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The Anthropic *** expansion story is less about diplomatic courtship and more about what happens when a government punishes a company for having principles. In late February, US Defence Secretary Pete Hegseth gave Anthropic CEO Dario Amodei a stark ultimatum: remove guardrails preventing Claude from being used for fully autonomous weapons and domestic mass surveillance, or face consequences. Amodei didn’t budge. He wrote that Anthropic could not “in good conscience” grant the Pentagon’s request, arguing that some uses of AI “can undermine rather than defend democratic values.” Washington’s response was swift. Trump directed every federal agency to immediately cease all use of Anthropic’s technology, and the Pentagon designated the company a supply chain risk, a label ordinarily reserved for adversarial foreign entities like Huawei. The US$200 million Pentagon contract was pulled. Defence tech companies instructed employees to stop using Claude and switch to alternatives. London, watching all of this unfold, saw something different. The ***’s pitch Staff at the ***’s Department for Science, Innovation and Technology (DSIT) have drawn up proposals for the US$380 billion company, ranging from a dual stock listing on the London Stock Exchange to an office expansion in the capital, according to multiple people with knowledge of the plans. Prime Minister Keir Starmer’s office has backed the effort, which will be put to Amodei when he visits in late May. Anthropic already has around 200 employees in Britain and appointed former prime minister Rishi Sunak as a senior adviser last year. The infrastructure for a meaningful *** presence is already there. What the British government is now offering is an explicit signal that Anthropic’s approach to AI–built on embedded ethical constraints–is an asset, not an obstacle. A dual listing in London, if it materialised, would give Anthropic access to European institutional investors at a moment when its domestic regulatory standing remains under active legal challenge. The Pentagon’s appeal of the court-ordered injunction blocking the supply chain designation is still before the Ninth Circuit, and the outcome remains uncertain. Ethics as a competitive advantage The dispute has been framed largely as a legal and political fight. But its implications for global AI governance run deeper. Anthropic’s lawyers argued in court filings that Claude was not developed to be used for lethal autonomous weapons without human oversight, nor deployed to spy on US citizens, and that using the tools in these ways would represent an abuse of its technology. US District Judge Rita Lin, who granted a preliminary injunction blocking the blacklist in March, found the government’s actions “troubling” and concluded they likely violated the law. That judicial finding matters in the *** context. Britain is positioning itself as a regulatory environment sitting between Washington’s current posture, which demands unrestricted military access, and Brussels, where the EU AI Act imposes its own constraints. The *** government presents itself as offering a less constrained environment for AI companies than either the US or the European Union. Crucially, that pitch doesn’t ask Anthropic to abandon the guardrails it went to court to defend. The courtship also sits alongside broader *** efforts to build domestic AI capability, including a recently announced £40 million state-backed research lab, after officials acknowledged the absence of a homegrown competitor to the leading US frontier labs. Competition in London The ***’s play for Anthropic is not happening in a vacuum. OpenAI has already committed to making London its biggest research hub outside the US. Google has anchored itself in King’s Cross since acquiring DeepMind in 2014. The race to secure frontier AI in London is already competitive, and Anthropic’s current circumstances make it the most consequential target yet. Anthropic has been expanding internationally regardless of its domestic legal battles, including opening a Sydney office as its fourth Asia-Pacific location. The global growth strategy is already in motion. What remains to be seen is how much of it London gets to claim. The company Washington blacklisted for having an AI ethics policy is now being actively courted by another G7 government that wants exactly that. The late May meetings with Amodei will be telling. See Also: Anthropic selected to build government AI assistant pilot Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Anthropic’s refusal to arm AI is exactly why the *** wants it appeared first on AI News. View the full article

AI systems are starting to move beyond simple responses. In many organisations, AI agents are now being tested to plan tasks, make decisions, and carry out actions with limited human input. It is no longer just about whether a model gives the right answer. It is about what happens when that model is allowed to act. Autonomous systems need clear boundaries. They need rules that define what they can access, what they are allowed to do, and how their actions are tracked. Without those controls, even well-trained systems can create problems that are hard to detect or reverse. One company working on this problem is Deloitte. The firm has been developing governance frameworks and advisory approaches to help organisations manage AI systems. From tools to AI agents Most AI systems in use today still depend on human prompts. They generate text, analyse data, or make predictions, but a person usually decides what happens next. Agentic AI changes that pattern. These systems can break down a goal into steps, choose actions, and interact with other systems to complete tasks. That added independence brings new challenges. When a system acts on its own, it may take paths that were not fully expected or use data in ways that were not intended. Deloitte’s work focuses on helping organisations prepare for these risks. Rather than treating AI as a standalone tool, the firm looks at how it fits into business processes, including how decisions are made and how data flows through systems. Building governance into the lifecycle Governance should not be added after deployment. It needs to be built into the full lifecycle of an AI system. This starts at the design stage. Organisations need to define what a system is allowed to do and where its limits are. This may include setting rules around data use and outlining how the system should respond in uncertain situations. The next stage is deployment. At this point, governance focuses on access and control, including who can use the system and what it can connect to. Once the system is live, monitoring becomes the main concern. Autonomous systems can change over time as they interact with new data. Without regular checks, they may drift away from their original purpose. The role of transparency and accountability As AI systems take on more responsibility, it becomes more difficult to trace how decisions are made. This creates a demand for stronger transparency. Deloitte’s work highlights the importance of keeping track of how systems operate. This includes logging actions and documenting decisions. These records help organisations in determining what happened if something goes wrong. If an autonomous system takes an action, there needs to be clarity about who is responsible. Research from Deloitte shows that adoption of AI agents is moving faster than the controls needed to manage them. Around 23% of companies already use them, and that figure is expected to reach 74% within two years. Only 21% report having strong safeguards in place to oversee how they behave. Real-time oversight for AI agents Once an autonomous system is active, the focus shifts to how it behaves in real-world conditions. Static rules are not always enough, and systems need to be observed as they operate. Deloitte’s approach includes real-time monitoring, allowing organisations to track what an AI system is doing as it performs tasks. If the system behaves in an unexpected way, teams can step in quickly. This may involve pausing certain actions or adjusting permissions. Real-time oversight also helps with compliance. In regulated industries, companies need to show that systems follow rules and standards. In practice, these controls are starting to appear in operational settings. Deloitte describes scenarios where AI systems monitor equipment performance across sites. Sensor data can signal early signs of failure, which can trigger maintenance workflows and update internal systems. Governance frameworks define what actions the system can take, when human approval is required, and how decisions are recorded. The process runs across multiple systems, but from a user’s point of view, it appears as a single action. Governance is part of discussions at AI & Big Data Expo North America 2026, taking place on May 18–19 in Santa Clara, California. Deloitte is listed as a Diamond Sponsor for the event, placing it among the firms contributing to conversations around how autonomous systems are deployed and controlled in practice. The challenge is not just building smarter systems, but ensuring they behave in ways organisations can understand, manage, and trust over time. (Photo by Roman) See also: Autonomous AI systems depend on data governance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post As AI agents take on more tasks, governance becomes a priority appeared first on AI News. View the full article

With the launch of KiloClaw, enterprises now have a tool to enforce governance over autonomous agents and manage shadow AI. While businesses spent the last year securing large language models and formalising vendor agreements, developers and knowledge workers started moving on their own. Employees are bypassing official procurement, deploying autonomous agents on personal infrastructure to automate their daily workflows. This practice, known as ‘Bring Your Own AI’ or BYOAI, exposes proprietary enterprise data to unregulated external environments. To address this vulnerability, software provider Kilo launched KiloClaw for Organizations, an enterprise-grade platform built to rein in decentralised agent deployments and restore architectural oversight. Kilo targets the lack of visibility surrounding agent deployment. When engineers set up autonomous agents to parse error logs, or financial analysts deploy local scripts to reconcile spreadsheets, they prioritise immediate efficiency over security protocols. These agents routinely gain access to corporate Slack channels, Jira boards, and private code repositories through personal API keys. Since these connections happen outside official IT purview, they create blind spots for data exfiltration and intellectual property leaks. KiloClaw provides a centralised control plane for security teams to identify, monitor, and restrict these autonomous actors without blocking their productivity gains. The unseen infrastructure of Bring-Your-Own-Agent The current shift mirrors the Bring Your Own Device (BYOD) era of the early 2010s, when employees used personal smartphones for corporate email and forced IT departments to adopt mobile device management. The AI equivalent carries higher stakes. A compromised phone might expose a static inbox, but an unmonitored autonomous agent has active execution privileges. It reads, writes, modifies, and deletes data across integrated platforms at speeds humans cannot replicate. These autonomous scripts also frequently rely on external computational power. An employee might run an agent locally while the agent sends corporate data to third-party inference servers to process queries. If those providers use the ingested data to train future models, the enterprise loses control of its intellectual property. KiloClaw, for its part, establishes a secure boundary around these processes. Instead of ignoring external deployments, the platform pulls them into a registry where compliance officers can audit behaviour and data flows. Identity and access management for autonomous AI agents Governing autonomous systems requires a different technical architecture than managing a human workforce. Traditional Identity and Access Management (IAM) systems are built for human credentials or static application-to-application communication. Autonomous agents, however, are dynamic. Agents chain tasks together sequentially, formulating new requests based on the output of previous actions. An agent might request access to an enterprise resource planning database halfway through a task, and standard security software struggles to determine if this is hostile behaviour or a legitimate operation. KiloClaw treats agents as distinct entities requiring restrictive, time-bound permission scopes. Instead of developers plugging permanent, high-level API keys into experimental models, KiloClaw issues short-lived, narrowly defined access tokens. If an agent designed to summarise weekly marketing emails attempts to download a customer database, the platform detects the scope violation and revokes access. This containment limits the blast radius within the corporate network if an open-source model behaves unpredictably. How tools like KiloClaw balance velocity and compliance Mandating a blanket ban on custom-built automation tools rarely works; it drives the behaviour underground, encouraging engineers to obfuscate traffic and hide workflows. Platforms like KiloClaw aim to construct a sanctioned environment where employees can safely register their tools. For this governance framework to work, IT leaders need to prioritise integration. KiloClaw connects directly into the continuous integration and deployment pipelines that software teams already utilise. By automating security checks and permission provisioning, security teams remove the friction that causes employees to bypass rules. Enterprises can establish baseline templates detailing what data external models can process, allowing workers to deploy agents within pre-approved boundaries. This maintains compliance without sacrificing workflow automation. The development of shadow AI governance tools points to a new phase of algorithmic regulation. Early corporate reactions to generative models focused on acceptable use policies for text-based chatbots. Now, the focus is shifting toward orchestration, containment, and system-to-system accountability. Regulators globally are also examining how companies monitor automated systems, pushing verifiable oversight toward legal obligation. As digital agents multiply within corporate networks, the concept of an ‘Agent Firewall’ is becoming a standard IT budget item. Platforms that map the relationships between human intent, machine execution, and corporate data will form the foundation of future security operations. KiloClaw’s entry into the organisational governance space highlights a shifting reality for the C-suite: the immediate threat includes well-meaning employees handing network keys to unregulated machines. Establishing structural authority over these non-human actors is necessary to safely harness their potential. See also: Autonomous AI systems depend on data governance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post KiloClaw targets shadow AI with autonomous agent governance appeared first on AI News. View the full article

A decade ago, it would have been hard to believe that artificial intelligence could do what it can do now. However, it is this same power that introduces a new attack surface that traditional security frameworks were not built to address. As this technology becomes embedded in critical operations, companies need a multi-layered defense strategy that includes data protection, access control and constant monitoring to keep these systems safe. Five foundational practices address these risks. 1. Enforce strict access and data governance AI systems depend on the data they are fed and the people who access them, so role-based access control is one of the best ways to limit exposure. By assigning permissions based on job function, teams can ensure only the right people can interact with and train sensitive AI models. Encryption reinforces protection. AI models and the data used to train them must be encrypted when stored and when moving between systems. This is especially important when that data includes proprietary code or personal information. Leaving a model unencrypted on a shared server is an open invitation for attackers, and solid data governance is the last line of defence keeping those assets safe. 2. Defend against model-specific threats AI models face a variety of threats that conventional security tools were not designed to catch. Prompt injection ranks as the top vulnerability in the OWASP top 10 for large language model (LLM) applications, and it happens when an attacker embeds malicious instructions inside an input to override a model’s behaviour. One of the most direct ways to block these attacks at the entry point is by deploying AI-specific firewalls that validate and sanitise inputs before they reach an LLM. Beyond input filtering, teams should run regular adversarial testing, which is essentially ethical hacking for AI. Red team exercises simulate real-world scenarios like data poisoning and model inversion attacks to reveal vulnerabilities before threat actors find them. Research on red teaming AI systems highlights that this kind of iterative testing needs to be built into the AI development life cycle and not bolted on after deployment. 3. Maintain detailed ecosystem visibility Modern AI environments span on-premise networks, cloud infrastructure, email systems and endpoints. When security data from each of these areas is in a separate silo, visibility gaps may emerge. Attackers move through those gaps undetected. A fragmented view of your environment makes it nearly impossible to correlate suspicious events into a coherent threat picture. Security teams need unified visibility in every layer of their digital environment. This means breaking down information silos between network monitoring, cloud security, identity management and endpoint protection. When telemetry from all these sources feeds into a single view, analysts can connect the dots between an anomalous login, a lateral movement attempt and a data exfiltration event not seeing each in isolation. Achieving this breadth of coverage is increasingly nonnegotiable. As the NIST’s Cybersecurity Framework Profile for AI makes clear, securing these systems requires organisations to secure, thwart and defend in all relevant assets, not the most visible ones. 4. Adopt a consistent monitoring process Security is not a one-time configuration because AI systems change. Models are updated, new data pipelines are introduced, user behaviours change and the threat landscape evolves with them. Rule-based detection tools struggle to keep pace because they rely on known attack signatures not real-time behavioural analysis. Continuous monitoring addresses this gap by establishing a behavioural baseline for AI systems and flagging deviations as they happen. Consistent monitoring can flag unusual activity in the moment, whether it’s a model producing unexpected outputs, a sudden change in API call patterns or a privileged account accessing data it normally shouldn’t. Security teams get an immediate alert with enough context to act fast. The change toward real-time detection is critical for AI environments, where the volume and speed of data far outpace human review. Automated monitoring tools that learn normal patterns of behaviour can detect low-and-slow attacks that would otherwise go unnoticed for weeks. 5. Develop a clear incident response plan Incidents are inevitable, even with strong preventive controls in place. Without a predefined response plan, companies risk making costly decisions under pressure, which can worsen the impact of a breach that could have been contained quickly. An effective AI incident response plan should cover containment, investigation, eradication and recovery: Containment: Limits the immediate impact by isolating affected systems Investigation: Establishes what happened and how far it reached Eradication: Removes the threat and patches the exploited weakness Recovery: Restores normal operations with stronger controls in place AI incidents require unique recovery steps, like retraining a model that was fed corrupted data or reviewing logs to see what the system produced while it was compromised. Teams that plan for these scenarios in advance recover faster and with far less reputational damage. Top 3 providers for implementing AI security Implementing these practices at scale requires purpose-built tooling. Three providers stand out for organisations looking to put a serious AI security strategy into practice. 1. Darktrace Darktrace is a premier choice for AI security, largely because of its foundational Self-Learning AI. The system builds a dynamic understanding of what normal looks like in an enterprise’s unique digital environment. Rather than relying on static rules or historical attack signatures, Darktrace’s core AI looks for anomalous events, reducing the false positives that plague more rule-based tools. A second layer of analysis is provided by its Cyber AI Analyst, which autonomously investigates every alert and determines whether it is part of a wider security incident. This can reduce the number of alerts that land in a SOC analyst’s ****** from hundreds to just two or three critical incidents that need attention. Darktrace was among the earliest adopters of AI for cybersecurity, giving its solutions a maturity advantage over newer entrants. Its coverage spans on-premise networks, cloud infrastructure, email, OT systems and endpoints – all manageable in unison or at the individual product level. One-click integrations from the customer portal mean brands can extend that coverage without long, disruptive deployment cycles. 2. Vectra AI Vectra AI is a strong option for organisations running hybrid or multi-cloud environments. Its Attack Signal Intelligence technology automates the detection and prioritisation of attacker behaviours in network traffic and cloud logs, surfacing the activity that matters most not flooding analysts with raw alerts. Vectra takes a behaviour-based approach to threat detection, focusing on what attackers do in an environment, not how they initially gained access. This makes it effective at catching lateral movement, privilege escalation and command-and-control activity that bypasses perimeter defenses. For teams managing complex hybrid architectures, Vectra’s ability to provide consistent detection in on-premise and cloud environments in a single platform is an advantage. 3. CrowdStrike CrowdStrike is recognised as a leader in cloud-native endpoint security. Its Falcon platform is built on a powerful AI model trained on an extensive body of threat intelligence, letting it prevent, detect and respond to threats at the endpoint, including novel malware. In environments where endpoints make up a large chunk of the attack surface, its lightweight agent and cloud-native setup make it easy to deploy without disrupting operations. Its threat intelligence integrations also help security teams connect the dots, linking what’s happening on a single device to a larger attack pattern playing out in the whole infrastructure. Chart a secure future for artificial intelligence As AI systems grow more capable, the threats designed to exploit them will also grow more sophisticated. Securing AI demands a forward-thinking strategy built on prevention, continuous visibility and rapid response – one that adapts as the environment evolves. 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China has approved its 15th Five-Year Plan [PDF] setting out the country’s economic, education, social, and industrial priorities through to 2030. As might be expected, there is a significant number of references to AI, with the technology mentioned in several contexts. AI is grouped alongside quantum computing, biotechnology, and energy as paths that are to be pursued as part of the country’s strategic science policy. The document calls for more work in developing high-performance AI chips and the software to support them in this context. There’s also a commitment to academic and industry research on new model architectures and the core algorithms underpinning them. Development to communications technologies such as satellite systems, 5G+ (sometimes referred to as 5G-A or 5G Advanced) and 6G networks is to support AI workloads as part of a broader push to improve the country’s infrastructure for data transmion, general communication and data processing. In the section of the Five-Year Plan dedicated to digital infrastructure, the use of AI falls into three components: computing power, AI models, and the organisation and dissemination of data across China. The government calls for national computing hubs described as “intelligent computing clusters”, and proposes market mechanisms such as the lease of computeing resources to give access to a large a swathe of the population as possible. There are also to be new ways in which government bodies will procure the computing services they need. The compute hubs the government proposes are also intended to reduce the barriers smaller firms face to access the very latest in technology. The government wants the theoretical work behind model training and inference to continue as research and in manufacturing, and refers specifically to multi-modal, agent-based, and “embodied” AI. It sees the technology as playing an increasing role in areas of the economy like manufacturing, energy, agriculture, and service industries. It cites industrial design, production processes, general operations, energy system management, and agricultural production as areas where the use of AI should be increased and encouraged. In the service sector, the text calls out the finance, logistics, and software services sectors. For the general technology-using ******** consumer, the government wants to see an increase in the number and type of AI-enabled devices, including phones, computers, and robots, and links the use of AI to education, healthcare, care for the elderly care, and social service provision. In these settings, it envisages adaptive learning systems in education, diagnostic support in healthcare, and ******** system management. At the national and local government levels, the Five-Year Plan wants the digital services provided by all elements of the public sector to increase in scope and ability, based on integrated data systems built around standard models. It calls for the use of AI models in general administration, and the assessment of risk to public safety. The government is generally quite conservative in its approach to cooperation with other nations, suggesting that it may be possible for the country to participate with outside organisations on international standards around data flows and infrastructure. The issue of governance and regulation of data forms a relatively substantial part of the discussion in the document, calling for specific leagal and regulatory frameworks for AI, including rules on the registration of new algorithms, security, and overall transparency. It cites common risks to AI use that may affect the economy, including data misuse and deepfakes. Given the size of the country’s population, it’s perhaps not surprising that there is little mention of specific steps the country will take to ensure its role in the evolution of AI. Over the course of the next five years, the details are more likely to emerge as events observable by China-watchers. But as the pages of this site can attest, the country’s chosen path for AI rests more on smaller, open, freely-available, efficient models than the approach more common in the West: large, proprietary models controlled by two or three major players based on hardware from mostly one supplier. The details of the ******** government’s implementations of AI in its economy will inform observers of whether the next five years will continue China’s chosen course, or whether the West’s ideology around the technology will force a change of approach. (Image source: “Beijing skyline from northeast 4th ring road (cropped)” by Picrazy2 is licensed under CC BY-SA 4.0. To view a copy of this license, visit [Hidden Content]) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post China’s Five-Year Plan details the targets for AI deployment appeared first on AI News. View the full article

Much of the current focus on AI safety has centred on models – how they are trained and monitored. But as systems become more autonomous, attention is changing toward the data those systems depend on. If the data feeding an AI system is fragmented, outdated, or lacks oversight, the system’s behaviour can become more unpredictable. Data governance is becoming a core part of how autonomous systems are controlled. Denodo is one of the companies working in this area, focusing on how organisations access and manage data in different sources. Autonomous AI systems carry out tasks with limited supervision, retrieving information, making decisions based on that information, and triggering actions in business workflows. The challenge is that these systems depend on a steady flow of data. In regulated industries, unpredictable results can create compliance risks. In customer-facing systems, it might result in poor decisions or incorrect responses. How data alters AI behaviour Data is often spread in multiple systems. Large organisations store information in cloud platforms, internal databases, and third-party services. This creates silos, where different parts of the business operate on different versions of the same data. Denodo addresses this problem by providing a way to access data without moving it into a single repository. Its platform creates a unified view of data from different sources for applications, including AI systems. It lets allows organisations apply consistent policies in all data sources. Access rules, compliance requirements, and use limits can be defined in one place. It also supports approaches that allow AI systems to query enterprise data using defined structures and policies. The platform logs how data is queried and what is returned, creating an audit trail. This can help organisations understand how an AI system reached a decision and support compliance requirements. It can also help teams monitor data use in real time and identify unusual activity. If multiple AI systems rely on the same governed data layer, they are more likely to produce aligned results which can help reduce the risk of conflicting outputs in different parts of the business. Governance in the stack As autonomous AI systems become more common, governance is being applied at several levels. Data governance, which sits underneath models and applications, helps ensure that the inputs to those systems are reliable. A well-governed model can still produce poor results especially if it relies on flawed data. Strong data governance can support better outcomes even when systems operate with some degree of independence. This is why data-focused companies are becoming part of the broader AI governance conversation. By controlling how data is accessed and used, they help alter how autonomous systems behave in practice. At AI & Big Data Expo North America 2026AI & Big Data Expo North America 2026AI & Big Data Expo North America 2026, discussions around AI include oversight and system behaviour. Denodo is among the companies taking part in those discussions, particularly around data management and enterprise AI. Early deployments often focused on what AI systems could do. Current discussions are more concerned with how those systems should be managed once they are in use. From ability to control The next stage of AI adoption is likely to depend less on new model features and more on how well organisations manage the systems around them. Governance is not an added feature, but a requirement for systems that are expected to act on their own. (Photo by Hyundai Motor Group) See also: SAP and ANYbotics drive industrial adoption of physical AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data ExpoAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Autonomous AI systems depend on data governance appeared first on AI News. View the full article

The same technology that financial institutions deploying is being weaponised against them. That is the core tension running through Experian’s 2026 Future of Fraud Forecast, and it’s a tension the company is in a position to name because it sits on both sides of it. According to FTC data cited in the forecast, consumers lost more than US$12.5 billion to fraud in 2024. As per Experian’s own data accompanying the report, nearly 60% of companies reported an increase in fraud losses from 2024 to 2025. Experian’s fraud prevention solutions helped clients avoid an estimated US$19 billion in fraud losses globally in 2025, a figure that underscores the scale of the problem and how much defence now depends on AI matching the speed and autonomy of attacks. The agentic AI issue The most pressing finding in Experian’s forecast is what the company calls machine-to-machine mayhem, the point at which agentic AI systems, designed to transact autonomously on behalf of users, become indistinguishable from the bots fraudsters deploy for the same purpose. According to Experian’s forecast, as organisations strive to integrate AI agents capable of independent decision-making, fraudsters are exploiting those same systems to run high-volume digital fraud at a scale and speed no human operation could sustain. The core challenge, as per the report, is that machine-to-machine interactions carry no clear ownership of liability; when an AI agent initiates a transaction that turns out to be fraudulent, the question of who is responsible has no settled answer. Kathleen Peters, chief innovation officer for Fraud and Identity at Experian North America, framed the problem: “Technology is accelerating the evolution of fraud, making it more sophisticated and harder to detect. By combining differentiated data with advanced analytics and cutting-edge technology, businesses can strengthen fraud defences, safeguard consumers, and deliver secure, seamless experiences.” Experian predicts that this will reach a tipping point in 2026, forcing substantive industry conversations around liability and the governance of agentic AI in commerce. Some organisations are already making preemptive moves. Amazon, for instance, has stated it blocks third-party AI agents from browsing and transacting on its platform, citing security and privacy concerns. Four other threats the forecast identifies Beyond the agentic AI issue, Experian’s forecast identifies four additional trends that financial institutions need to consider in 2026. Deepfake candidates infiltrating remote workforces; Generative AI tools can now produce tailored CVs and real-time deepfake video capable of passing job interviews. According to the forecast, employers will onboard individuals who are not who they claim to be, granting bad actors access to internal systems. The FBI and Department of Justice issued multiple warnings in 2025 about documented instances of North Korean operatives using this approach to gain employment at US companies. Website cloning overwhelms fraud teams; AI tools have made it easier to create replicas of legitimate sites, and harder to eliminate them permanently. As per the forecast, even after takedown requests are actioned, spoofed domains continue to resurface, forcing fraud teams into reactive patterns. Emotionally intelligent scam bots; Generative AI means bots can conduct complex romance fraud and relative-in-need scams without human operators. According to Experian’s forecast, such bots respond convincingly, build trust over extended periods, and are becoming increasingly difficult distinguish from genuine human interaction. Smart home vulnerabilities: Devices including virtual assistants, smart locks, and connected appliances create new entry points for fraudsters. Experian forecasts that bad actors will exploit these devices to access personal data and monitor household activity as the connected home becomes a more greater part of everyday financial behaviour. Financial institutions’ responses According to Experian’s Perceptions of AI Report, drawing on responses from more than 200 decision-makers at leading financial institutions, 84% identify AI as a critical or high priority for their business strategy over the next two years. A further 89% say AI will play an important role in the lending lifecycle. The governance dimension, however, is where institutions struggle. According to the same report, 73% of respondents are concerned about the regulatory environment around AI, and 65% identify AI-ready data as one of their biggest deployment challenges. Data quality was rated the single most important factor in choosing an AI vendor, which positions Experian’s data-first positioning at the intersection of what financial institutions say they need most. On the compliance side, Experian’s AI-powered Assistant for Model Risk Management addresses one of the most resource-intensive requirements facing institutions deploying AI. According to a 2025 Experian study of more than 500 global financial institutions, 67% struggle to meet their country’s regulatory requirements, 79% report more frequent supervisory communications from regulators than a year ago, and 60% still use manual compliance processes. In Experian’s announcement, the company states that more than 70% of larger institutions report model documentation compliance involves over 50 people, a figure that signals the scale of the automation opportunity. Vijay Mehta, EVP of Global Solutions and Analytics at Experian Software Solutions, described the challenge the product addresses: “The AI-enabled speed of data analytics and model development is driving unprecedented business opportunities for financial institutions, but it comes with a challenge: global regulations that require time-consuming documentation. Experian Assistant for Model Risk Management helps solve this labour and resource-intensive requirement with end-to-end model documentation automation.” The data quality foundation Running underneath Experian’s fraud and compliance products is the same structural argument that appears in both IBM and Salesforce’s AI narratives that appeared this week: AI is only as reliable as the data it runs on. As per Experian’s Perceptions of AI Report, 65% of financial institution decision-makers consider AI-ready data one of their biggest challenges, and data quality is the most critical factor influencing trust in AI vendors. That is not a coincidence of messaging. It reflects a constraint facing financial services institutions as they move AI from pilots into production credit decisioning, fraud detection, and regulatory reporting; functions where explainability and auditability are not optional. Experian’s CDAO Paul Heywood is among the confirmed speakers at the AI & Big Data Expo, part of TechEx North America, taking place 18 – 19 May 2026 at the San Jose McEnery Convention Centre, California. Experian is a Platinum Sponsor at TechEx Global. See also: Hershey applies AI in its supply chain operations Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Experian uncovers fraud paradox in financial services’ AI adoption appeared first on AI News. View the full article

Global AI investment is accelerating, yet KPMG data shows the gap between enterprise AI spend and measurable business value is widening fast. The headline figure from KPMG’s first quarterly Global AI Pulse survey is blunt: despite global organisations planning to spend a weighted average of $186 million on AI over the next 12 months, only 11 percent have reached the stage of deploying and scaling AI agents in ways that produce enterprise-wide business outcomes. However, the central finding is not that AI is failing; 64 percent of respondents say AI is already delivering meaningful business outcomes. The problem is that “meaningful” is doing a lot of heavy lifting in that sentence, and the distance between incremental productivity gains and the kind of compounding operational efficiency that moves the needle on margin is, for most organisations, still substantial. The architecture of a performance gap KPMG’s report distinguishes between what it labels “AI leaders” (i.e. organisations that are scaling or actively operating agentic AI) and everyone else. The gap in outcomes between these two cohorts is striking. Steve Chase, Global Head of AI and Digital Innovation at KPMG International, said: “The first Global AI Pulse results reinforce that spending more on AI is not the same as creating value. Leading organisations are moving beyond enablement, deploying AI agents to reimagine processes and reshape how decisions and work flow across the enterprise.” Among AI leaders, 82 percent report that AI is already delivering meaningful business value. Among their peers, that figure drops to 62 percent. That 20-percentage-point spread might look modest in isolation, but it compounds quickly when you consider what it reflects: not just better tooling, but fundamentally different deployment philosophies. The organisations in that 11 percent are deploying agents that coordinate work across functions, route decisions without human intermediation at every step, surface enterprise-wide insights from operational data in near real-time, and flag anomalies before they escalate into incidents. In IT and engineering functions, 75 percent of AI leaders are using agents to accelerate code development versus 64 percent of their peers. In operations, where supply-chain orchestration is the primary use case, the split is 64 percent versus 55 percent. These are not marginal differences in tool adoption rates; they reflect different levels of process re-architecture. Most enterprises that have deployed AI have done so by layering models onto existing workflows (e.g. a co-pilot here, a summarisation tool there…) without redesigning the process those tools sit inside. That produces incremental gains. The organisations closing the performance gap have inverted this approach: they are redesigning the process first, then deploying agents to operate within the redesigned structure. The difference in return on AI spend between these two approaches, over a three-to-five-year horizon, is likely to be the defining competitive variable in several industries. What $186 million actually buys—and what it does not The investment figures in the KPMG data deserve scrutiny. A weighted global average of $186 million per organisation sounds substantial, but the regional variance tells a more interesting story. ASPAC leads at $245 million, the Americas at $178 million, and EMEA at $157 million. Within ASPAC, organisations including those in China and Hong Kong are investing at $235 million on average; within the Americas, US organisations are at $207 million. These figures represent planned spend across model licensing, compute infrastructure, professional services, integration, and the governance and risk management apparatus needed to operate AI responsibly at scale. The question is not whether $186 million is too much or too little; it is what proportion of that figure is being allocated to the operational infrastructure required to derive value from the models themselves. The survey data suggests that most organisations are still underweighting this latter category. Compute and licensing costs are visible and relatively easy to budget for. The friction costs – the engineering hours spent integrating AI outputs with legacy ERP systems, the latency introduced by retrieval-augmented generation pipelines built on top of poorly structured data, and the compliance overhead of maintaining audit trails for AI-assisted decisions in regulated industries – tend to surface late in deployment cycles and often exceed initial estimates. Vector database integration is a useful example. Many agentic workflows depend on the ability to retrieve relevant context from large, unstructured document repositories in real time. Building and maintaining the infrastructure for this – selecting between providers such as Pinecone, Weaviate, or Qdrant, embedding and indexing proprietary data, and managing refresh cycles as underlying data changes – adds meaningful engineering complexity and ongoing operational cost that rarely appears in initial AI investment proposals. When that infrastructure is absent or poorly maintained, agent performance degrades in ways that are often difficult to diagnose, as the model’s behaviour is correct relative to the context it receives, but that context is stale or incomplete. Governance as an operational variable, not a compliance exercise Perhaps the most practically useful finding in the KPMG survey is the relationship between AI maturity and risk confidence. Among organisations still in the experimentation phase, just 20 percent feel confident in their ability to manage AI-related risks. Among AI leaders, that figure rises to 49 percent. 75 percent of global leaders cite data security, privacy, and risk as ongoing concerns regardless of maturity level—but maturity changes how those concerns are operationalised. This is an important distinction for boards and risk functions that tend to frame AI governance as a constraint on deployment. The KPMG data suggests the opposite dynamic: governance frameworks do not slow AI adoption among mature organisations; they enable it. The confidence to move faster – to deploy agents into higher-stakes workflows, to expand agentic coordination across functions – correlates directly with the maturity of the governance infrastructure surrounding those agents. In practice, this means that organisations treating governance as a retrospective compliance layer are doubly disadvantaged. They are slower to deploy, because every new use case triggers a fresh governance review, and they are more exposed to operational risk, because the absence of embedded governance mechanisms means that edge cases and failure modes are discovered in production rather than in testing. Organisations that have embedded governance into the deployment pipeline itself (e.g. model cards, automated output monitoring, explainability tooling, and human-in-the-loop escalation paths for low-confidence decisions) are the ones operating with the confidence that allows them to scale. “Ultimately, there is no agentic future without trust and no trust without governance that keeps pace,” explains Steve Chase, Global Head of AI and Digital Innovation at KPMG International. “The survey makes clear that sustained investment in people, training and change management is what allows organisations to scale AI responsibly and capture value.” Regional divergence and what it signals for global deployment For multinationals managing AI programmes across regions, the KPMG data flags material differences in deployment velocity and organisational posture that will affect global rollout planning. ASPAC is advancing most aggressively on agent scaling; 49 percent of organisations there are scaling AI agents, compared with 46 percent in the Americas and 42 percent in EMEA. ASPAC also leads on the more complex capability of orchestrating multi-agent systems, at 33 percent. The barrier profiles also differ in ways that carry real operational implications. In both ASPAC and EMEA, 24 percent of organisations cite a lack of leadership trust and buy-in as a primary barrier to AI agent deployment. In the Americas, that figure drops to 17 percent. Agentic systems, by definition, make or initiate decisions without per-instance human approval. In organisational cultures where decision accountability is tightly concentrated at the senior level, this can generate institutional resistance that no amount of technical capability resolves. The fix is governance design; specifically, defining in advance what categories of decision an agent is authorised to make autonomously, what triggers escalation, and who carries accountability for agent-initiated outcomes. The expectation gap around human-AI collaboration is also worth noting for anyone designing agent-assisted workflows at a global scale. East Asian respondents anticipate AI agents leading projects at a rate of 42 percent. *********** respondents prefer human-directed AI at 34 percent. North American respondents lean toward peer-to-peer human-AI collaboration at 31 percent. These differences will affect how agent-assisted processes need to be designed in different regional deployments of the same underlying system, adding localisation complexity that is easy to underestimate in centralised platform planning. One data point in the KPMG survey that deserves particular attention from CFOs and boards: 74 percent of respondents say AI will remain a top investment priority even in the event of a recession. This is either a sign of genuine conviction about AI’s role in cost structure and competitive positioning, or it reflects a collective commitment that has not yet been tested against actual budget pressure. Probably both, in different proportions across different organisations. What it does indicate is that the window for organisations still in the experimentation phase is not indefinite. If the 11 percent of AI leaders continue to compound their advantage (and the KPMG data suggests the mechanisms for doing so are in place) the question for the remaining 89 percent is not whether to accelerate AI deployment, but how to do so without compounding the integration debt and governance deficits that are already constraining their returns. See also: Hershey applies AI across its supply chain operations Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post KPMG: Inside the AI agent playbook driving enterprise margin gains appeared first on AI News. View the full article

AI is everywhere in the enterprise. The translation workflow often is not. That is the core finding of DeepL’s 2026 Language AI report, “Borderless Business: Transforming Translation in the Age of AI,” published on March 10. Despite broad AI investment across business functions, the report reveals that language and multilingual operations–workflows that touch sales, legal, customer support, and global expansion–remain the most underautomated part of the enterprise technology stack. The automation gap hiding in plain sight According to DeepL’s Borderless Business report, 35% of international businesses still handle translation entirely through manual processes, while a further 33% rely on traditional automation paired with systematic human review. Only 17% have implemented next-generation AI tools–large language models or agentic AI–for multilingual operations. That means, as per the report’s findings, 83% of enterprises have not transitioned to modern language AI capabilities despite investing in AI across other parts of the business. The report, which draws on survey data from business leaders across the United States, United Kingdom, France, Germany, and Japan, also found that enterprise content volume has grown 50% since 2023, yet 68% of companies still rely on workflows built for a different era. Jarek Kutylowski, CEO and founder of DeepL, put it plainly: “AI is everywhere, but efficiency is not. Most companies have deployed AI in some form, yet few achieve real productivity at scale because core workflows remain designed around people, not systems.” Why language AI is becoming infrastructure The angle that makes this more than a translation story is where language AI is now being deployed. According to DeepL’s research, global expansion is the top driver of language AI investment at 33%, followed by sales and marketing at 26%, customer support at 23%, and legal and finance at 22%. These are mission-critical business functions, not peripheral content tasks. DeepL’s broader research from December 2025, surveying 5,000 senior business leaders across the same five markets, found that 54% of global executives say real-time voice translation will be essential in 2026, up from 32% today. As perthat research, the *** and France are leading early adoption at 48% and 33% respectively, while Japan sits at 11%, a gap that points to significant variance in enterprise readiness across global markets. The company now serves over 200,000 business customers across 228 markets, and at the AI & Big Data Expo in London in February 2026, Scott Ivell, vice president of product marketing at DeepL, told SiliconANGLE that the company has 2,000 customers globally deploying AI agents — being used for report analysis, sales targeting, and legal document review. The sovereign AI dimension What separates DeepL’s positioning from general-purpose AI competitors is where it sits on the enterprise trust spectrum.As enterprises in regulated industries–financial services, healthcare, legal, government–accelerate AI adoption, data sovereignty is increasingly the deciding factor in platform selection. DeepL is ISO 27001, SOC 2 Type 2, and GDPR certified, and offers Bring Your Own Key encryption for enterprise customers, giving organisations the ability to withdraw data access in seconds, a control level that most large language model providers do not offer. As per DeepL’s own security documentation, this means data can effectively be placed beyond anyone’s reach, including DeepL itself, at the customer’s discretion. Sebastian Enderlein, CTO at DeepL, has framed 2026 as a year of execution rather than experimentation: “I believe 2026 will be the year AI stops experimenting and starts executing, at a scale we haven’t yet seen. After a cycle of pilots and proofs of concept, businesses are now ready to scale, and they’re betting big on agentic AI to do it.” DeepL Agent and the broader pivot DeepL’s product direction in 2026 reflects the same shift visible across enterprise AI broadly, from single-function tools to autonomous workflow execution. DeepL Agent, launched in general availability in November 2025, is designed to navigate business systems, execute multi-step workflows, and operate across CRM, email, calendars, and project management tools without requiring complex integrations. According to DeepL’s announcement, the agent operates with enterprise-grade security and data sovereignty built in by default, a deliberate positioning choice that targets the segment of enterprises that cannot send sensitive documents to OpenAI or Microsoft’s public cloud endpoints. DeepL’s chief scientist, Stefan Miedzianowski, has described the current moment as a transition on the technology adoption curve: “2026 will undoubtedly be the year of the agent. 2025 was the year when public awareness caught up with the science showing what agents can do, but enterprise adoption at scale will happen now. We are moving from the innovators to the early majority.” As per the Borderless Business report, 71% of business leaders say transforming workflows with AI is a priority for 2026, with expected returns across customer experience, employee productivity, and time to market. The gap between that ambition and the 17% who have actually modernised their language operations is the market DeepL is squarely targeting. DeepL is a Platinum Sponsor at TechEx Global, appearing at the AI & Big Data Expo and co-located events at Olympia London, February 3 & 4, 2027. See also: Automating complex finance workflows with multimodal AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post DeepL’s Borderless Business report reveals 83% of enterprises are still behind on language AI appeared first on AI News. View the full article

Artificial intelligence is moving beyond software and further into the physical side of business. Companies in food production and logistics are starting to use data systems to support day-to-day decisions, not long-term planning. That change is visible in The Hershey Company’s latest strategy update. At its Investor Day, the company said it plans to use AI in its operations, from sourcing analytics to plant automation and fulfilment, with a focus on how the business runs behind the scenes. Hershey said it plans to apply AI to sourcing and fulfilment. This includes using data to guide how ingredients are bought and how products are distributed. In its Investor Day material, the company said it aims to build “a faster, smarter and more resilient supply chain powered by automation and AI-enabled decision making”. Supply chains in food and snack markets are under steady pressure: Costs can change quickly, demand can change by season, by market, or by product category, and retailers still expect goods to arrive on time and in the right mix. Hershey said its digital planning tools are meant to connect different parts of the business. The company said those systems are designed to reduce waste and improve inventory levels. It also said digital operational planning can connect data in the supply chain and help raise service levels. From reporting to action Part of Hershey’s update is its use of the phrase “AI-enabled decision-making.” The company said its approach will link sourcing and delivery more closely and plans to use automated fulfilment systems for custom assortments and to improve speed to market. This is a useful way to read strategy. A hard task is turning data into decisions that help operations move faster or with fewer mistakes. This is where AI is starting to play a ******* role, according to Hershey’s. The value comes from how operations are connected. AI in the supply chain and plant operations The changes also extend into manufacturing. Hershey said it will increase plant automation to improve manufacturing efficiency and use AI in more parts of its operating model. What is changing is how AI fits into those systems. Instead of sitting apart from production, it is being positioned as part of the process used to guide planning and support execution. That may help companies improve planning and respond more quickly when conditions change. In a business where input costs and consumer demand can change often, even small gains in timing can matter. Food and snack companies deal with constant swings in input costs and demand. Ingredients like cocoa and sugar are affected by weather, trade flows, and supply issues. Companies still have to keep factories running and products moving through retail channels. Hershey’s plan to use sourcing analytics is one example of how AI may be applied in that setting. By analysing supplier data and market trends, the company may improve how it buys raw materials and manages risk. The company also said it wants to better connect workers in its operations. That suggests the strategy is not only about automation. It is also about coordination in the business. Hershey said it plans to “incorporate AI in every stage of its operations,” including sourcing analytics and worker connectivity, as well as automated fulfilment and plant automation. That makes the company a useful case study for a wider change in enterprise AI. Firms are moving away from narrow pilots and toward broader use in business functions. In that model, AI is treated as a part of supply and delivery systems. CEO Kirk Tanner framed the plan around growth and execution, saying, “The strategy is clear. The team is ready. The next chapter of growth and leading performance starts now”. Where this may lead The kind of change is likely to spread as more companies look for ways to connect data with operational decisions. Hershey’s strategy shows how AI is starting to take a larger role in industries built on physical goods. The technology may sit in the background, but its role in daily operations is becoming harder to ignore. (Photo by Janne Simoes) See also: JPMorgan begins tracking how employees use AI at work Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Hershey applies AI across its supply chain operations appeared first on AI News. View the full article

Heavy industry relies on people to inspect hazardous, dirty facilities. It’s expensive, and putting humans in these zones carries obvious safety risks. Swiss robot maker ANYbotics and software company SAP are trying to change that. ANYbotics’ four-legged autonomous robots will be connected straight into SAP’s backend enterprise resource planning software. Instead of treating a robot as a standalone asset, this turns it into a mobile data-gathering node within an industrial IoT network. This initiative shows that hardware innovation can now effectively connect with established business workflows. Underscoring that broader trend, SAP is sponsoring this year’s AI & Big Data Expo North America at the San Jose McEnery Convention Center, CA, an event that is fittingly co-located with the IoT Tech Expo and Intelligent Automation & Physical AI Summit. When equipment breaks at a chemical plant or offshore rig, it costs a fortune. People do routine inspections to catch these issues early, but humans get tired and plants are massive. Robots, on the other hand, can walk the floor constantly, carrying thermal, acoustic, and visual sensors. Hook those sensors into SAP, and a hot pump instantly generates a maintenance request without waiting for a human to report it. Cutting out the reporting lag Usually, finding a problem and logging a work order are two disconnected steps. A worker might hear a weird noise in a compressor, write it down, and type it into a computer hours later. By the time the replacement part gets approved, the machine might be wrecked. Connecting ANYbotics to SAP eliminates that delay. The robot’s onboard AI processes what it sees and hears instantly. If it hears an irregular motor frequency, it doesn’t just flash a warning on a separate screen, it uses APIs to tell the SAP asset management module directly. The system immediately checks for spare parts, figures out the cost of potential downtime, and schedules an engineer. This automates the flow of information from the floor to management. It also means machinery gets judged on hard, consistent numbers instead of a human inspector’s subjective opinion. Putting robots in heavy industry isn’t like installing software in an office—companies have to deal with unreliable infrastructure. Factories usually have awful internet connectivity due to thick concrete, metal scaffolding, and electromagnetic interference. To make this work, the setup relies on edge computing. It takes too much bandwidth to constantly stream high-def thermal video and lidar data to the cloud. So, the robots crunch most of that data locally. Onboard processors figure out the difference between a machine running normally and one that’s dangerously overheating. They only send the crucial details (i.e. the specific fault and its location) back to SAP. To handle the network issues, many early adopters build private 5G networks. This gives them the coverage they need across huge facilities where regular Wi-Fi fails. It also locks down access, keeping the robot’s data safe from interception. Of course, security is a major issue. A walking robot packed with cameras is effectively a roaming vulnerability. Companies must use zero-trust network protocols to constantly verify the robot’s identity and limit what SAP modules it can touch. If the robot gets hacked, the system has to cut its connection instantly to stop the attackers from moving laterally into the corporate network. These robots generate a massive amount of unstructured data as they walk around. Turning raw audio and thermal images into the neat tables SAP requires is difficult. If companies don’t manage this right, maintenance teams will drown in alerts. A robot that is too sensitive might ***** out hundreds of useless warnings a day, making the SAP dashboard completely ignored. IT teams have to set strict rules before turning the system on. They need exact thresholds for what triggers a real maintenance ticket and what just needs to be watched. The setup usually uses middleware to translate the robot’s telemetry into SAP’s language. This software acts as a filter, throwing out the noise so only actual problems reach the ERP system. The data lake storing all this information also needs to be organised for future machine learning projects. Fixing broken machines is the short-term goal; the long-term payoff is using years of robot data to predict failures before they happen. Ensuring a successful physical AI deployment Dropping robots into a factory naturally makes people nervous. The project’s success often comes down to how human resources handles it. Workers usually look at the robots and assume layoffs are next. Management has to be clear about why the robots are there. The goal is to get people out of dangerous areas like high-voltage zones or toxic chemical sectors to reduce injuries. The robot collects the data, and the human engineer shifts to analysing that data and doing the actual repairs. This requires retraining. Workers who used to walk the perimeter now have to read SAP dashboards, manage automated tickets, and work with the robots. They have to trust the sensors, and management has to make sure operators know they can take manual control if something unexpected happens. Companies need to take the rollout slowly. Because syncing physical robots with enterprise software is complicated, large-scale rollouts should start as small, targeted pilots. The first test should be in one specific area with known hazards but rock-solid internet. This lets IT watch the data flow between the hardware and SAP in a controlled space. At this stage, the main job is making sure the data matches reality. If the robot sees one thing and SAP records another, it has to be audited and fixed daily. Once the data pipeline actually works, the company can add more robots and connect other systems, like automated parts ordering. IT chiefs have to keep checking if their private networks can handle more robots, while security teams update their defenses against new threats. If companies treat these autonomous inspectors as an extension of their corporate data architecture, they get a massive amount of information about their physical assets. But pulling it off means getting the network infrastructure, the data rules, and the human element exactly right. See also: The rise of invisible IoT in enterprise operations Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post SAP and ANYbotics drive industrial adoption of physical AI appeared first on AI News. View the full article

Financial institutions are learning to deploy compliant AI solutions for greater revenue growth and market advantage. For the better part of ten years, financial institutions viewed AI primarily as a mechanism for pure efficiency gains. During that era, quantitative teams programmed systems designed to discover ledger discrepancies or eliminate milliseconds from automated trading execution times. As long as the quarterly balance sheets reflected positive gains, stakeholders outside the core engineering groups rarely scrutinised the actual maths driving these returns. The arrival of generative applications and highly complex neural networks completely dismantled that widespread state of comfortable ignorance. Today, it’s not acceptable for banking executives to approve new technology rollouts based simply on promises of accurate predictive capabilities. Across Europe and North America, lawmakers are aggressively drafting legislation aimed at punishing institutions that utilise opaque algorithmic decision-making processes. Consequently, the dialogue within corporate boardrooms has narrowed intensely to focus on safe AI deployment, ethics, model oversight, and legislation specific to the financial industry. Institutions that choose to ignore this impending regulatory reality actively place their operational licenses in jeopardy. However, treating this transition purely as a compliance exercise ignores the immense commercial upside. Mastering these requirements creates a highly efficient operational pipeline where good governance functions as a massive accelerant for product delivery rather than an administrative handbrake. Commercial lending and the price of opacity The mechanics of retail and commercial lending perfectly illustrate the tangible business impact of proper algorithmic oversight. Consider a scenario where a multinational bank introduces a deep learning framework to process commercial loan applications. This automated system evaluates credit scores, market sector volatility, and historical cash flows to generate an approval decision in a matter of milliseconds. The resulting competitive edge is immediate and obvious, as the institution reduces administrative overhead while clients secure necessary liquidity exactly when they require it. However, the inherent danger of this velocity resides entirely within the training data. If the deployed model unknowingly utilises proxy variables that discriminate against a specific demographic or geographic area, the ensuing legal consequences are swift and punishing. Modern regulators demand total explainability and categorically refuse to accept the complexity of neural networks as an excuse for discriminatory outcomes. When an external auditor investigates why a regional logistics enterprise was denied funding, the bank must possess the capability to trace that exact denial directly back to the specific mathematical weights and historical data points that caused the rejection. Investing capital into ethics and oversight infrastructure is essentially how modern banks purchase speed-to-market. Constructing an ethically-sound and thoroughly vetted pipeline enables an institution to release new digital products without constantly looking over its shoulder out of fear. Guaranteeing fairness from the absolute beginning prevents nightmarish scenarios that involve delayed product rollouts and retrospective compliance audits. This level of operational confidence translates directly into sustained revenue generation while entirely avoiding massive regulatory penalties. Engineering unbroken information provenance Achieving this high standard of safety is impossible without adopting a brutal and uncompromising approach toward internal data maturity. Any algorithm merely reflects the information it consumes. Unfortunately, legacy banking institutions are infamous for maintaining highly fractured information architectures. It remains incredibly common to discover customer details resting on thirty-year-old mainframe systems, transaction histories floating in public cloud environments, and risk profiles gathering dust within entirely separate databases. Attempting to navigate this disjointed landscape makes achieving regulatory compliance physically impossible. To rectify this, data officers must enforce the widespread adoption of comprehensive metadata management across the entire enterprise. Implementing strict data lineage tracking represents the only viable path forward. For example, if a live production model suddenly exhibits bias against *********-owned businesses, engineering teams require the exact capability to surgically isolate the specific dataset responsible for poisoning the results. Constructing this underlying infrastructure mandates that every single byte of ingested training data becomes cryptographically signed and tightly version-controlled. Modern enterprise platforms must maintain an unbroken chain of custody for every input, stretching all the way from a customer’s initial interaction to the final algorithmic ruling. Beyond data storage, integration issues arise when connecting advanced vector databases to these legacy systems. Vector embeddings require massive compute resources to process unstructured financial documents. If these databases are not perfectly synchronised with real-time transactional feeds, the AI risks generating severe hallucinations, presenting outdated or entirely fabricated financial advice as absolute fact. Furthermore, as we’re currently all too aware, economic environments change at a rapid pace. A model trained on interest rates from three years ago will fail spectacularly in today’s market. Technology teams refer to this specific phenomenon as concept drift. To combat this, developers must wire continuous monitoring systems directly into their live production algorithms. These specialised tools observe the model’s output in real-time, actively comparing results against baseline expectations. If the system begins to drift outside approved ethical parameters, the monitoring software automatically suspends the automated decision-making process. Exceptional predictive accuracy means absolutely nothing without real-time observability; without it, a highly-tuned model becomes a corporate liability waiting to explode. Defending the mathematical perimeter Of course, implementing governance over financial algorithms introduces an entirely new category of operational headaches for CISOs. Traditional cybersecurity disciplines focus primarily on building protective walls around endpoints and corporate networks. Securing advanced AI, however, requires actively defending the actual mathematical integrity of the deployed models. This represents a complex discipline that most internal security operations centres barely understand. Adversarial attacks present a very real and present danger to modern financial institutions. In a scenario known as a data poisoning attack, malicious actors subtly manipulate the external data feeds that a bank relies upon to train its internal fraud detection models. By doing so, they essentially teach the algorithm to turn a blind eye to specific and highly-lucrative types of illicit financial transfers. Consider also the threat of prompt injection, where attackers utilise natural language inputs to trick generative customer service bots into freely handing over sensitive account details. Model inversion represents another nightmare scenario for executives, occurring when outsiders repeatedly query a public-facing algorithm until they successfully reverse-engineer the highly confidential financial data buried deep within its training weights. To counter these evolving threats, security teams are forced to bury zero-trust architectures deep within the machine learning operations pipeline. Absolute device trust becomes non-negotiable. Only fully-authenticated data scientists, working exclusively on locked-down corporate endpoints, should ever possess the administrative permissions required to tweak model weights or introduce new data to the system. Before any algorithm touches live financial data, it must successfully survive rigorous adversarial testing. Internal red teams must intentionally attempt to break the algorithm’s ethical guardrails using sophisticated simulation techniques. Surviving these simulated corporate attacks serves as a mandatory prerequisite for any public deployment. Eradicating the engineering and compliance divide The highest barrier to creating safe AI is rarely the underlying software itself; rather, it is the entrenched corporate culture. For decades, a very thick wall separated software engineering departments from legal compliance teams. Developers were heavily incentivised to chase speed and rapid feature delivery. Conversely, compliance officers chased institutional safety and maximum risk mitigation. These groups typically operated from entirely different floors, used different software applications, and followed entirely different performance incentives. That division has to come down. Data scientists can no longer construct models in an isolated engineering vacuum and then carelessly toss them over the fence to the legal team for a quick blessing. Legal constraints, ethical guidelines, and strict compliance rules must dictate the exact architecture of the algorithm starting on day one. Leaders need to actively force this internal collaboration by establishing cross-functional ethics boards. Banks should pack these specific committees with lead developers, corporate counsel, risk officers, and external ethicists. When a particular business unit pitches a new automated wealth management application, this ethics board dissects the entire project. They must look past the projected profitability margins to deeply interrogate the societal impact and regulatory viability of the proposed tool. By retraining software developers to view compliance as a core design requirement rather than annoying red tape, a bank actively builds a lasting culture of responsible innovation. Managing vendor ecosystems and retaining control The enterprise technology market recognises the urgency surrounding compliance and is aggressively pumping out algorithmic governance solutions. The major cloud service providers now bake sophisticated compliance dashboards directly into their AI platforms. These tech giants offer banks automated audit trails, reporting templates designed to satisfy global regulators, and built-in bias-detection algorithms. Simultaneously, a smaller ecosystem of independent startups offers highly specialised governance services. These agile firms focus entirely on testing model explainability or spotting complex concept drift exactly as it happens. Purchasing these vendor solutions is highly tempting. Buying off-the-shelf software offers operational convenience and allows the enterprise to deploy governed algorithms without writing heavy auditing infrastructure from scratch. Startups are rapidly building application programming interfaces that plug directly into legacy banking systems, providing instant, third-party validation of internal models. Despite these advantages, relying entirely on outsourced governance introduces a risk of vendor lock-in. If a bank ties its entire compliance architecture to one hyperscale cloud provider, migrating those specific models later to satisfy a new local data sovereignty law becomes an expensive and multi-year nightmare. A hard line must be drawn regarding open standards and system interoperability. The specific tools tracking data lineage and auditing model behaviour have to be completely portable across different environments. The bank must retain absolute control over its compliance posture, regardless of whose physical servers actually hold the algorithm. Vendor contracts require ironclad provisions guaranteeing data portability and safe model extraction. A financial institution must always own its core intellectual property and internal governance frameworks. By fixing internal data maturity, securing the development pipeline against adversarial threats, and forcing legal and engineering teams to actually speak to one another, leaders can safely deploy modern algorithms. Treating strict compliance as the absolute foundation of engineering guarantees that AI drives secure and sustainable growth. See also: Ocorian: Family offices turn to AI for financial data insights Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Secure governance accelerates financial AI revenue growth appeared first on AI News. View the full article

Glia, a customer service platform providing AI-powered interactions for the banking sector, has been named a winner in the Banking and Financial Services Category at the 2026 Artificial Intelligence Excellence Awards. The awards recognises achievements in a range of industries and use cases, spotlighting “companies and leaders moving AI beyond experimentation and into practical, accountable deployment.” Speaking on the awards, Russ Fordyce, Chief Recognition Officer at Business Intelligence Group commented, “AI has arrived! 2026 is about execution and results. Glia stood out because its work in banking reflects where the market is headed: practical AI that solves real problems, earns trust, and delivers measurable value. The recognition highlights a team that is not participating in the AI shift, but helping define what meaningful progress looks like.” Glia’s Banking AI platform helps financial institutions navigate security and regulatory risks common in generative AI. It was chosen by a panel of AI experts and analysts as a platform that deploys AI trained precisely for banking workflows. It helps banks and credit unions automate up to 80% of all interactions, according to Glia. For the customer service and member care functions, this can free up time for other tasks, including strengthening client relationships and expanding lending and deposit portfolios; in other words, doing what humans can do and AI can’t. Dan Michaeli, CEO and co-founder of Glia, said: “The award celebrates the future of banking in an time where AI is everywhere. With consumers in every demographic now using AI to manage their lives, the pressure on financial institutions to provide instant, intelligent service has never been higher.” “Our platform is designed to help banks and credit unions lead this transition, using secure, banking-specific AI to amplify their efficiency while protecting the human connection that defines their brand,” he said. Glia has enjoyed positive business momentum recently, with the company announcing recently it will be the first to contractually promise to resist AI hallucinations and circumvent prompt injections for its clients’ use of the platform. As AI becomes increasingly complex, particularly in financial institutions, Glia’s focus on AI safety provides a model that banks and credit unions might rely on to help them use AI effectively and securely. (Image source: “Space Invaders does cones and safety barriers” by Gene Hunt is licensed under CC BY 2.0. To view a copy of this license, visit [Hidden Content]) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Glia wins Excellence Award for safer AI in banking appeared first on AI News. View the full article

When Pew Research Centre analysed 68,879 Google searches in March 2025, one finding stood out: users who encountered an AI-generated summary clicked on a traditional result just 8% of the time. Those who didn’t see a summary clicked nearly twice as often, at 15%. A quarter of users who saw an AI summary ended their session without clicking on anything at all. That gap tells you something important about where brand discovery is heading. With generative AI platforms like ChatGPT now pulling in 5.72 billion monthly visits (according to SimilarWeb data from January 2026), brands already know AI search matters. The more pressing question is whether your content is structured for the two distinct ways AI retrieves and presents information. SimilarWeb’s framework for AEO vs GEO draws a useful line between these approaches, and it’s one worth understanding before your competitors do. Where your clicks went and why they’re not coming back People are searching more than ever. They’re just not clicking. BrightEdge reported in May 2025 that Google search impressions climbed 49% in the year following the launch of AI Overviews. Over that same *******, click-throughs dropped nearly 30%. Seer Interactive’s September 2025 study, covering 25.1 million organic impressions in 42 organisations, found the decline was even steeper for queries triggering AI Overviews specifically: Organic CTR fell 61%, from 1.76% to 0.61% Paid CTR dropped 68%, from 19.7% to 6.34% Even queries without AI Overviews saw organic CTR decline 41% year-over-year By March 2025, one in five Google searches produced an AI summary (Pew Research Centre) Gartner predicted in early 2024 that traditional search volume would fall 25% by 2026. The exact figure remains debatable, but the direction is clear. Impressions are up. Engagement with links is collapsing. The answer itself has become the destination, and the brands inside that answer are the ones getting noticed. Getting cited by the machine This is where the AEO vs GEO distinction earns its weight. Answer Engine Optimisation (AEO) is about structuring content so AI systems can extract a clean, direct answer. Think featured snippets, People Also Ask boxes, voice assistant results. It’s tactical: question-based headings, answer-first paragraphs of 40 to 80 words, FAQ and HowTo schema markup. If someone asks a specific question and your content gives the clearest answer, AEO is what gets you cited at snippet level. Generative Engine Optimisation (GEO) operates at a broader level. It’s about making your brand a trusted source for RAG-powered platforms (ChatGPT, Perplexity, Gemini) that synthesise answers from multiple sources. GEO involves semantic content clusters, entity-rich data, multimodal assets and building domain authority through co-mentions in third-party sites, directories and publications. Here’s the part most brands are missing: you can win the featured snippet and still be completely absent from a ChatGPT response. McKinsey’s AI Discovery Survey (August 2025, surveying 1,927 consumers) found that a brand’s own website accounts for only 5 to 10% of the sources AI search platforms reference. The other 90% comes from publishers, user-generated content, affiliate sites and review platforms. So your AEO might be flawless on Google, while your GEO presence in the wider web remains thin. Worth noting: BrightEdge found that 89% of AI Overview citations come from results ranked beyond position 100. Traditional ranking position is becoming less relevant than content structure and authority signals. The brands that get cited will be the brands that get chosen The data on citation advantage is hard to ignore. Seer Interactive’s study found that brands cited in AI Overviews earn 35% more organic clicks and 91% more paid clicks compared to those left out of the summary entirely. The investment case is building, too. According to Conductor research reported by MarTech in February 2026, 32% of digital marketing leaders now rank GEO as their top priority for the year, and 97% report positive results from their efforts so far. An average of 12% of 2025 digital budgets went to GEO initiatives. Perhaps more telling, 93% of leaders are building these abilities in-house, treating AI search visibility as too strategically important to outsource. High-maturity organisations are already spending nearly twice as much on GEO as their lower-maturity peers. That gap will be difficult to close once the default answers are set. If 44% of consumers already prefer AI-powered search as their primary source of insight (McKinsey), and your brand doesn’t appear in those AI-generated responses, where does that leave you in the buying process? The new front door is already open AEO and GEO are distinct in their mechanics, but they serve the same purpose: making your brand the one AI systems trust, retrieve and cite. The practical starting point is straightforward. Audit your current AI visibility by prompting the major platforms with questions your customers ask. Identify where you appear, where you don’t and what sources are being cited instead. Then layer AEO (structured answers, schema, question-led content) with GEO (semantic depth, third-party co-mentions, multimodal assets) on top of your existing SEO foundations. The stakes are rising. As generative AI moves beyond summaries and toward agentic systems that act on users’ behalf (booking, purchasing, recommending), the brands AI cites will increasingly be the brands AI chooses. If your content strategy still measures success by clicks alone, what happens when the click becomes optional? (Image source: Bazoom) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. This comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How AEO vs GEO reshapes AI-driven brand discovery in 2026 appeared first on AI News. View the full article

As artificial intelligence becomes a driving force in financial prediction, the reliability of its forecasting tools faces increasing scrutiny. Many traders question whether claims of high accuracy translate into consistent results under live market conditions. Understanding how these AI systems are evaluated reveals important distinctions between performance in theory and practice. Few financial domains are as dependent on accurate prediction as forex trading, where slight changes in exchange rates can have consequences for participants. The surge of AI powered price forecasting tools has brought new abilities, but it has also raised questions about what constitutes meaningful accuracy. Readers in this rapidly evolving landscape of predictive technology seek clarity on how well these tools perform and which factors should inform their assessment of forecasts in live environments. Scrutinising claims of accuracy in predictive tools Accuracy claims regarding AI forecasting in currency markets are often presented optimistically, particularly when based on controlled demonstrations. These scenarios typically reflect historical data or optimised backtests, which can differ sharply from the volatility and unpredictability seen in live trading environments. The central issue lies in the gap between demonstration results and how models react to real-time market changes. While technical accuracy metrics are frequently referenced, their practical meaning for financial decision-making can remain ambiguous. When evaluating the accuracy of AI powered price forecasting tools, it is crucial to clarify what “accuracy” represents in this context. For some, accuracy might mean correctly predicting the direction of currency moves, while for others, it could relate to the exact magnitude or timing of price changes. The complexity of forex, with its fast moving variables and interdependencies, underscores why simplistic accuracy scores rarely provide the full picture. Professional users often demand both statistical rigor and domain expertise to interpret results effectively. Understanding the mechanics behind AI market predictions AI powered price forecasting tools commonly employ machine learning models specialised for time series prediction. These tools typically use advanced architectures like recurrent neural networks, convolutional neural networks, or transformer-based models designed to capture sequential patterns in financial data. They rely on inputs ranging from historical pricing and trading volumes to macroeconomic indicators and alternative data sources, including geopolitical events or sentiment analysis from news and social media. There are varied approaches in predictive modeling, with some systems focusing on point predictions that offer specific future prices, while others generate probabilistic forecasts reflecting outcome likelihoods in confidence intervals. The distinction affects how users interpret and trust model outputs. Although probabilistic methods can better accommodate market uncertainty, understanding distributional forecast accuracy and related concepts requires additional expertise. This complexity highlights why headline accuracy figures alone are not sufficient for assessing a system’s practical value. Evaluating model performance with robust accuracy metrics Practitioners typically assess AI powered price forecasting tools using a range of evaluation metrics, each shedding light on different facets of prediction quality. Directional accuracy measures whether forecasts correctly predict upward or downward movement of currency pairs, while metrics like mean absolute error or root mean squared error focus on the magnitude of prediction errors. Calibration, which reflects how well predicted probabilities align with actual market occurrences, adds another important dimension. Meaningful assessment requires benchmarks and rigorous out-of-sample testing, because models effective on past data may not remain reliable as markets change. Overfitting, where models treat noise as signal, can cause high-scoring tools to lose effectiveness once deployed. Similarly, regime shifts and nonstationarity in forex can quickly undermine predictive accuracy, highlighting the importance of ongoing monitoring and validation. It is recognised that participants benefit from understanding both the strengths and limitations of these tools before integrating them into operational processes. Navigating real world frictions and effective risk controls When AI powered price forecasting tools are integrated into live strategies, various real world frictions become significant. Issues like latency – the delay between signal and execution – with slippage, spread widening, and inconsistent execution quality, may degrade results observed in backtesting. And, data quality concerns and the risk of look ahead bias present ongoing challenges, particularly if datasets inadvertently include future information unavailable at decision time. As algorithmic signals become more prevalent, financial markets may adapt, reducing the effectiveness of commonly used forecasting techniques. Effective deployment requires a blend of quantitative insight and robust risk management. Rather than relying solely on single-point forecasts, applying confidence intervals and scenario analysis can yield greater operational stability. Position sizing rules and drawdown controls, with continuous stress testing during volatile periods, help mitigate the effects of erroneous predictions. Ongoing review and adaptation, grounded in an understanding of model limitations and maintained with human oversight, are essential for the sustainable application of AI powered price forecasting tools in currency markets. (Image source: Bazoom) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Assessing AI powered price forecasting tools in currency markets appeared first on AI News. View the full article

A developer of API and AI connectivity technologies, Kong, has announced that Bruce Felt has joined it as CFO. Felt is a seasoned finance leader who brings experience guiding enterprise software companies through their growth phases, including several IPOs, acquisitions, and global expansions. Mr. Felt has led finance organisations from early-stage environments to significant global enterprises. Over his career, he’s taken three companies public as CFO: FullTime Software, SuccessFactors, and Domo. At Domo, a cloud-based analytics and business intelligence software company, he helped scale the business and led the company to its public offering. Bruce Felt, new CFO at Kong. Source: AZK Media Augusto Marietti, chief executive officer and co-founder of Kong said:”Bruce has repeatedly helped high-growth software companies scale through transformative periods, pairing operational discipline with strategic insight and several crossings into public markets. As Kong continues to expand its leadership in API and AI connectivity, his experience building durable, globally scaled organisations will be a unique asset in our next journey.” “He brings the right mix of operational rigor and public company experience, while keeping a growth-oriented profile. We’re extremely excited to welcome Bruce onto the Kong team, and I look forward to partnering with and learning from him.” Bruce Felt serves on the boards of directors of several organisations, including Veradigm, Human Interest, Betterworks, and Cambium Networks. He has held board and audit committee leadership roles at public and private companies. (Image source: Pixabay under licence.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Kong names Bruce Felt as chief financial officer appeared first on AI News. View the full article

Banking house JPMorgan Chase is asking its roughly 65,000 engineers and technologists to use AI tools as part of their regular workflow. Business Insider reported that managers are tracking how often staff use these tools. That use may also influence performance reviews. The report states employees are encouraged to use tools like ChatGPT and Claude Code when writing code, reviewing documents, or handling routine tasks. Internal systems then classify workers based on their level of use. Some are labelled “light users,” while others fall into a “heavy user” category. JPMorgan has been using in fraud detection and risk analysis. What stands out here is not the technology itself, but how it is being woven into day-to-day expectations for staff. According to internal materials cited by Business Insider, managers are paying close attention to how employees use AI tools. JPMorgan shows AI adoption in banks Many companies have spent the past two years rolling out AI tools in departments. In most cases, adoption has been uneven. Some teams experiment heavily, while others stick to existing workflows. JPMorgan is treating AI as a standard part of the job. That creates a more uniform level of adoption in teams. In the past, performance reviews focused on output and accuracy. Now, they may also include how effectively employees use AI tools to reach those results. That raises a practical question for large organisations. If AI can reduce the time needed for certain tasks, should employees be expected to produce more work in the same amount of time? Keeping pace with internal change By tracking use, the bank may be trying to avoid a familiar problem in enterprise software rollouts. Tools are deployed, but adoption is slow, limiting their impact. Making AI part of performance reviews creates a stronger incentive to engage with the technology. It also suggests that AI literacy is becoming a baseline skill, similar to how spreadsheets or code tools became standard over time. New challenges include employees feeling pressure to use AI even in cases where it does not clearly improve the outcome. There is also the matter of how to measure “good” use, as opposed to simply frequent use. JPMorgan’s AI risks and efficiency gains Banks operate in a regulated environment, where introducing AI into more workflows increases the need for oversight. Tools like ChatGPT and Claude Code can help summarise information or generate drafts, but they can also produce incorrect or incomplete results. That means employees still need to verify outputs before using them in decision-making or client-facing work. JPMorgan has developed internal controls for AI systems in areas like trading and risk. Expanding use in a broader group of employees may require similar safeguards, creating a situation for the bank in which it wants to improve efficiency, but also needs to ensure that heavier AI use does not introduce new risks. Other financial institutions are likely watching closely. If tying AI use to performance leads to measurable gains in productivity, similar models may spread in the sector. The bank’s approach may reshape how companies hire and train employees, and skills like prompt writing and output checks could become part of standard job requirements. JPMorgan’ approach suggests that this change is already underway, at least in banking. (Photo by IKECHUKWU JULIUS UGWU) See also: RPA matters, but AI changes how automation works Want to experience the full spectrum of enterprise technology innovation? Join TechEx in Amsterdam, California, and London. Covering AI, Big Data, Cyber Security, IoT, Digital Transformation, Intelligent Automation, Edge Computing, and Data Centres, TechEx brings together global leaders to share real-world use cases and in-depth insights. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post JPMorgan begins tracking how employees use AI at work appeared first on AI News. View the full article

RPA (robotic process automation) is a practical and proven way to reduce manual work in business processes without AI systems. By using software bots to follow fixed rules, companies can automate repetitive tasks like data entry and invoice processing, and to a certain extent, report generation. Adoption grew quickly in many sectors, especially in finance, operations, and customer support. In recent years the technology has matured. While RPA is still used, business processes can become more complex. Many systems handle unstructured data, like messages and documents. Rule-based automation struggles to handle these inputs, since it depends on predefined steps and structured formats. RPA works best in stable environments where processes do not change often. When conditions change or inputs vary, bots can fail or need updating, adding maintenance overhead and reducing the value of automation over time. Gartner has pointed to more adaptive automation systems on the market, designed to handle variation and uncertainty, combining automation with machine learning or language models, allowing them to process a broader set of inputs. From RPA rules to AI-driven automation AI has changed how companies think about automation, as systems from vendors already known in the RPA space, like Appian and Blue Prism, can now interpret context and adjust their activities, especially relevant for tasks that involve text or images. Large language models’ ability to summarise documents and extract important details, and respond to queries in natural language offers automation in areas previously difficult to manage. McKinsey & Company research suggests generative AI could automate decision-making and communication work tasks, not routine data handling. The change does not replace automation, but rather modifies it. Rather than building chains of rules, businesses could use AI to handle variations in input media. Automation becomes more flexible, with systems able to adjust to different inputs without reconfiguration. That’s the theory. AI systems produce inconsistent outputs, and their behaviour is not predictable. Firms can combine AI with existing automation tools, using each where it fits best. Getting the balance right – intelligent automation – is a hot topic at industry events and on the pages of the RPA and AI media outlets. Where RPA still fits with AI Despite these changes, RPA remains relevant in many settings. Tasks that involve structured data and stable workflows still benefit from rule-based automation. Common examples include payroll processing and compliance checks, as well as system integrations. In these circumstances, RPA’s predictability can be an advantage. Bots follow defined steps and produce consistent results, which is useful in regulated environments. Financial reporting and auditing processes, for example, frequently require strict control and traceability. Rather than being replaced, RPA is often used with AI. Automation workflows may begin with AI systems that interpret input, then pass structured data to RPA bots for execution. The combination allows companies to extend automation without discarding existing systems. Blue Prism and the change toward intelligent automation Vendors that built their business around RPA are adapting to this change. Blue Prism, now part of SS&C Technologies, has expanded its focus to include what it describes as intelligent automation. This approach combines RPA with AI tools capable of processing more complex inputs. Platforms combine automation with abilities like document processing and decision support, frequently through integrations with AI tools. The move toward AI-enabled automation also changes how platforms get used. Workflows bring together data sources and decision points, along with execution steps in a single process. A gradual transition, not a full replacement Many organisations continue to rely on existing RPA systems, especially where processes are stable and well understood. Replacing these systems would take time and money, which may not always be justified. Instead, the transformation is gradual. Companies can add AI abilities to extend what automation can handle, while RPA is still in place for tasks where it still works well. This may change how automation is designed and deployed over time, but rule-based systems will remain necessary. See also: AI agents enter banking roles at Bank of America Want to experience the full spectrum of enterprise technology innovation? Join TechEx in Amsterdam, California, and London. Covering AI, Big Data, Cyber Security, IoT, Digital Transformation, Intelligent Automation, Edge Computing, and Data Centres, TechEx brings together global leaders to share real-world use cases and in-depth insights. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post RPA matters, but AI changes how automation works appeared first on AI News. View the full article

To gain financial data insights, the majority of family offices now turn to AI, according to new research from Ocorian. The global study reveals 86 percent of these private wealth groups are utilising AI to improve their daily operations and data analysis. Representing a combined wealth of $119.37 billion, these organisations want machine learning to modernise their workflows. The technology offers practical benefits for institutions handling complex portfolios, particularly in detecting anomalies, streamlining reporting, and navigating strict regulatory frameworks. Securing financial data insights via AI and system governance Implementing these tools requires careful alignment with existing enterprise architectures. Financial institutions frequently rely on major cloud ecosystems, such as Microsoft Azure or Google Cloud, to provide the necessary computing power and security protocols for advanced data processing. By using these platforms, operations teams can deploy machine learning models that identify potential fraud patterns or compliance breaches much faster than manual reviews allow. While 26 percent of surveyed wealth executives strongly agree that AI will reshape administration and boost performance within the next year, 72 percent expect the broader effects to materialise over a two to five-year horizon. This cautious timeline reflects the reality of integrating complex algorithms into highly-regulated environments. Integrating new systems without disrupting daily client services presents a major challenge. Legacy data architectures often require heavy re-engineering before they can fully support predictive analytics. Michael Harman, Commercial Director for the *** and Channel Islands at Ocorian, said: “Family offices are gradually adopting AI and technology as part of their operations and are particularly using it for data insights … there is a realisation that it will have a major impact and family offices need to start exploring the sector and will need support in making the transition.” Balancing operational upgrades with capital exposure Despite high operational adoption rates, direct capital allocation into the AI sector remains low. Only seven percent of respondents across 16 territories – including the ***, US, UAE, and Singapore – are currently seeking direct investment opportunities in such technology firms. This current hesitation highlights a preference for using proven enterprise solutions rather than absorbing the venture-style risks associated with emerging startups. Leaders are focused on immediate operational stability and verifiable returns on investment. However, this dynamic is likely to change rapidly over the next three years, as 74 percent of these organisations expect to increase their investments in digital assets. Within that group, 20 percent plan to increase their financial commitment to the sector dramatically. Outsourcing the technical burden to established service providers allows institutions to benefit from enhanced fraud detection and compliance monitoring without directly managing the algorithmic infrastructure. Success will depend on establishing clean data pipelines and ensuring cross-functional teams understand how to interpret algorithmic outputs for risk assessment. By prioritising secure and scalable cloud platforms, and focusing on specific operational pain points like regulatory reporting, financial leaders can effectively use these AI capabilities to bolster their data insights while maintaining the necessary oversight required in modern wealth management. See also: AI agents enter banking roles at Bank of America Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Ocorian: Family offices turn to AI for financial data insights appeared first on AI News. View the full article

AI agents are starting to take on a more direct role in how financial advice is delivered, as large banks move beyond internal tools and into systems that support real client interactions. Bank of America is now deploying an internal AI-powered advisory platform to a subset of financial advisors, rolled out to around 1,000 financial advisers, according to Banking Dive. The move is one of the clearer early examples of how AI is being used in core banking roles rather than back-office tasks or limited pilots. It also reflects a broader shift across the industry, where AI is moving from basic assistance to systems that can support decision-making in real time. The platform is based on Salesforce’s Agentforce, which enables the creation of AI agents to handle tasks. It is designed to help advisors handle client queries and prepare recommendations. It can also help manage daily workflows. According to Banking Dive, the system is part of a wider push among major banks to test how AI agents can work alongside human staff rather than operate as standalone tools. Bank of America has been expanding its use of AI across the business. The bank has said its virtual assistant Erica handles work equivalent to about 11,000 employees, while all 18,000 of its software developers use AI coding tools that have improved productivity by around 20%, according to Banking Dive. These figures give a sense of how widely AI is already embedded across different parts of the organisation. AI agents move closer to financial decision-making This approach differs from earlier deployments of AI in banking, which focused mainly on chatbots or internal productivity tools. In those cases, AI was used to answer simple questions or automate routine tasks. The newer systems are built to handle more complex work, including analysing client data and suggesting next steps. That shift brings AI closer to the core of financial decision-making. Instead of acting as a support layer, the technology is now embedded within the advisory process itself. Other large banks are moving in a similar direction. The same Banking Dive report notes that firms such as JPMorgan, Wells Fargo, and Goldman Sachs are also testing AI tools aimed at improving productivity and helping staff in client-facing roles, though these efforts vary and are not always focused on advisor-specific AI agent systems. While each bank is taking a different approach, the common goal is to increase output without expanding headcount at the same rate. Early data suggest these tools can improve efficiency, though results vary. In some cases, banks report gains in how quickly advisors can access information or prepare for meetings, based on industry reporting and early deployment feedback cited by Banking Dive. At the same time, there are ongoing concerns about accuracy and oversight, especially when AI systems are used to suggest financial decisions. A wider pattern is emerging across financial services. Many institutions are investing in AI, but they are doing so in a controlled way, often limiting deployment to specific teams or use cases. The goal is to test how the technology performs in real settings before expanding further. Some analysts remain cautious about how quickly AI is changing banking. Wells Fargo analyst Mike Mayo wrote that recent developments have yet to produce major new products, describing the current phase as “a little boring from a product standpoint,” according to Banking Dive. Human oversight remains central Bank of America’s rollout stands out because of its scale and placement. Financial advisors sit at the centre of the bank’s relationship with clients, particularly in wealth management. Introducing AI into that role suggests a growing level of trust in the technology. It also shows a willingness to let it influence how advice is formed and delivered. At the same time, the system is not replacing advisors. Instead, it is meant to work alongside them. Human monitoring remains an essential part of the process, particularly when dealing with complex financial decisions or high-value clients. Industry executives also acknowledge that AI is unlikely to completely replace expert roles, particularly in complex financial workflows where context and judgement still matter. This hybrid model is becoming more common across the sector. Rather than removing people from the loop, banks are trying to combine human judgement with machine-generated insights. Some firms are starting to treat AI as a part of the workforce rather than a tool, with staff expected to work alongside these systems on day-to-day tasks. Progress comes with limits and trade-offs There are also practical challenges. AI systems depend on clean, structured data, which is not always easy to achieve in large organisations with legacy systems. Integration with existing tools can take time, and staff may need training to use new systems effectively. Regulation adds another layer of complexity. Financial institutions must ensure that AI-driven recommendations meet compliance standards. They must also be able to explain them if questioned by regulators. This requirement may limit the amount of autonomy provided to AI systems, particularly in areas like lending or investment advice. Despite these constraints, banks are starting to move beyond experimentation and into operational use, even if progress remains uneven. Some estimates imply that up to one-third of banking jobs, or parts of those roles, could eventually be handled by AI, though timelines remain unclear. The introduction of AI agents into advisory roles also raises questions about how the job itself may change. If systems can handle more of the analytical work, advisors may spend more time on client relationships and less on preparation. Over time, this could shift the skills required for the role. At the same time, reliance on AI introduces new risks. Errors in data or model output could affect recommendations, and overreliance on automated systems may reduce critical review by human staff. These issues are still being studied as deployments expand. What sets the current phase is not just the technology, but where it is being used. Moving AI into frontline roles suggests that banks regard it as a tool for shaping outcomes rather than simply improving efficiency behind the scenes. Bank of America’s rollout offers a view into how that transition may play out. It shows a large institution testing how far AI can be integrated into everyday work, while still keeping human oversight in place. As more banks follow a similar path, the focus is likely to shift from whether AI should be used to how it should be managed once it becomes part of core operations. See also: Visa prepares payment systems for AI agent-initiated transactions Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI agents enter banking roles at Bank of America appeared first on AI News. View the full article

Finance leaders are automating their complex workflows by actively adopting powerful new multimodal AI frameworks. Extracting text from unstructured documents presents a frequent headache for developers. Historically, standard optical character recognition systems failed to accurately digitise complex layouts, frequently converting multi-column files, pictures, and layered datasets into an unreadable mess of plain text. The varied input processing abilities of large language models allow for reliable document understanding. Platforms such as LlamaParse connect older text recognition methods with vision-based parsing. Specialised tools aid language models by adding initial data preparation and tailored reading commands, helping structure complex elements such as large tables. Within standard testing environments, this approach demonstrates roughly a 13-15 percent improvement compared to processing raw documents directly. Brokerage statements represent a tough file reading test. These records contain dense financial jargon, complex nested tables, and dynamic layouts. To clarify fiscal standing for clients, financial institutions require a workflow that reads the document, extracts the tables, and explains the data through a language model, demonstrating AI driving risk mitigation and operational efficiency in finance. Given these advanced reasoning and varied input needs, Gemini 3.1 Pro is arguably the most effective underlying model currently available. The platform pairs a massive context window with native spatial layout comprehension. Merging varied input analysis with targeted data intake ensures applications receive structured context rather than flattened text. Building scalable multimodal AI pipelines for finance workflows Successful implementation requires specific architectural choices to balance accuracy and cost. The workflow operates in four stages: submitting a PDF to the engine, parsing the document to emit an event, running text and table extraction concurrently to minimise latency, and generating a human-readable summary. Utilising a two-model architecture acts as a deliberate design choice; where Gemini 3.1 Pro manages complex layout comprehension, and Gemini 3 Flash handles the final summarisation. Because both extraction steps listen for the same event, they run concurrently. This cuts overall pipeline latency and makes the architecture naturally scalable as teams add more extraction tasks. Designing an architecture around event-driven statefulness allows engineers to build systems that are fast and resilient. Integrating these solutions involves aligning with ecosystems like LlamaCloud and Google’s GenAI SDK to establish connections. However, processing pipelines rely entirely on the data fed into them. Of course, anyone overseeing AI deployments for workflows as sensitive as finance must maintain governance protocols. Models occasionally generate errors and should not be relied upon for professional advice. Operators must double-check outputs before relying on them in production. See also: Palantir AI to support *** finance operations Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Automating complex finance workflows with multimodal AI appeared first on AI News. View the full article

Evidence cited in an eBook titled “AI Quantum Resilience”, published by Utimaco , shows organisations consider security risks as the leading barrier to effective adoption of AI on data they hold. AI’s value depends on data amassed by an organisation. However, there are security risks to building models and training them on that data. These risks are in addition to better-publicised threats to intellectual property that exist around the point of inference (prompt engineering, for example). The eBook’s authors state that organisations need to manage threats throughout their AI development and implementation processes. At the same time, companies can and should prepare to change their security protocols, changes that will become mandatory if quantum computing-powered decryption tools become easily available to bad actors. Utimaco lists three areas under threat: Training data can be manipulated by bad actors, degrading model outputs in ways are hard to detect, Models can be extracted or copied, eroding intellectual property rights, Sensitive data used during training or inference can be exposed. Current public key cryptography will become vulnerable in the next ten years, the report’s authors attest; a ******* in which capable quantum systems may emerge. Regardless of the timescale, it’s thought that better organised groups currently collect encrypted data and store it to decrypt when or if quantum facilities become available. Any dataset with long-term sensitivity, including model training data, financial records, or intellectual property, may require protection against future decryption, therefore, Utimaco says. A migration to quantum-resistant cryptography will affect protocols, key management, system interoperability, and performance, so any migration is likely to take several years. The report’s authors suggest what they term ‘crypto-agility’, which it defines as changing cryptographic algorithms without redesigning underlying systems. ‘Crypto-agility’ is based on the principle of hybrid cryptography – combining established algorithms with post-quantum methods, such as those suggested by NIST. The eBook’s authors concur that cryptography on its own doesn’t address all possible areas of risk. It advocates the use of hardware-based trust devices that can isolate cryptographic keys and sensitive operations from normal working environments. If companies are developing their own AI tools and processes, protection on that basis should extend throughout the AI lifecycle, from data ingestion through to training, model deployment, and inference in production. Hardware keys used to encrypt data and sign models can be generated and stored inside a boundary. Model integrity can then be verified before deployment, and sensitive data processed during inference remains protected. Hardware-based enclaves isolate workloads so that even system administrators with sufficient privileges can’t access any of the data being processed. Hardware modules can verify that the data enclave is in a trusted state before releasing keys – a process of external attestation – helping create a ‘chain of trust’ from hardware to application. Hardware-based key management produces tamper-resistant logs covering access and operations to support compliance frameworks such as the EU AI Act. Many of the risks inherent in AI systems are well known if not already exploited. The risk from quantum computing’s ability to decrypt data currently considered safe is less immediate, but the implications should affect data and infrastructure decisions made today, Utimaco states. It advocates: A strengthening of controls throughout the AI development and deployment lifecycle, The introduction of ‘crypto-agility’ to allow transition to post-quantum security, Establishing hardware-based trust mechanisms wherever high-value assets are in play. (Image source: “Scanning electron micrograph of an apoptotic HeLa cell” by National Institutes of Health (NIH) is licensed under CC BY-NC 2.0. To view a copy of this license, visit [Hidden Content]) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Securing AI systems under today’s and tomorrow’s conditions appeared first on AI News. View the full article

*** authorities believe improving efficiency across national finance operations requires applying AI platforms from vendors like Palantir. The country’s financial regulator, the FCA, has initiated a project leveraging AI to identify illicit activities. The FCA is currently testing the Foundry platform from Miami-based software vendor Palantir. This three-month pilot costs upwards of £30,000 per week and focuses on mining the regulator’s internal data lake. The objective centres on detecting money laundering, insider trading, and fraud across the 42,000 financial services businesses under the FCA’s supervision. Navigating unstructured data lakes Traditional oversight methods struggle with the sheer volume of information generated by modern markets. AI platforms excel at parsing unstructured intelligence, which regulators gather during investigations into harmful activities like human trafficking and the narcotics trade. The information fed into these systems spans from highly-confidential internal files and reports on problematic companies to consumer ombudsman complaints. Machine learning tools digest audio recordings from phone calls, social media activity, and email archives. Uncovering patterns within such a vast array of inputs helps direct enforcement resources exactly where they are needed most. Industry experts note a historical under-exploitation of the intelligence housed within regulatory bodies, making advanced analytics a valuable tool for tackling financial crimes. When validating AI models, there is often a debate about the merits of synthetic information versus live environments. While standard guidelines encourage using artificial datasets for preliminary testing, the ***’s finance regulatory authority determined that evaluating AI software like Palantir’s required actual operational inputs. Expanding into national security operations This public sector adoption extends well beyond financial compliance. In September 2025, the *** government established an AI partnership with Palantir aimed at accelerating military decision-making and targeting capabilities. Palantir plans to invest up to £1.5 billion to establish London as its European defence headquarters, an initiative expected to generate up to 350 jobs. As businesses evaluate these platforms, the defence sector provides a high-stakes testing environment for data fusion. Military planners utilise these tools to consolidate open-source and classified intelligence, rapidly generating options to neutralise enemy targets. This forms an element of the Digital Targeting Web, which relies on a diverse supplier ecosystem. Palantir and the military will collaborate on identifying opportunities worth up to £750 million over a five-year *******. To foster broader ecosystem growth, the defence agreement includes provisions for mentoring local startups, assisting smaller British technology firms with expanding into US markets on a pro-bono basis. Deploying private AI like Palantir’s in *** finance operations CDOs deploying AI solutions often struggle when balancing processing capabilities with privacy mandates. During an enforcement action, regulators frequently compel companies to surrender extensive records. Such datasets regularly include the personal bank details, telephone numbers, and complete communication logs of individuals tangentially related to a case. Establishing exact boundaries regarding how a software provider interacts with this intelligence is vital. Before selecting Palantir from a two-vendor shortlist, the FCA claims to have run a competitive procurement process and established strict data protection controls. To mitigate risks associated with information exposure, the FCA structured its agreement with Palantir so the vendor acts strictly as a data processor. Under this arrangement, the software provider operates solely upon instruction. The regulatory agency maintains exclusive possession of encryption keys for the most classified files, and all hosting and storage remain securely within the ***. Similar data sovereignty principles apply to the defence partnership, ensuring military intelligence remains freely available across the Ministry of Defence while entirely under national control. The financial contract explicitly forbids the vendor from copying the ingested intelligence to train its own commercial products. Once the pilot concludes, the vendor must destroy the information. Any intellectual property generated during the analysis phase automatically belongs to the regulator. Setting limitations on data retention and processing rights ensures internal security standards remain intact while achieving efficiency gains from deploying private AI from vendors like Palantir to improve the ***’s finance operations. See also: Visa prepares payment systems for AI agent-initiated transactions Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Palantir AI to support *** finance operations appeared first on AI News. View the full article

Payments rely on a simple model: a person decides to buy something, and a bank or card network processes the transaction. That model is starting to change as Visa tests how AI agents can initiate payments. New work in the banking sector suggests that, in some cases, software agents may soon take on that role. A recent example comes from Visa, which is rolling out its “Agentic Ready” programme in Europe to test how financial systems handle AI-initiated transactions. The effort involves collaboration with banks, including Commerzbank and DZ Bank. The aim is to prepare existing payment infrastructure for a scenario where software agents can search for products and make decisions, then complete purchases on behalf of users. According to information published by Visa and reported by The Paypers, the programme focuses on enabling secure transactions where AI systems act as the initiating party. Instead of a customer confirming a purchase, an AI agent could carry out the task after being given a goal or set of rules. How transactions begin Payment systems are built around human identity and intent. A card transaction today depends on verifying that a person has authorised a purchase. If AI agents begin to initiate transactions, banks will need new ways to confirm identity and intent at the system level. That includes deciding how an agent proves it is acting on behalf of a user, and how much autonomy it should have. In Visa’s model, software agents could handle routine or repeat purchases with limited human input, based on user-defined rules. A system could, for example, monitor supply levels and compare prices, then complete a transaction when certain conditions are met. Reporting from Die Welt and Investing.com says the company sees this as similar in scale to the early change toward online payments, when banks had to adapt to a new type of transaction flow. Control and compliance Banks involved in early trials are testing how these ideas work in practice. Commerzbank and DZ Bank are exploring how AI agents can be integrated into existing systems without breaking compliance rules. This includes checks related to fraud, audit trails, and customer consent. These areas are tightly regulated, which means any change to how transactions are initiated must still meet oversight standards. A RepRisk report found that banks are already dealing with more frequent and costly issues linked to AI. The report states that these incidents can lead to multi-million-dollar losses. Visa’s work is focused on infrastructure not consumer-facing tools. It’s working on how payment networks should behave when the “customer” is a piece of software. That includes defining how agents are authenticated and how transactions are approved. It also covers how disputes are handled if something goes wrong. AI and enterprise purchasing In large organisations, procurement often involves multiple approval steps. AI agents could compress that process by handling routine purchases in set limits. This could reduce manual work, but it also means companies need clear rules about what agents are allowed to do. Without that, the risk of errors or misuse increases. Large institutions are investing in AI to automate back-office work and reduce costs. Some are also reorganising teams to focus more on data and AI strategy. Regulators are paying closer attention to how AI is used in decision-making, especially in areas like credit and fraud detection. Taken together, these developments suggest that payments could become one of the first areas where AI agents could act with greater autonomy. Banks will still need to set rules, monitor activity, and handle exceptions. But the day-to-day act of initiating a transaction may, in some cases, require less direct human input. Visa’s current phase is focused on testing and system design. As AI systems take on more responsibility, financial infrastructure will need to adapt to a new type of user, one that does not hold a card but can still make a purchase. (Photo by CardMapr.nl) See also: Goldman Sachs sees AI investment change to data centres Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Visa prepares payment systems for AI agent-initiated transactions appeared first on AI News. View the full article

The NVIDIA Agent Toolkit is Jensen Huang’s answer to the question enterprises keep asking: how do we put AI agents to work without losing control of our data, our systems, and our liability? Announced at GTC 2026 in San Jose on March 16, the NVIDIA Agent Toolkit is an open source software stack designed to help enterprises and developers build autonomous AI agents–ones that can perceive, reason, and act on their own, across internal systems, without needing a human to babysit every step. The timing makes sense. The agent conversation has moved well past the pilot phase. What’s stalling broader deployment isn’t capability–it’s trust. Agents that can take action inside enterprise systems need guardrails, and until now, those have been hard to standardise at scale. OpenShell and the safety problem The centrepiece of the toolkit is NVIDIA OpenShell, an open source runtime that enforces policy-based security, network, and privacy guardrails for autonomous agents. In NVIDIA’s terminology, individual agents are called “claws”, and OpenShell is what keeps them in check. Huang framed the stakes plainly at GTC: “Claude Code and OpenClaw have sparked the agent inflexion point–extending AI beyond generation and reasoning into action. Employees will be supercharged by teams of frontier, specialised, and custom-built agents they deploy and manage.” That last part is the pitch. The ambition isn’t a single AI assistant; it’s a workforce of specialised agents, each handling a domain, coordinated at scale. OpenShell is the layer that’s supposed to make that deployable without IT teams having heart attacks. NVIDIA is working with Cisco, CrowdStrike, Google, Microsoft Security, and TrendAI to build OpenShell compatibility into their respective security tools, which signals that this isn’t being positioned as a standalone product, but as infrastructure others build on top of. The research and cost angle Also inside the toolkit is NVIDIA AI-Q, an agentic search blueprint built with LangChain. It uses a hybrid architecture–frontier models handle orchestration while NVIDIA’s open Nemotron models do the research-heavy lifting. According to NVIDIA, this approach can cut query costs by more than 50% while still producing accuracy that tops the DeepResearch Bench and DeepResearch Bench II leaderboards. That cost figure will matter to enterprise buyers who’ve been burned by consumption-based AI pricing that looked manageable in pilots and became a budget problem at scale. Who’s already on board? The partner list at GTC was extensive. Adobe, Atlassian, SAP, Salesforce, ServiceNow, Siemens, Cisco, CrowdStrike, Red Hat, Box, Cadence, Cohesity, Dassault Systèmes, IQVIA, and Synopsys are all advancing enterprise AI agents using the NVIDIA Agent Toolkit. A few specifics stand out. Salesforce is building a reference architecture where employees use Slack as the orchestration layer for Agentforce agents–pulling from data in both on-premises and cloud environments–powered by NVIDIA infrastructure. Atlassian is integrating Agent Toolkit into its Rovo AI strategy across Jira and Confluence. ServiceNow’s “Autonomous Workforce of AI Specialists” is built on the toolkit alongside NVIDIA AI-Q. And Siemens launched the Fuse EDA AI Agent, which uses NVIDIA Nemotron to autonomously orchestrate workflows across its electronic design automation portfolio, from design conception through manufacturing sign-off. IQVIA’s deployment numbers offer a real-world data point: the company has already deployed more than 150 agents across internal teams and client environments, including 19 of the top 20 pharma companies. The ******* shift What NVIDIA is really doing here is positioning itself not just as the hardware backbone of AI, but as the software infrastructure layer for enterprise agentic deployment. The Agent Toolkit, OpenShell, Nemotron models, AI-Q-these are components of a stack that NVIDIA wants sitting underneath an enormous swath of enterprise software. Whether that bet pays off depends on how quickly enterprises move from agent experimentation to agent operations. The toolkit is available now on build.nvidia.com, with support across AWS, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure. See also: AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post NVIDIA wants to make enterprise AI agents safe enough to actually deploy appeared first on AI News. View the full article