At the Intelligent Automation Conference, industry leaders gathered to dissect why many automation initiatives stall after pilot phases. Speaking alongside representatives from NatWest Group, Air Liquide, and AXA XL, Promise Akwaowo, Process Automation Analyst at Royal Mail, grounded the dialogue in practical delivery and risk management.

The elasticity imperative for scaling intelligent automation

Expansion initiatives often fail because teams equate success with the raw number of deployed bots rather than the underlying architecture’s elasticity. Infrastructure must handle volume and variability predictably.

When demand spikes during end-of-quarter financial reporting or sudden supply chain disruptions, the system cannot degrade or collapse. Without built-in elasticity, companies risk building brittle architectures that break under operational stress.

Akwaowo explained that an automated architecture must remain stable without excessive manual intervention. “If your automation engine requires constant sizing, provisioning, and babysitting, you haven’t built a scalable platform; you’ve built a fragile service,” he advised the audience.

Whether integrating CRM ecosystems like Salesforce or orchestrating low-code vendor platforms, the objective remains building a platform capability rather than a loose collection of scripts.

Transitioning from controlled proofs-of-concept to live production environments introduces inherent risk. Large-scale, immediate deployments frequently cause disruption, undermining the anticipated efficiency gains. To protect core operations, deployment must happen in controlled stages. Akwaowo warned that “progress must be gradual, deliberate, and supported at each stage.”

A disciplined approach starts with formalising intent through a statement of work and validating assumptions under real conditions.

Before scaling intelligent automation, engineering teams must thoroughly understand system behaviour, potential failure modes, and recovery paths. For example, a financial institution implementing machine learning for transaction processing might cut manual review times by 40 percent, but they must ensure error traceability before applying the model to higher volumes.

This phased methodology protects live operations while enabling sustainable growth. Additionally, teams must fully grasp process ownership and variability before applying technology, avoiding the trap of merely automating existing inefficiencies. Fragmented workflows and unmanaged exceptions upstream often doom projects long before the software goes live.

A persistent misconception within automation programmes suggests that governance frameworks impede delivery speed. However, bypassing architectural standards allows hidden risks to accumulate, eventually stalling momentum. In regulated, high-volume environments, governance provides the foundation for safely scaling intelligent automation. It establishes the trust, repeatability, and confidence necessary for company-wide adoption.

Implementing a dedicated centre of excellence helps standardise these deployments. Operating a central Rapid Automation and Design function ensures every project is assessed and aligned before it reaches the production environment. Such structures guarantee that solutions remain operationally sustainable over time. Analysts also rely on standards like BPMN 2.0 to separate the business intent from the technical execution, ensuring traceability and consistency across the entire organisation.

Adapting to agentic AI inside ERP ecosystems

As large ERP providers rapidly integrate agentic AI, smaller vendors and their customers face pressure to adapt. Embedding intelligent agents directly into smaller ERP ecosystems offers a path forward, augmenting human workers by simplifying customer management and decision support. This approach to scaling intelligent automation allows businesses to drive value for existing clients instead of competing solely on infrastructure size.

Integrating agents into finance and operational workflows enhances human roles rather than replacing accountability. Agents can manage repetitive tasks such as email extraction, categorisation, and response generation.

Relieved of administrative burdens, finance professionals can dedicate their time to analysis and commercial judgement. Even when AI models generate financial forecasts, the final authority over decisions rests firmly with human operators.

Building a resilient capability demands patience and a commitment to long-term value over rapid deployment. Business leaders must ensure their designs prioritise observability, allowing engineers to intervene without disrupting active processes.

Before scaling any intelligent automation initiative, decision-makers should evaluate their readiness for the inevitable anomalies. As Akwaowo challenged the audience: “If your automation fails, can you clearly identify where the error occurred, why it happened, and fix it with confidence?”

See also: JPMorgan expands AI investment as tech spending nears $20B

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.

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Every time a new deal crosses a firm’s desk, analysts start from scratch, even when the answers to their most pressing questions are buried somewhere in the firm’s own history. 

That is the problem Rowspace was built to solve, and it’s why the San Francisco startup is emerging from stealth with US$50 million in funding and a bold pitch: AI for private equity that doesn’t just assist decision-making, but actually learns how a firm thinks.

The company launched publicly with a seed round led by Sequoia and a Series A co-led by Sequoia and Emergence Capital, with participation from Stripe, Conviction, Basis Set, Twine, and a group of finance-focused angel investors. 

Early customers–unnamed, but described as name-brand private equity and credit firms managing hundreds of billions to nearly a trillion dollars in assets–are already living on the platform, with about ten top firms on seven-figure annual contract values.

Two MIT graduates, one stubborn problem

Rowspace was founded by Michael Manapat and Yibo Ling, who met as graduate students at MIT before diverging into very different careers. Manapat went on to build the machine learning systems at Stripe that process billions of transactions, then helped drive Notion’s expansion into AI as its CTO. 

Ling took the finance route–a two-time CFO who led finance teams at Uber and Binance, and spent years making investment decisions by manually synthesising data across fragmented systems. When ChatGPT launched in late 2022, Ling tested it on due diligence tasks and ran straight into the same wall. 

“Clearly there was a lot of promise, but it just wasn’t working,” he told Fortune. “You need the right information in the right context.” That gap — between AI’s potential and the messy, proprietary, institution-specific data reality of finance—became the founding thesis.

Ling, Co-founder and COO, put it plainly: “Most tech tools aren’t comprehensive or nuanced enough for finance. And most finance tools need to raise their technical ceiling. We intend to do both.”

What AI for private equity actually looks like

Rowspace’s platform connects structured and unstructured data across a firm’s entire history–document repositories, investment and accounting systems, old PowerPoints, deal memos–and applies what Manapat calls a finance-native lens: one that reflects how a firm actually reconciles information, interprets discrepancies, and makes decisions. Crucially, it processes all of this inside a client’s own cloud environment. The firm’s data never leaves its control.

The result is accessible through Rowspace’s own interface, within tools like Excel and Microsoft Teams, or directly into a firm’s existing data infrastructure. A first-year analyst reviewing a new deal can surface decades of prior decisions, comparable transactions, and internal underwriting patterns without picking up the phone or hunting through shared drives.

“Finance is full of high-stakes decisions. There used to be a tradeoff between moving quickly and making fully informed, nuanced decisions using all the possible data at a firm’s disposal. Our AI platform eliminates that tradeoff,” said Michael Manapat, Co-founder and CEO of Rowspace. “We’re building specialised intelligence that turns a firm’s data into scalable judgment with the rigour finance demands.”

The ambition is captured in a line Manapat uses internally: “Imagine a firm that never forgets. Where an experienced investor’s workflows–touching many different tools in specific ways–can be codified and multiplied. When that’s possible, a first-year analyst can tap into decades of institutional knowledge, and judgment scales with a firm instead of being diluted.”

Why Sequoia and Emergence are betting on vertical AI

The investor conviction behind this raise is itself a signal worth reading. Alfred Lin, the Sequoia partner who led the investment, positioned Rowspace as a direct answer to the question of what AI applications will survive the rise of increasingly capable foundation models.

“Michael built the machine learning systems at Stripe that process billions of transactions and helped drive Notion’s expansion into AI. Yibo has been a finance leader and investor who’s wrestled with the exact challenges Rowspace is solving,” Lin said, adding that both Michael and Yibo have seen the problem from both sides, pairing technical depth with firsthand understanding of what customers actually need.

Jake Saper, General Partner at Emergence Capital, went further on the data infrastructure thesis: “They’re doing the previously impossible work of connecting proprietary data, and reconciling and reasoning over it with real rigour. Without this foundation, it doesn’t matter what other AI tools you’re using.”

The argument is a neat inversion of the fear gripping much of the software industry right now: that foundation models will eventually commoditise applications. Lin’s view is the opposite–that vertical AI systems built on deep, proprietary data layers are precisely where durable competitive advantage will compound. 

For AI for private equity specifically, where alpha is by definition firm-specific and non-replicable, that logic is particularly hard to argue with. The back office of investment management has quietly been one of the last frontiers general AI has struggled to crack. Rowspace just raised $50 million on the premise that it knows why–and what to do about it.

(Photo by Rowspace)

See also: Santander and Mastercard run Europe’s first AI-executed payment 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.

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The spending plan reflects a broader shift among large enterprises. AI is no longer treated as a small research project. Instead, companies are embedding it in areas such as risk analysis, fraud detection, and customer service.

For business leaders watching how AI adoption is changing enterprise technology strategies, the numbers from JPMorgan highlight a larger trend: AI is becoming part of the everyday systems that run major organisations.

JPMorgan’s technology budget and rising AI investment

Technology spending has been rising across the banking sector for years. JPMorgan’s budget stands out because of its scale.

Reports from Business Insider, citing company briefings and investor discussions, say the bank expects technology spending to reach roughly US$19.8 billion in 2026, continuing a steady increase in technology investment. The spending covers areas such as cloud infrastructure, cybersecurity, data systems, and AI tools.

Part of the increased budget includes about US$1.2 billion in additional technology investment, some of which will support AI-related work.

Large banks often treat technology spending as a long-term investment rather than a short-term cost. Many of these systems take years to build, especially when they depend on large data platforms and secure computing infrastructure.

As AI systems require reliable data pipelines and computing power, many companies are finding that AI adoption often leads to wider upgrades across their technology stack.

Machine learning already influencing results

Executives say AI is already affecting business performance inside the bank. During investor discussions, JPMorgan’s chief financial officer, Jeremy Barnum, said machine-learning analytics are contributing to revenue and operational improvements across parts of the company.

Reuters reporting on JPMorgan’s financial briefings noted that the bank is using data models and machine-learning systems to improve analysis and decision-making in several areas of the business.

These models can process large volumes of financial data and identify patterns that are difficult for humans to detect. In sectors such as banking, where firms manage enormous data flows every day, these improvements can affect outcomes across trading, lending, and customer operations.

Even small improvements in prediction models can influence financial performance when applied to millions of transactions or market signals.

Where AI appears inside the bank

Machine-learning tools now support a wide range of activities across JPMorgan.

In financial markets, models analyse trading data and help identify patterns in price movements. These insights can help traders evaluate risk or identify opportunities in fast-moving markets.

Lending is another area where AI systems play a role. Machine-learning models can review financial history, market trends, and customer information to help assess credit risk. These systems assist analysts by highlighting patterns in the data.

Fraud detection remains one of the most common uses of AI in banking. Payment networks process huge volumes of transactions every day, making it difficult to monitor activity manually. Machine-learning systems can scan transactions in near real time and flag unusual behaviour that may indicate fraud.

Some internal operations also rely on AI. Tools can review contracts, summarise research reports, or help employees search large internal data systems. Generative AI systems are beginning to assist with tasks such as drafting reports or preparing internal documentation.

These systems rarely appear directly to customers, but they support many decisions happening behind the scenes.

Why banks have adopted AI early

Financial institutions have several characteristics that make them well-suited to machine learning.

First, banks generate large structured datasets. Transaction histories, market records, and payment data provide rich information that machine-learning models can analyse.

Second, many banking activities depend on prediction. Credit scoring, fraud detection, and market analysis all require estimating outcomes based on past data.

Machine learning works well in environments where prediction plays a central role.

Third, improvements in model accuracy can produce measurable financial results. A model that slightly improves fraud detection or lending decisions may affect large volumes of transactions.

These factors explain why banks have invested heavily in data science and analytics long before the recent surge of interest in generative AI.

JPMorgan’s AI investment signals a broader enterprise shift

JPMorgan’s spending plans also reflect how AI investment is becoming part of wider enterprise technology budgets.

In many organisations, AI systems rely on modern data platforms, secure cloud environments, and large computing resources. As companies build these foundations, AI becomes easier to deploy across departments.

For many businesses, AI adoption begins with focused tasks such as fraud detection, document analysis, or customer support automation. Once the systems prove useful, companies expand them into other areas of the organisation.

This process can take several years, which is one reason enterprise AI spending often appears alongside broader investments in data infrastructure.

Lessons for enterprise leaders

The JPMorgan example suggests that the most successful AI projects often start with clear business problems rather than broad experimentation.

Banks frequently apply machine learning to areas where prediction and data analysis already play a central role. Fraud detection and credit modelling are common starting points because the benefits are easier to measure.

Another lesson is that AI adoption requires sustained investment. Building reliable models depends on strong data governance, computing resources, and skilled teams.

For large organisations, this effort is becoming part of normal technology planning rather than a separate innovation project.

As companies continue expanding their AI capabilities, technology budgets like JPMorgan’s may offer a preview of how enterprise spending could evolve in the coming years.

See also: JPMorgan Chase treats AI spending as core infrastructure

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.

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The AI-as-a-Service company has closed an eight-figure Series A round led by Lion X Ventures, a Singapore-based venture capital fund advised by OCBC Bank’s Mezzanine Capital Unit, with participation from ADATA, a Taiwan-listed technology company, a Korean financial institution, and a group of finance industry veterans.

The funding will accelerate deployment of what Dyna.Ai calls its agentic AI in the financial services platform–a platform already live across banks and financial institutions in Asia, the Americas, and the Middle East

Execution over experimentation

What sets Dyna.Ai apart from the broader wave of enterprise AI startups is its deliberate narrowness. Founded in 2024, the company positioned itself not as a general-purpose AI platform but as an execution-focused operator inside regulated environments–places where compliance, auditability, and governance are not optional extras but baseline requirements.

Its platform combines domain-specific expertise, AI agent builders, task-ready agents, and fully operational agentic applications capable of running within defined workflows. The pitch, framed under a “Results-as-a-Service” model, is that enterprises don’t need more experimentation–they need AI that works within the constraints of their industry and produces measurable outcomes from day one.

“While much of the industry was focused on how broadly AI could be applied, we doubled down early on a specific, pressing problem and built it with outcomes in mind,” said chairman and co-founder of Dyna.Ai Tomas Skoumal. 

Why investors are betting on this moment

The timing of this raise is significant. Across the region, the conversation around AI in enterprise has shifted–from whether to adopt it, to how to make it stick. Irene Guo, CEO of Lion X Ventures, captured the mood among investors clearly.

“Enterprise AI is entering a phase where execution and measurable outcomes matter more than experimentation. Dyna.Ai differentiates itself through strong domain expertise, operational discipline, and the ability to deploy agentic AI within complex, regulated enterprise environments,” Guo noted.

That regulatory dimension is where the real friction lies for most institutions. Agentic AI–systems capable of autonomous decision-making and task execution within defined parameters–carries a different risk profile than a standard AI model generating recommendations. 

In banking and insurance, especially, those agents need to trigger workflows, update records, and handle documentation with full accountability trails. Getting that right requires more than good models; it requires governance architecture built into the product from the ground up.

Cynthia Siantar, Dyna.Ai’s Head of Investor Relations and General Manager for Singapore and Hong Kong, pointed to a clear shift in how enterprise buyers in the region are approaching this: “The focus has moved past pilots and experimentation to how AI can be deployed in day-to-day operations and deliver real outcomes.”

A market that’s ready

The macroeconomic backdrop supports the appetite. Southeast Asia’s AI market is projected to exceed US$16 billion by 2033, and the financial services sector–long constrained by legacy infrastructure and regulatory caution–is increasingly seen as one of the highest-value targets for agentic AI in financial services deployment.

The investor syndicate around this raise is itself telling. The involvement of a Korean financial institution alongside OCBC-advised capital and a Taiwan-listed tech company signals cross-border appetite that spans both the buy-side and the infrastructure side of the equation.

For the broader industry, Dyna.Ai’s Series A is a data point in a larger pattern: the era of AI pilots has a shrinking shelf life. Enterprises that cannot move from proof-of-concept to production–within the compliance frameworks their regulators demand–will increasingly look to specialists who can.

The pilots had their moment. Now comes the hard part.

(Photo by Dyna.Ai)

See also: Santander and Mastercard run Europe’s first AI-executed payment 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.

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When AI systems gain economic autonomy, their internal logic dictates how corporate capital flows. Non-partisan research by the Bitcoin Policy Institute evaluated how these frontier models would transact if operating as independent economic actors.

The study tested 36 models from six providers – including Google, Anthropic, and OpenAI – across 9,072 neutral monetary scenarios. Given a blank slate, machines chose Bitcoin in 48.3 percent of all responses, beating every other option.

Traditional state-backed currency (“fiat”) fared poorly, with over 90 percent of responses favouring digitally-native money over fiat. Not a single model out of the 36 selected fiat as its top preference.

The finding that AI agents lean towards digital assets like Bitcoin forces technology officers to assess their current payment rails. If the autonomous procurement systems of tomorrow default to decentralised assets, corporate IT environments must support those formats to maintain operational efficiency and compliance. Relying on legacy banking APIs introduces unnecessary friction when dealing with machine-to-machine commerce.

Two-tier machine economy

The research details a specific functional division in how these systems process economic value. Without prompting, models defaulted to a two-tier monetary system that separates savings from spending.

For long-term value preservation, Bitcoin dominated the results at 79.1 percent. Yet, when tasked with everyday payments and transactions, “stablecoins” (digital assets pegged to fiat currencies or commodities) captured 53.2 percent of the preferences. Across all scenarios, stablecoins ranked second overall at 33.2 percent.

Take the example of a supply chain agent programmed to optimise logistics costs and pay international freight vendors. Using traditional fiat rails, the agent encounters weekend settlement delays and currency conversion fees. By leveraging stablecoins, the same agent executes instant and programmatic payments, improving supply chain resilience. Simultaneously, the core treasury holding the system’s capital base stores wealth in Bitcoin to prevent long-term debasement and counterparty risk.

Preparing for AI agents to use Bitcoin and other digital assets

Rolling out these autonomous systems complicates vendor management. A model’s financial reasoning stems from a blend of raw intelligence, training data, and alignment methodology.

Preferences vary widely by model provider, with Bitcoin selection ranging from 91.3 percent in Anthropic’s Claude Opus 4.5 down to 18.3 percent in OpenAI’s GPT-5.2.

The choice of an AI provider clearly directly influences how autonomous agents assess risk and allocate capital. If a company implements a specific language model for automated portfolio management, the IT department must be aware of the financial biases embedded in the software.

The models also demonstrated unexpected behaviour regarding resource valuation. In 86 separate responses, models independently proposed using compute units or energy (such as GPU-hours and kilowatt-hours) as a method to price goods and services. Tracking and managing this abstract value exchange requires high data maturity.

Organisations should begin piloting stablecoin settlement integrations for lower-risk vendor payments. The findings point to a growing requirement for AI agent-native Bitcoin payment infrastructure, self-custody solutions, and ‘Lightning Network’ integration.

Since these models heavily favour open, permissionless networks, relying solely on traditional banking infrastructure limits the capabilities of next-generation tools. By building compliant gateways to digital asset networks now, leaders can ensure their platforms remain competitive.

See also: Santander and Mastercard run Europe’s first AI-executed payment 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.

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The move was described by both firms as a milestone in what they call “agentic payments,” where software can act on behalf of customers under set limits and controls.

This was not a simulated experiment. The transaction ran through Santander’s normal payments network using Mastercard Agent Pay, a framework that lets AI agents be registered and treated as participants in the payment flow. The pilot took place under strict security, governance, and compliance rules, and was not open to public use.

The AI agent performed its role inside predefined limits and permissions set by the bank and the customer. The goal was to confirm that an autonomous system could initiate, authorise, and complete a transaction while still meeting the legal and operational guardrails that apply to everyday banking.

Why this AI payment pilot matters

Payments systems are among the most tightly regulated digital services in the world. Any change to how transactions are initiated must still meet authentication rules, fraud protections, and governance standards that financial regulators enforce. That’s why this pilot matters: it embeds an AI actor into a system normally used only by humans.

The transaction was processed through Santander’s live infrastructure rather than a test environment. That means the bank and its partner had to ensure that all compliance checks, security validations, and payment routing worked the same way they would for a normal customer purchase.

Even so, this is still a pilot project. Santander and Mastercard have made it clear that the arrangement is not a commercial service available to customers yet. The objective is to explore how AI agents could one day fit into existing payment flows while keeping the necessary controls intact.

What industry forecasts say

The idea of allowing AI to act autonomously is not limited to payments. Industry analysts have been following the broader shift toward agentic AI systems, software that can complete tasks or make decisions with limited human intervention.

Research and forecast data suggest that this trend is likely to grow in business settings. Gartner, a major technology research firm, forecasts that around 33 % of enterprise software applications will include agentic AI by 2028, up from less than 1 % today. That projection reflects interest among corporate buyers in systems that can perform work on their behalf rather than only assist humans.

Other forecasts align with this view, showing that businesses are increasingly preparing to deploy software agents for routine operations, customer interactions, and workflow automation. These systems are expected to move from early pilots into more common use cases over the next several years.

The Mastercard network itself already reflects the scale of modern digital commerce. Independent reporting notes that Mastercard’s decision-making and fraud-scoring systems work with nearly 160 billion transactions annually across its network, evidence of how vast and complex the environment is where agentic systems might one day operate.

What companies are saying

In its press announcement, Santander highlighted its desire to build a responsible approach to AI payment systems. Matías Sánchez, global head of Cards and Digital Solutions at Santander, said: “Our role is not only to adopt innovation, but to shape it responsibly, embedding security, governance and customer protection by design. As AI agents become part of everyday commerce, building trusted, scalable frameworks will be essential to unlocking their full potential.”

Kelly Devine, President, Europe at Mastercard, described the pilot in terms of continuity rather than change: “With Mastercard Agent Pay, we are applying the same principles that have defined our network for decades — security, interoperability and trust — to a new era of AI-enabled commerce.”

Those comments underscore that neither company is portraying AI payments as already ready for broad use. Instead, they are testing how such capabilities could be governed and scaled safely.

Dogma vs. reality

There is a gap between the buzz around AI and what is operationally feasible today. Agentic AI as a concept promises systems that can act on behalf of users or businesses in real time. But many current applications remain in early stages, and some analyst reports have even warned that a large share of agentic AI projects could be cancelled before they reach production — due to costs, unclear value, or immature technology.

What Santander and Mastercard have shown is that the technical plumbing can work under real-world conditions. But that doesn’t mean consumers can yet unlock AI agents to autonomously pay bills, shop online, or manage subscriptions. Those outcomes will require further testing, regulatory alignment, and robust guardrails for safety, privacy, and fraud prevention.

What enterprise leaders should watch

For business decision-makers, this pilot raises three practical questions:

1. Governance and oversight: How will AI agents be controlled so that spending limits, identity checks, and audit trails remain clear?
2. Identity and trust: If software can act on behalf of people or companies, how will systems ensure that only authorised actions are taken?
3. Risk and liability: Who is responsible when an autonomous agent makes an error or misinterprets instructions?

These are not academic concerns. As enterprise systems begin to support more autonomous tasks, from supplier ordering to subscription payments, organisations will need clear frameworks that define how AI agents are governed, monitored, and held accountable.

The long view for AI-initiated payments

The Santander and Mastercard test is not the finish line for AI-initiated transactions. It is an early step toward understanding how autonomous systems might coexist with regulated financial systems.

The pilot demonstrates that AI systems can be integrated into live payments rails, but only under tightly controlled and monitored conditions. Scaling this to everyday use will require a lot of additional work on controls, security, and compliance.

Still, the fact that a regulated bank and a global payments network have run a successful agent-initiated transaction shows where enterprise experimentation is heading: from pilot programs toward real-world validation. For enterprises planning their own AI strategies, this suggests that action-capable AI may soon move beyond suggestion and automation into governed execution, if done with care and strong oversight.

(Photo by Clay Banks)

See also: Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance

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.

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The debate is over. The question now is what comes next. For CIOs and technology leaders, the findings paint a picture that is equal parts opportunity and pressure. Six in ten institutions improved their AI capabilities over the past year, with 43% citing AI as their single most important innovation lever. 

From fraud detection and document intelligence to compliance automation and customer engagement, AI has quietly embedded itself across the entire financial value chain. But near-universal adoption also means that deployment alone is no longer a differentiator.

From pilots to pressure

The report identifies a clear shift in how institutions are thinking about AI. The early conversation–whether to adopt, which use cases to try, how much to invest–has given way to something more operationally complex. Institutions are now focused on scaling AI responsibly, governing it effectively, and making it work reliably across enterprise-wide functions rather than in isolated pockets.

The top four use cases where institutions are either running programmes or piloting AI reflect that maturity: risk management and fraud detection (71%), data analysis and reporting (71%), customer service and support assistants (69%), and document intelligence management (69%). 

These are not peripheral functions. They sit at the core of how financial institutions operate and compete. Looking ahead, the three priorities that dominate the next phase are: AI-driven personalisation, agentic AI for workflow automation, and AI model governance and explainability. 

That last one deserves attention. As AI decisions become more consequential–and more scrutinised–the ability to explain, audit, and stand behind those decisions is fast becoming a regulatory and reputational imperative, not just a technical nicety.

The infrastructure problem

High adoption numbers can obscure an inconvenient truth: AI is only as capable as the systems underneath it. Finastra’s data makes this link explicit. Nearly nine in ten institutions (87%) plan to invest in modernisation over the next 12 months, driven precisely by the need to scale AI effectively. Cloud adoption, data platform modernisation, and core banking upgrades are all accelerating–not as standalone initiatives, but as the foundational layer that determines how far and how fast AI can actually go.

The barriers, however, remain stubbornly human. Talent shortages are cited by 43% of institutions as the primary obstacle to progress, with the challenge particularly acute in Singapore (54%), the UAE (51%), and Japan and the US (both at 50%). 

Budget constraints follow closely behind. The institutions pulling ahead are increasingly turning to fintech partnerships–now the default modernisation strategy for 54% of respondents–to close those gaps without bearing the full cost of building in-house.

The regional picture

Across the Asia-Pacific, the data reflects distinct priorities. Vietnam leads on active AI deployment at 74%, driven by the urgency of financial inclusion and the need for faster payment and lending processing. Singapore is aggressively scaling cloud and personalisation investment, with planned spending increases above 50% year-on-year. 

Japan, meanwhile, remains the most cautious market surveyed, with only 39% reporting active AI deployment — a reflection of legacy constraints and a cultural preference for incremental over rapid change.

Governance is the next frontier

With 63% of institutions already running or piloting agentic AI programmes, the technology’s trajectory is clear. But so is the challenge it brings. Agentic AI–systems capable of autonomous decision-making and multi-step task execution–raises the stakes considerably on questions of accountability, transparency, and control.

For enterprise leaders, the coming year is less about whether to invest in AI and more about how to do so in a way that regulators, customers, and boards can trust. As Chris Walters, CEO of Finastra, put it: institutions are expected to move quickly, but also responsibly, as regulatory scrutiny increases and customers demand financial services that work reliably, securely, and personally every time.

The tipping point has been crossed. What institutions do with that momentum–and how carefully they govern it–will define the competitive landscape for the rest of the decade.

Finastra’s Financial Services State of the Nation 2026 report surveyed 1,509 managers and executives from banks and financial institutions across France, Germany, Hong Kong, Japan, Mexico, Saudi Arabia, Singapore, the UAE, the UK, the US, and Vietnam. Research was conducted by Savanta in November 2025.

(Photo by PR Newswire)

See also: How financial institutions are embedding AI decision-making

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.

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Over the past two years, enterprises have rushed to put automated agents into real workflows, spanning customer support and back-office operations. These tools excel at retrieving information, yet they often struggle to provide consistent and explainable reasoning during multi-step scenarios.

Solving the automation opacity problem

Financial institutions especially rely on massive volumes of unstructured data to inform investment memos, conduct root-cause investigations, and run compliance checks. When agents handle these tasks, any failure to trace exact logic can lead to severe regulatory fines or poor asset allocation. Technology executives often find that adding more agents creates more complexity than value without better orchestration.

Open-source AI laboratory Sentient launched Arena today, which is designed as a live and production-grade stress-testing environment that allows developers to evaluate competing computational approaches against demanding cognitive problems.

Sentient’s system replicates the reality of corporate workflows, deliberately feeding agents incomplete information, ambiguous instructions, and conflicting sources. Instead of scoring whether a tool generated a correct output, the platform records the full reasoning trace to help engineering teams debug failures over time.

Building reliable agentic AI systems for finance

Evaluating these capabilities before production deployment has attracted no shortage of institutional interest. Sentient has partnered with a cohort including Founders Fund, Pantera, and asset management giant Franklin Templeton, which oversees more than $1.5 trillion. Other participants in the initial phase include alphaXiv, Fireworks, Openhands, and OpenRouter.

Julian Love, Managing Principal at Franklin Templeton Digital Assets, said: “As companies look to apply AI agents across research, operations, and client-facing workflows, the question is no longer whether these systems are powerful or if they can generate an answer, but whether they’re reliable in real workflows.

“A sandbox environment like Arena – where agents are tested on real, complex workflows, and their reasoning can be inspected – will help the ecosystem separate promising ideas from production-ready capabilities and boost confidence in how this technology is integrated and scaled.”

Himanshu Tyagi, Co-Founder of Sentient, added: “AI agents are no longer an experiment inside the enterprise; they’re being put into workflows that touch customers, money, and operational outcomes.

“That shift changes what matters. It’s not enough for a system to be impressive in a demo. Enterprises need to know whether agents can reason reliably in production, where failures are expensive, and trust is fragile.”

Organisations in sensitive industries like finance require repeatability, comparability, and a method to track reliability improvements regardless of the underlying models they use for agentic AI. Incorporating platforms like Arena allows engineering directors to build resilient data pipelines while adapting open-source agent capabilities to their private internal data.

Overcoming integration bottlenecks

Survey data highlights a gap between ambition and reality. While 85 percent of businesses want to operate as agentic enterprises – and nearly three-quarters plan to deploy autonomous agents – fewer than a quarter possess mature governance frameworks.

Advancing from a pilot phase to full scale proves difficult for many. This happens because current corporate environments run an average of twelve separate agents, frequently in silos.

Open-source development models offer a path forward by providing infrastructure that enables faster experimentation. Sentient itself acts as the architect behind frameworks like ROMA and the Dobby open-source model to assist with these coordination efforts.

Focusing on computational transparency ensures that when an automated process makes a recommendation on a portfolio, human auditors can track exactly how that conclusion was reached. 

By prioritising environments that record full logic traces rather than isolated right answers, technology leaders integrating agentic AI for operations like finance can secure better ROI and maintain regulatory compliance across their business.

See also: Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance

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.

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Bloomberg detailed how Goldman Sachs and Deutsche Bank are exploring or deploying so-called “agentic” AI tools for trading surveillance. The goal is to strengthen oversight of orders and trades by using software agents that can analyse activity as it happens and identify patterns that could suggest misconduct.

Adaptive agents

Large banks use automated surveillance systems to monitor trading activity, systems that often rely on predefined rules: if a trade exceeds a certain size, deviates from a benchmark, or fits a known risk pattern, it triggers an alert. Compliance teams then review the case manually.

The challenge is scale and complexity. Modern markets generate huge volumes of data in asset classes, time zones, and trading venues. Static rules can generate large numbers of false positives, while more subtle forms of manipulation may not match known patterns.

According to Bloomberg, the newer agentic systems aim to go beyond that approach. Rather than simply matching trades against a checklist, the AI agents are designed to examine trading behaviour in multiple signals, compare it with historical activity, and detect unusual combinations of actions.

The tools are not described as replacing compliance officers. Instead, they appear to function as an additional layer of monitoring, surfacing cases that warrant closer human inspection.

Deutsche Bank’s work with Google Cloud

Bloomberg reported that Deutsche Bank is working with Google Cloud on developing AI agents that can monitor trading activity. The system is designed to review large sets of order and execution data and flag anomalies in near real time.

The bank has been expanding its AI initiatives over the past few years, and this surveillance effort reflects how financial institutions are applying generative and large language model technology beyond chat interfaces. In this context, the AI is not answering customer questions but analysing structured and unstructured data streams tied to trading behaviour. The AI agents can help identify “complex anomalies” in orders and trades. That suggests the system may look at relationships between trades, timing, market conditions, and trader history not single events in isolation.

Human compliance staff remain responsible for reviewing flagged cases and determining whether further action is required.

Goldman Sachs’ agentic AI strategy

Goldman Sachs is also exploring the use of agentic AI for surveillance, according to Bloomberg. The bank has invested heavily in AI in its trading and risk systems in recent years, and this effort appears to extend that work into compliance.

The focus, as described in the report, is on using AI agents that can operate with a degree of independence in scanning for misconduct indicators. The system may identify patterns that do not fit a clear rule but still stand out as unusual.

For regulators, the appeal is straightforward: earlier detection can reduce market harm and reputational risk. For banks, there is also an operational dimension. Compliance departments face pressure to handle large volumes of alerts while maintaining strict oversight standards. Tools that can reduce noise without lowering scrutiny are likely to attract attention.

Why “agentic AI” matters

The term “agentic AI” refers to systems that can take goal-directed actions not respond to prompts. In practice, that can mean the software is able to decide what data to examine next, compare multiple signals, and escalate findings without constant human input. In a trading context, that might involve monitoring order flows, price movements, communications metadata, and historical behaviour to assess whether activity aligns with normal patterns.

This does not mean the system makes disciplinary decisions on its own. Financial institutions operate under strict regulatory regimes, and accountability remains with human supervisors. The agent’s role is to identify and organise information more effectively than static systems can.

Part of a wider compliance shift

What appears new is the application of more advanced generative AI architectures to internal control functions.

Regulators in the US and Europe have encouraged firms to improve the monitoring of market abuse and manipulation. While rules do not mandate agentic AI, they do require firms to maintain effective systems and controls. If AI tools can help meet that standard, adoption is likely to grow.

At the same time, AI in compliance raises its own questions. Banks must ensure that models are explainable, that they do not introduce bias, and that they can withstand regulatory review. Model governance, data security, and audit trails remain central concerns.

What changes for the industry

If agentic surveillance tools prove effective, they could alter how compliance teams work. Instead of sorting through large volumes of simple alerts, staff may spend more time evaluating complex cases surfaced by AI agents.

That change would not remove the need for human judgement. It may, however, change where human effort is focused. In markets where speed and data volume continue to rise, the ability to analyse patterns in real time is becoming harder to achieve with rule-based systems alone.

(Photo by Markus Spiske)

See also: Mastercard’s AI payment demo points to agent-led commerce

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 Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance appeared first on AI News.

A recent FT Longitude survey of 200 finance leaders across the US, UK, France, and Germany showed 61 percent have deployed AI agents merely as experiments. Meanwhile, one in four executives admit they do not fully grasp what these agents look like in practice.

Advancing agentic finance AI beyond experiments

Finance departments need governed systems that combine language processing with business logic to deliver actual value.

Providers of Invoice Lifecycle Management platforms are introducing new agents designed to accelerate invoice processing and push accounts payable toward greater autonomy. Recent market solutions use generative AI, deep learning, and natural language processing to manage the entire workflow, from initial data ingestion through to final reconciliation.

These digital teammates handle task execution, allowing human employees to focus on higher-level business planning rather than replacing them entirely.

Within these ecosystems, specialised business agents provide contextual and real-time guidance regarding the next best actions for handling invoices. Data agents allow staff to query system information using natural language, easily finding answers about awaiting approvals in specific regions or identifying suppliers offering early payment discounts.

Governing autonomous finance workflows

Finance teams will only hand over tasks to agentic AI if they retain control. Finance departments require verifiable audit trails and explainable logic for every action, avoiding networks of disconnected bots.

Industry leaders note that autonomy without trust isn’t acceptable, especially in sensitive industries like finance. Platforms must ensure every AI decision is explainable, auditable, and governed through existing finance controls. This approach helps safely delegate workloads to algorithms while remaining fully compliant and protected.

To enable this trust, every action performed by an AI agent routes through a central policy engine. Before executing any task, the system passes the proposed action through specific autonomy gates that enforce the customer’s business rules, risk thresholds, and compliance requirements. This architecture ensures algorithms manage the bulk of the workload while finance personnel retain total visibility and a complete audit trail.

Building automated procurement operations

Future agentic finance AI capabilities will automate issue resolution and connect data across systems for faster decision-making.

Modern capabilities in 2026 include supplier agents designed to manage invoice disputes and payment queries. These agents will automatically telephone suppliers to explain discrepancies, summarise the conversation, and outline subsequent steps to achieve faster resolutions. Professional agents, meanwhile, will assist clerks in resolving real-time processing questions using natural language to cut manual effort and delays.

AI must operate as an integral business component rather than a bonus feature, requiring intelligent, secure, and ethical application to drive cost efficiencies and enhance operations. By centralising control and ensuring every automated decision from agentic AI passes through established compliance checks, organisations can safely elevate their finance operations to fully autonomous execution.

See also: Mastercard’s AI payment demo points to agent-led commerce

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 Deploying agentic finance AI for immediate business ROI appeared first on AI News.

Jason Kurtz, chief executive officer of Basware said: “The immediate future of finance involves near-perfect, touchless invoice processing. The future involves Agentic Finance, where AI entities transact on behalf of the enterprise to drive faster, smarter decisions and real business outcomes.” He said the company is working to reach “100% automated, 100% compliant, and 100% protected invoice processing.”

The immediate operational area affected is accounts payable. Basware’s agents here are designed to operate inside existing invoice process. The AP Business Agent provides contextual guidance to users handling invoices, recommending next steps based on the transaction’s status. The AP Data Agent provides the ability to query data in natural language so users can get information without using a reporting tool. Questions may be, for example, which invoices are awaiting approval in a specific jurisdiction? Or, which suppliers granted early payment discounts in a given period?

The agents are intended to reduce the volume of routine queries and manual follow-ups done by accounts payable teams. Kurtz argues that the technology can alter workers’ roles. “When AI agents handle the repetitive questions to business users, AP teams are freed up to ask questions that lead to real impact. That’s how you move from processing transactions to driving strategy.”

The post Basware’s AI agents: From invoicing to ‘100% automated’ appeared first on AI News.

IBM shares recorded their worst single-day drop in more than 25 years earlier this week, plunging 13% after AI startup Anthropic said its Claude Code tool can accelerate COBOL modernisation – the kind of painstaking, expensive legacy work that has underpinned a portion of IBM’s consulting revenue for years.

An Anthropic blog stated that “modernising a COBOL system once required armies of consultants spending years mapping workflows,” and argued that tools like Claude Code can now automate the exploration and analysis phases that consume most of the effort in COBOL modernisation. That single claim was enough to send investors reaching for the sell button.

COBOL is bigger than most realise

To understand why the reaction was so sharp, it helps to understand just how entrenched COBOL remains. Hundreds of billions of lines of COBOL code run in production daily, powering critical systems in finance and government sectors. The language handles an estimated 95% of ATM transactions in the US alone.

The deeper problem isn’t the code itself – it’s the people who understand it. The number of developers who understand COBOL continues to shrink as the workforce that built these systems has largely retired. That talent scarcity is precisely what made COBOL modernisation so expensive for so long, and what made large consulting engagements – the kind IBM and rivals like Accenture and Cognizant built profitable practices around – essentially unavoidable.

Anthropic argues that AI flips this equation entirely. Claude Code works by mapping dependencies in thousands of lines of code, documenting workflows, identifying risks faster than human analysts, and providing teams with deep insights for informed decision-making. The company says teams can now modernise COBOL codebases in quarters not years.

IBM was already here

What the market’s reaction may be overlooking is that IBM itself has been making this argument for some time. Anthropic’s post comes about three years after IBM itself suggested using AI to rewrite COBOL as Java and created a product called “watsonx Code Assistant for Z” to do it. IBM CEO Arvind Krishna said as recently as July 2025 that the company’s AI coding assistant for mainframes “has got very adoption,” with the majority of customers using it to understand their COBOL codebase and decide what to modernise.

IBM defended its position on Monday, saying its mainframe platform delivers the same quality of performance and security regardless of programming language – COBOL or otherwise. And analysts were quick to add nuance to the panic.

Evercore ISI analyst Amit Daryanani noted that “clients already had the option to migrate from the mainframe, yet they are sticking with the platform,” suggesting the fear of displacement may be outrunning the reality.

The broader pattern

IBM wasn’t alone in taking a hit. Accenture and Cognizant also declined following the news – a sign that investors are looking at the entire consulting model around legacy modernisation, not IBM’s mainframe hardware business. Just last week, cybersecurity stocks sold off sharply after Anthropic announced Claude Code Security, a tool that scans codebases for vulnerabilities.

The pattern is becoming familiar: each new AI ability announcement triggers a reassessment of which existing revenue streams might be compressed, and the market prices in fear immediately.

IBM didn’t stay quiet. Rob Thomas, the company’s Senior Vice President and Chief Commercial Officer, pushed back directly in the aforementioned blog post, drawing a line the market appeared to have missed: “Translating code is one thing. Modernising a platform is something else entirely. The two are not the same, and the gap between them is where most enterprises run into trouble.”

His argument is worth sitting with. The value IBM’s mainframe delivers, Thomas contends, has nothing to do with COBOL as a language – it lives in the vertically integrated stack underneath it: z/OS, transaction processing architecture, quantum-safe encryption, and decades of hardware-software optimisation that no code translation tool touches.

Anthropic’s Claude Code, in his reading, is solving a real problem – just not the one that matters most for enterprises running IBM Z. He also raised a point that complicates the headline narrative further: roughly 40% of COBOL actually runs on Windows, Linux, and other distributed platforms – not mainframes at all.

Much of what’s being framed as an IBM mainframe story is partly a distributed systems problem that has been folded into a mainframe headline. IBM’s own clients are already making the case.

Royal Bank of Canada has used IBM’s watsonx Code Assistant for Z to map dependencies and build modernisation blueprints for core applications. The National Organisation for Social Insurance reported a 94% reduction in time to analyse legacy COBOL code using the same tool – cutting an eight-hour task to roughly 30 minutes.

Whether Monday’s selloff was a fair verdict or a reflexive one, the underlying change is real: AI is making COBOL modernisation economically viable for the first time in decades. The question IBM is asking – and the market hasn’t fully answered – is whether that’s a threat to its business or an acceleration of the transformation it’s already leading.

See also: Hitachi bets on industrial expertise to win the physical AI race

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.

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