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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The term itself–physical AI–is simple enough. It describes AI systems that don’t just process data or generate content, but perceive, reason, and act in the real world–robots, autonomous vehicles, machines that adapt. Nvidia CEO Jensen Huang called it “the ChatGPT moment for robotics” at CES in January–a deliberate framing, and a useful one. 

The ChatGPT comparison isn’t about hype. It signals that a technology once confined to research environments is being adopted for mainstream commercial deployment. That crossing is exactly what we are watching unfold from factory floors in Silicon Valley to stages in Shanghai.”

The West is building the stack

On the Western side, the physical AI push is fundamentally a platform race. The companies investing most aggressively aren’t primarily robotics companies–they’re infrastructure companies that see robotics as the next surface on which AI gets monetised.

Nvidia has released new Cosmos and GR00T open models for robot learning and reasoning, alongside the Blackwell-powered Jetson T4000 module, which delivers 4x greater energy efficiency for robotics computing. Arm has carved outan entirely new Physical AI business unit focused on semiconductor design for robotics and intelligent vehicles. 

Siemens and Nvidia announced plans to build what they’re calling an Industrial AI Operating System, with ambitions to create the world’s first fully AI-driven adaptive manufacturing site. Then there’s Google, which last week brought its robotics software unit Intrinsic fully in-house–out of Alphabet’s “Other Bets” and into Google’s core. 

The move positions Google to offer manufacturers a vertically integrated stack: AI models from DeepMind, deployment software from Intrinsic, and cloud infrastructure from Google Cloud. The Android analogy being floated internally is instructive. Android didn’t win smartphones by building the best phone. It won by becoming the layer everything else ran on. 

That is precisely what Google is attempting with physical AI.

The enterprise implications are significant. A Deloitte survey of more than 3,200 global business leaders found that 58% are already using physical AI in some capacity, rising to 80% with plans over the next two years. The demand is there. The question has shifted from whether to adopt to how fast and on whose platform.

The East is building the machines

China’s physical AI story is different in character–and arguably more visceral. At this year’s Spring Festival Gala, humanoid robots from multiple Chinese startups performed kung fu routines, aerial flips, and choreographed dances before hundreds of millions of viewers–a sharp contrast from the stumbling prototypes that drew scepticism just a year prior. 

It was a spectacle, yes. It was also a statement. China accounted for over 80% of global humanoid robot installations in 2025 and over half of the world’s industrial robots. That dominance is underpinned by structural advantages that go beyond software. China controls roughly 70% of the global lidar sensor market, leads in harmonic reducer production–the gears critical to robot movement–and has driven hardware costs down through the same economies of scale that propelled its EV industry. 

Alibaba has entered the race with RynnBrain, an open-source AI model designed to help robots comprehend the physical world and identify objects–positioning itself alongside NVIDIA’s Cosmos and Google DeepMind’s Gemini Robotics in the foundation model layer. With over 140 domestic humanoid manufacturers and more than 330 humanoid models already unveiled, China’s push into embodied AI is no longer experimental–it’s commercial.

Why it matters beyond the headlines

The convergence of Western platform strategies and Eastern manufacturing scale is creating something genuinely new: a global physical AI ecosystem that is advancing on multiple fronts simultaneously, with different competitive advantages colliding.

What makes this moment distinct from prior robotics waves is the removal of the expertise bottleneck. Historically, deploying industrial robots required specialised engineering teams, months of custom programming, and a high tolerance for downtime. The platforms being built now–by Google, Nvidia, Siemens, and their Chinese equivalents–are explicitly designed to lower that barrier. 

Companies like Vention, which raised US$110 million in January, claim their physical AI platforms can reduce automation project timelines from months to days. When that claim becomes routine, the economics of manufacturing change structurally.

There is also a geopolitical dimension that sits quietly beneath the product announcements. Every foundation model for robotics, every platform layer, every semiconductor architecture being developed right now carries with it questions of supply chain dependency, data sovereignty, and long-term infrastructure control. 

The country–or company–that governs the software layer of physical AI will have unusual leverage over industrial operations globally for years to come.

Physical AI is not a trend. It is the next significant reconfiguration of how the world makes things, moves things, and operates at scale. The conversations happening now–from semiconductor boardrooms to factory floors in Shenzhen and Silicon Valley–are not preliminary. They are the thing itself, already underway.

(Photo by Hyundai Motor Group)

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

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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.

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The company will remain a distinct group within Google, working closely with Google DeepMind and tapping into Gemini AI models and Google Cloud. No purchase price was disclosed.

On the surface, this looks like a routine internal reshuffle. It isn’t.

From Moonshot to Mandate

Intrinsic graduated into an independent Alphabet-owned company in 2021 after five years of development within Alphabet’s X, the moonshot research division–the same factory that produced Waymo and Wing. Its mission from the start: make industrial robotics AI accessible to manufacturers who don’t have armies of specialist engineers.

While hardware like robotic arms has become cheaper, programming them remains incredibly complex, often requiring hundreds of hours of manual coding by specialised engineers that can vary based on the particular robot. Intrinsic’s answer to that is Flowstate–a web-based platform that allows users to build robotic applications without having to write thousands of lines of code. 

The platform is designed to be hardware-, software-, and AI-model-agnostic. Think of it less as a product and more as an operating layer–one that Google CEO Sundar Pichai has reportedly compared directly to Android. “He said this is the Android of robotics,” Intrinsic CEO Wendy Tan White said, noting that Pichai worked on Chrome and Android before becoming CEO. 

Why now, why Google?

The timing isn’t arbitrary. The sequence of hiring Boston Dynamics’ CTO, releasing a standalone robotics SDK, and now absorbing Intrinsic represents a deliberate consolidation of robotics capability inside Google’s core. Taken together, these moves position Google to offer manufacturers something no competitor has assembled quite as cleanly: AI models from DeepMind, deployment software from Intrinsic, and cloud infrastructure from Google Cloud–all under one roof.

Last month, Google also teamed up with Boston Dynamics to integrate Gemini into Atlas humanoid robots built for manufacturing environments, while Google DeepMind hired the former CTO of Boston Dynamics in November. 

The industrial robotics AI market Google is chasing is not small. McKinsey projects that the market for general-purpose robots could reach US$370 billion by 2040. 

What it means for the enterprise

For enterprise decision-makers, the more interesting signal here isn’t the technology–it’s the accessibility shift. Google plans to integrate Intrinsic’s robotics development platform and vision models with its broader AI ecosystem, combining advanced reasoning, perception and learning capabilities with industrial-grade robotics software to allow machines to interpret sensor data better, adapt to dynamic environments and execute complex tasks. 

Intrinsic has also expanded through acquisitions–acquiring the Open Source Robotics Corp. in 2022, the for-profit arm of the foundation behind the Robot Operating System (ROS). And its commercial pipeline is already in motion: in October 2025, Intrinsic formed a strategic partnership with Foxconn focused on developing general-purpose intelligent robots for full factory automation within electronics manufacturing. 

White framed the integration in terms enterprise leaders will find hard to ignore: production economics, operational transformation, and what she described as truly advanced manufacturing — all within reach once Google’s infrastructure is fully behind it.

That’s a significant claim. But with Gemini, DeepMind, and Google Cloud now aligned behind it, the infrastructure to back it up is, for the first time, actually there.

See also: Physical AI adoption boosts customer service ROI

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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As businesses navigate shrinking labour pools, they are finding that simply automating routine workflows is no longer enough. A new partnership between KDDI and AVITA demonstrates how companies can address complex operational gaps through humanoid deployment.

While traditional industrial robots excel at repetitive, single-function tasks, they lack the versatility required to manage unexpected anomalies like equipment failures. Customer-facing roles demand nonverbal communication, including synchronised nodding, natural eye contact, and reassuring facial expressions. 

By integrating AVITA’s avatar creation expertise with KDDI’s communications infrastructure, the two organisations are building domestically developed humanoids capable of operating smoothly in real-world commercial environments.

Blending hardware with advanced data infrastructure

Deploying humanoids into active commercial spaces requires high-capacity and low-latency network infrastructure to transmit visual data and control commands in real time. KDDI provides this operational backbone, facilitating remote control capabilities alongside intensive cloud-based data processing. The resulting visual and motion data collected during customer interactions feeds back into the system to train the AI, improving the precision and autonomy of the humanoid’s behaviour.

To support the demanding computational requirements of physical AI adoption, the companies plan to utilise GPUs hosted at the Osaka Sakai Data Center, which commenced operations in January 2026. They are also exploring integration with an on-premises service for Google’s Gemini high-performance generative AI model. This alignment with major enterprise platforms ensures that data processing remains secure and capable of handling complex dialogue requirements.

The hardware itself departs from standard utilitarian machinery. Based on a concept model designed by Hiroshi Ishiguro, the humanoid features a compact skeletal structure approximating a typical Japanese physique.

Silicone skin and specialised mechanical systems enable warm, approachable facial expressions that sync directly with spoken dialogue. Embedded camera sensors track objects in motion to create natural eye contact, while quiet pneumatic actuation allows for fluid and continuous movement with natural “micro-variations”. This design specifically addresses the historical difficulty of deploying automation in operations requiring hospitality and reassurance.

Preparing for commercial adoption of physical AI

This initiative builds upon earlier joint projects between KDDI and AVITA, which introduced a “next-generation remote customer service platform” using digital avatars for remote assistance at retail locations like Lawson and au Style shops.

Transitioning from digital and language-driven communication to physical units capable of free movement represents a logical progression for enterprises looking to scale their customer service capabilities. The partners intend to begin trials in actual commercial facilities starting in Autumn 2026. Deployment at customer touchpoints such as au Style shops will also be considered.

Integrating physical AI demands environments capable of sustaining continuous, high-volume data streams without latency interruptions. As visual and motion data becomes central to machine learning models, governance frameworks must adapt to manage customer data usage within physical spaces.

Organisations facing demographic workforce pressures should evaluate current bottlenecks to identify where non-verbal, empathetic engagement is necessary. Setting up high-speed network foundations and piloting digital AI avatar programmes today allows enterprises to prepare for the adoption of physical humanoids as the hardware further matures.

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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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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The company’s research shows that companies that fail to embed AI into their human workflows are falling behind the competition as productivity slows down. Datatonic says a hybrid human-AI approach speeds up decision-making, thus improving overall operations. Scott Eivers, CEO of Datatonic says, “AI [is] about redesigning how work gets done. The biggest risk we see in the market is productivity leakage when AI exists in isolation from the people who actually run the business.”

After years of AI investment, pressure is mounting on businesses to show returns. However, some research shows some initiatives remaining in their pilot stage due to limited trust among users. As a result, organisations are failing to use AI-powered insights to positively affect decisions and workflows, meaning efficiency gains never materialise.

According to Datatonic, HiTL models are crucial for future success, providing a combination of AI speed with human judgement and accountability. This is evident in agent-assisted software development, where AI systems create code from loose prompts and transform them into code. In this case, human teams decide what needs to be developed, inspect all requirements, and review plans before being brought into existence. Once this direction is clear, AI agents construct modular components.

The trend for AI in the workplace is starting to appear in finance and operations. For instance, in back-office and finance departments, AI-powered document processing is already delivering a 70% reduction in invoice-processing costs according to some, but finance teams still approve the final outcomes.

“They’re partnership stories,” says Andrew Harding, CTO of Datatonic. “Humans create evaluation systems, validate plans, set guardrails, and make decisions. AI executes at speed and scale. That combination is where real enterprise value shows up.”

Many enterprises are failing to deploy fully autonomous agents safely, according to Datatonic, with shortfalls in security controls and governance frameworks. Autonomy can only scale when organisations introduce approval checkpoints and benchmark performance standards. Evaluation systems must also be implemented as AI models evolve, ensuring they always operate safely and as intended without violating any compliance obligations.

Harding says, “As trust builds, companies can responsibly delegate more to AI. But skipping governance doesn’t build speed, it creates risk.”

Datatonic predicts major acceleration in workloads in the next two years, with preparation and validation handled by AI agents. AI systems may also be implemented to test and invalidate decisions before teams invest resources.

Scott Eivers believes the future “looks like expert departments run by smaller, nimble teams – finance, HR, marketing – each amplified by AI. The companies that win will be those that teach people to work with AI — not around it,” he said.

(Image source: “Waterfall” by PMillera4 is licensed under CC BY-NC-ND 2.0.)

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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

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In the demo, as reported by Times of India, an AI agent searched for a product, assessed the website, and completed the purchase using stored payment credentials, without the user opening an app or entering card details. The company said the transaction took place inside a secure payment framework designed to verify both the user and the AI acting on their behalf.

The demonstration was controlled, not a public rollout. Mastercard executives told reporters that broader deployment would depend on regulatory approval and ecosystem readiness. Still, the test highlights a change that many enterprises may need to prepare for: the possibility that customers – or corporate systems – will increasingly rely on AI agents to initiate and complete transactions.

Assisted checkout to delegated spending

Digital payments have usually focused on reducing friction for human users through tokenisation, saved credentials, and one-click checkout. Agentic commerce goes further. Instead of helping a user complete a purchase, the system allows software to handle the process from start to finish once permission rules are in place.

The model relies on several building blocks already used in modern payments: identity verification, tokenised card data, and risk monitoring. What changes is who performs the action. If AI agents can act in defined limits, like spending caps or merchant restrictions, checkout may change from a user interaction to a background workflow.

For enterprises, the issue is if software can spend money automatically, procurement rules, approval chains, and audit trails need to account for machine decisions, not human ones. Finance teams may need clearer policies on when an AI agent can commit funds, how liability is assigned if something goes wrong, and how fraud detection should treat automated transactions.

Payment networks position for machine customers

Mastercard is not alone in exploring this direction. Across the payments sector, providers are testing ways to embed transactions into AI-driven tools and digital assistants. The goal is to ensure that when autonomous software begins purchasing goods or services, payment networks remain part of the trust and verification layer.

In public statements tied to the summit demo, Mastercard framed the effort as building infrastructure that allows AI agents to transact safely on behalf of users. That framing points to a broader industry race: not to build smarter AI shopping tools, but to control the authentication systems that make those tools safe enough for financial use.

For banks and fintech firms, the change could affect how customer identity is managed. Traditional authentication often assumes a person is present, entering a password or approving a prompt. Agentic commerce assumes the opposite: the user may not be involved at the moment of purchase. That means identity systems must verify both the account owner’s prior consent and the agent’s authority at the time of transaction.

Merchants may need API-ready storefronts

If AI agents begin acting as buyers, merchant systems may also need to adapt. Online stores built mainly for human browsing may struggle if automated agents become a meaningful share of customers.

To support machine-driven purchases, product catalogues, pricing data, and checkout processes may need to be accessible through structured APIs not only visual web pages. Inventory accuracy, transparent pricing, and clear return policies become more important when decisions are made by software trained to compare options instantly.

This could also influence competition. If agents optimise for price and delivery speed, merchants with inconsistent data or hidden fees may be filtered out before a human even sees them.

Security risks move, not disappear

While agentic commerce promises convenience, it also introduces new risks. A compromised AI assistant with payment authority could execute purchases at scale before detection. Fraud models that look for unusual user behaviour may need updating to distinguish between legitimate automated spending and malicious activity.

Regulators are likely to take a cautious approach. Mastercard’s own comments that the system still awaits approvals suggest that compliance frameworks for AI-initiated payments are still taking shape.

In enterprises deploying AI internally, similar concerns apply. Automated purchasing agents integrated into enterprise resource planning systems could streamline routine procurement, but they also expand the attack surface. Access controls and spending thresholds will matter more when software can execute financial actions without real-time human confirmation.

Where commerce may head

Mastercard’s demonstration does not mean agent-led payments will reach consumers immediately. Yet it offers a glimpse of how commerce may change as AI systems move from advisory roles into operational ones.

If the model matures, the most visible change may be that checkout disappears as a distinct step. Instead of visiting a site and paying, users or companies may set rules, and their software will handle the rest.

For enterprises, the important takeaway is less about Mastercard’s AI technology and more about the direction of travel. As AI agents gain the authority to act, payment systems, identity frameworks, and digital storefronts may need to treat software not as a tool, but as a participant in the transaction.

(Photo by Cova Software)

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Amul, the world’s largest dairy cooperative, has launched what it calls Amul AI: a platform built on five decades of cooperative data, designed to give every farmer in its network round-the-clock, personalised guidance in their own language.

Amul was launched just ahead of India’s AI Impact Summit 2026 and backed by the Ministry of Electronics and Information Technology (MeitY) with the EkStep Foundation. It is a test case for whether AI – the kind being debated in boardrooms and policy forums globally – can actually reach the last mile.

Meet Sarlaben: The AI dairy farming assistant

Sarlaben draws from one of India’s most comprehensive agricultural data repositories. It’s accessible via the Amul Farmer mobile app – already downloaded by over 10 lakh (one million) users on Android and iOS – as well as through voice calls for farmers using feature phones or landlines.

The system is integrated with Amul’s Automatic Milk Collection System (AMCS) and the Pashudhan application, allowing it to offer personalised, cattle-specific guidance.

What makes Amul AI substantially different from most agricultural chatbots is the scale of its training data. The platform was built on a digital backbone managing over 200 crore (two billion) milk procurement transactions annually, veterinary treatment records from more than 1,200 doctors covering nearly 3 crore (30 million) cattle, approximately 70 lakh (seven million) artificial inseminations conducted each year, ISRO satellite imagery for fodder production mapping, and a cattle census conducted every five years.

Every animal in the system carries a unique ID, with individual records of feed intake, disease history and milking status. “Amul AI is about taking dependable, verified information directly to the farmer – instantly and in a language they are comfortable with,” said Jayen Mehta, Managing Director of the Gujarat Cooperative Milk Marketing Federation (GCMMF), which markets the Amul brand.

He said how, by using decades of structured data and integrating it with their operational systems, the platform will help farmers make timely decisions that improve animal productivity and income.

India’s productivity paradox

India is the world’s largest producer of milk, generating 347.87 million tonnes in 2024-25 according to the Department of Animal Husbandry and Dairying – more than double the US’s 102.70 million tonnes. And yet despite leading in volume, India’s per-animal milk yield remains among the lowest globally.

The reasons are structural. India’s dairy sector is characterised by small herd sizes, low-quality feed, limited access to veterinary care in rural areas, and widespread lack of awareness about modern breeding and husbandry practices. Amul’s network spans more than 18,600 villages in Gujarat, where farmers supply over 350 lakh litres (35 million litres) of milk daily.

But information asymmetry has long been a bottleneck – a farmer facing a sick animal at midnight in a remote village has few places to turn; the gap Amul AI is designed to close.

Available initially in Gujarati – the primary language of the cooperative’s farmer base – the platform is built on the government’s Bhashini multilingual framework and could, in principle, be extended to 20 Indian languages, reaching Amul’s presence in 20,000 villages in 20 states.

The cooperative model

The technology story here is inseparable from the institutional one. Amul’s cooperative structure – built over five decades under the original White Revolution – created the data infrastructure that makes Amul AI possible.

Most private agri-tech startups are working backwards: collecting data first, building products second. Amul already had the data. What was needed was a way to make it actionable at the farmer level.

Experts tracking the dairy-tech space see this as significant. Sreeshankar Nair, Founder of Brainwired, a dairy-tech startup, identifies three specific challenges that Amul AI could meaningfully address: farmer awareness, access to quality veterinary guidance, and connectivity to grazing and feed resources.

“If AI can integrate local dialects of Indian languages, India can have White Revolution 2.0,” Nair said, pointing to the transformative potential of vernacular AI in a sector where not every farmer speaks the same dialect.

Saswata Narayan Biswas, Director of the Institute of Rural Management, Anand (IRMA) – the institution closely associated with Amul’s founding ethos – frames it as an AI embedded in a cooperative framework. It becomes “not a technology upgrade, but an instrument of inclusive rural transformation.”

For Biswas, the specific abilities Amul AI brings – predictive disease detection, oestrus tracking, optimised feed formulation, localised weather risk advisories – are abilities Amul had been building for years. AI accelerates and democratises them.

Scale and the test ahead

The launch has drawn backing from the highest levels of government. Gujarat Chief Minister Bhupendra Patel launched the platform and confirmed it will be showcased at the AI Impact Summit 2026. The cooperative has acknowledged MeitY and the EkStep Foundation – an open digital infrastructure nonprofit – as partners in building the AI layer.

Farmers not affiliated with Amul can also access general dairying and animal husbandry information through the app. At its current scale, Amul AI already covers more cattle – nearly 3 crore (30 million) – than most national veterinary databases anywhere in the world.

The harder question, as with most AI deployments at a population scale, is whether the tool will serve those who need it most. The farmers most likely to benefit first – those already comfortable with smartphones, already plugged into Amul’s digital system – may not be the ones with the greatest information deficit.

The rollout of Bhashini-enabled dialect support, the adoption rate among feature-phone users relying on voice calls, and whether AI-driven advisories translate into measurable yield improvements will be the metrics that determine whether this is genuinely White Revolution 2.0.

Amul has built an AI system grounded in half a century of real cooperative transactions, real animals, and real farmers. Such an infrastructure is, arguably, the most credible foundation for AI dairy farming at scale. Whether it fulfils its promise will depend on execution – and on whether Sarlaben’s voice can reach in the last few miles; those that have always been the hardest to cross.

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

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Dense urban stores, high labour churn, and competitive quick-commerce ecosystems are driving the uptake. A Q4 2025 survey by GlobalData found that 45 percent of consumers in Asia and Australasia are very or quite likely to purchase a product based on AI recommendations or endorsements.

Jaya Dandey, Consumer Analyst at GlobalData, said: “Whether shoppers realise it or not, machine-learning systems have long been deciding when to encourage consumers to make purchases, which products they can see, and what discounts they can avail.

“Now, agentic systems can also complete shopping-related tasks end-to-end.” 

Computer vision and store automation

Enterprises evaluating computer vision and machine learning can observe early implementations in the region.

Lawson, for example, introduced AI-enabled ‘Lawson Go’ stores in Japan during 2022. The retailer collaborated with technology provider CloudPick in 2025 to integrate AI, machine learning, and computer vision. This integration eliminates check-out lines and cashiers to enhance the customer experience.

In South Korea, retail AI company Fainders.AI launched a compact and cashier-less MicroStore inside a gym in 2024. This deployment improved the accessibility of autonomous retail across different businesses.

AI also aids the forecasting and automation of retail replenishment—a capability that applies well to the APAC market, where store footprints are small and replenishment frequency is high.

Japanese food retail chain Coop Sapporo uses a camera-based AI system named Sora-cam, developed by Soracom. The system helps the chain avoid overstocking and reduce unsold merchandise on store shelves. Coop Sapporo employs an analytics team to evaluate the generated images. The team determines the optimal shelf display ratio. The Sora-cam system also alerts staff members to apply discount labels on food items close to expiry to prevent wastage.

AI models track waste and markdown timing while improving promotion efficiency. In Southeast Asian (SEA) markets characterised by high price sensitivity, minor improvements in promotion efficiency increase profit margins.

AI-driven labour optimisation measures include scheduling, task priority lists, and workload balancing. These measures assist retailers in Japan and South Korea, which face structural labour shortages. They also provide efficiency benefits in high-growth SEA markets.

Agentic AI systems in retail are improving APAC consumer interaction

“In food retail, agentic AI is best understood as an AI ‘operator’ that can understand a goal, plan steps, stay within budget or allergen constraints, execute actions across systems, ask clarifying questions, and learn preferences over time,” says Dandey. 

Customers can bypass individual item searches by outlining their overall intent. A customer, for example, might request an AI agent to “Plan five dinners for a family of four, mostly Asian recipes, no shellfish, under 45 minutes.” The agent then generates recipes, builds a shopping cart, sizes quantities, and adds missing staples to the cart.

This retail agentic AI capability aligns with regional behaviours, as many APAC households cook frequently and shop fresh. AI agents that recognise local cuisines – such as Korean banchan, Japanese bentos, and Indian spice bases – fit regional habits better than generic Western meal plans.

“In many APAC markets, shopping is already deeply integrated with digital wallets, messaging apps, ride-hailing, and delivery ecosystems, making it easier for agentic AI to plug into daily routines,” explains Dandey.

“Nevertheless, some key challenges need to be overcome; ensuring private data sharing consent, minimising hallucinations in terms of allergens and ingredients, and implementing proper localisation of the system with language nuance.”

See also: DBS pilots system that lets AI agents make payments for customers

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The working paper [PDF], published by the National Bureau of Economic Research and produced by teams from the Federal Reserve Bank of Atlanta, the Bank of England, the Deutsche Bundesbank and Macquarie University, found that over 90% of firms report no measurable change headcount attributable to AI over the past three years. Given the short time horizon and the concentration of AI use in discrete functions, such incremental rather than transformative effects are consistent with how general purpose technologies have evolved historically.

Adoption of AI is widespread. Around 69% of firms are already using some form of AI, led by LLM-based text generation at 41%, data processing via machine learning at 28% and visual content creation at 29%. In the UK, firm-level adoption rose from 61% to 71% across 2025. AI tools are embedded in day-to-day workflows, and although measured impact at firm level often lags adoption, the trend is generally upwards.

The forward AI impact numbers indicate acceleration

Executives expect stronger effects to take place over the next three years. On average, they expect a 1.4% increase in productivity and a 0.8% rise in output. US executives project a 2.25% productivity gain, while UK firms expect 1.86%. In economies that have struggled with weak productivity growth for over a decade, gains of that magnitude are notable – incremental improvements, compounded across sectors, shift national outputs.

On the thorny subject of employment, executives expect a modest 0.7% reduction in headcount across the four countries over the same period. In the UK, around two-thirds of this adjustment is expected to come through slower hiring rather than outright redundancies. That pattern suggests a gradual reallocation of roles rather than abrupt terminations. As with previous waves of automation, aggregate figures do not capture job creation in adjacent roles, and in the case of AI, these might include roles around data governance, model oversight, prompt engineering, and AI-enabled service development, many of which would be new roles.

Interpreting the expectation gap

The study also compares executive expectations with those of workers. Researchers fielded parallel questions to US employees through the Survey of Working Arrangements and Attitudes. Employees expect AI to increase employment at their firms by 0.5% over the next three years, while US executives expect a 1.2% reduction. Employees foresee productivity gains of 0.92%, below the executive forecast of 2.25%.

This divergence reflects different vantage points. Executives observe cost structures and competitive pressure, while employees experience task-level augmentation and new capabilities. In practice, AI systems are often deployed to assist rather than replace, particularly in knowledge-intensive work. Evidence from controlled trials, including large language model use in customer support and professional services, shows productivity gains concentrated among less experienced staff, with quality improvements appearing alongside better output figures. Where communication and training are clear, adoption tends to proceed with limited resistance.

Why this AI impact data merits attention

Survey design influences inferences from any statistics, and in this particular case, the researchers noted variation between their own figures and those from, for example, a McKinsey survey taken in the same period that put adoption at 88% of organisations (the survey in question here pegs the figure at just 69%). On the other hand, the US Census Business Trends and Outlook Survey, which draws on a broader respondent base, estimated AI use at around 9% in early 2024, rising to 18% by December 2025. This gap reflects differences in sampling, question framing and respondent seniority. Executive surveys tend to capture intent and enterprise-level deployments, while broader business surveys may reflect narrower definitions of AI or earlier stages of implementation.

In the study in question, respondents were phone-verified, unpaid, and predominantly CEOs and CFOs, with over 90% drawn from the UK and Germany. The data was cross-checked against ten years of macro output and employment figures from national statistics agencies.

The inflection point executives anticipate may unfold over the next three years as deployments mature and integration improves, in the way that many new technologies have emerged into the workplace until they become everyday tools. The central question is less whether AI will affect productivity and employment, and more how quickly organisations can change the technology’s wider adoption into measurable economic gains.

See also: OpenAI’s enterprise push: The hidden story behind AI’s sales 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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