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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Ensuring operational continuity in isolated environments has become increasingly vital for businesses. Facilities lacking continuous internet access face unique constraints where external dependencies become unacceptable.

Microsoft recently expanded its capabilities to allow regulated industries and public sectors to participate independently in the digital economy. Trust in these systems stems from confidence that data remains protected, controls are enforceable, and operations proceed regardless of external conditions.

The company now offers full stack options across connected, intermittently connected, and fully disconnected modes. This architecture unifies Azure Local, Microsoft 365 Local, and Foundry Local into a single sovereign private cloud.

Bringing these elements together provides a localised experience resilient to any connectivity condition. By standardising governance across all deployments, it helps enterprises to prevent fragmented architectures.

Azure Local disconnected operations enable organisations to run vital infrastructure using familiar Azure governance and policy controls completely offline. Execution, management, and policy enforcement stay entirely within customer-operated facilities. 

This approach allows companies to maintain uninterrupted operations and keep identities protected within their established boundaries. Implementations scale from minor deployments to demanding and data-intensive workloads.

Improving resilience and AI data governance in tandem

Deploying AI in sovereign environments introduces high compute requirements. Foundry Local enables enterprises to run multimodal large models completely offline.

Utilising modern hardware from partners like NVIDIA, customers deploy AI inferencing on their own physical servers. This ensures data and application programming interfaces operate strictly within customer-controlled boundaries. Customers maintain complete authority over their hardware even as AI inferencing demands increase over time.

Gerard Hoffmann, CEO of Proximus Luxembourg, said: “The availability of Azure Local disconnected operations represents a breakthrough for organisations that need control over their data without sacrificing the power of the Microsoft Cloud.

“For Luxembourg, where digital sovereignty is not just a principle but a strategic necessity, this model offers the resilience, autonomy and trust our market expects. By combining Microsoft’s technological leadership with Proximus NXT’s sovereign cloud expertise, we are enabling our customers to innovate confidently—even in fully-disconnected mode.”

CIOs planning offline deployments must map workloads to the correct control posture based on risk, regulation, and specific mission requirements. Since disconnected environments are not one-size-fits-all, businesses can start fast with smaller deployments and expand their capabilities over time.

Implementing a disconnected private cloud with AI support answers a business requirement for highly-regulated sectors, enabling secure data governance even when external connectivity is absent.

See also: Deploying agentic finance AI for immediate business 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.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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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

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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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

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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Corporate finance departments face pressure from market volatility, regulatory demands, and digital finance requirements. Ashish Kumar, head of Infosys Oracle Sales for North America, and CM Grover, CEO of IBS FinTech, recently discussed the realities of corporate treasuries.

IBS FinTech has operated for 19 years and currently ranks in the top five globally according to an IDC report. Grover notes that while AI-powered automation has reached many areas of corporate life, treasury departments often still rely on manual spreadsheets.

“IBS FinTech has identified the gap in the CFO’s office in corporations where they are managing their most critical information system, that is, treasury management on Excel,” Grover said.

Treasury teams manage cash, liquidity, and risk. Companies face foreign currency risk through imports and exports, alongside related commodity risks. Cash surplus companies also need to invest in operations to generate returns.

The key problem for many enterprises is a lack of real-time data connection. Teams often execute trades on platforms like Bloomberg, Reuters, or 360D, manually enter the data into spreadsheets, and then post accounting entries into an enterprise resource planning system.

Successfully implementing AI in enterprise treasury management

AI implementations in finance depend on resolving these manual bottlenecks. Enterprise leaders often view the technology as a fast solution, but the technology requires digitised and automated data as a foundation.

“It is not by talking you can do AI in treasury,” Grover said. “You have to create that underlying data set that has to be digitised and automated.”

Integrating treasury management systems with existing enterprise resource planning platforms allows companies to establish this data foundation. IBS FinTech built its backend on Oracle databases from its inception and now integrates with Oracle Cloud, NetSuite, and Fusion.

A connected ecosystem requires the treasury management system to communicate directly with the enterprise resource planning platform, trading platforms, and banks. This integration provides executives with accurate information to manage liquidity, mitigate risk, and monitor compliance violations across the system.

Grover expects global volatility to increase due to geopolitical and economic factors impacting commodities, equities, and foreign exchange. Executives must prioritise automation and real-time information systems to operate in this uncertain environment.

Kumar noted that modernising treasury management with AI and connecting it to enterprise resource planning systems builds financial resilience. Enterprise leaders should audit their existing data workflows. If a finance team relies on manual entry between a trading platform and an enterprise resource planning platform, AI initiatives will fail due to poor data quality.

Implementing direct integrations ensures data flows in real time without error, providing the necessary baseline for future technology deployment.

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

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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While early adoption centred on content generation and efficiency in isolated workflows, the current requirement is to industrialise these capabilities. The objective is to create systems where AI agents do not merely assist human operators, but actively run processes within strict governance frameworks.

This transition presents specific architectural and cultural challenges. It requires a move from disparate tools to joined-up systems that manage data signals, decision logic, and execution layers simultaneously.

Financial institutions integrate agentic AI workflows

The primary bottleneck in scaling AI within financial services is no longer the availability of models or creative application, it is coordination. Marketing and customer experience teams often struggle to convert decisions into action due to friction between legacy systems, compliance approvals, and data silos.

Saachin Bhatt, Co-Founder and COO at Brdge, notes the distinction between current tools and future requirements: “An assistant helps you write faster. A copilot helps teams move faster. Agents run processes.”

For enterprise architects, this means building what Bhatt terms a ‘Moments Engine’. This operating model functions through five distinct stages:

• Signals: Detecting real-time events in the customer journey.

• Decisions: Determining the appropriate algorithmic response.

• Message: Generating communication aligned with brand parameters.

• Routing: Automated triage to determine if human approval is required.

• Action and learning: Deployment and feedback loop integration.

Most organisations possess components of this architecture but lack the integration to make it function as a unified system. The technical goal is to reduce the friction that slows down customer interactions. This involves creating pipelines where data flows seamlessly from signal detection to execution, minimising latency while maintaining security.

Governance as infrastructure

In high-stakes environments like banking and insurance, speed cannot come at the cost of control. Trust remains the primary commercial asset. Consequently, governance must be treated as a technical feature rather than a bureaucratic hurdle.

The integration of AI into financial decision-making requires “guardrails” that are hard-coded into the system. This ensures that while AI agents can execute tasks autonomously, they operate within pre-defined risk parameters.

Farhad Divecha, Group CEO at Accuracast, suggests that creative optimisation must become a continuous loop where data-led insights feed innovation. However, this loop requires rigorous quality assurance workflows to ensure output never compromises brand integrity.

For technical teams, this implies a shift in how compliance is handled. Rather than a final check, regulatory requirements must be embedded into the prompt engineering and model fine-tuning stages.

“Legitimate interest is interesting, but it’s also where a lot of companies could trip up,” observes Jonathan Bowyer, former Marketing Director at Lloyds Banking Group. He argues that regulations like Consumer Duty help by forcing an outcome-based approach.

Technical leaders must work with risk teams to ensure AI-driven activity attests to brand values. This includes transparency protocols. Customers should know when they are interacting with an AI, and systems must provide a clear escalation path to human operators.

Data architecture for restraint

A common failure mode in personalisation engines is over-engagement. The technical capability to message a customer exists, but the logic to determine restraint is often missing. Effective personalisation relies on anticipation (i.e. knowing when to remain silent is as important as knowing when to speak.)

Jonathan Bowyer points out that personalisation has moved to anticipation. “Customers now expect brands to know when not to speak to them as opposed to when to speak to them.”

This requires a data architecture capable of cross-referencing customer context across multiple channels – including branches, apps, and contact centres – in real-time. If a customer is in financial distress, a marketing algorithm pushing a loan product creates a disconnect that erodes trust. The system must be capable of detecting negative signals and suppressing standard promotional workflows.

“The thing that kills trust is when you go to one channel and then move to another and have to answer the same questions all over again,” says Bowyer. Solving this requires unifying data stores so that the “memory” of the institution is accessible to every agent (whether digital or human) at the point of interaction.

The rise of generative search and SEO

In the age of AI, the discovery layer for financial products is changing. Traditional search engine optimisation (SEO) focused on driving traffic to owned properties. The emergence of AI-generated answers means that brand visibility now occurs off-site, within the interface of an LLM or AI search tool.

“Digital PR and off-site SEO is returning to focus because generative AI answers are not confined to content pulled directly from a company’s website,” notes Divecha.

For CIOs and CDOs, this changes how information is structured and published. Technical SEO must evolve to ensure that the data fed into large language models is accurate and compliant. 

Organisations that can confidently distribute high-quality information across the wider ecosystem gain reach without sacrificing control. This area, often termed ‘Generative Engine Optimisation’ (GEO), requires a technical strategy to ensure the brand is recommended and cited correctly by third-party AI agents.

Structured agility

There is a misconception that agility equates to a lack of structure. In regulated industries, the opposite is true.

Agile methodologies require strict frameworks to function safely. Ingrid Sierra, Brand and Marketing Director at Zego, explains: “There’s often confusion between agility and chaos. Calling something ‘agile’ doesn’t make it okay for everything to be improvised and unstructured.”

For technical leadership, this means systemising predictable work to create capacity for experimentation. It involves creating safe sandboxes where teams can test new AI agents or data models without risking production stability.

Agility starts with mindset, requiring staff who are willing to experiment. However, this experimentation must be deliberate. It requires collaboration between technical, marketing, and legal teams from the outset.

This “compliance-by-design” approach allows for faster iteration because the parameters of safety are established before the code is written.

What’s next for AI in the financial sector?

Looking further ahead, the financial ecosystem will likely see direct interaction between AI agents acting on behalf of consumers and agents acting for institutions.

Melanie Lazarus, Ecosystem Engagement Director at Open Banking, warns: “We are entering a world where AI agents interact with each other, and that changes the foundations of consent, authentication, and authorisation.”

Tech leaders must begin architecting frameworks that protect customers in this agent-to-agent reality. This involves new protocols for identity verification and API security to ensure that an automated financial advisor acting for a client can securely interact with a bank’s infrastructure.

The mandate for 2026 is to turn the potential of AI into a reliable P&L driver. This requires a focus on infrastructure over hype and leaders must prioritise:

• Unifying data streams: Ensure signals from all channels feed into a central decision engine to enable context-aware actions.

• Hard-coding governance: Embed compliance rules into the AI workflow to allow for safe automation.

• Agentic orchestration: Move beyond chatbots to agents that can execute end-to-end processes.

• Generative optimisation: Structure public data to be readable and prioritised by external AI search engines.

Success will depend on how well these technical elements are integrated with human oversight. The winning organisations will be those that use AI automation to enhance, rather than replace, the judgment that is especially required in sectors like financial services.

A handbook from Accuracast for CMOs is available here (registration required)

See also: Goldman Sachs deploys Anthropic systems with success

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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While US-based labs have historically held the performance advantage, open-source alternatives like the Qwen 3.5 series are closing the gap with frontier models. This offers enterprises a potential reduction in inference costs and increased flexibility in deployment architecture.

The central narrative of the Qwen 3.5 release is this technical alignment with leading proprietary systems. Alibaba is explicitly targeting benchmarks established by high-performance US models, including GPT-5.2 and Claude 4.5. This positioning indicates an intent to compete directly on output quality rather than just price or accessibility.

Technology expert Anton P. states that the model is “trading blows with Claude Opus 4.5 and GPT-5.2 across the board.” He adds that the model “beats frontier models on browsing, reasoning, instruction following.”

Alibaba Qwen’s performance convergence with closed models

For enterprises, this performance parity suggests that open-weight models are no longer solely for low-stakes or experimental use cases. They are becoming viable candidates for core business logic and complex reasoning tasks.

The flagship Alibaba Qwen model contains 397 billion parameters but utilises a more efficient architecture with only 17 billion active parameters. This sparse activation method, often associated with Mixture-of-Experts (MoE) architectures, allows for high performance without the computational penalty of activating every parameter for every token.

This architectural choice results in speed improvements. Shreyasee Majumder, a Social Media Analyst at GlobalData, highlights a “massive improvement in decoding speed, which is up to nineteen times faster than the previous flagship version.”

Faster decoding ultimately translates directly to lower latency in user-facing applications and reduced compute time for batch processing.

The release operates under an Apache 2.0 license. This licensing model allows enterprises to run the model on their own infrastructure, mitigating data privacy risks associated with sending sensitive information to external APIs.

The hardware requirements for Qwen 3.5 are relatively accessible compared to previous generations of large models. The efficient architecture allows developers to run the model on personal hardware, such as Mac Ultras.

David Hendrickson, CEO at GenerAIte Solutions, observes that the model is available on OpenRouter for “$3.6/1M tokens,” a pricing that he highlights is “a steal.”

Alibaba’s Qwen 3.5 series introduces native multimodal capabilities. This allows the model to process and reason across different data types without relying on separate, bolted-on modules. Majumder points to the “ability to navigate applications autonomously through visual agentic capabilities.”

Qwen 3.5 also supports a context window of one million tokens in its hosted version. Large context windows enable the processing of extensive documents, codebases, or financial records in a single prompt.

If that wasn’t enough, the model also includes native support for 201 languages. This broad linguistic coverage helps multinational enterprises deploy consistent AI solutions across diverse regional markets.

Considerations for implementation

While the technical specifications are promising, integration requires due diligence. TP Huang notes that he has “found larger Qwen models to not be all that great” in the past, though Alibaba’s new release looks “reasonably better.”

Anton P. provides a necessary caution for enterprise adopters: “Benchmarks are benchmarks. The real test is production.”

Leaders must also consider the geopolitical origin of the technology. As the model comes from Alibaba, governance teams will need to assess compliance requirements regarding software supply chains. However, the open-weight nature of the release allows for code inspection and local hosting, which mitigates some data sovereignty concerns compared to closed APIs.

Alibaba’s release of Qwen 3.5 forces a decision point. Anton P. asserts that open-weight models “went from ‘catching up’ to ‘leading’ faster than anyone predicted.”

For the enterprise, the decision is whether to continue paying premiums for proprietary US-hosted models or to invest in the engineering resources required to leverage capable yet lower-cost open-source alternatives.

See also: Alibaba enters physical AI race with open-source robot model RynnBrain

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 Alibaba Qwen is challenging proprietary AI model economics appeared first on AI News.

While general AI projects saw return on investment rise to 67 percent last year, autonomous agents delivered an average ROI of 80 percent by handling complex processes without human intervention. This performance gap demands a change in how CIOs allocate automation budgets.

Agentic AI systems are now advancing the enterprise from theoretical value to hard returns. Unlike generative tools that summarise data or draft text, these agents execute workflows within strict rules and approval thresholds.

Boardroom pressure drives this pivot. A report by Basware and FT Longitude finds nearly half of CFOs face demands from leadership to implement AI across their operations. Yet 61 percent of finance leaders admit their organisations rolled out custom-developed AI agents largely as experiments to test capabilities rather than to solve business problems.

These experiments often fail to pay off. Traditional AI models generate insights or predictions that require human interpretation. Agentic systems close the gap between insight and action by embedding decisions directly into the workflow.

Jason Kurtz, CEO of Basware, explains that patience for unstructured experimentation is running low. “We’ve reached a tipping point where boards and CEOs are done with AI experiments and expecting real results,” he says. “AI for AI’s sake is a waste.”

Accounts payable as the proving ground for agentic AI in finance

Finance departments now direct these agents toward high-volume, rules-based environments. Accounts payable (AP) is the primary use case, with 72 percent of finance leaders viewing it as the obvious starting point. The process fits agentic deployment because it involves structured data: invoices enter, require cleaning and compliance checks, and result in a payment booking.

Teams use agents to automate invoice capture and data entry, a daily task for 20 percent of leaders. Other live deployments include detecting duplicate invoices, identifying fraud, and reducing overpayments. These are not hypothetical applications; they represent tasks where an algorithm functions with high autonomy when parameters are correct.

Success in this sector relies on data quality. Basware trains its systems on a dataset of more than two billion processed invoices to deliver context-aware predictions. This structured data allows the system to differentiate between legitimate anomalies and errors without human oversight.

Kevin Kamau, Director of Product Management for Data and AI at Basware, describes AP as a “proving ground” because it combines scale, control, and accountability in a way few other finance processes can.

The build versus buy decision matrix

Technology leaders must next decide how to procure these capabilities. The term “agent” currently covers everything from simple workflow scripts to complex autonomous systems, which complicates procurement.

Approaches split by function. In accounts payable, 32 percent of finance leaders prefer agentic AI embedded in existing software, compared to 20 percent who build them in-house. For financial planning and analysis (FP&A), 35 percent opt for self-built solutions versus 29 percent for embedded ones.

This divergence suggests a pragmatic rule for the C-suite. If the AI improves a process shared across many organisations, such as AP, embedding it via a vendor solution makes sense. If the AI creates a competitive advantage unique to the business, building in-house is the better path. Leaders should buy to accelerate standard processes and build to differentiate.

Governance as an enabler of speed

Fear of autonomous error slows adoption. Almost half of finance leaders (46%) will not consider deploying an agent without clear governance. This caution is rational; autonomous systems require strict guardrails to operate safely in regulated environments.

Yet the most successful organisations do not let governance stop deployment. Instead, they use it to scale. These leaders are significantly more likely to use agents for complex tasks like compliance checks (50%) compared to their less confident peers (6%).

Anssi Ruokonen, Head of Data and AI at Basware, advises treating AI agents like junior colleagues. The system requires trust but should not make large decisions immediately. He suggests testing thoroughly and introducing autonomy slowly, ensuring a human remains in the loop to maintain responsibility.

Digital workers raise concerns regarding displacement. A third of finance leaders believe job displacement is already happening. Proponents argue agents shift the nature of work rather than eliminating it.

Automating manual tasks such as information extraction from PDFs frees staff to focus on higher-value activities. The goal is to move from task efficiency to operating leverage, allowing finance teams to manage faster closes and make better liquidity decisions without increasing headcount.

Organisations that use agentic AI extensively report higher returns. Leaders who deploy agentic AI tools daily for tasks like accounts payable achieve better outcomes than those who limit usage to experimentation. Confidence grows through controlled exposure; successful small-scale deployments lead to broader operational trust and increased ROI.

Executives must move beyond unguided experimentation to replicate the success of early adopters. Data shows that 71 percent of finance teams with weak returns acted under pressure without clear direction, compared to only 13 percent of teams achieving strong ROI.

Success requires embedding AI directly into workflows and governing agents with the discipline applied to human employees. “Agentic AI can deliver transformational results, but only when it is deployed with purpose and discipline,” concludes Kurtz.

See also: AI deployment in financial services hits an inflection point as Singapore leads the shift to production

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 post Agentic AI drives finance ROI in accounts payable automation appeared first on AI News.

As distinct business units race to adopt generative technologies, CIOs especially find their ecosystems populated by fragmented and unmonitored assets. This mirrors the shadow IT challenges of the cloud era, but involves autonomous actors capable of executing business logic and accessing sensitive data.

IDC projects the number of actively deployed AI agents will exceed one billion by 2029—a forty-fold increase from current levels. In the first half of 2025 alone, agent creation surged by 119 percent. For enterprise leadership, the immediate challenge shifts from building these agents to locating, auditing, and governing them across platforms.

Salesforce has responded to this fragmentation by expanding its MuleSoft Agent Fabric capabilities, introducing automated discovery tools designed to centralise the management of AI agents regardless of their origin.

Automating discovery

Visibility remains the core issue for security and operations teams. When marketing teams deploy AI agents on one platform and logistics teams build on another, effective governance becomes difficult as central IT loses a consolidated view of the organisation’s digital workforce.

MuleSoft’s updated architecture addresses this via ‘Agent Scanners’. These tools continuously patrol major ecosystems – including Salesforce Agentforce, Amazon Bedrock, and Google Vertex AI – to identify running agents. Rather than relying on developers to manually register their deployments, the system automates detection.

Finding an agent is only the first step; compliance leaders need to understand the logic behind it. The scanners extract metadata detailing the agent’s capabilities, the LLMs driving it, and the specific data endpoints it is authorised to access. This information is then normalised into standard Agent-to-Agent (A2A) specifications, creating a uniform profile for assets regardless of the underlying vendor.

Andrew Comstock, SVP and GM of MuleSoft, said: “The most successful organisations of the next decade will be those that harness the full diversity of the multi-cloud AI landscape. The expanded capabilities of MuleSoft Agent Fabric give you the freedom to innovate across any platform while maintaining the unified visibility and control needed to scale.”

Governance and cost control for AI agents

Unmanaged agents create financial inefficiency and risk exposure. Consider a CISO in the banking sector. Under standard operations, verifying a new loan-processing agent involves manually chasing documentation from development teams. Automated cataloguing allows security teams to immediately view which financial databases an agent accesses and verify its authorisation levels without manual intervention. This capability ensures security teams view real-time data rather than outdated snapshots.

From a financial perspective, visibility drives consolidation. Large enterprises frequently suffer from redundancy where regional teams independently procure or build similar tools. A multinational manufacturer, for instance, might have three separate teams paying for distinct summarisation agents on different platforms.

By using the MuleSoft Agent Visualizer to filter the estate by job type, operations leaders can identify these overlaps. Consolidating these into a single high-performing asset reduces redundant licensing costs and allows budget reallocation toward novel development.

Transitioning successfully to an ‘Agentic Enterprise’

Innovation often occurs at the edges, where data scientists build bespoke tools outside formal procurement channels.

The expanded Agent Fabric addresses this by allowing the registration of “homegrown” agents and Model Context Protocol (MCP) servers via URL. This is particularly relevant for sectors like logistics, where teams may build internal tools for proprietary database optimisation. Instead of remaining hidden, these assets can be registered and made discoverable for reuse across the company.

Jonathan Harvey, Head of AI Operations at Capita, said: “Agent Scanners will let us focus on innovation instead of inventory management. Knowing that every agent is automatically discovered and catalogued allows our teams to collaborate, reuse work, and build smarter multi-agent solutions.”

Similarly, AT&T is utilising the framework to orchestrate agents across customer support, chat, and voice interactions.

Brad Ringer, Enterprise & Integration Architect at AT&T, explained: “With AI moving so fast, MuleSoft Agent Fabric provides the framework we need to scale. It brings together and helps us orchestrate all of the agents and MCP servers we’re building in customer support, chat, and voice interactions. It isn’t just a tool; it’s a huge enabler for everything we’re doing next.”

The transition to an “Agentic Enterprise” requires a change in governance around how IT assets are tracked, rendering the days of managing integrations via stale spreadsheets incompatible with the speed of AI agent deployment. 

Leaders must assume their inventory of AI agents is incomplete and deploy automated scanning tools to establish a baseline of truth. Once this baseline is established, governance policies should mandate that all agents – whether bought or built – expose their capabilities and data access privileges in a standardised format like A2A to facilitate monitoring.

Finally, executives can use the visibility provided by these tools to audit spend, identifying duplicate functionalities across cloud environments and merging them to control the Total Cost of Ownership (TCO). 

As organisations move from pilot programmes to mass deployment, the differentiator will not be the intelligence of individual agents, but the coherence of the network that connects them.

See also: Balancing AI cost efficiency with data sovereignty

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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For over a year, the generative AI narrative focused on a race for capability, often measuring success by parameter counts and flawed benchmark scores. Boardroom conversations, however, are undergoing a necessary correction.

While the allure of low-cost, high-performance models offers a tempting path to rapid innovation, the hidden liabilities associated with data residency and state influence are forcing a reassessment of vendor selection. China-based AI laboratory DeepSeek recently became a focal point for this industry-wide debate.

According to Bill Conner, former adviser to Interpol and GCHQ, and current CEO of Jitterbit, DeepSeek’s initial reception was positive because it challenged the status quo by demonstrating that “high-performing large language models do not necessarily require Silicon Valley–scale budgets.”

For businesses looking to trim the immense costs associated with generative AI pilots, this efficiency was understandably attractive. Conner observes that these “reported low training costs undeniably reignited industry conversations around efficiency, optimisation, and ‘good enough’ AI.”

AI and data sovereignty risks

Enthusiasm for cut-price performance has collided with geopolitical realities. Operational efficiency cannot be decoupled from data security, particularly when that data fuels models hosted in jurisdictions with different legal frameworks regarding privacy and state access.

Recent disclosures regarding DeepSeek have altered the math for Western enterprises. Conner highlights “recent US government revelations indicating DeepSeek is not only storing data in China but actively sharing it with state intelligence services.”

This disclosure moves the issue beyond standard GDPR or CCPA compliance. The “risk profile escalates beyond typical privacy concerns into the realm of national security.”

For enterprise leaders, this presents a specific hazard. LLM integration is rarely a standalone event; it involves connecting the model to proprietary data lakes, customer information systems, and intellectual property repositories. If the underlying AI model possesses a “back door” or obliges data sharing with a foreign intelligence apparatus, sovereignty is eliminated and the enterprise effectively bypasses its own security perimeter and erases any cost efficiency benefits.

Conner warns that “DeepSeek’s entanglement with military procurement networks and alleged export control evasion tactics should serve as a critical warning sign for CEOs, CIOs, and risk officers alike.” Utilising such technology could inadvertently entangle a company in sanctions violations or supply chain compromises.

Success is no longer just about code generation or document summaries; it is about the provider’s legal and ethical framework. Especially in industries like finance, healthcare, and defence, tolerance for ambiguity regarding data lineage is zero.

Technical teams may prioritise AI performance benchmarks and ease of integration during the proof-of-concept phase, potentially overlooking the geopolitical provenance of the tool and the need for data sovereignty. Risk officers and CIOs must enforce a governance layer that interrogates the “who” and “where” of the model, not just the “what.”

Governance over AI cost efficiency

Deciding to adopt or ban a specific AI model is a matter of corporate responsibility. Shareholders and customers expect that their data remains secure and used solely for intended business purposes.

Conner frames this explicitly for Western leadership, stating that “for Western CEOs, CIOs, and risk officers, this is not a question of model performance or cost efficiency.” Instead, “it is a governance, accountability, and fiduciary responsibility issue.”

Enterprises “cannot justify integrating a system where data residency, usage intent, and state influence are fundamentally opaque.” This opacity creates an unacceptable liability. Even if a model offers 95 percent of a competitor’s performance at half the cost, the potential for regulatory fines, reputational damage, and loss of intellectual property erases those savings instantly.

The DeepSeek case study serves as a prompt to audit current AI supply chains. Leaders must ensure they have full visibility into where model inference occurs and who holds the keys to the underlying data. 

As the market for generative AI matures, trust, transparency, and data sovereignty will likely outweigh the appeal of raw cost efficiency.

See also: SAP and Fresenius to build sovereign AI backbone for healthcare

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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For enterprise leaders, deploying AI is less a technical hurdle than a complex exercise in change management. The reality for many organisations is that, while algorithms offer efficiency, the human element dictates the speed of adoption.

Data from the TUC indicates that 51 percent of UK adults are concerned about the impact of AI and new technologies on their job. This anxiety creates a tangible risk to ROI; resistance halts the innovation leaders seek to foster.

Allister Frost, a former Microsoft leader and expert on business transformation, argues this friction stems from a misunderstanding of the technology’s capability.

Address the misconception of true intelligence

A common error in corporate strategy treats generative AI and Large Language Models (LLMs) as autonomous agents rather than data processors. This anthropomorphism drives the fear that machines will make human cognition obsolete.

“The greatest misconception is that AI is as intelligent as its name suggests and can perform human-like tasks,” Frost notes. He clarifies the reality: “AI is primarily pattern-matching at scale, offering opportunities to help people work smarter, innovate faster, and explore new pathways to growth.”

Communicating this distinction is essential. When employees view these tools as pattern-matchers rather than sentient replacements, the narrative changes from competition to utility. Frost emphasises that “AI doesn’t have the ability to replicate human intelligence, it exists to augment it.”

Some finance and operations leaders view AI integration primarily as a mechanism to reduce salary overheads. Yet stripping away experienced staff for automation often degrades institutional memory.

Frost warns against this tactic: “Too often, businesses see AI as a shortcut to headcount reduction, putting experienced workers at risk for short-term savings. This approach overlooks the enormous economic and societal cost of losing skilled staff.”

Data confirms the workforce is on edge regarding this scenario. Acas reports that 26 percent of British workers cite job losses as their biggest concern regarding AI at work. History suggests, however, that technological integration expands rather than contracts the labour market.

“The reality is that AI is not poised to eliminate jobs indiscriminately, but rather to evolve the nature of work,” states Frost.

Operationalising augmentation

Successful integration requires changing how AI use cases are identified. Rather than looking for roles to remove, enterprise leaders should identify high-volume, low-value tasks that bottleneck productivity.

“AI tools have the potential to automate mundane tasks and free up human labour to focus on creative and strategic aspects,” explains Frost.

This allows leaders to move staff toward high-touch areas where algorithms struggle.

“As AI handles repetitive tasks, it frees up time to allow staff to upskill and transition into more complex roles that require a higher level of critical thinking and emotional intelligence.”

These competencies – empathy, ethical decision-making, and complex strategy – remain outside the grasp of current computational models.

Resistance to AI is often a symptom of “change fatigue,” a common response to the pace of digital updates. With 14 percent of UK workers explicitly worried about AI’s impact on their current job, transparent governance is required.

Leaders must recognise that “resisting AI’s integration can hinder progress and limit opportunities for innovation.” Active engagement is the solution. “Engaging employees in discussions about AI’s role within the organisation can help demystify its functions and build trust,” Frost advises.

This requires moving beyond top-down mandates. It involves creating a culture where staff feel safe to experiment with new tools without the immediate fear of displacing their own roles.

“Once leaders have cultivated an environment of transparency and inclusion, businesses can alleviate anxieties, ensuring all team members are aligned and prepared to harness AI’s benefits.”

Adapting the workforce for successful AI integration

Enterprise technology advancements have always demanded adaptation, and AI – while a larger transformation than many technologies in recent decades – is no different.

“Throughout history people have been resistant to new technological advancements, yet history shows us humans have repeatedly risen to the challenge of integrating new technologies.”

For enterprise leaders, success involves investing in resilience and continuous learning. By framing AI as a transformative tool rather than a threat, organisations can protect their talent pipeline while modernising operations.

A summary of advice to ensure successful AI integration:

• Reframe the narrative: Explicitly communicate AI as a “pattern-matching” tool for augmentation, not a sentient replacement, to lower cultural resistance.

• Audit for augmentation: Identify the mundane and high-volume process bottlenecks for automation, specifically to free up staff for more rewarding creative work.

• Invest in “human” skills: Allocate learning and development budgets toward critical thinking, empathy, and ethical decision-making, as these are the non-replicable assets in an AI-driven market.

• Combat change fatigue: Ensure transparent and two-way dialogue regarding AI integration roadmaps and governance to build trust and mitigate the fear factor regarding job losses.

“My mission is to save one million working lives by showing that AI works best when it empowers humans, rather than replaces them,” Frost concludes.

See also: How Shopify is bringing agentic AI to enterprise 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. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Allister Frost: Tackling workforce anxiety for AI integration success appeared first on AI News.

The Department for Science, Innovation & Technology (DSIT) recently launched a call for evidence on a proposed ‘AI Growth Lab’. This cross-economy sandbox is designed to accelerate the deployment of autonomous technologies by granting “time-limited regulatory exemptions” to firms. The government’s position is that many regulations are outdated, having been designed before autonomous software existed, often assuming that decisions are made by people rather than machines.

Ministers believe that if the UK can move faster than its global competitors, it can secure a defining economic advantage, with a potential  £140 billion boost to national output by 2030. Their preliminary analysis specifically flags legal services as a sector where removing “unnecessary legal barriers” could generate billions in value over the next decade.

Yet, the legal profession – supposedly the beneficiary of this deregulation – isn’t asking for exemptions. In its formal response, the Law Society made clear that the existing framework is robust enough. The friction lies not in the rules themselves, but in the lack of certainty surrounding them. While two-thirds of lawyers already use AI tools, confusion remains the primary brake on deeper integration.

Ian Jeffery, CEO of The Law Society, said: “AI innovation is vital for the legal sector and already has great momentum. The existing legal regulatory framework supports progress. The main challenges don’t stem from regulatory burdens, but rather from uncertainty, cost, data and skills associated with AI adoption.”

Rather than a regulatory overhaul, the profession is asking for a practical roadmap. Firms are currently navigating a grey area regarding liability and data protection. Solicitors need definitive answers on whether client data must be anonymised before it is fed into AI platforms, and they require standardised protocols for data security and storage.

The questions get thornier when errors occur. If an AI tool generates harmful legal advice, it is currently unclear where the buck stops (i.e. with the solicitor, the firm, the developer, or the insurer.) There is also ambiguity about supervision requirements, specifically whether a human lawyer must oversee every instance of AI deployment.

Such concerns are particularly acute for “reserved legal activities” like court representation, conveyancing, and probate, where practitioners need to know if using automated assistance puts them in breach of their professional duties.

AI laws must retain safeguards

The government has tried to reassure the public that the sandbox will have “red lines” to protect fundamental rights and safety. However, The Law Society remains wary of any move that might dilute consumer protection in the name of speed.

“Technological progress in the legal sector should not expose clients or consumers to unregulated risks,” Jeffery stated. “Current regulation of the profession reflects the safeguards that Parliament deemed vital to protect clients and the public. It ensures trust in the English and Welsh legal system worldwide.”

The body is willing to collaborate on a “legal services sandbox,” but only if it upholds professional standards rather than bypassing them. For The Law Society, the priority is maintaining the integrity of the justice system in the AI era.

“The Law Society strongly supports innovation provided it remains aligned with professional integrity and operates in a solid regulatory environment,” Jeffery explained. “The government must work with legal regulators and bodies to ensure adherence to the sector’s professional standards. Any legal regulatory changes must include parliamentary oversight.”

See also: Inside China’s push to apply AI across its energy system

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. Click here for more information.

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