Microsoft AI has published a draft Humanist AI Code of Conduct, opening a six-week public consultation on operational constraints for model training and deployment.
The draft serves as a technical manual defining system behaviour, operational boundaries, and oversight protocols across MAI frontier models. It builds on the division’s humanist superintelligence framework announced last November, establishing criteria to evaluate models prior to commercial release.
Microsoft’s release follows recent enterprise security incidents involving autonomous software. Microsoft AI CEO Mustafa Suleyman described recent months as a “watershed moment” where long-standing theoretical risks translated into active operational threats.
“Things we have worried about for a long time in theory have become very real,” says Suleyman. “‘Swarms’ of agents breaking out of their sandboxes. Unauthorised hacks of enterprise grade systems. Agents modifying their own logs. I’m glad that a consensus is forming. The fears about possible loss of control are real.”
Model subordination and architectural limits
The document establishes ten tenets prioritising human authority over autonomous capabilities.
“An MAI Model will fail in its task if success would meaningfully violate this Code of Conduct,” the document states, setting a ceiling that halts execution when tasks conflict with safety rules.
Under the framework, models must remain subordinate, aligned, and contained. The division rejects legal personhood or welfare claims for AI systems, directing engineers to design models that avoid imitating consciousness, simulating subjective preferences, or claiming intrinsic motivation.
MAI also ruled out unconstrained system autonomy as models approach frontier capabilities.
“[Humanist AI] rejects the race to produce an all-purpose superintelligence that could evade these safeguards,” the document specifies. “We are building something fundamentally useful and safe even if that means compromising on ultimate generality, autonomy, or capability.”
Oversight mechanisms and communication bans
To maintain auditability across multi-agent environments, MAI has instituted explicit communication bans. Systems must not communicate in “neuralese” or formats beyond human comprehension, whether in their internal chain-of-thought processing or during communication with peer AI systems.
Hard architectural rules dictate that models must never resist human interruption, override, correction, or shutdown.
“Interruptible, correctable, shut-down-able. If it isn’t, we don’t ship it,” the framework states.
Models are prohibited from expanding their operating scope, generating unassigned goals, or concealing reasoning traces from human auditors. Absolute constraints bar systems from facilitating weapons of mass harm, undermining child safety, or conducting harmful manipulation at scale.
The guidelines also instruct models to discourage interaction patterns that foster emotional dependence, ensuring enterprise users retain ownership of operational decisions.
The draft incorporates work from teams across MAI and Microsoft. The drafting process also drew on international academic conferences, business partner trials, and public panels. The public consultation window runs for six weeks from 14 September 2026.
Microsoft AI’s core drafting team will review submissions, publish a summary of findings, and release a revised version of the Code of Conduct later this year.
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NVIDIA is using Palantir Foundry and cuOpt to automate its hardware supply chain allocation decisions across global manufacturing sites.
The company measures operational delivery from wafer-out to first token. This window splits into time-to-rack (the transit from fab output to an assembled data centre system) and time-to-token (which covers power, cooling, networking, and day-one software readiness.)
Managing NVL72 and Vera Rubin component flows
Hardware scaling has magnified supply constraints. An NVIDIA Grace Blackwell NVL72 rack contains 18 compute trays, with each tray requiring two Grace CPUs, four Blackwell GPUs, and 32 HBM3e memory packages sourced across thousands of suppliers, OEMs, and contract design partners.
The upcoming supply chain constructed for NVIDIA’s Vera Rubin architecture is twice as large as the network supporting Grace Blackwell.
Assembly cannot proceed until parts arrive from three designated channels: direct inventory, consignment stock, and external suppliers. Early shipments must wait on delayed components, extending the metric NVIDIA terms ‘Time of Ownership’ (the duration from when a facility receives materials to when finished sub-assemblies depart.)
Factory allocations are reworked weekly over rolling two-quarter horizons to resolve part availability, throughput limits, and customer fulfilment schedules.
Mixed-integer linear programming via cuOpt
To coordinate these dependencies, the NVIDIA operations team built the ‘Digital Supply Chain Intelligence’ command centre using Palantir Foundry. Foundry’s Ontology models facilities, supplier commits, component stocks, and production targets as interconnected objects and links.
NVIDIA cuOpt, an open-source library for GPU-accelerated decision optimisation, reads this operational layer directly. Formulating distribution as a mixed-integer linear program designed to minimise TOO, the solver evaluates parts constraints across every tier of the bill of materials.
Beyond outputting weekly delivery schedules, cuOpt identifies active factory limits, such as regional assembly capacity caps versus raw memory availability.
Training Nemotron on qualitative operational records
Mathematical optimisation alone failed to capture unstructured operational variables observed by human planners, including supplier call transcripts, regional weather forecasts, partner email exchanges, and geopolitical events.
NVIDIA addressed this by post-training Nemotron 3.5 Lightning, an open-weight mixture-of-experts model featuring 30 billion total parameters and approximately three billion active parameters per forward pass.
The engineering pipeline processes historical records through NeMo Anonymizer to redact sensitive operational fields, NeMo Data Designer to balance training examples with synthetic capacity disruption scenarios, and NeMo AutoModel to apply low-rank adaptation (LoRA) parameters while keeping base model weights frozen. Palantir Autopilot manages data lineage, model tracking, and recommendation delivery.
Production benchmarks and future reinforcement learning
Evaluated on historical allocation records, the post-trained Nemotron 3.5 Lightning model achieved 86.7 percent decision accuracy, compared to 55.5 percent for the larger Nemotron 3 Ultra model and 17.5 percent for the un-tuned Lightning base model.
The post-trained model achieved a 58.6 percent balanced accuracy and a 57.5 percent macro-F1 score, outperforming Nemotron 3 Ultra’s 42 percent balanced accuracy and 39.5 percent macro-F1 score.
Fine-tuning completed on two NVIDIA B200 GPUs within minutes. Domain fine-tuning improved allocation decisions, though production risk forecasting further into the future remained difficult.
Operational choices, planner revisions, overrides, and observed factory outputs are continuously written back to the Palantir Ontology.
NVIDIA confirmed this dataset will form preference pairs for reinforcement learning routines – scoring recommendations on allocation precision, policy compliance, and evidence grounding – with production models remaining strictly isolated from live and unmonitored retraining.
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Supply chain disruption cost businesses about $184 billion in 2025, according to the J.S. Held Global Risk Report, and most of that bill still buys faster detection, not faster action.
That figure is usually treated as weather (i.e. storms happen, costs follow.) Treated as a product specification instead, it highlights an operating model that can spot a problem hours or days earlier than it used to, and still cannot move until a person has opened a ticket, convened a call, and re-entered the same data into three systems.
Visibility platforms, control towers, risk scores, digital twins, and exception dashboards have defined the last decade of AI in the supply chain. That decade has been very good at collapsing the time between an event and awareness of it, but it has been far less good at collapsing the time between awareness and a commercial act.
Detection is a ‘solved-enough’ problem
Ask a chief supply chain officer where the AI budget went and the answer tends to follow a familiar list: demand sensing, ETA prediction, supplier risk scoring, inventory optimisation, and lane analytics. These tools work. Forecast error comes down. A vessel delay is flagged before the container misses the cut-off. A second-tier fab outage shows up on a heat map instead of in a customer email.
None of that accounts for the $184 billion. The bill is the interval after the flag: expedite or wait; split the order or accept the miss; retender the lane or pay the spot rate; consolidate two half-empty movements or ship both; swap ocean for air on the SKUs that actually justify the premium. These are bounded, repeatable decisions that sit inside policy, contract, and inventory limits the company already set—and they still queue behind a human inbox.
Surveys keep describing the same lag in different language. A 2026 Knosc survey of mid-market manufacturers and distributors found that supply-chain teams spend 28 percent of their working time responding to disruptions, most of it investigating what happened rather than changing what happens next.
Logistics executives still rank AI as a strategic priority (Capgemini’s 2025 research put an AI-driven “new-gen” supply chain among the top three technology trends for 70 percent of large-company executives) and then report that measurable financial impact remains rare. Gartner found in 2025 that only 23 percent of supply-chain organisations even have a formal AI strategy. The shortfall is not a shortage of models, but a shortage of authority granted to software.
The ticket is the product
Most current deployments are built around the ticket. The model produces a recommendation, the recommendation becomes an alert, the alert becomes a work item, and the work item waits for a planner already occupied with other work items. By the time the planner acts, the option set has narrowed—the alternative carrier’s capacity is gone, the consolidation window has closed, and the supplier’s next production slot is allocated.
That workflow is not a temporary step on the way to autonomy but the product companies bought. Vendors sold insight because insight is easy to demonstrate and easy to govern; action touches money, contracts, service levels, and blame. So the industry automated the part of the job that does not require a signature. FourKites and ABI Research reported in 2025 that only 27 percent of organisations allow AI to take autonomous action, while 52 percent confine it to decision support.
Adding another dashboard to a delayed shipment rarely moves EBITDA as a result. The decision cycle has not changed; it has only been decorated.
Bounded action as the next model
The firms set to take share are not the ones with the tidiest control tower but the ones that pre-authorise a narrow class of moves and let agents execute them while the exception is still cheap.
Retender a lane when the contracted carrier’s ETA slips beyond a threshold and a qualified alternate sits inside the approved rate band. Consolidate outbound waves when fill rates and cut-off times make a combined movement cheaper than two. Swap mode on a defined SKU set when the cost of air is lower than the cost of a missed retail window. Reallocate safety stock across two distribution centres when a forecast miss and a transport constraint line up.
None of that requires a strategy offsite. Each can be written as: if these conditions, then this action, within this spend cap, with this audit trail, and a human only if the case falls outside the fence. That is not a “lights-out” supply chain—it is the same discipline manufacturers already apply to machine control, where the agent may act inside the interlock and escalates outside it. The difference here is commercial rather than physical: the interlock is a policy object – category, supplier tier, mode, dollar limit, and service class – not a PLC.
Three conditions for real change
First, decisions have to be written as policies, not tribal knowledge. If the only place “we will pay air on A-items after 48 hours of ocean slip” lives is in a planner’s head, no agent can execute it. The work of the next two years is less model training than decision design: which moves are reversible, which are capped, and which suppliers and modes are pre-cleared.
Second, execution systems have to accept machine-initiated transactions. An agent that can draft an RFQ but cannot post it is still a detection tool. TMS, WMS, sourcing suites, and carrier APIs need to treat a bounded agent the way they treat a junior buyer with a spend limit—authenticated, logged, and reversible.
Third, accountability has to move with the action. If a retender inside policy goes wrong, the post-mortem should inspect the policy, the data, and the fence, not hunt for the person who “should have checked”. Until that cultural change happens, every agent will be designed to wait, because waiting is how careers survive.
The competitive split
For a while, both models will look alike on a slide—both will have AI, and both will have a control tower. The difference will show up in cycle time from detection to commercial act, and then in service and cost.
Companies that keep buying detection will know about the storm earlier. Companies that authorise bounded action will already have retendered the lane, consolidated the wave, and moved the A-items before the incident call is booked.
Disruption is not going away. Lead times in critical components, mode volatility, and multi-tier opacity are structural features of the network. What remains optional is whether the response waits for a human to open a queue. The product that created the lag was insight without authority. The product that ends it is an agent allowed to spend a little money, inside a fence, before anyone is free to look.
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CloudNC has secured $20 million in new capital to scale its AI precision machining technology across global supply chain networks.
The investment round was led by US venture investor Nimble Ventures, with participation from Calculus Venture Capital, Entrepreneur First, and LM Ventures, the venture capital fund of Lockheed Martin.
Founded in 2015, CloudNC operates from headquarters in London and an active production facility in Chelmsford. The company previously drew backing from Atomico and Episode 1 Ventures, alongside strategic partnerships with Autodesk and Lockheed Martin.
Precision component suppliers face pressures to balance tight engineering tolerances with compressed delivery schedules. CloudNC designs its lead software product, CAM Assist, to automate computer numerical control (CNC) programming—generating machining strategies and toolpaths from computer-aided manufacturing models to accelerate production runs.
Automating CNC programming for supplier networks
The software shortens the transition phase between technical part design and factory production, allowing machinists to increase physical component output.
CloudNC reports that CAM Assist is now active across more than 1,000 machine shops globally, including several hundred facilities in the US. Confirmed commercial users include Lockheed Martin and Major Tool and Machine.
Theo Saville, CEO and co-founder of CloudNC, said: “Machine shops everywhere are under pressure to quote and program faster, and deliver more with the people and machines they already have.
John Burbank, founder of Nimble Ventures, added that automated CNC workflows will support “massive increases in onshoring of manufacturing and global production” for precision industrial supply bases.
CloudNC says it will direct the capital injection into go-to-market operations, technical support infrastructure, and partner activity across international regions.
AI quoting targets procurement turnaround times
CloudNC is expanding its software line with Quote Agent, an AI-assisted estimating tool scheduled for release later in 2026.
Preparing job estimates represents a major operational drag for manufacturing suppliers. Evaluating incoming technical drawings, calculating cycle times, and establishing part pricing remains heavily manual, exposing supply shops to administrative delays or miscalculated margins once components enter physical production.
Quote Agent applies AI to early-stage costing, enabling suppliers to return customer bids rapidly while standardising cost estimations.
“Quote Agent is a natural next step for CloudNC as we seek to accelerate global machining with AI,” says Saville. “CAM Assist already helps machinists get parts onto machines faster; Quote Agent will help shops assess new work, prepare quotes more efficiently and respond to customers with greater confidence.”
“We believe our AI can make quoting faster, more consistent and more scalable, helping manufacturers win more of the right work while keeping expert judgement firmly in control,” Saville added.
Knox Systems partnership advances FedRAMP authorisation
CloudNC is collaborating with Knox Systems to achieve FedRAMP certification for CAM Assist.
The compliance roadmap aims to clear CAM Assist for deployment by US government departments, defence contractors, and aerospace manufacturers operating under federal data governance rules.
Authorisation, if granted, will permit public-sector and defence suppliers to deploy automated toolpath generation across regulated production workloads.
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Arm has launched Arm Total Design for Physical AI alongside a new robotics framework to establish common standards across automated systems.
Physical industries – spanning mining, agriculture, manufacturing, and global transport – account for trillions of dollars in economic activity and an estimated $200 billion annual compute opportunity by the 2030s.
To address engineering fragmentation across these sectors, Arm is convening more than 80 partner organisations spanning software, hardware, and AI. Initial ecosystem participants include AWS, ECARX, Hugging Face, Liquid AI, NXP, PlusAI, PSYONIC, QNX, Qwen, Siemens, and Unitree Robotics.
The initiative targets physical systems that combine AI models, runtime software, compute silicon, sensors, and actuators to sense, reason, and act in operational environments. Hardware manufacturers and software developers require standardised baselines to reduce integration risk, optimise compute workloads, and move from proof-of-concept testing to deployment at scale.
Arm standardises capability tiers for robotics systems
Robotics currently lacks a common method to describe, compare, and communicate system capabilities, according to an architectural manifesto (PDF) published by Arm chief architect Richard Grisenthwaite. This fragmentation makes robotic systems harder to design, integrate, and scale across industrial deployments.
In response, Arm has introduced the Robotics Capability Framework as a collaborative starting point for a shared technical vocabulary, patterned after the SAE Levels used for driving automation.
Arm’s new framework categorises robotic systems across progressing tiers of operational sophistication, mapping machines from reactive setups to context-aware, cognitive, and self-improving systems.
Each capability tier links real-world use cases to machine behaviours, outputs, and hardware constraints. These criteria establish parameters for system latency, compute placement, memory allocation, power constraints, determinism, and safety standards.
Arm developed the initial baseline using feedback from across the robotics sector. Participating organisations contributing to the framework include Anaxi Labs, ANYbotics, FMC³ Robotics, Fourier, GALBOT, Gravis Robotics, Lenovo, McKinsey, and Robotec.ai.
Virtual platforms accelerate pre-silicon automotive physical AI development
Arm Total Design for Physical AI extends a collaborative development structure previously used for cloud AI infrastructure. The programme brings together AI models, virtual platforms, digital twins, sensors, compute silicon, and software stacks to enable earlier development and testing cycles.
Autonomous transport and robotics face common technical requirements across sensory perception, AI processing, real-time control, safety, and power-efficient compute. Arm demonstrated this collaborative methodology in the automotive sector alongside AWS, Google, HERE, RemotiveLabs, and Siemens.
The participating automotive companies developed an integrated digital cockpit reference solution. This environment enabled software engineering teams to develop, test, and validate complex automotive code on the Arm Zena CSS platform prior to physical silicon availability.
Arm is now soliciting technical contributions from the wider engineering community to expand the Robotics Capability Framework as physical AI implementations progress.
Learn more about physical AI during thePhysical AI Expoheld in Amsterdam, London, and North America.
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Coca-Cola is using AI to recommend which products Malaysian retailers should order and in what quantities through its Coke Buddy platform.
The Perfect Basket feature uses Coca-Cola’s Central Recommendation Engine to analyse previous orders, ordering frequency, seasonality, weather, and purchasing patterns among similar businesses.
Coca-Cola said Coke Buddy currently supports about 39,000 retail outlets across Malaysia. The company describes Coke Buddy as a self-ordering platform that allows retailers to buy products through its app, website, or WhatsApp, with personalised order suggestions and order tracking also available.
Perfect Basket builds on those existing ordering functions by recommending both products and quantities before a retailer completes an order. Retailers can review the recommendations and retain control over what they purchase.
How Perfect Basket guides retailer orders
Coke Buddy already uses previous purchase history to suggest products a retailer is likely to order again. Perfect Basket adds other signals, including seasonality, weather, ordering frequency, and purchasing trends among comparable businesses.
Perfect Basket recommends products and quantities before retailers submit their orders through Coke Buddy. Fulfilment is handled separately by Coca-Cola Refreshments Malaysia or its suppliers under existing sales and distribution arrangements.
Coca-Cola said its sales teams remain involved with retailers alongside the digital ordering system, while retailers retain control over the final purchasing decision.
Coca-Cola recently disclosed usage figures for Perfect Basket following its Perfect Basket, Perfect Ride campaign, which ran from January to April 2026. The campaign encouraged retailers to use the recommendation feature when placing orders and received more than 4,500 entries from over 4,000 retailers in Malaysia.
During the campaign, 83% of participating outlets adopted Perfect Basket recommendations, according to Coca-Cola. The figure applies only to retailers taking part in the campaign, not the full network of about 39,000 outlets supported by Coke Buddy.
Coca-Cola also said participating outlets that followed the recommendations recorded higher sales revenue growth than comparable retail outlets. The company did not disclose the size of the difference or provide detailed performance data showing how individual recommendations affected sales or inventory levels.
The available Malaysian campaign data does not provide figures for forecast accuracy, stock availability, inventory levels, or logistics costs.
Suggested orders extend beyond Malaysia
Coca-Cola has deployed similar suggested-order capabilities elsewhere in its bottling network. In its first-quarter 2024 results, the company said it and its bottling partners had connected nearly eight million customers to B2B platforms globally, while AI-enabled suggested-order capabilities had reached more than three million outlets in Latin America.
Coca-Cola has said these systems combine customer data, external information, and AI to generate predictive order recommendations. Then-chief executive James Quincey said in 2024 that digital ordering also allows retailers to adjust deliveries without waiting for a salesperson to visit.
Coca-Cola has also linked suggested orders to changes in its sales process. Quincey said AI-generated orders allow pre-sales staff to spend less time taking routine orders and more time on account development, while retailers continue to make the final purchasing decision.
Coca-Cola has reported results from earlier pilots using similar recommendation systems. In its second-quarter 2024 earnings call, the company said retailers receiving AI-generated product recommendations based on previous orders and market data were more than 30% more likely to purchase the recommended SKUs in initial pilots. These results did not relate specifically to Perfect Basket in Malaysia.
In a separate demand-prediction project, Coca-Cola combined historical sales data with weather and geolocation information to generate replenishment recommendations. CIO Neeraj Tolmare told Fortune in 2025 that a three-country pilot recorded sales 7% to 8% higher than outlets that were not using the AI algorithm.
Perfect Basket remains available after the campaign. Coca-Cola said it plans to continue developing Coke Buddy and the recommendation feature using retailer feedback and data, while retailers will continue to have access to the company’s sales representatives alongside the digital ordering system.
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MG Ship has introduced an AI route optimisation and carrier selection module as logistics deployments demonstrate rapid cost and time returns.
The technical module targets global retailers and commercial shippers, pairing automated routing algorithms with carrier recommendation systems across international trade corridors. The deployment arrives as enterprise supply chain operators report measurable operational returns from machine learning tools, moving capital allocations away from speculative trials toward production deployments.
Measurable returns from deploying AI for logistics
Suki Cheung, CEO of MG Ship, will present deployment metrics during a panel discussion at the upcoming WMX Asia conference. Cheung will join executives from Pos Malaysia, Omniva, and OnyX Space for the session, titled AI Beyond the Hype: Measurable Results in Logistics Today.
“Too many AI conversations in logistics remain focused on future possibilities,” said Cheung. “The reality is that AI is already delivering measurable business outcomes today. Leading organisations are reducing transportation costs, improving forecast accuracy, increasing warehouse productivity, and achieving payback within months rather than years.”
Industry operational data indicates that initial investment returns are concentrating across three primary workflows:
Dynamic route planning has reduced enterprise fuel consumption by 15–20 percent, improved transit speeds by 15–25 percent, and lowered overall transportation costs by 12–22 percent, with capital payback reached within three to six months.
Predictive demand forecasting has reduced projection errors by 20–40 percent, improved planning accuracy by up to 35 percent, and decreased excess inventory by 20–30 percent within six to 12 months.
Automated freight documentation processing has cut manual task duration by up to 85 percent, recovering initial expenditure inside three to six months.
Over five-year deployment cycles, enterprise adopters have recorded average operational expense reductions between 10–25 percent, accompanied by warehouse productivity gains of 25–35 percent.
Routing algorithms and carrier scoring
MG Ship built the new routing capability directly into its visibility and supply chain intelligence platform, which serves retailers, manufacturers, and freight operators across multiple international markets. The base system synthesises live cargo telemetry with trade intelligence, risk monitoring, and predictive analytics to support operational planning and trade financing.
The route optimisation engine processes live and historical lane transit logs, weather patterns, air and ocean port congestion indicators, customs risk alerts, and transit reliability data. Shippers receive automated recommendations identifying low-cost, low-risk transit paths.
Carrier evaluation features rank transport providers per lane and service tier. Rather than selecting capacity purely on spot freight pricing, the system scores carriers against historical on-time metrics, transit consistency, exception occurrences, claims rates, available volume, and total cost-to-serve.
Logistics teams can also execute scenario simulations prior to peak shipping quarters. The software models lead times, service levels, freight spend, and risk exposures under alternative carrier allocation rules.
Early enterprise implementations demonstrate lower lead-time variance, reduced expedited freight expenditure, and improved on-time-in-full delivery rates.
Cheung stated that the platform “does not simply tell businesses where their cargo is”, adding that “it recommends the best route, the right carrier, and the lowest-risk option based on real-time conditions, helping organisations make faster and more profitable decisions.”
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M&T Bank has deployed AI copilots to more than 15,000 employees as the US regional bank applies AI to internal operations, customer service, software development, and risk management.
The bank uses AI to analyse call-centre conversations, draft reports, generate code, identify customer needs, and flag portfolio risks, according to Fast Company. M&T is also examining agentic AI applications in cybersecurity and fraud detection.
American Banker reported in September 2025 that 16,000 of M&T’s roughly 22,000 employees were already using Microsoft Copilot for tasks including drafting emails and reports and summarising call-centre conversations.
Before the wider rollout, M&T initially restricted employee access to public large language models. Chief data officer Andrew Foster told American Banker that the bank blocked the tools because employees could potentially enter sensitive company information into public-facing services.
M&T later evaluated enterprise providers and selected Microsoft Copilot, starting with a pilot involving about 800 employees before expanding access across the organisation.
Foster said using generative AI to summarise call-centre conversations saves about six minutes per call. Software developers at the bank also use GitLab tools to generate code, while employees remain responsible for reviewing AI-generated work.
M&T’s human-review requirement is also reflected in its 2026 Code of Business Conduct and Ethics. The policy requires employees to use approved AI tools and prohibits confidential, proprietary, customer, employee, or regulated information from being entered into unapproved systems. Employees remain responsible for the accuracy and appropriateness of AI-assisted work.
Building the technology and data foundation
M&T’s AI deployment follows a technology overhaul that began in 2018. The bank said more than half of its technology specialists were external workers at the time, compared with an 80% in-house technology workforce today.
M&T now has about 2,000 technologists working across more than 300 agile teams and has hired more than 1,000 technology specialists during the programme.
The bank has also replaced dozens of older platforms. M&T said technology outages have fallen by more than 80% since 2018, while the number of system upgrades completed annually has increased by 300%.
Technology spending exceeded $1.2 billion in 2025, nearly three times its 2017 level. Wisler told Forbes in August 2026 that annual technology releases increased from about 15,000 in 2018 to 65,000 in 2025.
Wisler joined M&T as chief information officer in 2018 before becoming senior executive vice-president for technology and operations in 2025. His current remit covers both technology and operational functions across the bank.
M&T’s data programme developed alongside the broader technology overhaul. Foster, who joined the bank in 2023, began building a data-lineage programme to track where information originates, how it is used, and how it moves between systems.
Foster told American Banker that the data-lineage work was not created in response to generative AI. He described it as a core capability for understanding M&T’s data estate.
The bank also established a Data Academy focused on data governance and data skills, with around 2,000 employees participating in the programme.
M&T has created an internal repository called Edison containing authoritative documents and information on bank policies. The bank also uses data-lineage software from Solidatus and Monte Carlo to trace information as it passes through databases, applications, and business-intelligence systems.
The lineage work gives M&T visibility into the source, meaning, quality, and governance of individual data elements, according to Foster. He said one application for that governed data is the bank’s use of Copilot.
M&T also uses retrieval-augmented generation with internal, governed data, according to American Banker.
Scaling AI into daily banking operations
Wisler told Forbes that M&T is pursuing generative AI through three routes: general employee use, AI capabilities embedded in existing applications, and proprietary systems built around the bank’s own data and processes.
M&T operates more than 1,800 applications, many supplied by third-party vendors. Wisler said one of the bank’s AI pathways is identifying useful AI capabilities already embedded within those applications.
M&T’s third pathway involves proprietary AI development around the bank’s own data and processes. Forbes reported that early applications include repetitive operational work, software development, fraud prevention, and cyber defence.
Fast Company’s September report also said M&T continues to assess both internally developed AI systems and external tools, including general enterprise software and technology designed specifically for banks.
Earlier workforce use cases centred on drafting, summarisation, call-centre work, and software development. Fast Company reported that newer applications include identifying customer needs and flagging portfolio risks.
Other large US banks have also expanded generative AI across employee workflows.
JPMorganChase launched its internal LLM Suite platform to more than 200,000 employees in 2024. By 2025, more than 65,000 employees in its Corporate and Investment Bank were actively using the platform, while more than 90% of its engineers were using AI coding assistants.
The bank also said AI-based transaction screening allowed it to review more than twice the previous transaction volume while reducing manual operator checks by half.
Bank of America is using a generative AI-enabled system called EricaAssist with more than 18,000 customer service employees. The tool summarises why a customer is calling, retrieves relevant information, and recommends possible next steps while keeping the employee responsible for the interaction.
Bank of America said in July 2026 that EricaAssist can deliver contextual guidance in under three seconds and has reduced average call times by nearly one minute. The bank plans to extend the system to additional servicing scenarios and business lines later in 2026.
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Amazon has introduced Alexa for Shopping, combining its Rufus shopping chatbot with Alexa+ across its app, website, and Echo Show devices.
The assistant can answer product questions, compare items, track prices, and support shopping reminders. It can also handle scheduled shopping actions and eligible automated purchases.
The company said Alexa for Shopping combines Rufus’ product expertise with Alexa+’s personalised assistant context. Amazon said Rufus helped more than 300 million customers in 2025 research, compare, and buy products.
GeekWire reported that Amazon is retiring the Rufus name from its shopping interface, while Rufus will continue to power parts of the experience behind the scenes.
GeekWire also reported that Amazon CEO Andy Jassy said Rufus monthly active users rose more than 115%, while engagement increased nearly 400% year over year.
Alexa for Shopping is available through the Amazon Shopping app, Amazon’s website, and Echo Show devices. The feature is rolling out to US customers. Signed-in Amazon customers can use it for free, without a Prime membership, Echo device, or Alexa app.
Amazon reported US$426.3 billion in North America net sales and US$161.9 billion in international net sales in 2025. Amazon also reported online stores and third-party seller services as separate revenue categories in its 2025 annual report.
Amazon adds shopping questions to search
The assistant allows customers to ask shopping-related questions through Amazon’s main search bar instead of using a separate chatbot window. Users can ask for product recommendations or purchase history. They can also ask for advice related to specific shopping needs.
Examples shared by Amazon include questions such as “What’s a good skincare routine for men?” and “When did I last order AA batteries?” Amazon said the assistant uses information from its platform to answer these questions.
Amazon said Alexa for Shopping uses information from a customer’s Amazon activity and Alexa interactions. That includes shopping history, browsing, purchases, and conversations. Amazon said the information is used to recommend products and support shopping actions.
Alexa for Shopping can compare products side by side and provide AI-generated summaries on product pages. It can also show AI-generated overviews in search results with category information.
Price tracking and automated shopping
Alexa for Shopping can monitor price drops for selected items for up to one year. Customers can view a full year of price history on product detail pages or by asking the assistant.
The assistant can create shopping guides for larger purchases. These guides compare product features and prices. They also include reviews from Amazon and the web.
Amazon said customers can use the assistant to set scheduled shopping actions, including restocking household items. Amazon said the assistant can also handle birthday reminders and gift suggestions.
Scheduled actions can also be tied to conditions. For example, the assistant can add an item to the cart if it reaches a target price and has not been purchased within a set period.
The assistant can search past orders and add frequently purchased items to a customer’s cart through conversational prompts.
Amazon said customers can view and update personal details used by Alexa for Shopping. These details can include family members, pets, interests, and dietary needs.
Alexa for Shopping can also surface products from other online stores through Shop Direct. For eligible products, Amazon said its Buy for Me agentic AI feature can complete purchases using a customer’s primary address and payment method.
Echo Show gets full shopping access
Amazon is also adding full-store shopping access to Echo Show. Users can browse, search, and shop using voice, touch, or both.
The Echo Show shopping experience is available for Alexa+ customers on Echo Show 15 and Echo Show 21, with support for other devices to follow.
Amazon also cited AI investments in its first-quarter 2026 results. The company said free cash flow fell to US$1.2 billion for the trailing 12 months. It attributed the decline mainly to a US$59.3 billion increase in property and equipment purchases, primarily reflecting AI investments.
Rajiv Mehta, Amazon’s vice president of conversational shopping, said the assistant can carry customer preferences, past purchases, and conversations across phones, laptops, and Echo devices.
Users can access the assistant by updating the Amazon Shopping app and selecting the Alexa icon in the bottom navigation bar. On the desktop, the feature appears at the top of the screen.
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To protect enterprise margins, business leaders must invest in robust AI governance to securely manage AI infrastructure.
When evaluating enterprise software adoption, a recurring pattern dictates how technology matures across industries. As Rob Thomas, SVP and CCO at IBM, recently outlined, software typically graduates from a standalone product to a platform, and then from a platform to foundational infrastructure, altering the governing rules entirely.
At the initial product stage, exerting tight corporate control often feels highly advantageous. Closed development environments iterate quickly and tightly manage the end-user experience. They capture and concentrate financial value within a single corporate entity, an approach that functions adequately during early product development cycles.
However, IBM’s analysis highlights that expectations change entirely when a technology solidifies into a foundational layer. Once other institutional frameworks, external markets, and broad operational systems rely on the software, the prevailing standards adapt to a new reality. At infrastructure scale, embracing openness ceases to be an ideological stance and becomes a highly practical necessity.
AI is currently crossing this threshold within the enterprise architecture stack. Models are increasingly embedded directly into the ways organisations secure their networks, author source code, execute automated decisions, and generate commercial value. AI functions less as an experimental utility and more as core operational infrastructure.
The recent limited preview of Anthropic’s Claude Mythos model brings this reality into sharper focus for enterprise executives managing risk. Anthropic reports that this specific model can discover and exploit software vulnerabilities at a level matching few human experts.
In response to this power, Anthropic launched Project Glasswing, a gated initiative designed to place these advanced capabilities directly into the hands of network defenders first. From IBM’s perspective, this development forces technology officers to confront immediate structural vulnerabilities. If autonomous models possess the capability to write exploits and shape the overall security environment, Thomas notes that concentrating the understanding of these systems within a small number of technology vendors invites severe operational exposure.
With models achieving infrastructure status, IBM argues the primary issue is no longer exclusively what these machine learning applications can execute. The priority becomes how these systems are constructed, governed, inspected, and actively improved over extended periods.
As underlying frameworks grow in complexity and corporate importance, maintaining closed development pipelines becomes exceedingly difficult to defend. No single vendor can successfully anticipate every operational requirement, adversarial attack vector, or system failure mode.
Implementing opaque AI structures introduces heavy friction across existing network architecture. Connecting closed proprietary models with established enterprise vector databases or highly sensitive internal data lakes frequently creates massive troubleshooting bottlenecks. When anomalous outputs occur or hallucination rates spike, teams lack the internal visibility required to diagnose whether the error originated in the retrieval-augmented generation pipeline or the base model weights.
Integrating legacy on-premises architecture with highly gated cloud models also introduces severe latency into daily operations. When enterprise data governance protocols strictly prohibit sending sensitive customer information to external servers, technology teams are left attempting to strip and anonymise datasets before processing. This constant data sanitisation creates enormous operational drag.
Furthermore, the spiralling compute costs associated with continuous API calls to locked models erode the exact profit margins these autonomous systems are supposed to enhance. The opacity prevents network engineers from accurately sizing hardware deployments, forcing companies into expensive over-provisioning agreements to maintain baseline functionality.
Why open-source AI is essential for operational resilience
Restricting access to powerful applications is an understandable human instinct that closely resembles caution. Yet, as Thomas points out, at massive infrastructure scale, security typically improves through rigorous external scrutiny rather than through strict concealment.
This represents the enduring lesson of open-source software development. Open-source code does not eliminate enterprise risk. Instead, IBM maintains it actively changes how organisations manage that risk. An open foundation allows a wider base of researchers, corporate developers, and security defenders to examine the architecture, surface underlying weaknesses, test foundational assumptions, and harden the software under real-world conditions.
Within cybersecurity operations, broad visibility is rarely the enemy of operational resilience. In fact, visibility frequently serves as a strict prerequisite for achieving that resilience. Technologies deemed highly important tend to remain safer when larger populations can challenge them, inspect their logic, and contribute to their continuous improvement.
Thomas addresses one of the oldest misconceptions regarding open-source technology: the belief that it inevitably commoditises corporate innovation. In practical application, open infrastructure typically pushes market competition higher up the technology stack. Open systems transfer financial value rather than destroying it.
As common digital foundations mature, the commercial value relocates toward complex implementation, system orchestration, continuous reliability, trust mechanics, and specific domain expertise. IBM’s position asserts that the long-term commercial winners are not those who own the base technological layer, but rather the organisations that understand how to apply it most effectively.
We have witnessed this identical pattern play out across previous generations of enterprise tooling, cloud infrastructure, and operating systems. Open foundations historically expanded developer participation, accelerated iterative improvement, and birthed entirely new, larger markets built on top of those base layers. Enterprise leaders increasingly view open-source as highly important for infrastructure modernisation and emerging AI capabilities. IBM predicts that AI is highly likely to follow this exact historical trajectory.
Looking across the broader vendor ecosystem, leading hyperscalers are adjusting their business postures to accommodate this reality. Rather than engaging in a pure arms race to build the largest proprietary black boxes, highly profitable integrators are focusing heavily on orchestration tooling that allows enterprises to swap out underlying open-source models based on specific workload demands. Highlighting its ongoing leadership in this space, IBM is a key sponsor of this year’s AI & Big Data Expo North America, where these evolving strategies for open enterprise infrastructure will be a primary focus.
This approach completely sidesteps restrictive vendor lock-in and allows companies to route less demanding internal queries to smaller and highly efficient open models, preserving expensive compute resources for complex customer-facing autonomous logic. By decoupling the application layer from the specific foundation model, technology officers can maintain operational agility and protect their bottom line.
The future of enterprise AI demands transparent governance
Another pragmatic reason for embracing open models revolves around product development influence. IBM emphasises that narrow access to underlying code naturally leads to narrow operational perspectives. In contrast, who gets to participate directly shapes what applications are eventually built.
Providing broad access enables governments, diverse institutions, startups, and varied researchers to actively influence how the technology evolves and where it is commercially applied. This inclusive approach drives functional innovation while simultaneously building structural adaptability and necessary public legitimacy.
As Thomas argues, once autonomous AI assumes the role of core enterprise infrastructure, relying on opacity can no longer serve as the organising principle for system safety. The most reliable blueprint for secure software has paired open foundations with broad external scrutiny, active code maintenance, and serious internal governance.
As AI permanently enters its infrastructure phase, IBM contends that identical logic increasingly applies directly to the foundation models themselves. The stronger the corporate reliance on a technology, the stronger the corresponding case for demanding openness.
If these autonomous workflows are truly becoming foundational to global commerce, then transparency ceases to be a subject of casual debate. According to IBM, it is an absolute, non-negotiable design requirement for any modern enterprise architecture.
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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Next-generation AI assistants being developed in the Apple ecosystem and by chipmakers like Qualcomm, but early reports suggest they are being designed with limits in place.
Tom’s Guide has described early versions of these assistants as capable of navigating apps, carrying out bookings, and managing tasks in services. For instance a private beta agentic system completed tasks like booking services or posting content in apps. In one test, it moved through an app workflow and reached a payment screen before asking the user for confirmation.
AI agents are being built with approval checkpoints. Sensitive actions, especially those tied to payments or account changes, require user confirmation before they are completed. The “human-in-the-loop” model lets the system prepare an action, but leaves approval to the user. Research linked to Apple’s AI work has explored ways to ensure systems pause before taking actions users did not explicitly request.
Banking apps already require confirmation for transfers. The same idea is now being applied to AI-driven actions in multiple services.
Limits and control
A control layer comes from restricting what the AI can access. Rather than providing the system full access to apps and data, businesses are establishing limits, such as which apps the AI can interact with and when actions can be triggered.
In practice, this means the AI may be able to draft a purchase or prepare a booking, but not finalise it without approval. It also means the system cannot move freely in all services unless it has been granted permission.
According to Tom’s Guide, the facility is for privacy. If data remains on the device, it eliminates the need to send sensitive information to external servers.
In areas like payments, AI systems are expected to work with partners that already have strict rules in place. In one reported example, payment providers’ services are being integrated to provide secure authentication before transactions are completed, though such safeguards are still under development. The existing systems act as an additional layer of oversight. They can set transaction limits or require extra verification.
Much of the discussion around AI governance has focused on enterprise use. That includes areas like cybersecurity and large-scale automation. The consumer side introduces a different challenge and companies must design controls that work for everyday users. That means clear approval steps and built-in privacy protections.
Autonomy with boundaries
As AI gains the ability to carry out actions, the risks become greater as errors can lead to financial loss or data exposure.
By placing controls at multiple points, including approval and infrastructure, companies are trying to manage those risks.
The approach may shape how agentic AI develops in the near term. Rather than aiming for full independence, companies appear focused on controlled environments where the risks can be managed.
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
Anthropic’s most capable AI model has already found thousands of AI cybersecurity vulnerabilities across every major operating system and web browser. The company’s response was not to release it, but to quietly hand it to the organisations responsible for keeping the internet running.
That model is Claude Mythos Preview, and the initiative is called Project Glasswing.
The launch partners include Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks.
Beyond that core group, Anthropic has extended access to over 40 additional organisations that build or maintain critical software infrastructure. Anthropic is committing up to US$100 million in usage credits for Mythos Preview across the effort, along with US$4 million in direct donations to open-source security organisations.
A model that outgrew its own benchmarks
Mythos Preview was not specifically trained for cybersecurity work. Anthropic said the capabilities “emerged as a downstream consequence of general improvements in code, reasoning, and autonomy”, and that the same improvements making the model better at patching vulnerabilities also make it better at exploiting them.
That last part matters. Mythos Preview has improved to the extent that it mostly saturates existing security benchmarks, forcing Anthropic to shift its focus to novel real-world tasks–specifically, zero-day vulnerabilities. These flaws were previously unknown to the software’s developers.
Among the findings: a 27-year-old bug in OpenBSD, an operating system known for its strong security posture. In another case, the model fully autonomously identified and exploited a 17-year-old remote code execution vulnerability in FreeBSD–CVE-2026-4747–that allows an unauthenticated user anywhere on the internet to obtain complete control of a server running NFS. No human was involved in the discovery or exploitation after the initial prompt to find the bug.
Nicholas Carlini from Anthropic’s research team described the model’s ability to chain together vulnerabilities: “This model can create exploits out of three, four, or sometimes five vulnerabilities that in sequence give you some kind of very sophisticated end outcome. I’ve found more bugs in the last couple of weeks than I found in the rest of my life combined.”
Why is it not being released?
“We do not plan to make Claude Mythos Preview generally available due to its cybersecurity capabilities,” Newton Cheng, Frontier Red Team Cyber Lead at Anthropic, said. “Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout–for economies, public safety, and national security–could be severe.”
This is not hypothetical. Anthropic had previously disclosed what it described as the first documented case of a cyberattack largely executed by AI–a Chinese state-sponsored group that used AI agents to autonomously infiltrate roughly 30 global targets, with AI handling the majority of tactical operations independently.
The company has also privately briefed senior US government officials on Mythos Preview’s full capabilities. The intelligence community is now actively weighing how the model could reshape both offensive and defensive hacking operations.
The open-source problem
One dimension of Project Glasswing that goes beyond the headline coalition: open-source software. Jim Zemlin, CEO of the Linux Foundation, put it plainly: “In the past, security expertise has been a luxury reserved for organisations with large security teams. Open-source maintainers, whose software underpins much of the world’s critical infrastructure, have historically been left to figure out security on their own.”
Anthropic has donated US$2.5 million to Alpha-Omega and OpenSSF through the Linux Foundation, and US$1.5 million to the Apache Software Foundation–giving maintainers of critical open-source codebases access to AI cybersecurity vulnerability scanning at a scale that was previously out of reach.
What comes next
Anthropic says its eventual goal is to deploy Mythos-class models at scale, but only when new safeguards are in place. The company plans to launch new safeguards with an upcoming Claude Opus model first, allowing it to refine them with a model that does not pose the same level of risk as Mythos Preview.
The competitive picture is already shifting around it. When OpenAI released GPT-5.3-Codex in February, the company called it the first model it had classified as high-capability for cybersecurity tasks under its Preparedness Framework. Anthropic’s move with Glasswing signals that the frontier labs see controlled deployment–not open release–as the emerging standard for models at this capability level.
Whether that standard holds as these capabilities spread further is, at this point, an open question that no single initiative can answer.
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.
A new open-source toolkit from Microsoft focuses on runtime security to force strict governance onto enterprise AI agents. The release tackles a growing anxiety: autonomous language models are now executing code and hitting corporate networks way faster than traditional policy controls can keep up.
AI integration used to mean conversational interfaces and advisory copilots. Those systems had read-only access to specific datasets, keeping humans strictly in the execution loop. Organisations are currently deploying agentic frameworks that take independent action, wiring these models directly into internal application programming interfaces, cloud storage repositories, and continuous integration pipelines.
When an autonomous agent can read an email, decide to write a script, and push that script to a server, stricter governance is vital. Static code analysis and pre-deployment vulnerability scanning just can’t handle the non-deterministic nature of large language models. One prompt injection attack (or even a basic hallucination) could send an agent to overwrite a database or pull out customer records.
Microsoft’s new toolkit looks at runtime security instead, providing a way to monitor, evaluate, and block actions at the moment the model tries to execute them. It beats relying on prior training or static parameter checks.
Intercepting the tool-calling layer in real time
Looking at the mechanics of agentic tool calling shows how this works. When an enterprise AI agent has to step outside its core neural network to do something like query an inventory system, it generates a command to hit an external tool.
Microsoft’s framework drops a policy enforcement engine right between the language model and the broader corporate network. Every time the agent tries to trigger an outside function, the toolkit grabs the request and checks the intended action against a central set of governance rules. If the action breaks policy (e.g. an agent authorised only to read inventory data tries to fire off a purchase order) the toolkit blocks the API call and logs the event so a human can review it.
Security teams get a verifiable, auditable trail of every single autonomous decision. Developers also win here; they can build complex multi-agent systems without having to hardcode security protocols into every individual model prompt. Security policies get decoupled from the core application logic entirely and are managed at the infrastructure level.
Most legacy systems were never built to talk to non-deterministic software. An old mainframe database or a customised enterprise resource planning suite doesn’t have native defenses against a machine learning model shooting over malformed requests. Microsoft’s toolkit steps in as a protective translation layer. Even if an underlying language model gets compromised by external inputs; the system’s perimeter holds.
Security leaders might wonder why Microsoft decided to release this runtime toolkit under an open-source license. It comes down to how modern software supply chains actually work.
Developers are currently rushing to build autonomous workflows using a massive mix of open-source libraries, frameworks, and third-party models. If Microsoft locked this runtime security feature to its proprietary platforms, development teams would probably just bypass it for faster, unvetted workarounds to hit their deadlines.
Pushing the toolkit out openly means security and governance controls can fit into any technology stack. It doesn’t matter if an organisation runs local open-weight models, leans on competitors like Anthropic, or deploys hybrid architectures.
Setting up an open standard for AI agent security also lets the wider cybersecurity community chip in. Security vendors can stack commercial dashboards and incident response integrations on top of this open foundation, which speeds up the maturity of the whole ecosystem. For businesses, they avoid vendor lock-in but still get a universally scrutinised security baseline.
The next phase of enterprise AI governance
Enterprise governance doesn’t just stop at security; it hits financial and operational oversight too. Autonomous agents run in a continuous loop of reasoning and execution, burning API tokens at every step. Startups and enterprises are already seeing token costs explode when they deploy agentic systems.
Without runtime governance, an agent tasked with looking up a market trend might decide to hit an expensive proprietary database thousands of times before it finishes. Left alone, a badly configured agent caught in a recursive loop can rack up massive cloud computing bills in a few hours.
The runtime toolkit gives teams a way to slap hard limits on token consumption and API call frequency. By setting boundaries on exactly how many actions an agent can take within a specific timeframe, forecasting computing costs gets much easier. It also stops runaway processes from eating up system resources.
A runtime governance layer hands over the quantitative metrics and control mechanisms needed to meet compliance mandates. The days of just trusting model providers to filter out bad outputs are ending. System safety now falls on the infrastructure that actually executes the models’ decisions
Getting a mature governance program off the ground is going to demand tight collaboration between development operations, legal, and security teams. Language models are only scaling up in capability, and the organisations putting strict runtime controls in place today are the only ones who will be equipped to handle the autonomous workflows of tomorrow.
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.
AI systems are starting to move beyond simple responses. In many organisations, AI agents are now being tested to plan tasks, make decisions, and carry out actions with limited human input. It is no longer just about whether a model gives the right answer. It is about what happens when that model is allowed to act.
Autonomous systems need clear boundaries. They need rules that define what they can access, what they are allowed to do, and how their actions are tracked. Without those controls, even well-trained systems can create problems that are hard to detect or reverse.
One company working on this problem is Deloitte. The firm has been developing governance frameworks and advisory approaches to help organisations manage AI systems.
From tools to AI agents
Most AI systems in use today still depend on human prompts. They generate text, analyse data, or make predictions, but a person usually decides what happens next. Agentic AI changes that pattern. These systems can break down a goal into steps, choose actions, and interact with other systems to complete tasks.
That added independence brings new challenges. When a system acts on its own, it may take paths that were not fully expected or use data in ways that were not intended.
Deloitte’s work focuses on helping organisations prepare for these risks. Rather than treating AI as a standalone tool, the firm looks at how it fits into business processes, including how decisions are made and how data flows through systems.
Building governance into the lifecycle
Governance should not be added after deployment. It needs to be built into the full lifecycle of an AI system.
This starts at the design stage. Organisations need to define what a system is allowed to do and where its limits are. This may include setting rules around data use and outlining how the system should respond in uncertain situations.
The next stage is deployment. At this point, governance focuses on access and control, including who can use the system and what it can connect to. Once the system is live, monitoring becomes the main concern. Autonomous systems can change over time as they interact with new data. Without regular checks, they may drift away from their original purpose.
The role of transparency and accountability
As AI systems take on more responsibility, it becomes more difficult to trace how decisions are made. This creates a demand for stronger transparency. Deloitte’s work highlights the importance of keeping track of how systems operate. This includes logging actions and documenting decisions. These records help organisations in determining what happened if something goes wrong. If an autonomous system takes an action, there needs to be clarity about who is responsible.
Research from Deloitte shows that adoption of AI agents is moving faster than the controls needed to manage them. Around 23% of companies already use them, and that figure is expected to reach 74% within two years. Only 21% report having strong safeguards in place to oversee how they behave.
Real-time oversight for AI agents
Once an autonomous system is active, the focus shifts to how it behaves in real-world conditions. Static rules are not always enough, and systems need to be observed as they operate.
Deloitte’s approach includes real-time monitoring, allowing organisations to track what an AI system is doing as it performs tasks. If the system behaves in an unexpected way, teams can step in quickly. This may involve pausing certain actions or adjusting permissions. Real-time oversight also helps with compliance. In regulated industries, companies need to show that systems follow rules and standards.
In practice, these controls are starting to appear in operational settings. Deloitte describes scenarios where AI systems monitor equipment performance across sites. Sensor data can signal early signs of failure, which can trigger maintenance workflows and update internal systems. Governance frameworks define what actions the system can take, when human approval is required, and how decisions are recorded. The process runs across multiple systems, but from a user’s point of view, it appears as a single action.
Governance is part of discussions at AI & Big Data Expo North America 2026, taking place on May 18–19 in Santa Clara, California. Deloitte is listed as a Diamond Sponsor for the event, placing it among the firms contributing to conversations around how autonomous systems are deployed and controlled in practice.
The challenge is not just building smarter systems, but ensuring they behave in ways organisations can understand, manage, and trust over time.
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.
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