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.

See also: Meta, Microsoft, Nvidia, IBM, and others back open-weight AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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

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Deloitte’s 2026 technology trends research puts the pilot-to-production failure rate for AI agents at 89%. A Teradata survey adds the shape of that gap: 78% of enterprises have at least one agent pilot running, but only 14% have scaled one to organisation-wide use. Adoption is nearly universal; deployment is rare. The difference is not model capability, since the same models power the pilots and the production systems, but everything around the model: data access, evaluation, ownership, and cost control. Closing that gap is precisely why Crunch-IS is a leader in AI agent development, with a delivery approach built around the operational layer that pilots routinely skip. Below are the six blockers that recur across the research, and what the 11–14% that make it through do differently.

The funnel, in numbers

Before the causes, the scale. Drawing on Gartner’s April 2026 survey of 782 infrastructure and operations leaders and related industry analysis, the funnel roughly runs:

• Of every 1,000 AI projects that receive a budget, around 120 reach production
• Of those, around 34 meet their ROI targets
• Gartner’s Agentic AI Pulse survey found 41% of deployments reach positive ROI within 12 months; 19% never reach payback

McKinsey’s 2026 work puts organisations running agents at genuine scale at 11%. S&P Global Market Intelligence counts 31% with at least one agent in production. “One agent in production” and “agents at scale” are very different milestones.

Blocker 1: Scope creep

Analysis of stalled agent projects attributes 61% of failures to two causes combined: scope creep and data quality. Pilots start narrow, succeed, and are then asked to handle adjacent workflows the underlying infrastructure was never built for. The agent that triaged support tickets is now expected to resolve them, then to update the CRM, then to issue refunds. Each expansion adds integrations, permissions, and failure modes without adding the operational foundation to support them.

Blocker 2: Data access that worked in the sandbox

Pilots run on curated data exports. Production runs on live systems with inconsistent schemas, access controls, and latency. Industry surveys suggest 83% of enterprises need infrastructure overhauls to support agentic AI. The pilot never touched the legacy ERP; production cannot avoid it.

Blocker 3: No evaluation harness

Only 38% of production agents have automated evaluations running on every prompt change, per Forrester’s 2026 panel. In a pilot, a human reviews every output. In production, nobody does, and without automated regression tests every prompt tweak is a gamble. Forrester’s data shows agents without automated evals had a 47% rollback rate versus 9% for agents with full coverage. Organisations using systematic evaluation frameworks achieved nearly six times higher production success rates in separate survey work.

Blocker 4: Nobody owns it

A pilot is owned by the innovation team. Production requires an operational owner: someone accountable when the agent makes a wrong call at 2 a.m. Enterprise governance surveys put agentic AI governance maturity at around 21%. Without a named owner, a defined escalation path, and a budget line for ongoing operation, the pilot has nowhere to be handed to.

Blocker 5: Costs that only appear at scale

Analysis of cancelled projects consistently finds costs ballooning two to three times beyond estimates. Token consumption, retry loops, and reasoning depth all scale with volume and edge cases. A pilot running 50 tasks a day is cheap. The same agent at 5,000 tasks a day, with production-grade retries and monitoring, frequently costs more than the process it replaced.

Blocker 6: Security clearance

Gravitee’s 2026 research found 54% of organisations experienced or suspected an agent-related security or data-privacy incident in the past year, and only about one in five fully secures agents in production. Security teams reviewing a pilot for production approval routinely find over-permissioned service accounts and no audit trail, and block the launch.

What the 14% do differently

Survey data on organisations that successfully scaled agents shows they were not outspending the ones that stalled. Total AI budgets were comparable. The difference was allocation:

1. More spend on evaluation infrastructure and less on prompt engineering
2. More spend on monitoring and observability: structured logs of every reasoning step and tool call
3. More spend on operational staffing: people whose job is running the agent, not building it
4. Graduated autonomy with human-verification gates mapped to the stakes of each action
5. A named governance owner per agent and per-phase ROI checkpoints with finance sign-off

The takeaway

Gartner projects over 40% of agentic AI projects will be cancelled by the end of 2027, and notes that many use cases positioned as agentic today do not require agentic implementations at all. The pilot-to-production gap is not evidence that agents do not work. It is evidence that most organisations build the demo and skip the operating model. The ones that reach deployment do the reverse.

The post Why Most Enterprise Agent Pilots Never Reach Deployment appeared first on AI News.

A video looks simple when you press play. There is a picture, some dialogue, perhaps music in the background, and a few minutes later it is over.

However, with the right use of AI, things can become a lot more interesting. To an artificial intelligence system, that same video can become speech to transcribe, faces and objects to recognise, scenes to classify, topics to identify, emotions to estimate, timestamps to organise, and text to summarise.

In other words, the same video can be transformed completely into something powerful and attractive. Wondering how? Let’s dig deep into the content

Table of Contents

●     Why Is Multimedia Becoming AI-Readable?

● What Actually Happens When AI Processes a Video?

● Why File Conversion Still Matters

● From Audio to Searchable Intelligence

● Where Multimedia AI Is Already Being Used

● The Quality Problem AI Cannot Ignore

● Why Multimedia Is Becoming AI Readable

Why Is Multimedia Becoming AI-Readable?

For a very long time, business data was conveniently machine friendly, its examples include spreadsheets, databases, forms, and text documents.

Video and audio were different. Though a two-hour webinar might contain a number of useful insights, it was truly a hassle to find one specific comment.

That’s when AI helps. Find multimedia AI systems working across texts, images, speeches, and videos. The AI news has covered this broader shift towards multimedia, where models take into consideration and combine different forms of information. Hence, it does not treat each of them separately.

The result?

A video library is formed, behaving like a searchable database. This way, you can ask for moments in which a customer has mentioned something or extract dialogues. Suddenly, the video is doing much more than sitting in storage.

What Actually Happens When AI Processes a Video?

There is no single magic button behind multimedia AI. In many workflows, the process involves several stages.

We can say that AI is not just watching a video, rather, it is taking notes, understanding the key comments, and then putting everything back and well structured.  And that’s how AI turns something sitting in a corner into something very useful and important.

Why File Conversion Still Matters in an AI Workflow

Preparing Data AI Tools Can Actually Use

Though AI models could be sophisticated, they still rely on the input they can process reliably. For example, imagine there is a marketing team that has an MP4 interview. The problem is they only need the spoken conversation for the transcription.

Hence, instead of sending the entire video through every AI tool, they can first convert the file into the format needed for the next step. This makes the workflow cleaner, faster, and more efficient.

Turning Video Content Into AI-Ready Audio

Doing all the conversion is only convenient when done by reliable tools like Convertio. It transforms the files into a format that accommodates a specific AI application. For example, converting an MP4 file into a WAV audio file removes the video portion.

It then creates a high quality audio file that can be used for speech recognition or analysis. That’s not it, Convertio further helps by allowing media files to be converted into a format required by the next tool in the AI workflow.

The facility helps the team prepare fields for transcription, analysis, or repurposing. Likewise, a workflow that specifically requires uncompressed audio can use an mp4 to wav conversion to extract the video’s audio track as a WAV file for subsequent speech processing.

It goes beyond only converting files by changing extensions. It’s about preparing the right data for the right tasks.

Why File Compatibility Matters for AI Performance?

One important technical detail is that different AI services support different input formats and configurations.

OpenAI’s current audio transcription API, for example, accepts several audio and media formats, including MP3, MP4, M4A, WAV, FLAC, and WebM. OpenAI Audio API documentation.

Furthermore, Google Cloud also recommends lossless audio like FLAC or LINEAR16. It is practical for speech recognition and notes that audio quality can influence results. Google Cloud Speech-to-Text best practices.

The conclusion is, don’t just ask “what an AI model can do with your content”, rather, ask if you are providing the right input.

From Audio to Searchable Intelligence

Everything becomes way easier and less stressful when you pass the phase of speech extraction and transcription.

A transcript can be:

● summarised into key points;

● translated into another language;

● divided by speaker;

● searched for specific terms;

● converted into subtitles;

● analysed for recurring topics;

● repurposed into articles, notes or social content.

Take an example of a company where there are 500 recorded customer interviews. Wouldn’t it be so annoying to watch the entire thing again?

A well-designed AI pipeline could instead turn the recordings into transcripts, identify common complaints, group similar themes, and surface the moments where customers discuss a particular feature.

Where Multimedia AI Is Already Being Used

In media production, AI systems are capable of analyzing footage deeply, generating titles for it, making summaries, and even producing audio relevant to the visuals.

Artificial Intelligence News previously examined Tencent’s Hunyuan Video-Foley system, which generates synchronised audio based on video content.

Other practical applications include:

● Meetings: turning recordings into searchable notes and action items.

● Education: generating transcripts, summaries and study materials from lectures.

● Customer service: analysing recorded interactions at scale.

● Media archives: automatically tagging large libraries of footage.

● Content creation: transforming long videos into transcripts, clips, captions and articles.

● Accessibility: producing captions and alternative content formats.

The Quality Problem AI Cannot Ignore

One has to understand that the outcome depends entirely on the input. If there is too much background noise, overlapping speakers, or low quality audio in a recording, speech recognition becomes quite difficult.

The deal is the same with visuals. If the recording is very blurry, or the lighting is poor, the results might not be satisfactory. That means the future of multimedia AI is not simply about building smarter models. It is also about creating better pipelines around those models.

Good preprocessing may involve:

1. choosing an appropriate file format.
2. extracting only the data needed.
3. preserving useful audio or visual quality.
4. checking privacy and permissions.
5. validating the AI-generated output before using it.

Conclusion

On the bottom line, AI today is changing the passive content into more valuable data. However, the success of any workflow is heavily dependent upon acquiring the right data, quality outputs and suitable formats.

As AI continues to evolve, the future will not be about simply storing the content. Instead, it is expected to be more about storing more content and discovering more value from every file that is being created.

The post From Video to Data: How AI Is Transforming Multimedia Content Processing appeared first on AI News.

Live translation has long been one of travel’s hardest unsolved problems: a single guide speaking to a mixed-language group, with no way to be understood by everyone at once. Vox Group, a 25-year-old guiding technology company operating in over 150 countries, has spent the past year rebuilding its AI-powered technology, Aura, to close that gap, now supporting simultaneous translation in up to 200 languages, live accessibility subtitles, and an AI companion designed to support guides rather than replace them. The update arrives as Vox celebrates its 25th Anniversary this month.

WHO IS VOX GROUP

Few outside the travel industry have heard of Vox Group, yet its technology underpins how millions of people experience a guided tour, powering cruise lines, tour operators, DMCs and destination experience partners in more than 150 countries. Founded 25 years ago on a simple radio link that let one guide be heard clearly by an entire group, the company has spent the years since expanding that idea, first into group guiding hardware, and more recently into Aura, its AI-powered guiding technology. Vox remains privately, family-led, an increasingly unusual profile among travel technology providers operating at this scale.

“Twenty-five years ago, we put a radio into a guide’s hand so that every guest could hear them,” says Elio Epifani, founder of Vox Group. “That principle has not changed. Aura begins with the guide and stays with the guide. What has changed is how much it can carry for them.”

THE TRANSLATION PROBLEM AI IS NOW SOLVING

Group tours have traditionally handled multiple languages in one of two ways: splitting visitors into separate language groups or adding a dedicated interpreter alongside the guide. Both add cost and complexity, and neither scale well for operators running mixed-nationality departures, increasingly the norm on cruise excursions and city tours. Aura’s translation library has grown from 50 to 200 languages over the past year, with live simultaneous translation now running in up to four languages at once on a single tour. Guests need no smartphone, app or download to take part, audio reaches them over the same radio infrastructure Vox has used for 25 years.

AN AI COMPANION BUILT TO SUPPORT GUIDES, NOT REPLACE THEM

Aura’s most significant update is its AI companion, a system designed to sit alongside the guide rather than in front of them. Rather than generating commentary from the open internet, it draws only on operator-approved content and verified sources, surfacing answers the moment a guest asks something unexpected, along with local context such as place names or regional sayings. It reflects a wider question the artificial intelligence sector is grappling with well beyond travel: where automation adds genuine value, and where it risks replacing the human expertise it was meant to support. Vox’s approach has been to keep the guide as the primary voice, using AI to extend what one person can reasonably be expected to know, rather than to generate the tour itself.

“One guide speaks once, and every guest receives a tour made for them in their own language, run from a single app,” says Fabio Primerano, Chief Executive Officer of Vox Group. “It gives guides more to work with and operators more to sell.”

ACCESSIBILITY BUILT IN, NOT BOLTED ON

Aura also addresses a gap much of the guiding technology on the market has overlooked. For guests who are deaf or hard of hearing, live subtitles are sent directly to their own phone as the guide speaks, letting them follow the tour in real time alongside the rest of the group rather than reading a summary afterwards. No separate equipment is issued, and no guest is visibly marked out as needing extra support, an accessibility approach built into the core product rather than offered as an add-on.

MULTIPLE CHANNELS, ONE DEPARTURE

Aura runs multiple commentary channels in parallel on a single tour, standard commentary, a historical deep dive, a children’s version, or a translated feed, so a family, a subject specialist and a first-time visitor can each follow a version suited to them without the guide repeating themselves. For operators, that means one departure can now carry visitors who would previously have needed splitting into separate language groups, with the tour adapting to each guest as it runs.

WHAT IT SIGNALS FOR AI IN TRAVEL

Aura’s evolution mirrors a pattern playing out across natural language processing applications more broadly: real-time translation and accessibility tools that once required specialist hardware or dedicated staff are increasingly built into existing infrastructure. For an industry still working out where AI adds value without eroding the human interactions travellers pay for, Vox’s approach, extending the guide rather than replacing them, offers one working answer.

Aura is available now for cruise lines, tour operators, DMCs and destination experience partners. voxtours.com/aura.

The post How Vox Group’s AI-Powered Technology Is Solving Real-Time Translation for Group Travel appeared first on AI News.

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.

See also: Supply chains detect fast, act slow: How AI agents fix it

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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

The post Palantir Foundry and cuOpt drive NVIDIA supply chain allocation appeared first on AI News.

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.

See also: JD.com expands physical AI in logistics with 3 million robots

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 Supply chains detect fast, act slow: How AI agents fix it appeared first on AI News.

JD.com is expanding AI and robotics across its logistics network under a new Physical AI Acceleration Plan, while reiterating a five-year target to procure 3 million robots, 1 million autonomous vehicles, and 100,000 delivery drones.

The company launched the plan at JDDiscovery 2026 in Beijing. JD Logistics also unveiled its industrial Wolf Robot series, designed for tasks across warehousing, sorting, transport, and delivery.

Specialised systems include equipment designed to operate in temperatures as low as minus 20 degrees Celsius, automated pharmacy dispatch systems, autonomous delivery vehicles, and drones.

The five-year procurement plan builds on automation that JD Logistics already has in operation. As of June 30, its LangzuTech Goods-to-Person automated warehousing system had been deployed in more than 30 warehouses across China, with deployments also launched in the UK and Germany.

JD Logistics’ broader warehouse network included more than 1,800 self-operated warehouses and more than 2,000 third-party cloud warehouses on its Open Warehouse Platform as of June 30. The network covered more than 36 million square metres in aggregate.

The company also had thousands of unmanned vehicles in regular operation across more than 20 Chinese provinces by the end of June. More than 100 domestic drone routes were operating across applications including parcel and food delivery, emergency medicine transport, and disaster relief.

From AI decisions to physical execution

JD Logistics is connecting its physical equipment with Meta Brain, an AI system used across warehousing, transportation, and delivery. JD said Meta Brain 3.0 can calculate optimal routes for hundreds of millions of parcels in seconds, compared with minutes previously.

Meta Brain also powers JD Logistics’ LangzuTech Packer robotic arm, which combines the model with multimodal sensor data to track, grasp, and place parcels with different shapes.

JD Logistics said in a first-quarter regulatory filing that the Packer uses parallel reinforcement learning in simulated environments to optimise parcel-placement sequences and loading layouts. The company said the system is designed to improve sorting efficiency and the use of available carrier space.

JD upgraded the robotic arm’s force-control technology during the second quarter to support more precise cage-loading operations. By June, the Packer was operating around the clock at multiple JD Logistics parks, according to the company’s interim report.

JD is also adding computing capacity to support AI development. JD Cloud plans to work with Chinese chipmaker Moore Threads on a cluster containing 100,000 GPUs for large-model training, inference, and embodied-AI workloads.

The two companies have previously worked on a 10,000-GPU cluster, according to Data Center Dynamics. Details of which Moore Threads GPU models will be used in the planned 100,000-GPU system have not been disclosed.

JD Cloud also plans to collect more than 10 million hours of video showing real-world human activities over the next two years for embodied-AI training.

Beyond warehouse operations, JD Logistics has expanded its autonomous vehicle network into night-time delivery. Its interim report said the company had launched night-time autonomous routes in Shenzhen, allowing vehicles to operate around the clock.

JD is also using drones in rural logistics. In June, JD Logistics launched a drone delivery network in Zizhong, Sichuan province, covering 78 administrative villages, and said deliveries to some mountain villages could be completed in as little as seven minutes.

Scaling automation across JD’s logistics network

JD did not disclose the total expected cost of the five-year procurement programme at JDDiscovery or provide a network-wide return-on-investment target.

JD Logistics spent RMB2.3 billion on research and development during the first half of 2026, up 23.7% from RMB1.9 billion a year earlier. The company attributed the increase to continued investment in technology and innovation but did not provide a breakdown showing how much was spent specifically on AI or robotics.

Depreciation of property and equipment and amortisation of other intangible assets rose 18.7% to RMB2.6 billion during the first half of 2026, from RMB2.2 billion a year earlier. JD Logistics attributed the increase mainly to additional logistics equipment and vehicles.

Purchases of property and equipment and investment properties totalled RMB3.09 billion over the same six-month period, compared with RMB2.70 billion a year earlier. Those figures cover the wider logistics business and are not disclosed as spending specifically associated with the new physical AI programme.

JD’s automation plans also come as China’s major ecommerce platforms expand fulfilment infrastructure. Reuters reported on September 3 that competition between JD.com, Alibaba, and Meituan had moved from heavy spending on delivery subsidies towards logistics infrastructure, broader supply, and order-level economics.

Alibaba and JD have been opening dark stores and fast-fulfilment “lightning warehouses” in densely populated areas to support deliveries within an hour, while Meituan has been building supermarkets to expand its grocery operations. Ministry of Commerce research cited by Reuters estimates China’s instant-retail market will reach RMB1.2 trillion, or about $178 billion, by the end of 2026.

JD and companies within its ecosystem employ around 700,000 delivery and logistics personnel, according to the South China Morning Post.

JD founder Richard Liu said earlier this year that robots would eventually take over parcel-delivery work now carried out by human couriers. The Financial Times reported in June that JD had signed agreements with around 120 educational institutions to retrain workers for roles including robot repair and maintenance.

JD said JD Logistics currently operates eight robot repair centres in China and plans to expand its robotics after-sales capabilities over the next five years. The company expects the expansion to support more than 100,000 robotics service engineer jobs.

(Photo by JD.com)

See also: Arm launches Total Design for Physical AI and robotics framework

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