How a Global Machinery Manufacturer Moved Fragmented AI Use Onto One Governed Platform
This enterprise GenAI platform manufacturing case study describes how an international industrial machinery manufacturer moved from scattered, ungoverned AI use to one company-wide platform on Microsoft Azure, built primarily for its software development and engineering organisation. inteliScience SRL covered the full path — requirements, architecture, development, then operations and maintenance, including CI/CD pipelines, containerised deployment and load balancing for high availability.
One note on scope, because we have published something adjacent: our enterprise AI platform for an Austrian bank describes a regulated financial services environment. This one is about an engineering organisation inside a manufacturing group — different users, different data, a different reason for centralising. It is also deliberately unquantified: the client released no performance figures, so what follows describes confirmed changes in how the organisation works, and nothing more.
In this enterprise GenAI platform manufacturing case study, an international machinery manufacturer runs generative AI for its development and engineering organisation on one sanctioned, centrally operated platform, instead of on whatever tool each team could reach by itself. The platform runs on Microsoft Azure, ships through CI/CD pipelines as containers behind a load balancer, and comes with an operating model that names an owner, an onboarding path and a maintenance routine. The client confirms the change qualitatively and released no metrics for publication.
LAST UPDATED AUGUST 21, 2026 · CONFIRMED BY THE CLIENT FOR ANONYMOUS PUBLICATION · NO METRICS RELEASED
- Client
- International industrial machinery manufacturerNOT NAMED
- Sector
- Industrial machinery
- Primary users
- The software development and engineering organisation
- Scope
- Requirements, architecture, development, operations and maintenance
- Environment
- Microsoft Azure — containerised deployment, CI/CD pipelines, load balancing
- Duration
- Not disclosed
- Evidence
- Client-confirmed, qualitative — no metrics released for publication
- Our role
- Platform architecture, development and operations — inteliScience SRL
The situation: AI had already arrived, just not through IT
Generative AI was in use at the Manufacturer long before there was a platform for it — the normal starting position, not a failure of discipline. People had work to do, capable tools were one browser tab away, and nobody waits for a governance framework before pasting a specification into a chat window.
The industry data shows the same pattern. In the Microsoft and LinkedIn 2024 Work Trend Index, a survey of 31,000 people across 31 countries, 75 percent of knowledge workers said they use generative AI at work — and 78 percent of those users bring their own tools rather than something the company provides. The OutSystems Enterprise AI Agent Report 2026 found that 94 percent of organisations consider AI sprawl a growing source of complexity, technical debt and security risk, while only 12 percent operate a centralised platform to manage it. Those are industry figures from named studies; they set the scene and are not this project’s results.
For an engineering organisation this size, that position cost in three ways. Exposure: nobody could answer which company data had reached which model under which terms. Duplication: every team that wanted an assistant began its own procurement, security review and hosting discussion, and the second team learned nothing from the first. And the cost that quietly decides everything — nothing composed. A dozen isolated experiments do not add up to a capability, which is the mechanism behind the MIT NANDA finding in The GenAI Divide: State of AI in Business 2025 that 95 percent of enterprise generative AI pilots produced no measurable return.
The Manufacturer treated this as a platform problem rather than a policy problem. A policy can forbid the browser tab. Only a platform can offer something better.
What changed in the working day
- ad hocWhoever needed AI found a tool and used it, often on a personal account
- unknownNobody could say which company data had reached which model
- per teamEvery use case started its own infrastructure and security discussion
- repeatedThe second team rebuilt what the first team had already solved
- stalledIsolated pilots had nowhere to grow into
- sanctionedOne company-wide entry point, with identity, roles and logging
- definedWhich data class may reach which model is a platform rule, not a habit
- onboardedA new use case joins the platform instead of starting a project
- reusedAccess, routing and integration work are built once and shared
- operatedCI/CD pipelines and containers behind a load balancer keep it available
What the client reports
The statements below were confirmed by the client for anonymous publication. They are qualitative: no before-and-after figures were released, so this case study publishes none. A qualitative result that is true is worth more than a precise number nobody can stand behind.
Released by the client for anonymous publication. No metric was attached to any of these statements, so none is shown — there are deliberately no progress bars in this block.
Where people go for AI at work
client-confirmed Qualitative statement, no metric attached. The platform did not forbid the old behaviour; it offered something better, with identity, roles and logging attached.
Standing up a new AI use case for a department
client-confirmed Qualitative statement, no metric attached. This is where the efficiency gain sits: integration, access and operations work is done once and reused instead of repeated per department.
Shipping a platform change
client-confirmed Qualitative statement, no metric attached. Containers deploy through the pipeline behind a load balancer. Availability was engineered in from the start, because a central platform that is down sends people straight back to their own tools.
Visibility of which data reaches which model
client-confirmed Qualitative statement, no metric attached. This is the reason the programme was funded, and the one result the client considers non-negotiable.
How does an enterprise GenAI platform actually get built?
An enterprise GenAI platform gets built in the reverse order of most AI projects: boundaries first, platform second, operating model third — and the operating model decides whether any of it is still running a year later. The model itself is the least interesting component, and deliberately the most replaceable.
The requirements decide what the architecture is allowed to do
Before anything was designed, the work was to establish which data classes exist, which may reach which kind of model, who may use what, and what has to be logged for an internal audit. In a group this size those answers come from several organisations at once, and gathering them is project work, not a preliminary.
OUTPUT — a written boundary the architecture can be checked againstA platform is what turns a working prototype into something operable
On Microsoft Azure: an access layer that resolves identity and roles, a routing layer that sends each request to a model according to its data class, and the integrations that make internal knowledge reachable. It ships as containers through CI/CD pipelines behind a load balancer, so releases are pipeline events and load is spread rather than survived.
OUTPUT — a service the engineering organisation can rely onThe layer that decides whether the platform is still there in a year
Running the platform means naming an owner, defining how a department onboards a use case, deciding how models are evaluated and swapped, and handling incidents and updates as routine. Most GenAI programmes leave this until last; it is what separates a platform from a pilot that happened to survive.
OUTPUT — AI as a maintained internal service, not a project with an end dateWorth stating plainly, because every security team asks: the platform decides access, routing, logging and retention by rules, not by the model. The language model answers questions. It does not decide who may ask, which data class it may see, or where an answer is stored. Those decisions stay deterministic in the platform layer, and that is what makes the whole thing auditable.
What the platform is built on
Managed cloud services and the conventions of the client. Nothing that depends on a single vendor decision.
Related work on the same ground: AI strategy and consulting, the engineering knowledge assistant for energy manufacturers, and, for the Azure side of this kind of build, our write-up on Azure AI Foundry.
What we would do differently
We would write the operating model in week one instead of after the architecture. Availability was never the hard part — containers, pipelines and a load balancer are well-understood engineering. The hard part was everything the architecture diagram does not show: who owns the platform after go-live, how a department gets a use case onboarded without opening a project, who approves a new data source, who decides when a model is swapped. Every one of those questions arrived anyway, just later and under time pressure, and a question that arrives late gets answered by whoever is in the room rather than by design. We would also build the self-service onboarding path much earlier: as long as adding a use case requires the platform team, the platform team is the bottleneck — and a bottleneck is indistinguishable from a policy that says no.
When does a central GenAI platform pay off?
A central GenAI platform pays off when several teams want the same capability and the data cannot leave the company unsupervised. The case is strong when more than a handful of departments are asking for AI support, when the same integration work would otherwise be repeated per team, when confidential engineering or commercial data is involved, and when someone has to answer an audit question about which data reached which model. It is weak when one team has one use case and the data is not sensitive — then a commercial subscription is the honest answer. The threshold is not company size; it is the number of independent teams that would otherwise each solve the same problem badly.
Frequently asked questions
What does an enterprise GenAI platform actually consist of?
How long does it take to stand up a platform like this?
Why not simply buy commercial AI licences for everyone?
Does the platform decide anything on its own?
Why is the client not named?
Is your AI usage a platform yet, or still a collection of tabs?
Tell us which teams already use AI at your company and which data they are not allowed to send anywhere — we will tell you honestly whether that is a platform problem or a licence problem.
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