Industrial R&D teams sit on a goldmine of testing and operational data. But until now, unlocking that data required costly, slow, and fragile technical integration. Today, autonomous AI agents are solving the infrastructure bottleneck, turning fragmented data into a competitive moat and allowing a single engineer to deliver the output of an entire team of specialists.
In aerospace, automotive, and industrial manufacturing, R&D teams generate enormous volumes of test data. This data holds the key to incredible research insights, but most of it sits dormant. Why? Because the engineering effort required to simply clean, connect, and organise it is too high.
Historically, this data fragmentation was viewed as an IT infrastructure problem. Test data arrives in countless different formats, naming conventions drift over the years, and vital context is often lost when senior experts retire, taking their “tribal knowledge” with them.
What ties these three issues together is the human labour involved in solving them. Each of them needs a skilled engineer to fix the formatting mismatches, map channel names and hunt down the relevant documentation. Agentic AI changes the economics of that work. LLM-powered agents can read documentation, inspect unfamiliar files, write code to reconcile formats, and apply expert knowledge that has been codified into the agent’s instructions. Leading manufacturers are now using that capability to change how their R&D organizations handle incoming data.
When AI agents take over the heavy lifting of data preparation, the business outcomes are transformative: the velocity of test cycles increases drastically, CapEx overheads on physical machinery are reduced, and decades of institutional knowledge are codified into an impenetrable competitive moat.
The key business outcomes I see most often are increased velocity, reduced CapEx and better knowledge management. Teams get through test cycles faster because engineers aren’t losing days to malformed files and format reconciliations. They avoid buying new test rigs, because each rig they already own ends up delivering more useful data per run. And the working knowledge of their most experienced employees gets codified in centralized, structured form since that’s what AI agents require before they can be useful.
That’s the high-level picture, but it’s hard to grasp what this means for your team if you don’t have some concrete examples to learn from, so I’ve pulled together three recent cases worth examining in a little more detail.
GKN Aerospace empowers engineers to deliver more value
At GKN Aerospace, structural engineers rely on complex data deliveries from OEMs to certify the airworthiness of jet engine components. In the past, if an OEM changed the format of the data delivery (such as saving a file as a YAML instead of a JSON, using imperial units instead of metric, or hiding a critical mathematical correction factor in a written note) the automated engineering scripts would instantly crash. Highly skilled structural engineers were forced to stop their actual work to spend hours debugging data formats rather than conducting safety analyses.
To solve this, GKN deployed an AI agent powered by Anthropic’s Claude, integrated into a system they call DUCTILE. Now, instead of rigid scripts failing, the AI agent autonomously reads the company’s official, documented design practices, inspects incoming files containing hundreds of physical load cases, spots the formatting errors, and writes custom code to fix them instantly. During testing, engineers trialled the AI using different working styles: one acting as a delegating manager who simply checked the final output, and another acting as a strict supervisor approving every single step. Both approaches achieved perfectly correct results across ten independent trials.
As a result, the integration bottleneck has completely collapsed. The engineer’s time has moved entirely away from fixing broken data pipelines to supervising high-level analysis, effectively allowing one engineer to deliver far greater strategic value.
BMW accelerates analysis cycles twelvefold
BMW’s fleet of around 3,500 development vehicles generates a staggering 5 to 10 terabytes of measurement data per car, per week. IoT devices inside the vehicles monitor more than 10,000 signals (such as braking patterns, torque, RPMs, and battery voltage) twice per second.
Finding specific insights within that ocean of data used to take hours or days, and access was largely restricted to IT specialists who knew how to write complex database queries.
By implementing a multi-agent AI system dubbed the “MDR Copilot,” BMW has democratised this data across its R&D teams. Built on Microsoft Azure, the system relies on a vast knowledge base captured from structured interviews with domain experts.
Engineers can now query the system in natural language. For example, “How many braking manoeuvres were performed by the development vehicles in the last two days?”. Relying on the codified expert knowledge, the AI agents collaborate to instantly retrieve the relevant telemetry, test hypotheses, and generate structured, written reports.
BMW reports this system can deliver analysis up to 12 times faster than previous workflows, providing engineers with critical insights before a test car even returns to the garage. By democratising data access, BMW has completely removed the IT bottleneck and empowered its broader engineering team to make faster, data-driven decisions.
Reducing CapEx through predictive precision
A German machinery manufacturer used AI-powered research to solve a high-cost R&D bottleneck. This is a project I’ve been personally involved in. The company (who need to remain anonymous) was trying to predict how many test runs it would take to balance heavy turbine rotors. Physical testing is incredibly expensive and ties up valuable machinery. The goal was to drastically reduce the number of physical balancing runs required per rotor, effectively increasing the capacity of their existing machines without having to buy new ones.
However, the required data was highly complex and scattered across different formats and network locations.
First, the team centralized the sensor and config data into a dedicated lakehouse (a system that stores raw data files cheaply and lets you query them like a database). They then deployed an AI agent to autonomously monitor network locations, detect new files, and make sure they conformed to the expected structure before ingesting new data into the lakehouse.
From there, human rotor specialists used natural language to interact with AI-powered research agents (who were equipped with specific analysis skills) to process high-resolution sensor data and build complex predictive models. Once the agents had access to a centralized data platform and a place to execute code, they could handle the heavy lifting of writing Python to extract features from the data, and the human engineers directed the investigation.
By combining human expertise with this agile data infrastructure, the team was able to achieve 97.2% prediction accuracy. By getting it right the first time, the manufacturer radically improved throughput and avoided the heavy CapEx overhead of commissioning new testing machinery.
The true moat is institutional knowledge powered by accessible infrastructure
It’s easy to get distracted by the sophistication of large language models, but the algorithm isn’t the secret. The competitive moat comes from pairing codified domain expertise with data infrastructure that’s actually usable
Every one of the examples above makes the same point. BMW’s system relies on a knowledge base built from structured interviews with domain experts. GKN’s agent reads directly from audited design practice documents. The rotor balancing project only worked because a human specialist told the agent which physical properties to investigate. The agent amplifies the expert; it does not replace them.
Documents full of expert knowledge doesn’t help an agent until the agent has a place to run its code and enough data to work on. Historically that meant a serious IT project. BMW built a bespoke architecture on Microsoft Azure with a dedicated engineering team behind it. At their scale, with 10 terabytes per vehicle per week, that’s the right answer. Most industrial organisations are not at that scale and cannot justify that build.
They no longer need to. Modern industrial data platforms come with the storage layer, the compute layer, and the connections between them already set up. An agent can read from the data, run its code against it, and write results back, without anyone having to build that stack in-house.
So the practical steps aren’t glamorous, and both get harder the longer you leave them.
The first step is getting your fragmented data into one place, we all know this by now. The temptation, (especially at organisations with capable software teams) is to treat this as a greenfield engineering project: pick your own stack, stand up your own cluster, write your own ingestion layer, build your own query layer on top. A team that spends eighteen months on this is a team that isn’t running agents against its data for eighteen months, and at the end of it they own a maintenance burden that will outlast the people who built it. Look for platforms that provide the first layer of infrastructure and ideally come with an existing AI agent framework, so you can focus on integrating your internal systems and knowledge.
The second step is sitting down with your most experienced engineers and recording how they actually solve problems. Those engineers are, on average, closer to retirement than to the start of their careers. The interviews don’t get easier to schedule, and they become impossible to schedule at all once the person has left.
Do both, and in parallel. An agent without usable data has nothing to work on. An agent without the expert knowledge will produce confident nonsense.
Author: Michael Rosam, CEO & Founder, quix
For more articles like this, visit our Industrial Data & AI channel
