A snapshot of challenges and solutions to scaling AI, drawn from our report ‘Why AI pilots succeed, but AI transformation fails at scale’

1. Why do so many AI pilots fail to scale in engineering organizations?

Many AI pilots get stuck because they are built as isolated experiments, rather than integrated systems. Even when there is ambition to make AI projects scalable, companies often hit systemic blockers.

Capgemini Engineering research has identified a number of internal blockers to scale, including:

  1. Fragmented and inaccessible engineering data and models
  2. Integration challenges across siloed and legacy IT and OT systems
  3. Governance, cybersecurity, and regulatory constraints that are not designed for AI
  4. Lack of trust in AI outputs in high-stakes environments
  5. Cultural resistance
  6. Skills gaps in both AI development and AI usage

2. Are there particular challenges to AI deployment in engineering?

Yes. Engineering organizations rely on multi-format data – including CAD models, simulations, diagrams, and sensor streams – that are often distributed across different systems and difficult for AI to integrate.

More importantly, AI outputs influence real-world products. Decisions informed by AI can affect safety, compliance, and reliability. AI systems in engineering must meet far higher standards for accuracy, traceability, and validation than typical enterprise AI applications.

3. What is Capgemini Engineering’s position on scaling AI in engineering organizations?

Capgemini Engineering believes AI must be embedded into data architecture, workflows, governance, and human processes. Only when AI becomes a reliable part of the company’s infrastructure – like electricity or the internet – can it deliver the promised transformation across engineering lifecycles. We call this AI-as-a-utility.

4. What do you mean by “AI-as-a-utility”?

AI-as-a-utility is Capgemini Engineering’s model for embedding AI as a secure, governed, scalable infrastructure capability across the entire engineering organization – similar to electricity, cloud, or the internet.

Under an AI-as-a-utility model:

  • Engineers can access domain-approved models
  • Data is structured and interoperable
  • Governance is built in
  • Validation is automated
  • AI systems integrate with legacy infrastructure

5. If organizations successfully set themselves up for AI at scale, how might AI transform engineering?

AI – particularly Generative and Agentic AI – promise to deliver transformation across the engineering lifecycle. Examples in engineering include:

  • Generating component designs and simulations
  • Automating lifecycle and sustainability assessments
  • Drafting compliance documentation
  • Supporting predictive maintenance
  • Accelerating R&D concept exploration

6. Why not keep deploying more point AI solutions?

The incremental gains of problem-by-problem AI use cases will plateau, whilst organizations with the right AI infrastructure will race ahead.

Without the right AI infrastructure, coordination between models will rely on brittle workarounds that compromise reliability and trust. AI agents will hit integration limits. When knowledge cannot flow between domains, AI systems designed for one product line will need to be completely reengineered for another.

7. How can companies scale AI in engineering and R&D?

Companies set themselves up to scale AI by transforming end-to-end processes. Capgemini Engineering’s recommended approach involves:

  1. Making Data and AI Accessible by standardizing engineering data structures, building shared model libraries, and establishing clear governance frameworks.
  • Transforming Processes with AI. To force an integrated, scalable approach, focus on full processes – e.g. production lines or supply chains – not points along them.
  • Creating Human–AI Chemistry. Redesign workflows for hybrid teams, provide high quality AI tools with checks baked in, and encourage curiosity and experimentation within safe environments.

8. Can generative AI be trusted in safety-critical engineering environments?

Fully trusting AI for safety-critical applications may be a way off. But it can play a valuable role in advancing engineering applications – like those mentioned in question 5 – as long as validation, traceability, and governance are built into the workflow.

Capgemini Engineering rejects the “human-in-the-loop solves everything” narrative. AI fails differently than humans. Trustworthy AI in engineering requires both human checks and embedded validation models, where outputs are checked against real-world constraints and regulations.

9. So, AI needs both humans and machines to check its results?

Yes, AI should use a variety of models to provide checks. Generative model outputs can be made more reliable by combining them with simulations, knowledge graphs, and mathematical or physics-based models. For example, a generative model proposing a component design can be automatically verified against a physics-based simulation, before being passed to a human expert, with attention drawn to areas that require judgment.

This is what we call Hybrid AI. Hybrid AI makes generative AI safe for engineering use.

10. What should engineering leaders do now?

To make AI scalable, Capgemini Engineering recommends that leaders:

  1. Stop treating AI as a side initiative
  2. Invest in data foundations and governance
  3. Select a strategic end-to-end process transformation to focus AI transformation strategies
  4. Engineer in AI safety and validation from the start through a Hybrid AI methodology
  5. Redesign workflows for human–AI collaboration

AI is at the inflection point that cloud computing once was. The organizations that embed it as infrastructure will redefine engineering performance.

Read the full report to explore how engineering organizations can move beyond pilots and unlock AI value at scale: Why AI pilots succeed, but AI transformation fails at scale.