Most organizations have run generative AI pilots; far fewer have production value. A roadmap for choosing use cases, preparing data, governing risk and scaling what works.
Pilots often prove that the technology works but fail to reach production because of unclear ownership, poor data, security concerns or no measurable business case. A structured roadmap addresses each.
Score candidates on value, feasibility, risk and data readiness. Strong early candidates include:
| Pattern | Use |
|---|---|
| SaaS copilots | Productivity in existing tools |
| Retrieval-augmented generation (RAG) | Answers grounded in your documents |
| Fine-tuning | Specialised style or domain tasks |
| Agents | Multi-step tasks with tools and approvals |
AI is only as good as the content it draws on. Clean up document repositories, fix permissions so the AI cannot surface data users should not see, and classify sensitive information.
Create a small AI centre of excellence to provide platforms, patterns and governance, while business teams own use cases. Reuse components across use cases rather than building each from scratch.
Track time saved, throughput, quality and error rates, customer satisfaction and adoption. Compare against a baseline measured before rollout.
| Month | Milestone |
|---|---|
| 1 | Use-case portfolio, governance and acceptable-use policy |
| 2 | Data readiness and platform selection |
| 3–4 | Two production pilots with measured baselines |
| 5 | Security testing and scale-out plan |
| 6 | Production rollout and value review |
Our digital transformation team identifies use cases, builds RAG and agent solutions, implements governance and measures value.
Next step: explore our Digital Transformation services or read about ISO 42001 AI governance.
NAZZTEC Editorial TeamWritten and reviewed by NAZZTEC practitioners. This briefing is general guidance, not legal advice.
Automating a broken process just makes it fail faster. How to pick the right processes, combine RPA with AI and APIs, and build an automation programme that pays back.
AI and analytics ambitions fail on fragmented, untrusted data. How to design a modern data platform that is governed, scalable and genuinely self-service.
The DCC extends the ECC with controls that scale with data classification. What it requires and how to align it with PDPL and national data management rules.
Tell us what you are working on. A senior NAZZTEC consultant will come back within one business day with a practical view.
We respond to every enquiry within one business day.