Integrating AI into legacy systems, managing multiple AI platforms, scaling successful pilots, accumulating technical debt, and rising infrastructure costs are the unglamorous engineering realities behind every AI announcement that never quite delivers.
Where this gets hard
- Legacy systems were rarely designed with AI integration in mind, creating significant technical friction.
- Managing multiple, overlapping AI platforms adds complexity and cost without necessarily adding value.
- Scaling a successful pilot to full production is a different, harder engineering challenge than building the pilot itself.
- Technical debt accumulated from rapid AI experimentation compounds over time if left unmanaged.
- Infrastructure and cloud dependency costs can escalate quickly and unpredictably as AI usage scales.
Where to start
- Assess integration feasibility with legacy systems before committing to a use case, not after building the pilot.
- Rationalise the AI platform portfolio deliberately, retiring redundant tools rather than accumulating them.
- Build a defined pilot-to-scale process with clear engineering criteria, not just business sign-off.
- Allocate dedicated time and budget to managing technical debt from AI initiatives, not just new feature delivery.
- Model infrastructure costs at scale before committing, not just at pilot volume.
The consulting document includes an integration feasibility checklist and a platform rationalisation template.
How many AI platforms is your organisation currently paying for — and could you name what each one is actually used for?