AI doesn't have to replace existing enterprise systems overnight.
The smart approach is to build AI features and systems in parallel with your current workflows, using custom models. Start small — route a limited percentage of traffic through the AI path while the core system continues uninterrupted.
Set Higher Standards for the AI Path
The AI route should aim for AI-specific metrics:
- Better accuracy
- Faster turnaround
- Improved learning over time
- Stronger customer outcomes
These aren't the same metrics you optimise for in a traditional system. AI earns its place by outperforming, not by replacing.
Test Beyond A/B
Testing should go beyond simple A/B experiments. Use:
- Interleaving — serve both paths to the same user session and compare quality signals
- Multivariate testing — isolate the contribution of individual model or pipeline changes
- Feature flags — control rollout granularity without redeployment
Maintain Audit Trails for Both Systems
Run both AI and legacy systems with full observability — automatically comparing results to catch failures or regressions. This dual audit trail is what gives you confidence to migrate, not guesswork.
Build Safety Valves
Don't wait for a post-mortem to handle edge cases. Design safety valves upfront:
- Route tricky or ambiguous cases to human review
- Throttle AI traffic if quality signals degrade
- Roll back instantly if needed — the legacy system should always be ready
Progressive Migration
Once confidence is proven, AI-driven go-to-market can progressively replace the traditional release path, aligned to your organisation's pace and risk tolerance.
Timelines will vary. But the principle remains constant:
Parallel build. Higher standards. Human oversight. Safe, reversible rollout.
This is how enterprise AI should be shipped — not as a big-bang replacement, but as a continuously validated, incrementally trusted system running alongside what already works.