How to introduce AI into existing systems without a rewrite
A practical, low-risk approach to adding AI to existing business systems: pick the right use case, integrate at the edges, evaluate, and keep humans in control.
- By
- PRAXIONIS Engineering
- Published
- Reading time
- 2 min
Short answer
You can add AI to existing business systems without replacing them by treating AI as a component at the edges of your current workflows: choose a narrow, valuable task, connect to existing systems through APIs or event streams, measure output quality against agreed criteria before launch, and keep a person in control of consequential decisions. Replace or modernise core systems only when the evidence shows you need to.
1. Start with a task, not a technology
Good first use cases are specific and frequent: extracting fields from documents, classifying and routing requests, drafting responses for a person to review, or finding answers across internal knowledge. Each has a clear input, a clear output and a person who can judge whether the output is right.
2. Integrate at the edges
Rather than rewriting a core platform, place the AI capability beside it: read from the system through its API or a replica, write results back through the same controls a human would use, and keep the system of record unchanged. This is the same principle as the strangler fig pattern (opens in a new tab) for gradual modernisation — new capability grows around the old system until replacing parts of it becomes a choice rather than a prerequisite.
3. Evaluate before you launch
- Agree what 'good' means with the people who do the work today, and collect representative examples — including the awkward ones.
- Measure accuracy, failure modes, latency and cost on that set before going live, and re-run it whenever prompts, models or data change.
- Decide in advance what happens when the system is unsure: route to a person, ask for more information, or decline.
4. Keep people in control and design for risk
Human review for consequential outputs, audit trails, access controls and data minimisation are engineering requirements, not afterthoughts. Useful frameworks include the NIST AI Risk Management Framework (opens in a new tab), the UK NCSC's guidelines for secure AI system development (opens in a new tab) and the OWASP Top 10 for LLM applications (opens in a new tab), which covers risks such as prompt injection and sensitive information disclosure.
5. Operate it like any other production system
Monitor quality over time, not just uptime. Track how often people override or correct the system, watch costs, and plan for model and vendor changes. The operational discipline described in Google's SRE book (opens in a new tab) applies to AI services as much as any other.
Sources and further reading
- Martin Fowler — Strangler Fig Application (opens in a new tab)
- NIST — AI Risk Management Framework (opens in a new tab)
- NCSC — Guidelines for secure AI system development (opens in a new tab)
- OWASP — Top 10 for LLM Applications (opens in a new tab)
- Google — Site Reliability Engineering (opens in a new tab)
About the author
PRAXIONIS Engineering
Articles written and reviewed by the PRAXIONIS engineering team. Individual author profiles will be added as team members approve their published bios.