Adding AI to existing business systems: what actually works
Most businesses don't need a new AI product. They need the systems they already run to take on some of the reading, sorting and re-typing their staff do every day.
Start with the work, not the technology
The AI projects that pay back quickly have three things in common: a task done many times a week, a clear right answer, and a person who can check the result. Good candidates:
- Document processing: pulling data out of invoices, orders, delivery notes, contracts and application forms, and entering it into your system.
- Email and enquiry triage: classifying incoming messages, routing them to the right person and drafting a first reply for review.
- Search over your own knowledge: letting staff or customers ask questions and get answers drawn from your manuals, policies and past cases, with links to the source.
- Summaries and reports: turning case notes, call transcripts or long threads into short, consistent summaries.
Build it into the system you already have
AI works best as a feature of your existing application, not a separate tool staff have to copy and paste into. In practice, that means:
- An API call from your existing .NET, Node.js or cloud application to a model provider such as Anthropic, OpenAI or Azure OpenAI.
- Results written back into your own database, where the normal screens, permissions and audit trail apply.
- A review step for anything that matters, so a person approves before it's sent, paid or filed.
Data protection and security
Under UK GDPR, sending personal data to an AI provider is data processing like any other:
- Use business API terms under which the provider doesn't train on your data, and put a data processing agreement in place.
- Send the minimum needed. Remove or mask personal details the task doesn't require.
- Choose where the data is processed, including UK or EU regions where required, and record it in your data protection documentation.
- Log what was sent and returned, so decisions can be explained and audited.
Keeping costs predictable
Model costs are usually small compared with the staff time saved, but they need controlling. Use the smallest model that meets the accuracy target, cache repeated work, set spending limits per feature and monitor usage like any other cloud cost.
Where to start
My fixed-price AI opportunity review looks at how your business works today and ranks the opportunities by value and effort, so you can start with the one most likely to pay back.