Generative AI and large language models are changing how businesses handle information. Yet there is a real gap between an impressive demo and an application that runs reliably in day-to-day operations. The right question is not “which AI model should we use?” but “which problem is worth solving with AI?”.
Good problems to start with
- Internal knowledge search: Q&A over processes, technical documents and company policies.
- Document data extraction: combining OCR and AI to read invoices, contracts and goods receipts.
- Drafting and summarization: emails, reports and meeting minutes.
- Request classification and routing: automatically tagging support tickets and sending them to the right team.
- Content analysis: consolidating customer feedback and spotting trends.
What these problems have in common: high volume, repetitive work, and outputs that a person can review.
Four questions before you build
- Data: is there enough of it, is it clean, and are you allowed to use it for this purpose?
- Accuracy: what error rate is acceptable, and what happens when an answer is wrong?
- Process: who reviews the AI's output, and how does it fit into the existing workflow?
- Security and cost: where is data processed, and how do operating costs scale as usage grows?
People stay at the center
For most enterprise applications, the effective approach is for AI to suggest and people to approve (human-in-the-loop). This reduces risk and generates feedback data that improves the system over time. Alongside it, organizations need clear AI usage guidelines: which data may be used, and who is accountable for the final result.
A suggested roadmap
Start with a small, measurable problem, build a prototype in a few weeks using real data, evaluate the results with business users, and only then decide whether to scale. Miagi Solution supports businesses from problem assessment and data preparation through to integrating AI and OCR into existing software and workflows.
