Enterprise AI in production: the complete guide

Why do enterprise AI pilots stall?

Most AI pilots stall because they were built to impress, not to run. A demo on sample data, with an engineer's credentials and no audit trail, proves that a model can do a task. It does not prove the task can be done safely inside a bank, an insurer or a brokerage. The gap between the two is where pilots die: nobody owns the permissions, nobody has written down what happens when the model is wrong, and the people who built the demo have moved on. The fix is to treat production as the starting condition, which is why FetchSky's pilots run on the client's own data and systems rather than a copy. The full argument is in why enterprise AI pilots never reach production.

What does production actually demand?

Production demands four things a demo can skip. First, real data: the AI reads from the same ledger, CRM or document store the business uses, with the same quirks. Second, permissions: each task gets only the access it needs, and no more. Third, an audit trail: everything the AI reads, decides and does is logged, so a compliance team can reconstruct any outcome. Fourth, on-call ownership: someone monitors it, receives alerts, and can switch it off or change it at any time. A pilot that meets those four conditions is already most of the way to production. A pilot that meets none of them is a prototype, however good the output looks. Assessing whether your organisation can meet them is the subject of what is AI readiness.

How does the four-stage process work?

FetchSky's AI transformation process has four stages, each with a defined output.

1. Find the right opportunity. A free consultation and a short look at how your teams actually work. The output is the one or two tasks where AI pays off fastest.
2. Prove it on your own data. A six-week pilot on your own data and systems. The output is one working version, not a slide deck.
3. Roll it out. Scoped with you, with cost and scope agreed before work starts.
4. Keep it running and improving. For as long as needed, with the engineers who built it on call.

Six weeks is deliberate. It is long enough to connect to real systems and short enough that the decision to continue is made on evidence rather than sunk cost. What the pilot includes, and what it does not, is covered in what is a six-week AI pilot.

What kinds of AI work in production?

The kinds of AI that work in production are the ones that attach to an existing workflow and replace a specific, repetitive step. FetchSky delivers six kinds:

• AI agents that carry out a multi-step task end to end, such as extracting, matching and sending. What makes an agent different from a chatbot is explained in what is agentic AI.
• Document and data extraction from PDFs, scans and ledgers using OCR and document AI.
• Language models in workflows, for scoring, summarising and drafting inside a process rather than in a separate chat window.
• Voice agents for calls and pre-screening.
• Computer vision, including signature verification.
• Monitoring and analytics, such as portfolio risk monitoring and announcement tracking.

Alongside these sit AI strategy and consulting (opportunity discovery, data readiness, roadmap) and AI integration and automation (workflow automation, system integration, audit trail). FetchSky uses leading commercial and open models, chosen per use case for accuracy, cost and data residency, and does not train foundation models.

What results have been published?

The published results come from live automations with anonymised clients. The table shows the four with full before-and-after figures.

Other published results, given on the site as a single figure: AI signature verification 95%, school assessment analysis 90%, multi-portfolio risk monitoring 80%, workforce analytics 85%, POS settlement and merchant analytics 90%, blood bank workflow 80%, and PSX announcement monitoring in near real time. In each case the percentage is the time saved compared with doing the work by hand.

The trade confirmation, fund report and newsletter stories are told in full in how AI agents send every trade confirmation within minutes, how three AI agents cut a four-day report to three minutes and how we automated a daily market newsletter.

How is security and control handled?

Security and control are built into the engagement rather than added at sign-off. Your data can stay in your own cloud or your own country. Each task gets only the access it needs. Your data is not used to train models. Everything the AI reads, decides and does is logged. A person reviews anything that affects a customer or moves money. The system is tested against misuse before launch and monitored with alerts after it. It can be switched off or changed at any time. Two of those points get their own articles: how to stop an AI agent doing something it shouldn't and data residency for AI.

How should you choose an AI partner?

Choose an AI partner on four tests. Will they work on your real data in the first six weeks, or on a sample? Will they agree scope and cost before work starts? Will the people who build it stay to run it? And have they shipped production software before AI, so they already know about permissions, staging, rollback and on-call? FetchSky's answer to the first three is the free consultation and six-week pilot before any larger commitment, and the builders staying on afterwards. The answer to the fourth is a company founded in August 2015 with 50+ engineers, six products of its own, and several client relationships over ten years old. The role that makes this work in practice is described in what is a forward deployed engineer.

Go deeper

• Why enterprise AI pilots never reach production
• What is AI readiness?
• What is a six-week AI pilot?
• What is agentic AI?
• How to stop an AI agent doing something it shouldn't
• Data residency for AI
• How AI agents send every trade confirmation within minutes
• How three AI agents cut a four-day report to three minutes
• How we automated a daily market newsletter
• What is a forward deployed engineer?

Start with a free consultation on AI transformation.