Most companies don't need an AI strategy deck to get started. They need one process that costs too much time, a safe way to automate it and a clear way to measure the result. This is how we approach a first project.
What “agentic” means in plain words
A chatbot answers questions. An AI agent does work: it reads an input, decides what to do, uses your systems to do it and hands over to a person when it is unsure.
For example, an invoice agent can read a supplier invoice from the inbox, check it against the purchase order, code it to the right account, enter it in the accounting system and send anything unusual to a person for approval.
Pick the right first process
A good first process has most of these:
- High volume: it happens many times a week.
- Repetitive: the steps are mostly the same each time.
- Clear rules: a new team member could learn it from a short guide.
- Digital input: emails, PDFs, forms or system data.
- Easy to measure: hours spent, turnaround time, error rate.
Typical winners are supplier invoices, document intake (contracts, onboarding and KYC documents, claims) and customer or supplier enquiries that arrive by email.
Poor first choices are processes that change every time, depend on judgement calls that nobody has written down, or where a single error is very costly. Those can come later, once trust is built.
A simple five-step approach
1. Map the process and measure today
Write down each step, who does it and how long it takes. Without a baseline, you can't show the result.
2. Design with a human in the loop
Decide what the agent does alone, what it prepares for approval and what it always hands over. Start cautious and widen the agent's freedom as results come in.
3. Connect it to your systems, with limited access
The value comes from integration: email, accounting or ERP, CRM, document storage. Give the agent only the access it needs, and nothing more.
4. Pilot on real work
Run it for four to six weeks on real cases, side by side with the team. Track accuracy, exceptions and time saved.
5. Measure, then scale
If the numbers hold, move to the next process. Each new one is faster, because the integrations and controls are already in place.
The controls a regulated business needs
Running AI safely is mostly good operational practice applied to a new tool:
- Approval limits. For example, the agent can process invoices up to a set amount; anything above goes to a person.
- A full audit log of what the agent saw, decided and did.
- Data protection. Know where data goes, which AI provider processes it and under what terms, in line with PDPA, GDPR or the rules that apply to you.
- Testing on real samples before go-live, including the messy cases.
- A clear owner in the business, plus a fallback to the manual process if something goes wrong.
Rule of thumb: if you would not let a new hire do it unsupervised in their first week, don't let the agent do it unsupervised either. Not yet.
What to expect
A well-chosen first process usually shows its value within weeks, not months, mainly as hours given back to the team and faster turnaround. We have seen this with invoice automation that cut a client's monthly processing time.
The bigger win is what comes next: once the first agent is live and trusted, the second and third are much quicker to build.
How we help
We start with a short process audit to find the quick wins, build the first workflow with your team, and set up the governance to run it safely. We work with SMEs and enterprises, and on projects where AI meets Web3 and digital assets.
Have a question about this for your organisation? A 30-minute call is the easiest way to start.
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