AI automation for organisations
A large share of any organisation's time goes on work that is repetitive but not fully rule-bound: reading a letter and working out which department it belongs to, pulling a few numbers off an invoice, building a report that looks the same every week. That is exactly where intelligent automation fits.
Where it has the most effect
- Document processing: extracting data from invoices, contracts, forms and letters without manual typing
- Automatic triage and routing of requests to the right department
- Producing the recurring reports that are currently assembled by hand
- First-pass review, flagging the cases that need human attention
How it runs
- 1
Pick one process and one metric
A specific high-volume process, and a number by which success is judged. Without that number you can never say whether the project worked.
- 2
Prototype on real data
The model is tested on a sample of the organisation's actual documents or requests. Real accuracy shows up here, not in a presentation.
- 3
Deploy with a human in the loop
At first the model's output is a suggestion, not a decision. As accuracy is demonstrated and trust builds, the level of automation rises. Doing it the other way round is a risk.
Frequently asked questions
How does this differ from ordinary office automation?
Ordinary automation executes fixed rules: if this, then that. When the input is unstructured — a letter, a handwritten form, a free-text request — fixed rules do not work. That is where a model has to understand and classify the content.
Which processes give the best return?
Those with three properties: high volume, repetitive, and rule-governed but not perfectly fixed. Inbound document processing, request triage and recurring reporting are usually the best starting points.
Where should we start?
With one specific process and one numerical measure, not with the whole organisation. If you cannot say "this takes x hours today and we want it at y", the project cannot be evaluated.
Do our existing systems have to change?
Usually not. In most cases the intelligent layer sits alongside the existing system and works through its API. Replacing a system outright is rarely necessary and almost always more expensive.
How long does it take?
It depends on scope. Our approach is to start with one narrow process and measure the real result before any larger commitment, because an estimate made before seeing real data is a guess.
Further reading
Which of your processes can be automated
A short session is enough to establish which process offers the most return — and which are better left alone.