AI opportunity discovery
Find the work worth automating before committing to a build
Work out where AI and automation would actually pay off in your business, and where they would not, before anyone writes a workflow.
Who this is for: Owners and managers who can see that something should be automated but not which thing, or what it would cost to be wrong.
Available to scope
This is work BrisAI is ready to scope, but there is no published case study for it yet. Treat the description below as what we deliver rather than evidence of a measured result.
You will recognise this if…
- You have been told AI could help, but every example you have seen is from a company that looks nothing like yours.
- Several processes feel slow, and there is no obvious way to rank them against each other.
- You want to know what a project would involve — the data, the risk, the review effort — before you commit budget to it.
What we deliver
- Workflow and process discovery, mapping what actually happens rather than what the process document says
- An AI opportunity audit with a prioritised shortlist
- Feasibility and data-readiness assessment
- A time-saving and cost model with the assumptions written down
- A prototype or proof of concept where the answer is genuinely uncertain
- Risk, privacy and human-review design
- Team training and adoption support
Common builds
Concrete systems in this family, described the same way the free Snapshot describes them.
Business intelligence assistant
Ask a question about your own numbers in plain English and get an answer you can check.
A question-answering layer over your business data that returns the figure and the query behind it, so every answer can be verified rather than trusted. Built on structured sources only — it will not infer numbers it cannot see.
How we work on this
01
Map the work
We sit with the people who do the process and record the real steps, including the exceptions everyone works around.
02
Size the opportunity
Each candidate gets an estimate of volume, time spent, error cost and how much of it could sensibly be automated.
03
Rank and rule out
Some things are not worth automating. Saying so early is most of the value of a discovery engagement.
04
Write the roadmap
A shortlist with what each build involves, what it would cost, and what has to stay under human control.
Evidence
We have not published a case study in this family yet. Rather than show an illustrative example and let it read as proof, we would rather say that plainly — and show you the work we have written up, so you can judge how we build and what we are willing to claim.
Questions
- Can we start with the free Snapshot instead?
- Yes, and most people should. The AI Opportunity Snapshot asks about your business and returns a prioritised set of opportunities using the same catalogue this page draws from. A discovery engagement goes further: it looks at your actual processes and data rather than your answers to a questionnaire.
- What if the answer is that we should not automate anything?
- Then that is the finding, and it is a cheaper one to receive at the discovery stage than after a build. A process that is unstable, low-volume or about to change is usually not a good first automation.
Next step
Describe the process that prompted you to read this page. If it is a good fit we will say so, and if it is not we will say that too.