# About AutoSpark

> AutoSpark finds the one place AI or automation is genuinely worth applying in an established business, then builds and deploys it. Led by Patrick Nesbitt, CA(SA), CFA and a former private-equity investor.

Source: https://autospark.ai/about

## What AutoSpark does

AutoSpark works with established small and mid-sized businesses that suspect AI could help but cannot say where. Most of them have been pitched a platform, a copilot or a transformation programme, and none of those start by asking what the business actually loses money on.

The work starts with interviews. We talk to the people doing the job, not only the people who own the process on paper, and we look for the places where work repeatedly gets stuck, gets rekeyed, or waits on someone. Those places are then ranked by what they plausibly cost per year. Only the ones where the arithmetic shows a clear payback get built.


## How the work is structured

- Interviews with the people doing the work, usually five to ten conversations.
- A ranked list of problems with an estimate of what each one costs annually.
- A recommendation on which single problem to fix first, and what fixing it returns.
- Build and deployment of that fix, with the system handed over working.

## Who runs it

AutoSpark is led by Patrick Nesbitt. He qualified as a CA(SA) in 2012 and became a CFA charterholder in 2017, spent time in audit and equity research, and then moved to the buy side, where he worked on private-equity transactions and sat as chairman of an alternative investment manager.

That background is the reason the method leads with cost rather than capability. An automation that saves four hours a month in a place nobody was waiting is not worth building, and an investor is trained to notice the difference. AI is treated here as a capital allocation question, which is to say it has to clear a hurdle rate like anything else.


## What this is not

It is not an AI audit, and it is not a transformation programme. There is no licence to buy and no platform to adopt before anything useful happens. If the interviews show that the honest answer is that nothing here is worth automating yet, that is a legitimate result and it gets said plainly.