AI & Automation
AI Strategy & Readiness
A costed plan, ranked by return — not a slide deck
- Ranked backlog, not a maturity model
- Honest build-versus-buy recommendations
- Two weeks, fixed price
- 2 weeks
- From kickoff to readout
- 12-20
- Opportunities identified and ranked
- 3
- Recommended starting projects
Fixed-scope AI Readiness Audit
Typical for a 100-500 person organisation
With effort, risk and savings modelled for each
Why most AI strategy work is worthless
It produces a maturity model, a two-by-two, and a recommendation to "build capability." None of that survives a budget review, because none of it says what to build, what it costs, or what comes back.
We do the opposite. The output is a ranked list of specific processes with an estimate against each one: hours currently spent, error rate today, engineering effort to automate, running cost once live, and a payback period. Some rows say "buy this SaaS tool instead." Some say "do not automate this." That is the point.
What we look at
Process reality. How work actually flows, including the spreadsheet nobody mentions and the person who reconciles two systems by hand every Friday.
Data readiness. Whether the inputs are consistent enough to automate against, who owns them, how they are accessed, and what the DPDP Act and any sector regulation permit you to do with them.
Systems and integration surface. What has an API, what needs one, and what is a mainframe with a screen-scraping habit.
Team capability. Whether you can maintain what gets built, and what would have to be true for you to own it in twelve months.
Commercial case. Cost of the status quo, cost of the change, and the realistic window before the return shows up in a report someone reads.
The uncomfortable findings
Roughly a third of our audits conclude that the highest-return action is not an AI project. It is fixing a broken handoff between two teams, replacing a manual export with an integration, or buying a product that already does the job.
We include those, prominently. An audit that only ever recommends work we happen to sell is a sales document wearing an audit's clothes, and it costs you far more than the fee.
How it runs
What the engagement looks like
Phases, not a proposal. Each one has an output you can see.
- 1
Interviews before inventories
Days 1-4We talk to the people doing the work and the people paying for it. The gap between those two accounts is usually where the opportunity is.
- 2
Trace the data
Days 4-7Where does it live, who owns it, is it clean enough, and are we allowed to send it anywhere. Plenty of promising ideas die here, cheaply, which is the point.
- 3
Model the economics
Days 7-10Time saved, error rate today, inference cost, engineering effort, and a payback period. Numbers you can defend in a budget conversation.
- 4
Rank and sequence
Days 10-12Ordered by return per unit of effort, adjusted for risk and for what unblocks what. Some things are worth doing second because they make the third thing cheap.
- 5
Read out and hand over
Days 13-14A working session with your leadership, then the full artefact set. Yours to execute with us, with someone else, or internally.
FAQ
Questions we get asked
Talk to someone who does ai strategy & readiness
Thirty minutes with an engineer who has delivered this, not an account manager. You will get a straight answer on feasibility, rough cost and where it would fail.
Or email [email protected] · we reply within 1 business day