VP / Head of AI · AI Center of Excellence Director
Find the AI opportunities nobody submitted — and know which one to fund first.
You don't have a shortage of AI ideas. You have no view of the ones nobody wrote down, and no way to sequence the ones you already know about. IntakeOS runs structured discovery inside every business unit, scores what it finds against the same eight deterministic rules, and returns a single ordered answer to what gets funded first, second, and not at all.
What IntakeOS does for you.
- 1Discovery interviews across every business unit — not just the teams that already shout the loudest
- 2A live, ranked backlog with ROI and payback calculated for every candidate — the answer to what gets funded first
- 3One 8-dimensional deterministic scoring model - the same evaluation, every time, fully auditable
- 4Export-ready specs the moment an opportunity clears the bar
“Our automation backlog had been measured in years. IntakeOS gave us 37 qualified opportunities in a single quarter.”
VP, Automation Center of Excellence
Have a question? Talk to our sales team before you decide.
Contact Our SalesWhat this role asks first.
Short answers to the questions VP / Head of AI teams raise before taking IntakeOS further.
How should an AI leader decide which use case to fund first?
Rank every candidate with one scoring model instead of comparing pitches. IntakeOS scores each opportunity on the same eight dimensions using deterministic rules, so the ordering reflects volume, effort, complexity, and value rather than which team argued hardest for its idea.
How is this different from running an AI use-case workshop?
A workshop captures the ideas of whoever attends; IntakeOS captures the process itself. VARA interviews the people who run each candidate process across every business unit, records volumes, handle times, and exception rates as structured data, and re-scores the backlog as new intakes complete instead of freezing it in a deck.
Can IntakeOS tell a generative AI candidate from an RPA candidate?
Yes - pattern selection is part of qualification. Scoring evaluates the process against seven automation patterns, including generative AI, machine learning, RPA, and document processing, and returns the recommended pattern with ranked alternatives and the reasoning behind each.
What can an AI leader take to the board?
A portfolio view rather than a list of pilots: every qualified opportunity with its score, recommended pattern, ROI model, payback period, and the rules that produced the ranking - so the funding case can be defended line by line.