AI Strategy, Value & Governance
Your AI cost is real. Is the return?
Independent AI advisory for measurable ROI impact.
Most companies already have AI running somewhere. Few can say what it returned. ArcheNex AI measures what’s working, kills what isn’t, and gives you a number you can stand behind.
The problem
You bought the AI. Can you defend the spend?
Most organizations have AI running somewhere. Few can answer these questions leadership will eventually ask:
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What did we pay for twice?
Duplicate tools, redundant builds, no single view of what’s already in the house.
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Is the AI winning trust or burning it?
An assistant that answers but doesn’t resolve sends the customer back — and costs goodwill.
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What moved on the P&L?
Seats and usage are easy to report. Impact isn’t.
MIT put the number at 95% of enterprise pilots with no measurable P&L impact. We’re the 5% — proving the dollars saved on every token spent.
What we do
Diagnose first. Build only if it’s worth it.
Three stages, in order. Each one earns the next.
Select a stage to see what it involves.
Deepdive
You can’t fix what you never measured.
We map every AI initiative already running — the seats, the tokens, the pilots, the shadow tools nobody logged. Then we baseline what each one actually costs and actually does.
You walk away with:
- A full list of AI-native products in the house — official and unofficial
- A real cost picture: per task, per outcome, not just per license
- The baseline nobody captured the first time
Decide
The hardest part isn’t building AI. It’s knowing what not to build.
We turn the findings into a ranked, board-ready call — scale it, fix it, or kill it. Every initiative scored on value, feasibility and risk, with a dollar figure attached, not a gut feel.
You walk away with:
- A prioritized AI portfolio — funded, paused or killed, with the reasoning
- A business case per initiative, tied to cost, revenue or risk
- The number your CFO can defend in a board review
Deploy
A decision on a slide changes nothing.
We stand up the initiatives that survived the cut — with owners named, success defined up front, and measurement wired in from day one. So the next review has answers, not excuses.
You walk away with:
- Live initiatives with a named owner and agreed success metric
- Governance and tracking built in, not bolted on later
- Proof that graduates from pilot to P&L
The firm
Consulting judgment, with technical depth behind it.
A senior team by design: a management consultant leading every engagement, backed by specialists in AI engineering, machine learning and product analytics. The person you meet is the person who does the work.
Snehasis Dutta
Founder & Principal Consultant
A management consultant working on AI — framing the problem, sizing the opportunity, building the business case, and staying in the room through implementation. He leads every engagement personally.
Behind him sits the technical bench. Doctorate-level machine learning experience from semiconductor and chip-design firms, practicing data science from a global measurement company, and product analytics from engineers who have shipped and scaled software. Between them they cover the ground a value question actually touches: what the technology can do, what it costs to run, and whether the number at the end holds up.
That mix is deliberate. Advice on AI value is only as good as the technical judgment underneath it, and a consultant working alone tends to accept a vendor's account of what is possible.
Questions
The things clients ask first.
We already have consultants and an internal AI team. Where do you fit?
Alongside them, not instead of them. Teams cannot mark their own homework on ROI, and neither can the partner who built the thing. We give an independent second read your existing people can argue with on the merits — and often use to make their own case internally.
You are a new firm. Why should we trust the output?
Because the entry engagement is built so you do not have to take it on faith. It is small and fixed in scope, and you can judge the framework and the quality of the findings before any larger commitment exists. We would rather earn the next engagement than argue for it.
What does an engagement cost?
Entry diagnostics are fixed-fee and deliberately sized to be approved without a committee. Larger work is scoped to the problem. We price after the problem is agreed and sized together, and if budget is tight we reduce what is delivered rather than quietly reduce quality.
What happens to our data?
It stays yours. Access is scoped to what the engagement requires, retention is agreed in writing before we start, and nothing you share is used to train anything or reused for another client. We work inside your environment wherever your policies require it.
What if the answer is that we should not be spending on AI?
Then that is the finding, with the reasoning attached. A fixed process, a cleaner report, or a decision someone simply needs to own often beats a model at a fraction of the cost. Saying so is the whole point of being independent.
Get in touch
Start with the number you cannot currently answer.
Tell us what you are running today and roughly what it costs. We read everything that arrives.