AI Feasibility Study: Know If It Works Before You Commit Budget
A two-to-four-week technical and economic audit of your proposed AI initiative, ending in a defensible Go or No-Go verdict with the evidence to back it.
Most AI initiatives fail for reasons that were visible before kickoff. The data could not support the use case, the token economics collapsed at production volume, or the problem never needed AI in the first place. Our expert team audits all three before you sign a build contract, and we are comfortable telling you the answer is no.
Most AI Initiatives Fail for Reasons Visible Before Kickoff
MIT's Project NANDA found that 95% of enterprise generative AI pilots deliver no measurable return, and S&P Global reports the average organization abandons 46% of its AI proofs of concept before production. The causes are almost always knowable in advance.
The Data Was Never There
Teams commit budget before confirming their data can support the use case. We audit volume, cleanliness, access rights, and labeling against what the initiative actually requires, then price the gaps.
The Economics Collapse at Scale
A pilot that costs four cents per request feels free at 50 requests a day. At 50K a day it becomes a $700K annual line item that nobody ever modeled. We cost production volume, not demo volume.
The Problem Never Needed AI
Some initiatives turn out to be a rules engine or a $50-per-month SaaS wearing an AI costume. A real feasibility study says so plainly and tells you which cheaper tool to buy instead of building.
Five Questions Every Feasibility Study Answers
Every study audits the same five dimensions, because these are the five places AI initiatives actually die.
Data Readiness & Accessibility
We audit volume, quality, schemas, labeling, access controls, and pipeline gaps against what the use case actually requires, then score what is missing and what it costs to fix.
Technical Feasibility
We test whether current models can hit your reliability bar on your data: accuracy ceilings, latency limits, hallucination exposure, and failure rates under real inputs rather than curated demos.
Token & Compute Economics
We model inference cost at production volume: cost per action, human-review overhead, margin impact, and the break-even threshold that decides whether the initiative ever pays back.
Failure Modes & Containment
We map what happens when the system is wrong: blast radius, rollback paths, human checkpoints, and the audit trail your compliance and security teams will ask for.
Build vs Buy vs Off-the-Shelf
We check whether a SaaS tool, a fine-tuned model, or a custom agent is the right answer. MIT's research found purchased tools succeed roughly twice as often as in-house builds. Sometimes the verdict is do not build.
One Document. One Verdict. Evidence for Both Audiences.
The study ends in a single 25-to-35-page report written for two audiences: executives who need a decision and engineers who may have to build it.
What's inside the report
- Executive brief with the verdict, conditions, and confidence level
- Data readiness scorecard with a priced gap-remediation list
- Technical verdict: reliability ceilings and the architecture sketch for viable paths
- Three-year TCO and ROI projection modeled at production volume
- Risk register with containment, rollback, and audit-trail design
- If the answer is Go: recommended tools and frameworks, plus a sprint-ready Phase 1 plan
Three ways a study ends
The evidence supports the build. You get the architecture sketch, the TCO model, and a Phase 1 plan your sprint board can absorb immediately.
Viable once specific gaps close, usually data remediation or a narrowed first phase. The report prices the remediation so the condition is a budget line, not a guess.
The data, economics, or use case do not hold up. You get the documented reasons and the cheaper alternative. A No-Go that prevents a failed six-figure build is the cheapest outcome we sell.
Two to Four Weeks, Four to Six Hours of Your Team's Time*
Calendar-light by design. We take on the inspection, modeling, and analysis. Your team answers questions, not homework assignments.
Kickoff & Scoping
One working session to pin down the initiative, the success bar, and the decision the report has to support. We leave with the access list and documentation pointers we need.
Data & Workflow Audit
We inspect the data sources, APIs, and workflows behind the initiative, interview the two or three people closest to the process, and test candidate models against real samples.
Modeling, Verdict & Readout
We build the TCO and ROI model, complete the risk register, and finalize the verdict. A readout walks your stakeholders through the evidence and answers challenges live.
*Timeline and effort depend on the scope of work.
Match the Tool to the Question
A feasibility study answers whether one specific AI initiative can succeed. If that is not your question, one of these fits better.
Frequently Asked Questions
What does an AI feasibility study include?
The study audits five dimensions: data readiness and accessibility, technical feasibility against real samples, token and compute economics at production volume, failure modes and containment design, and build-versus-buy options. The deliverable is a 25-to-35-page report containing an executive brief with the verdict, a data readiness scorecard, a technical verdict, a three-year TCO and ROI model, a risk register, and a Phase 1 roadmap if the answer is Go.
What is the difference between an AI feasibility study and an AI readiness assessment?
A feasibility study asks whether a specific initiative can succeed technically and economically. A readiness assessment asks whether your organization's data, infrastructure, and team can support it. Our study covers both for the initiative in scope, so you get one verdict that accounts for the idea and the environment it has to survive in.
How much does an AI feasibility study cost?
Studies start from $2K. The final fee depends on the scope of work, mainly the number of data sources, workflows, and integrations under review. Once scoped, the fee is fixed, confirmed before kickoff, and does not change mid-engagement.
How much of our team's time does a feasibility study take?
Four to six hours total across two to four weeks: a 90-minute kickoff, two or three short interviews during the audit phase, and a readout where we walk your stakeholders through the verdict and the evidence behind it.
What happens if the verdict is No-Go?
You keep the report. It documents exactly why the initiative does not hold up, what would have to change for it to become viable, and the cheaper alternatives worth considering, such as an off-the-shelf tool, a rules engine, or a narrower scope. A documented No-Go that prevents a failed six-figure build is a successful engagement.
What happens after a Go verdict?
The report includes a sprint-ready Phase 1 plan, a recommended stack of tools and frameworks, and a three-year cost model, so your internal team can execute it or our engineers can build the first phase. If the open question is which of several initiatives deserves budget first, that is the job of an AI strategy consulting engagement.
Get a Verdict Before You Commit Budget
Schedule a 30-minute working session with our expert team. We will review the initiative you are considering, pressure-test the data behind it, and tell you honestly whether a study is the right next step.