Everyone wants AI. Almost nobody knows where to start.
The board read an article. The CEO saw a demo. A vendor pitched a platform that "transforms everything." And now someone, usually the CTO, has been handed the assignment: "Build us an AI strategy." S&P Global reports that 42% of companies abandoned their AI initiatives in 2025. Only 1% scaled beyond pilots. The gap between excitement and execution is enormous, and it's littered with six-figure pilots that produced nothing.
The failure pattern is predictable. Companies start with the technology ("Let's use GPT!") instead of the problem ("What decision takes our team 40 hours a week and could be 80% automated?"). They pick use cases based on what sounds impressive in a board meeting, not what's actually feasible given their data, systems, and organizational readiness. The pilot works in a demo. It collapses when it meets production data, edge cases, and users who don't behave the way the training set assumed.
The AI Opportunity Assessment flips the approach. We start with your actual operations, your actual data, and your actual problems. Then we identify the 3-5 places where AI can deliver measurable value, score each one for feasibility and ROI, and hand you a roadmap your engineering team can execute. Not "you should explore AI." Instead: "Here are the three specific things worth building, in this order, and here's what it'll take."
Every use case gets scored on two axes. Feasibility and impact.
AI vendors sell possibilities. We score probabilities. Each candidate use case is evaluated across six feasibility criteria and four impact criteria, producing a composite score that separates the use cases worth building from the ones that sound exciting but would cost more than they return.
Quick Wins
High feasibility, high impact. Build these first. Data is available, complexity is manageable, and the business case is clear.
Strategic Bets
High impact but lower feasibility. Worth pursuing, but data or infrastructure needs work first. These become quick wins after prerequisite investments.
Exploratory
Easy to build but modest returns. Fine for internal learning and team skill-building. Don't bet the budget on them.
Avoid
Low feasibility, low impact. The use cases that sound impressive in a meeting but would cost more than they return. We'll tell you which ones these are.
Each use case gets plotted based on composite feasibility and impact scores. The quadrant placement determines sequencing: quick wins fund strategic bets, and strategic bets become quick wins once their prerequisites are addressed.
Two to three weeks. From "where should we use AI?" to "here's the plan."
Most assessments land at two and a half weeks. Smaller organizations with focused scope can finish in two; complex environments with many candidate use cases sometimes need three. The sequence stays the same.
Discovery: map the operations, the data, and the pain points
Your Conductor starts by understanding your business, not your technology. Six to eight stakeholder interviews across operations, leadership, and frontline teams surface the workflows, decisions, and bottlenecks where AI could change something meaningful. We're looking for the 40-hour-per-week manual process, the decision that depends on stale data, the classification task that three people do inconsistently.
In parallel, we run a lightweight data landscape review. Not a full Data Readiness Assessment (that's a separate engagement), but enough to understand what data exists, where it lives, how accessible it is, and whether it's clean enough to train or feed an AI system. This shapes feasibility scoring: a brilliant use case with no usable data isn't a quick win, it's a strategic bet that needs prerequisites.
Scoring: evaluate every candidate against feasibility and impact
The longlist gets narrowed. Each candidate use case runs through our scoring framework: six feasibility criteria (data availability, data quality, technical complexity, integration effort, team readiness, and regulatory risk) and four impact criteria (revenue potential, cost reduction, time savings, and strategic alignment). This isn't a gut-feel ranking. Each criterion gets a 1-5 score with documented evidence and rationale.
Your Conductor also evaluates build complexity for the highest-scoring candidates. What model architecture fits? What infrastructure is needed? Where does human-in-the-loop need to be maintained? What are the governance implications? BCG's 10-20-70 rule holds here: algorithms are 10% of AI success, technology and data are 20%, and people and process are the other 70%. The scoring framework captures all three layers.
Roadmap: sequence the build, estimate the investment, and present the plan
The top 3-5 use cases get full treatment: recommended architecture, technology choices, team requirements, estimated cost range, timeline, and expected ROI. Your Conductor sequences them by dependency and risk. Quick wins go first because early successes build organizational confidence and fund the bigger bets. Strategic bets get prerequisite plans: "This use case scores high on impact but needs these three data quality improvements before it's feasible."
The final presentation walks your leadership team through the complete picture. Not a generic "AI can help with customer service, sales, and operations" deck. A specific, scored, sequenced plan built from your actual data, your actual workflows, and your actual constraints. If the honest answer is "you're not ready for AI yet, and here's what to fix first," we'll say so. That's a more valuable deliverable than a false start.
Five deliverables. Each one designed to drive a decision, not collect dust.
Your CTO presents the scored matrix to the board. Your engineering team picks up the roadmap and starts planning sprints. Your CFO evaluates the ROI projections against budget. Everything connects.
Scored use case matrix
Every candidate use case plotted on the feasibility/impact grid with documented scores across all ten criteria. The visual your board needs to understand where AI fits and why some ideas scored low, even the ones the CEO was excited about.
ROI projections
Per-use-case business case with estimated cost, timeline, and expected return. Conservative numbers your CFO can stress-test, not vendor math designed to make everything look like a 10x return.
Data readiness gap analysis
For each recommended use case: what data exists, what's missing, what quality issues need fixing, and what infrastructure prerequisites need to be in place. The bridge between "we want AI" and "here's what has to happen first."
Architecture recommendations
For the top 3-5 use cases: recommended model families, infrastructure requirements, integration patterns, human-in-the-loop design, and governance considerations. Written by an engineer who'll have to build it if you proceed.
Prioritized implementation roadmap
The primary deliverable. A sequenced plan: which use cases to build first, which prerequisites need addressing, and estimated timeline and investment per phase. Scoped at a level where your engineering team can start planning, not at the "consider adopting AI" altitude.
Fixed scope. Clear price. A roadmap before the month is out.
Depends on organizational complexity and number of candidate use cases. Most assessments land around $35K.
Two and a half weeks is the sweet spot. Two for focused environments; three when candidate use cases span multiple business units.
A senior engineer-architect who's built production AI systems. Not a strategist who'll hand you a deck and disappear. Someone who evaluates feasibility because they've shipped the kind of system they're recommending.
The assessment is the on-ramp, not the destination.
The roadmap feeds directly into an Applied AI & Intelligent Systems engagement. The same Conductor who identified and scored your use cases leads the build. Every feasibility evaluation, every architecture recommendation, and every stakeholder insight carries forward. No re-explaining your business to a new team.
If the assessment reveals that data infrastructure needs work before AI can stick, it connects naturally to a Data Engineering & Platform Modernization engagement or the deeper Data & AI Readiness Assessment. The path forward is always clear.
Just need the roadmap?
That's fine. Some clients use the scored matrix to build internal consensus. Others take it to the board to secure an AI budget. PE operating partners use it to prioritize across portfolio companies. The deliverables stand on their own regardless of what comes next.
Four situations where this assessment changes the trajectory.
CTOs who've been handed "build us an AI strategy"
The board wants AI. Your CEO read an article. You know it's more complicated than the C-suite thinks, and you need a credible plan built on evidence rather than enthusiasm. The assessment gives you a scored, defensible roadmap to present at the next board meeting, with honest feasibility analysis that protects you from committing to something that won't work.
Companies that tried AI and got burned
The pilot looked great in the demo. Then it met real data, real edge cases, and real users. You spent six figures and have nothing in production. You're not anti-AI; you're anti-waste. This assessment starts by asking why the last attempt failed, then identifies use cases with a genuine path to production. Skepticism is the right starting posture. We share it.
PE portfolio companies with AI in the thesis
The investment thesis includes AI-driven value creation. The operating partner needs measurable results for the next board meeting, not another strategy deck. The scored use case matrix gives you a clear picture of where AI creates value in the portfolio company, with ROI projections tied to business outcomes the board cares about.
Organizations with clean data wondering what's next
You've done the hard work: modern data platform, reliable pipelines, and solid governance. Now what? The assessment identifies the highest-value AI applications sitting on top of that foundation. You're the ideal starting point. The high failure rate everyone cites doesn't apply to you because you've already solved the prerequisite problem.
Great fit
- CTOs tasked with building an AI strategy who need a credible, defensible plan
- Companies that tried AI, got burned, and want a realistic path to production
- PE portfolio companies with AI-driven value creation in the investment thesis
- Organizations with clean data wondering which AI use cases are worth pursuing
Not the right fit
- Companies whose data infrastructure is fundamentally broken (do Data Readiness first)
- Teams that want a chatbot demo to impress the board next week
- Organizations that are not willing to hear 'you are not ready for AI yet'
- Projects with budgets under $25K for the assessment