AI Opportunity Diagnostic
Before you commit to AI, set yourself up for success.
A focused 1–2 week engagement that identifies the AI opportunities in your business worth pursuing — ranked by value, feasibility, and strategic fit. Built for leadership teams that want a practical roadmap, not a pile of pilots.
80%
of enterprise AI projects fail to reach production
The 20% that succeed share a pattern.
Which AI projects succeed isn’t random — it’s predictable. The right opportunity, the right scope, the right data, the right integration path. The diagnostic exists to put your project in the 20%, not the 80%.
Sources: RAND (2024), Gartner research on enterprise AI deployments.
The pattern
What separates the 20% that thrive?
Each one sits at the intersection of business judgment and technical depth. Get either side wrong and the project joins the 80%.
A real business problem.
The strongest predictor of success: starting from a workflow that actually costs the business, not from a desire to “do something with AI.” Most failures are AI-in-search-of-a-problem.
Business judgment
Identifying the workflows where AI will move a real metric, not just demo well.
Engineering depth
Pressure-testing whether AI can credibly solve the problem, or if a simpler tool would do.
Leveraging real business data.
AI doesn’t run on theoretical data. It runs on what your business actually produces — the records, documents, conversations, and decisions that flow through your operations. Most pilots stall when the data turns out to be fragmented, locked up, or not what was assumed.
Business judgment
Understanding what data the business actually produces, who controls it, and what it’s worth.
Engineering depth
Assessing quality, structure, access, and governance — and the work needed to make it AI-ready.
Connected to your team and workflow.
AI that doesn’t fit how people actually do their jobs gets quietly ignored. The 20% build AI into the workflow — and set up the team, the integrations, and the operating model to keep the system running long after launch.
Business judgment
Understanding the workflow in use and naming the team that owns the system after launch.
Engineering depth
Building the integrations and operational surfaces that put AI inside the work, and the training that lets your team run it.
Why East Rock
This is what we do.
Each of the three above — the real problem, the real data, the connection to your team and workflow — fails without both halves: engineering depth and business judgment. Large firms can frame the strategy but can’t tell you what’s actually buildable. Dev shops can build but don’t connect to where AI moves the business. East Rock runs the diagnostic with senior engineers who do both — and who stay through implementation if you want them to.
Business judgment AND technical execution
The people identifying the opportunity are the people who'd build it. Every recommendation reflects what's actually buildable and what actually moves the business — not what sounds plausible in a workshop.
Senior engineering, not junior consultants
Every diagnostic is led by an East Rock principal — PhD-level engineers who've shipped production AI systems. Not a junior associate working off a template, not a contractor without business context.
No lock-in
The roadmap is yours. Take it to your internal team, your existing vendors, or use it as the starting point for an implementation engagement with us. We're trying to be useful, not to trap you.
Most companies know they should be exploring AI. The hard part is deciding where it’s actually worth applying, what’s technically feasible, and which projects are likely to produce measurable ROI.
The real risk isn’t being slow to adopt AI. It’s spending time and budget on pilots that look impressive but never become useful operational systems. The AI Opportunity Diagnostic is built to surface that distinction early, before anyone commits to a larger implementation.
The deliverable
From AI question to AI plan, in three moves.
Not a strategy deck. A tailored plan you can act on — built from three concrete things we do during the engagement.
AI Opportunity Roadmap
Prepared for [Your Company]
Move 1
Map the opportunity.
Where AI fits in your business — the workflows, data, and decisions worth instrumenting, based on what we learn in the diagnostic.
Move 2
Pressure-test the options.
Seven concrete AI project ideas tailored to your business — each evaluated for business impact, feasibility, data and integration needs, and implementation path.
Move 3
Pick what to do first.
A clear recommendation — the one or two projects most worth pursuing now, with the artifacts to act on them.
The roadmap is yours — take it to your internal team, your existing vendors, or East Rock if you want help implementing it.
Why now
The cost of waiting compounds.
A few situations where running the diagnostic now beats running it later. If any of these describe where you are, every one gets harder the longer it sits.
Pressure from above
The board is asking.
Your sponsor, board, or leadership team wants an AI plan — and the answer needs to be more than “we're exploring it.”
Stalled momentum
Pilots that didn't ship.
Previous AI work has stalled — vendor demos, internal experiments, off-the-shelf tools. The next attempt has to land differently.
Budget timing
Annual planning is now.
AI is in this year's budget conversation. You need a defensible point of view before the commitments get made — not after.
Visible leverage
You can see where it should go.
A workflow, a data source, or a customer experience you know AI could change. Turning that intuition into a real build path is the missing step.