AI Strategy & Opportunity Assessment
A short, evidence-led engagement that tells you which AI use cases are worth building, which are not, what each will cost, and in what order to do them.
- Typical duration
- 3–5 weeks
- Investment
- from €22,000
- Team
- One principal consultant plus an AI architect
Short answer
Rubra runs three to five week AI discovery engagements that produce a ranked portfolio of use cases with feasibility assessments, cost and effort estimates, data readiness findings, and a sequenced roadmap. The most valuable output is usually the list of things I recommend you do not build.
Last reviewed
Most organisations do not have an AI capability problem. They have a prioritisation problem: twenty ideas, no shared basis for comparing them, and a budget that will fund three.
Discovery is a short engagement that replaces opinion with evidence.
How it runs
Week one. Inventory. Structured interviews across the functions that would use or be affected by these systems. I am looking for tasks that are repetitive, text-heavy, currently slow, and where the information needed already exists somewhere in writing. I am also listening for the constraints people mention in passing, which are usually the ones that matter.
Week two. Feasibility probes. For the strongest candidates I test the riskiest assumption directly against your data. Not a build: a probe. Can I actually extract this field from these documents? Does retrieval find the right passage for these fifty real questions? Is this dataset as complete as everyone believes? Two days of this routinely changes the ranking.
Week three. Economics. Effort, cost, running cost, time to value, and the organisational change required. The last is the one most often underestimated, and it is why technically successful projects sometimes deliver nothing.
Weeks four and five. Roadmap and readout. A sequenced plan with decision points, a written recommendation for each candidate, and a session with your leadership team where I present the case against the weak ones as clearly as the case for the strong ones.
What makes this different from a strategy deck
The people running discovery are the people who would build the system. That changes the estimates: an architect who has shipped six retrieval systems knows what the scanned-PDF corpus is going to cost, and will say so in week two rather than discovering it in month four.
It also means the recommendation is accountable. I am proposing work I would have to deliver.
The most valuable output
Usually the no list. Nearly every discovery I run identifies at least one use case with visible executive enthusiasm that will not work, because the data does not exist, the accuracy bar cannot be met, or the process around it would have to change in ways nobody has agreed to.
Finding that in week three costs a fraction of finding it in month nine.
What you get
- Use case inventory from structured stakeholder interviews
- Feasibility assessment against your real data, not a hypothetical
- Data readiness findings per use case
- Cost, effort and time-to-value estimates
- Build/buy/wait recommendation for each candidate
- Sequenced 12-month roadmap with decision points
Outcomes
- A ranked shortlist with honest feasibility and cost attached
- A clear no on the use cases that will not work, with reasons
- A sequenced roadmap your board can actually approve
Frequently asked questions
What does an AI discovery engagement produce?
A ranked portfolio of use cases, each with a feasibility assessment based on your actual data, an effort and cost estimate, and a build, buy or wait recommendation, plus a sequenced roadmap. It also produces an explicit list of use cases I recommend against, with the reasoning, which is usually the part that saves the most money.
How do you assess feasibility without building anything?
I test the assumptions that would sink the project, on real data, in days rather than weeks. Does the information needed to answer these questions actually exist in the documents? Can I retrieve it reliably on a sample? Is the source data clean enough, or is there a data engineering project hiding underneath? These probes are quick and they resolve most of the uncertainty that otherwise gets discovered three months into a build.
Will you recommend we build nothing?
Sometimes, and I have. If your data is not ready, or the use cases on the table are better solved by a search upgrade or a process change, saying so is the useful outcome. Delivering an uncomfortable finding in week four beats a failed build in month nine, and clients who get that answer tend to come back when the situation changes.
Can discovery run alongside an existing project?
Yes, and it often should. A common pattern is that a team has one pilot in flight and no view of the broader portfolio. Discovery runs in parallel, places that pilot in context, and frequently reveals that a different use case has better economics and should have gone first.
Get a straight answer on your AI roadmap
A 30-minute call with the engineer who would do the work, not a salesperson. You will get an honest read on what is worth building, what is not, and roughly what it costs.
No NDA needed to talk. EU and UK hours in full, with afternoons overlapping US Eastern and Central.