---
title: AI Strategy & Opportunity Assessment
source: https://rubradigital.com/services/ai-strategy
site: Rubra Digital
description: 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.
updated: 2026-05-30
duration: 3–5 weeks
starting_price: from €22,000
team: One principal consultant plus an AI architect
---

# AI Strategy & Opportunity Assessment

**Summary:** 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.

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.


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Source: https://rubradigital.com/services/ai-strategy
Rubra Digital. Independent LLM and RAG consulting for regulated and document-heavy organisations in Europe and North America.
Contact: hello@rubradigital.com