Feasibility per use case
What each one needs, what exists, and what closing the gap costs.
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AI readiness appraisal
AI readiness appraisal, including whether to wait.
Before committing budget to AI, it is worth knowing whether your data can support it. This engagement assesses the use cases you have in mind against the data you actually hold, and ranks them by feasibility and value.
- Use case triage
- Data feasibility
- Honest no
What you get
Find out before the budget is committed.
Ranked by commercial value
Grounded in current process cost, not technical interest.
Build, buy or wait
A clear recommendation per use case, with reasoning.
A starting point likely to work
Sequenced from the use case most likely to prove value.
The problem
AI projects fail on data, not models.
The failure mode is almost never the model. It is that the data the use case depends on is incomplete, inconsistently labeled, locked in a system with no API, or simply not collected. Teams discover this three months into a build, after the budget is committed and expectations are set. The second most common failure is choosing a use case that is technically achievable and commercially pointless, because it was picked from a vendor deck rather than from where the cost actually sits.
What the appraisal covers
What's included.
- A structured inventory of candidate use cases, including ones your team has not considered
- Data feasibility per use case: what it needs, what exists, and what the gap costs to close
- Value estimation grounded in current process cost, so the ranking is commercial rather than technical
- A build, buy or wait recommendation per use case, with reasoning
- Integration and access assessment, since many blockers are systems rather than data
- A sequenced plan starting with the use case most likely to succeed and prove value
How we work
How we run it.
Step 1
Workshop candidate use cases with the people who own the affected processes
Step 2
Profile the underlying data for completeness, consistency and accessibility
Step 3
Score each use case on feasibility and value, and be explicit about what fails
Step 4
Deliver the ranked plan with a recommended starting point and what to fix before the rest
What we use
Tools, platforms and methods.
- Data profiling
- Use case workshops
- Feasibility modeling
- Vendor landscape review
Related work
Projects we've delivered.
FAQ
Common questions.
Will you tell us not to do AI?
When that is the answer, yes, and it has happened. The most common version is not "never" but "not this use case yet": fix the data dependency first, or start with a narrower version that will actually work. An appraisal that approves everything is worthless as a decision input.
What data quality is realistically needed?
It depends entirely on the use case, which is the point of appraising rather than generalizing. A retrieval assistant over documentation tolerates messy data well. A forecasting model needs consistent history. An agent acting on budgets needs data that is correct and current, because a wrong action costs real money.
Do you consider off-the-shelf tools too?
Yes, and quite often they win. For plenty of use cases a mature product is cheaper and better than anything custom. We flag those explicitly so you do not commission a build that a license would solve.
How long does it take?
Typically two to three weeks: a workshop round, data profiling, then findings and a review session. Faster if the candidate use cases are already narrow and well defined.
Go deeper
Guides from our engineers.
Guide
AI Agents for E-commerce: From Support Chatbots to Autonomous Ad Optimization
AI agents in e-commerce go far beyond chatbots. Here's a practical look at where autonomous agents genuinely help and where human judgment still wins.
Read the guideGuide
RAG Explained: How Retrieval-Augmented Generation Improves Business AI Search
RAG is the technique that stops AI assistants from making things up about your business data. Here's a clear, non-hype explanation of how it works.
Read the guideRelated
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Tell us the use cases you are considering and we will assess whether your data can support them.