AI Capability Assessment: where AI really pays off.
Concrete answers, not generic advice: which AI use cases to build, for how much and what they bring — done by people who deliver AI into production.
Most companies already run AI pilots. Few know which ones matter.
The same functionality shows up as separate use cases in every department, each built on its own. Prototypes stay prototypes. Decisions rest on estimates. The assessment turns that into a scored portfolio and a clear path to production.
Done by people who deliver AI
DNAi is a hands-on AI company: eight years with industry leaders, from research to production. We bring what works — and what does not.
Concrete answers
Real analysis of your use cases: what to build, for how much and what it brings — impact quantified in cost and FTE.
A method you keep
Tuned for your domain, measurable and yours to reuse across the company after we leave.
Every use case scored twice — by AI and by people.
A decision, backed by evidence.
Scaled to you: a few weeks for a smaller company or one team, up to three to four months for a large division like a European car maker’s Sales division. Always two phases — a broad analysis with workshops, then deep dives on the use cases with the highest value — with a decision after each step.
- AI strategic direction: where AI moves your KPIs, based on evidence
- A broad map of use cases across the whole value chain, beyond the obvious ideas and isolated pilots
- A scored portfolio: effort vs. value, impact in cost and FTE, ranked by feasibility
- Deep dives with working PoCs on your own data, demonstrated live
- Platform and data recommendation: which shared AI platforms and data sources to build first
- The next six months: first pilots with a go/no-go at every step
AI Capability Assessment in Sales.
An extensive assessment that helped a European automotive manufacturer identify and prioritise its key AI projects in Sales: 130+ use cases from about 50 workshops, scored and validated with the business, three deep dives with working prototypes — and a recommendation to build a few shared AI platforms instead of many isolated ones.
We can also build what we recommend.
Deep dive → PoC → MVP → production. The platforms that usually come out on top — demand forecasting (MyIO), manufacturing cost (COGS), AI modernization of legacy systems, computer vision and knowledge assistants — are the ones we already run in production.
Frequently asked questions
What is an AI Capability Assessment?
A structured analysis that maps AI use cases across a division’s value chain, scores each by AI and by people on six axes, places them in a value × feasibility portfolio and tests the best ones in deep dives with working prototypes.
How long does it take?
It scales with the scope: a few weeks for a smaller company or a single team, up to three to four months for a large division such as a European car maker’s Sales division. Always in two phases: a broad analysis with workshops, then deep dives on the highest-value use cases.
Who has DNAi done it for?
a European automotive manufacturer, in Sales: 130+ use cases from about 50 workshops, with roughly 80% of the value concentrated in four shared AI platforms.
What happens after the assessment?
The value stars go through deep dive, proof of concept, MVP and production. DNAi can build them, or your teams can reuse the method on their own.