Find where AI pays off
Most companies have a list of AI ideas and no way to rank them. In 1‑2 weeks we map where AI pays back in your business, check whether your data can support it, and tell you what to build, buy, or skip.
The problem is rarely the model
of EU companies that considered AI but held back cited a lack of relevant expertise. Only 20.7% said AI was not useful to them.
Source: Eurostat, Use of AI in enterprises, 2025
What goes wrong
- Picking tools before defining the problem
- Starting with the hardest, most visible use case
- Discovering data gaps after the build has started
- No baseline, so nobody can say whether it worked
What we do instead
- Start from the workflows that cost you the most time or money
- Rank ideas by value, feasibility and risk
- Check the data before anyone writes code
- Agree success metrics and a baseline up front
Who it's for
SME leaders
You know AI matters but not where to start, and you cannot afford a wasted year.
Enterprise innovation teams
You have a long list of ideas and a backlog of stalled pilots, and need to know which to stop, fix or scale.
Founders
You need to decide where AI belongs in your product before you raise or rebuild.
How it works
Typical timeframe: 1‑2 weeks. Every engagement is scoped to your situation after the discovery call.
Workflow and opportunity interviews
We talk to the people who do the work, not just the people who sponsor it, and map where time, errors and cost pile up.
- Workflow map
- Long list of AI opportunities
- Current baseline metrics
Data and feasibility check
We look at the data each opportunity would need: where it lives, how clean it is, and what GDPR and the EU AI Act mean for using it.
- Data readiness score per use case
- Risk and compliance flags
- Technical constraints
Ranking and roadmap
We score every opportunity on value, effort and risk, then recommend what to build, what to buy off the shelf, and what to leave alone.
- Ranked opportunity matrix
- Build, buy, or skip recommendation
- 90-day roadmap with success metrics
You leave with
- A shortlist of 3-5 AI use cases ranked by value and feasibility
- A clear view of the data work each one needs
- Success metrics agreed before anything is built
- A recommendation for your first validation sprint
Typical opportunities we find
- Document and email triage in operations
- Support ticket drafting with human review
- Sales research and proposal preparation
- Internal knowledge search across tools
- Forecasting and anomaly detection
- AI features inside an existing product
As Head of Product at Stryber, a venture builder that launched 30+ ventures across the EU, MENA and APAC, our founder was product lead on six of them and made 1-day smoke tests the standard way to validate ideas. We bring the same discipline to deciding where AI belongs.
About UnivotCommon questions
We're a small company. Is AI relevant for us?
Often more than for large firms, because small teams feel the time savings directly. Most SME opportunities use existing tools and your current data. Part of our job is telling you when a simple automation beats AI.
What if you conclude we should not build anything?
Then we say so, and explain why. A clear "not yet" with the reasons is a useful outcome. It saves you from a project that would have stalled in the pilot stage.
Do you recommend a particular AI vendor?
No. We are model-neutral and do not resell any provider. We pick the model and tools that fit your problem, data and compliance needs, and design so you can switch later.
How does the EU AI Act affect our plans?
Most business uses, such as copilots, internal automation and chatbots, fall under transparency and AI literacy duties, not the high-risk rules. We flag any use case in high-risk areas like hiring, credit scoring, education, or risk assessment and pricing in life and health insurance, where obligations apply from 2 December 2027.
What happens after the strategy work?
Usually a validation sprint on the top-ranked use case, so you see it working on your data before committing to a full build. You are free to take the roadmap to your own team or another partner.
Related services
AI Validation Sprints
A working AI prototype on your real data in 2‑4 weeks, with evaluations, cost per task and a clear go or no‑go decision.
AI Product Development
AI-first MVPs and AI features in existing products, built in 6‑12 weeks with quality testing, interfaces users can trust, and cost control.
AI Agents & Automation
AI agents for well-defined workflows, running in your systems, with human escalation, audit trails and measured savings.
AI Governance & Enablement
Proportionate AI governance for the EU AI Act and GDPR, plus hands-on AI training for your team on the tools they actually use.
Know what to build before you build it
Book a 30-minute call. We will ask about your workflows, data and goals, and tell you honestly whether a strategy sprint makes sense.