but it's unclear which use cases pay off, and where the first pilot should happen. Before money flows into technology, prioritization pays off.
Strategy & orientation
Develop an AI strategy: prioritize before you start your first project
We help you identify the right use cases, assess your data situation honestly, and develop a roadmap that is economically viable and can build internal consensus.
When is this a fit?
The three most common
The three most common
starting situations
AI strategy isn't the right investment for every phase. Here are the three constellations in which strategic orientation has the greatest leverage, and when we'd rather start differently.
and management or a board needs a solid basis with use cases, business case, risks and roadmap.
but an overarching strategy is missing: no unified target picture, no clear prioritization model, no assessment of the data foundation.
How we work
Step by step.
Step by step.
No surprises
Our approach is structured and transparent. From the first workshop day you know what happens in each phase and what comes out at the end.
1
Understand the context
We get to know your starting point: business model, data situation, ongoing initiatives and strategic goals. Without a sound understanding there is no sensible prioritization.
2
Gather use cases in a structured way
Workshop with management and business units: Which problems should be solved? Which decisions should get better? Which automations are conceivable?
3
Assess the data situation honestly
Which data exists, which is reliable, where are the gaps? This question determines which use cases are realistic today, and which prerequisites must be created first.
4
Prioritize use cases
Scoring by benefit, effort, data maturity and risk. The result is a prioritized use-case map, not a list based on gut feeling, but on criteria.
5
Develop the roadmap and business case
12-month roadmap with pilot, rollout and milestones. Business case with investment calculation, sensitivities and risks, investment-ready for management and board.
Format
Strategy Sprint
2 weeks. Field-proven.
We deliver AI strategy and data strategy in the format of the Strategy Sprint: 2 weeks, clearly structured, with a committed investment.
See the sprint in detail
Measurable impact
What a good strategy
What a good strategy
concretely changes
Strategy sounds abstract. Its impact is not. These three outcomes are typical for companies that chose a sound AI strategy as their first step.
Avoid misinvestments
Companies that prioritize strategically first avoid, on average, one or more failed pilots. A use-case map is cheaper than a failed project.
Faster to the first pilot
Instead of months of internal discussion: with clear prioritization the first productive pilot starts 4 to 8 weeks after the strategy phase is completed.
Build internal consensus
A traceable, criteria-based prioritization is the most important prerequisite for acceptance across business units, IT and management.
Clear scope
What we deliver
What we deliver
and what we don't
Being honest about the scope is part of our approach. Those who know what they get can decide better.
We deliver
- Prioritized use-case map with scoring criteria
- Honest data-situation assessment (what exists, what's missing)
- 12-month roadmap with pilot, rollout, milestones
- Investment-ready business case for management / board
- Clear recommendation: pilot now, or build the data foundation first
- 30 days of follow-up support for questions
Not part of the strategy phase
- Software implementation or tool configuration
- Data migration or ETL development
- AI model training or agent development
- Operation or support after project completion
- Procurement or licensing of AI platforms
Implementation, data foundation and AI agents are separate solution areas, we cover all three and connect them where it makes sense.
Technology selection
Sovereignty as a
Sovereignty as a
selection criterion
The selection is guided by data residency within the European Union, by sovereignty requirements and by the existing IT landscape. European providers and open standards take priority. International platforms are used exclusively in EU regions with documented data processing agreements.
From practice
Strategy in action:
Strategy in action:
an example
The Strategy Sprint as an entry point into AI strategy: how a food manufacturer condensed 12 use-case ideas into 2 prioritized pilots.
Case · Food production · 600 employees
12 use-case ideas became 2 prioritized pilots, and one pilot started 6 weeks after the sprint ended
Before the sprint there were many ideas and no decision. After the sprint: a clear use-case map, a data-situation assessment and a roadmap that management and IT could support together.
Read the full case
12 → 2
Use cases prioritized
6 wks
to pilot start
2 wks
Strategy Sprint duration
Frequently asked questions
What companies ask
What companies ask
before the first step
Specific to AI & data strategy. General questions about working with LYN on the homepage.
What is the difference between AI strategy and data strategy?
Data strategy clarifies how your data is organized, structured and made accessible. AI strategy clarifies which problems it should solve and how. In practice they are interdependent: without a reliable data foundation there is no productive AI, which is why we treat both together.
Do we need a strategy if we already have a pilot running?
Often yes. A running pilot does not answer the question of which use cases come next and on which data foundation they should build. A strategy phase helps embed the individual pilot into a scalable roadmap.
How concrete is the roadmap in the end?
Very concrete. The roadmap contains prioritized use cases with effort and benefit assessment, a 12-month timeline with phases and milestones, as well as a business case with investment calculation and sensitivities. No generic framework, but your company, your data, your situation.
What happens if it turns out that AI is not yet ready?
That's a valid result. A strategy phase that shows the data foundation must be created first is not a failure, but exactly the insight that prevents misinvestments. We then deliver a clear data-foundation roadmap as the next step.
Who needs to be involved on our side?
Ideal: management or CDO/CTO for the target picture and investment decision, IT for the data landscape and infrastructure, one or two business-unit representatives for use-case relevance. In total around 8 to 12 hours of time spread over 2 weeks.
All three solution areas
Strategy is the beginning.
Strategy is the beginning.
Not the end
AI strategy creates clarity. The data foundation creates the basis. AI agents deliver the productive value. We accompany all three phases, as a single engagement or as a continuous partnership.
AI & data strategy
Prioritize sensible use cases, assess the data situation, develop a reliable roadmap and business case.
Current pageReliable data foundation & BI
Consolidate data sources, build a single point of truth, modernize reporting: the basis on which AI can reliably build.
View solution →AI agents & delivery
Business-oriented AI solutions on your enterprise data, from pilot through rollout to productive use.
View solution →
Ready to prioritize
AI sensibly?
In a 30-minute discovery call we clarify whether a Strategy Sprint fits your situation, or whether a different entry point makes more sense. No pitch, no slides.