What Does a Data and AI Strategy Do?
What Does a Data and AI Strategy Do?
Data and AI strategy has become a regular item on the agendas of senior leadership teams — and for good reason.
But despite the attention, it often remains vague: What exactly is it? Why is it important? And what makes it strategic?
Recently, I had the opportunity to visit three excellent books:
- Data Strategy – Bernard Marr
- AI Strategy – Bernard Marr
- The Business Case for AI – Kavita Ganesan
These readings deeply resonated with my own real-world experiences and helped clarify what a Data and AI Strategy should truly entail. Most importantly, they reinforced a powerful insight:
Strategy is not just about alignment or high-level goals — it’s about making deliberate choices that position a firm uniquely in the market.
What Is a Data and AI Strategy?
At its core, a Data and AI Strategy answers two key questions:
- Why data and AI are crucial to achieving your business ambitions
- What specific actions, use cases, and enablers will set you apart
More than a checklist or alignment exercise, a strategy is a choice — about where to compete, how to win, and how to build advantage through data and AI.
Example: Turning Strategic Goals into Competitive Differentiation
Imagine a company whose business strategy aims to stand out from its peers by focusing on:
- Achieving net-zero emissions, and
- Delivering a best-in-class customer experience
A high-impact Data and AI Strategy shouldn’t just support these goals — it should fundamentally shape how the company achieves them in ways that competitors can’t easily replicate.
The Why: Why Data & AI Enable True Differentiation
The commpany may analyze and come to the conclusion that Data and AI create new possibilities:
- Enable real-time tracking and predictive optimization of carbon emissions — going beyond after-the-fact reporting.
- Power hyper-personalized customer experiences, anticipating needs and proactively delivering value.
But to truly unlock differentiation, a company must go further and ask themselves:
- Does this create a unique advantage over competitors?
- Does it reshape the relationship with customers — from reactive to anticipatory?
- Does it elevate the company’s role in the partner ecosystem or open new collaborative opportunities?
- Does it reduce the threat of substitution by increasing customer dependency or stickiness or simply better offering than any others in the market?
- Does it raise barriers to entry by building capabilities or data assets that are difficult to copy?
These are the strategic angles where Data & AI create not just value — but defensible, long-term advantage.
The What: From Strategy to Execution
The company would then define a guiding policy — for example:
“Make sustainability and customer intimacy data-driven by default.”
And support it with a coherent set of actions, such as:
- Build proprietary AI models to forecast and optimize emissions, giving the company a unique carbon intelligence edge.
- Deploy a real-time personalization engine to tailor content, offers, and interactions across every customer channel.
- Embed AI practitioners within business teams, enabling rapid experimentation, iteration, and innovation at the edge.
- Establish strong data governance, focused not just on compliance but on creating trusted, reusable, and shareable data products.
- Modernize the data platform to enable fast, scalable deployment of new use cases — while competitors remain bogged down in legacy systems.
👉 This is what real differentiation looks like:
Not just doing the same things more efficiently — but doing entirely different, smarter things, faster, deeper, and at scale.
How Is Data and AI Strategy Evolving?
Traditionally, organizations approached strategy by asking:
“How can data and AI help us achieve our business goals?”
But in today’s AI-first world, the question must evolve into:
“What new goals, business models, or opportunities can data and AI make possible for us?”
The best firms now build business strategy and Data/AI strategy together. That’s because:
- Data and AI aren’t just enablers — they’re becoming core to value creation
- Competitive advantage is increasingly defined by how effectively organizations apply intelligence at scale
What’s the Difference Between Strategy and an Implementation Plan?
It’s common to confuse the two — but they’re not the same.
- Strategy = The Why and What
- Implementation Plan = The How, When, Where, and Who
| Strategy | Implementation Plan |
|---|---|
| Explains purpose and positioning | Details execution and delivery |
| Prioritizes where to play and how to win | Breaks down timeline, teams, budget |
| Defines enablers (data, tech, skills, org) | Defines scope, sprints, and responsibilities |
Think of it this way:
Strategy sets the direction. Implementation maps the path.
🔑 20 Practical Insights for a Strong Data & AI Strategy
1. No Data? Start Rule-Based.
Use rule-based logic to begin automating and collecting useful data before jumping into ML.
2. Use B-CIDS to Assess Readiness.
Evaluate Business, Culture, Infrastructure, Data, and Skills as your AI foundation.
3. Top-Down or Bottom-Up — Both Work.
Start with a clear business goal or discover pain points in existing processes.
4. Hiring Alone Won’t Get You There.
Transformation requires structure, operating models, and leadership — not just a few data scientists.
5. Iterate Your Strategy.
Avoid big-bang efforts. Deliver in small, valuable steps to build momentum.
6. Use the Right Tools.
Don’t overuse ML. Sometimes simpler techniques (stats, heuristics) do the job just fine.
7. Understand ML Types.
- Analytical ML = insights and discovery
- Operational ML = real-time, continuous decisions (e.g., personalization, fraud)
8. Play the Long Game.
AI transformation pays off gradually. Value compounds over time.
9. Invest in Data Quality Early.
You can’t outsmart bad data. AI is only as good as its input.
10. Don’t Bet Everything on One Use Case.
A single high-profile POC won’t transform your business. Build broader readiness.
11. Measure What Matters.
Track:
- Business value
- User adoption
- Technical performance
12. Clarify Strategy vs. Plan vs. TOM.
- Strategy = Why & What
- Plan = Where, When, How
- TOM (Target Operating Model) = Who
13. Start Small, Think Big.
Build MVPs that scale. Compound value through iteration.
14. Use Bernard Marr’s Framework:
For each top use case, define:
- Data needs
- Governance
- Tech foundation
- Skills & teams
- Change management
15. Move Toward an AI-Native Model.
Don’t just digitize old processes — reimagine them through an AI lens.
16. Stay Strategic.
Avoid “AI for AI’s sake.” Focus on areas where it moves the needle.
17. Leadership Must Lead.
AI literacy at the executive level is a must. Change starts at the top.
18. Pilot with Purpose.
Build MVPs that are scalable and budget for operational rollout — not just POCs.
19. Be Financially Iterative.
Don’t wait for perfect ROI models. Deliver early wins, then reinvest.
20. Actively Manage AI Risk.
Cover all angles — from security and compliance to fairness and explainability.
Final Thought
A good Data and AI Strategy doesn’t just support the business —
It redefines what’s possible, and charts a bold, differentiated path to get there.
In the age of AI, strategy isn’t about following — it’s about leading through intelligence.