Vincent Pirenne
AI is no longer just a tech initiative.
It’s becoming the lens through which strategy, operations, and growth are viewed. But while most companies are experimenting with AI, very few are doing it with the right strategic intent. And that often comes down to one thing – leaders aren’t asking the right questions.
The goal isn’t to chase hype or stack up pilots. The goal is to drive impact. And that starts by asking better questions.
“The goal isn’t to chase hype or stack up pilots. The goal is to drive impact. And that starts by asking better questions.”
⸻ Vincent Pirenne
Here are 10 questions we believe every executive team should be tackling right now.
These won’t solve everything, but they will help spark the conversations
that move you forward.
1. Are we building the AI operating model we’ll need in two years?
AI is moving fast, but your operating model probably isn’t. Many companies still manage AI like IT projects. But the next phase requires something else entirely.
If you’re still using legacy structures to drive next-gen
transformation, it’s time to redesign the system.
2. How do we prioritize AI use cases that scale across the organization?
Scaling AI isn’t about launching more pilots. It’s about identifying the few initiatives with real impact potential and doubling down.
Without clear priorities, AI becomes an expensive distraction. AI has the power to transform, but only when applied with laser focus to what matters most.
Johnson & Johnson recently narrowed down from 900+ AI ideas to a select few that were moving the needle.
“Now we’ve moved from the thousand flowers to a prioritized focus.”
⸻ J&J’s CIO for WSJ, 2025
3. Are we building differentiators, or just catching up?
Many companies use AI to reduce costs. Fewer use it to create something new.
The real strategic opportunity with AI lies in differentiation — rethinking how you deliver value, not just doing things faster or cheaper.
“If your AI roadmap is only about finding efficiency, you’re not playing to win, you’re just trying not to fall behind.”
⸻ Vincent Pirenne
We argue that AI adoption is unfolding in three waves:
Wave 1: Time, cost, efficiency
Wave 2: Quality, better output
Wave 3: New systems, transformation
4. What data foundation do we need to unlock AI’s potential?
AI success doesn’t start with a model — it starts with data. Clean, connected, labeled, and accessible. If your data is siloed, incomplete, or buried in legacy systems, even the best models won’t help.
“Most companies overestimate their AI readiness and underestimate their data problems.”
⸻ Vincent Pirenne
5. How do we prevent siloed AI experiments?
It’s easy to let teams launch experiments in isolation. But without
shared learning, central oversight, and a clear plan for scaling, these
initiatives rarely deliver value. Create a system to track, review, and sunset
pilots that don’t scale.
6. What’s our plan for developing internal AI talent?
If your AI expertise lives mostly in vendor contracts, it’s worth asking
whether you’re building enough capability internally. External partners can
play a key role in accelerating progress, but the long-term impact also depends
on internal teams having the skills and context to drive initiatives forward.
Offer learning paths, build hybrid roles, and enable people to rotate into AI
squads.
7. What are we doing to increase AI fluency across leadership?
AI is no longer just for data teams. It’s a board-level topic. As we pointed out in our latest Fast Company article, companies led by AI-literate teams are more likely to spot value and act on it. Start by building shared language and base knowledge across your leadership bench.
“Companies led by AI-literate teams are more likely to identify where AI can create value — and act on it.”
⸻ Fast Company, 2025
8. Is our vision strong enough to weather future AI shifts?
The tools will change. The hype cycles will come and go. What matters is
whether your vision is durable. Are you building toward a future-proof business
model that gets better with AI? Or are you stuck chasing trends?
9. Who in our leadership team is accountable for AI?
This might be the most important question. If no one owns it, it won’t
happen. AI doesn’t belong in a silo; it needs cross-functional leadership,
clear KPIs, and integration into core strategy.
10. Do we understand how agents will reshape our workforce?
With agents becoming more capable, certain tasks may no longer need direct human coordination. That’s not just automation, that’s a shift in your workforce. Have you through about what happens when agents start owning outcomes, not just tasks? Which parts of your value chain change? Having a clear point of view now will help you lead the shift rather than react to it.
These questions aren’t meant to be overwhelming. They’re meant to start better conversations that move you from exploration to execution, from scattered pilots to real transformation.
And in a world that’s becoming more AI-native by the day, these
conversations might be your strongest competitive edge.
AI-Native Operating Model
Aligning talent, workflows, and governance to scale AI across the
enterprise.
Challenges we tackle
Challenges that are top of
mind for leaders today
Our solution
Defining and enabling AI at scale with an AI-native operating model
We partner with leadership teams to define the operating model that enables AI at scale—aligning strategy, talent, and governance.
Defining the operating
model that will enable AI at scale. Aligning
strategy, talent and governance.
Key elements of AI-Native Operating Model Design
Establishing an AI Center of Excellence
Aligning AI strategy with business functions
Defining AI governance and ethical guidelines
Creating AI-driven decision-making workflows
Implementing MLOps for automated AI lifecycle
management
Defining AI-specific roles & upskilling strategies
https://tinyurl.com/4836n4jz
Комментариев нет:
Отправить комментарий