Introduction
In the two or more decades (yikes) of working with organisations on their data strategies and now coupling that with AI, I’ve seen a recurring theme: many strategies aren’t strategies at all. Instead, they’re collections of activities or glorified project plans, often anchored in technology and infrastructure but missing the mark on real, impactful change. If that sounds familiar, then raise your hand!
This post was inspired by a chat I had with Mark Stouse, this week in which we discussed strategy and causality. I do love my conversations with Mark, and afterwards, my head hurts from having to think too much! A good thing, as it makes me question the things I know and accept the fact that I don’t know a whole lot of stuff! In fact, in our conversation, we discuss this too and I love the quote stated about not knowing a whole bunch of stuff: “Use it to curate your ignorance”, apparently this was mentioned to him during a conversation he had with a physicist friend!
So, thank you Mark for inspiring this post!
We also discussed all things to do with economic uncertainty, VUCA and the gap in really understanding strategy and how it can make or break a company.
So, what exactly is strategy, and why does it feel like we’ve lost touch with its roots?
Strategy Is About Choice, Not Checklists
True strategy isn’t a to-do list. It’s about making hard, focused choices on where to play and how to win. A strategy should address the why behind what you’re doing and define a clear path to differentiate your organisation. For data and AI, this means framing decisions around the unique advantages your company can create in the market, not just building another dashboard or implementing the latest analytics tool.
Because the reality is, too many data and AI strategies are technical documents masquerading as strategic visions. They often focus on capabilities, systems, or where the data will be stored and managed etc., but miss the strategic punch of a compelling, value-driven direction.
Why Strategy Needs to Embrace Volatility
In my conversation with Mark, we discussed that the current business landscape is complex, fast-paced, and unpredictable and we used the terms VUCA to describe it. For those who may have not heard the acronym: volatility, uncertainty, complexity, and ambiguity.
Traditional strategy, which is often anchored in stability and long-term forecasting, needs to adapt to this volatile world. But as we saw the pandemic blew that out of the water! A real strategy should equip you to pivot, evolve, and respond to new challenges, not lock you into a rigid roadmap.
For data and AI, this means integrating a dynamic approach that prioritises adaptability. Are your data initiatives aligned with key business objectives and flexible enough to meet changing market demands? If not, there’s a good chance what you’re calling “strategy” is just a framework of tactics that won’t carry you through the next disruption/pandemic!
Remember what Mike Tyson said: “Everyone has a plan: until they get punched in the face!!”
The Problem with “Strategic Planning” in Data and AI
Let’s be clear, strategic planning is not the same as strategy.
I’m going to say that again, strategic planning is not the same as strategy.
Planning is about coordination and project execution; strategy is about direction and differentiation. Many organisations equate the two, often falling into a cycle of project-focused planning that bypasses the actual purpose of strategy: to give your organisation an edge in a competitive world.
For a data or AI strategy to be effective, it must go beyond listing projects or technologies. It should tackle the bigger questions:
How will these initiatives contribute to our unique position in the market?
What customer needs are we meeting in a way no one else can?
These questions force you to consider your organisation’s value proposition and to align your data and AI initiatives with that core purpose. This is why I always want to understand the business strategy and where it’s headed.
Getting Back to the Core of Strategy
To develop a real strategy, start with foundational questions:
- What unique value can we create? Identify where data and AI can drive unique, measurable value in your market. This goes beyond improving internal efficiency to creating something your competitors can’t replicate.
- How do we sustain that value in the face of change? A strategic vision should anticipate market shifts and position your organisation to lead, not follow.
- What are we choosing not to do? Strategy requires clear boundaries. Knowing what to avoid is just as important as knowing where to invest. An AI strategy, for example, should involve tough calls on which technologies and initiatives aren’t worth pursuing, even if they’re “nice to have.”
Here Is A Real Example of Strategy
Matthew Small and I recently worked with a global CPG company to build an enterprise data and AI strategy. Within just two weeks, we identified several substantial opportunities to drive revenue and cut costs. One initiative alone held a $55M revenue potential, while another identified about $30M in cost efficiencies.
We achieved this because we approach data and AI strategy with a clear focus: aligning it with business strategy to create real, measurable value. While there’s still plenty of work ahead, discovering this level of impact in such a short time shows what’s possible when strategy leads.
Avoid the easy route of prioritising technology first. Many organisations have gone down this route, and the results show many are leaning towards mistrust. As I stated on a post by Kyle Winterbottom this morning, many organisations have invested heavily in Data & Analytics teams and what has happened, “bit investment in tech + not much value to show from the efforts = mistrust”
A successful data and AI strategy must start with value and business alignment to truly succeed. Yes, I know that’s a broken record, but I will keep saying it until it sinks in!
Real Strategy Means Real Impact
In the real world example above, a strategy should move the needle on what truly matters for the organisation’s growth and competitiveness. For data and AI, this is a call to move beyond technical specifics and focus on tangible business outcomes. It’s also asking some tough questions:
Are your AI models actually creating value for customers, or are they just projects checked off a list?
Are your analytics driving strategic decision-making, or are they isolated reports that never reach the boardroom?
Real strategy has lasting impact because it’s driven by purpose and choice, not just deliverables and KPIs.
Where Next?
The strategies that will thrive in today’s economy are those that combine data, AI, and business insights in ways designed for resilience and growth. Of course, everyone purports to know that! But few do this well.
From my experience it’s time for organisations to break away from mere planning and rediscover what real strategy means: making tough choices, standing out from the competition, and staying adaptable to change. The road to true strategy isn’t easy, but it’s necessary if you want to lead, not just keep up.
If your organisation wants to win in a volatile world, start by asking hard questions. That’s often the crux, finding the real question that needs to be tackled; and therefore, you must challenge your current approach. You MUST demand a data and AI strategy that aligns with your business vision and drives measurable value. Embrace the origins of real strategy, where choices are intentional, differentiation is sharp, and adaptability is your strongest asset.
Act now and review your current data & AI strategy, does it stack up, is it littered with technology statements or focused on moving data from one area to another, if so, realign it to what matters most, and don’t settle for less. The stakes are high, and organisations that get this right now will set themselves up for lasting success.