What Every CEO Needs To Know About Their Data And AI Strategies
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I’ll start with the most essential point. Stop worrying about whether your business is moving fast enough. Moving fast feels very slow right now.
Talk to people who left Meta and Google a few years ago. Both went through the same growing pains that most adopters are facing now. They struggled to put reliable models into customers’ and users’ hands. Early innovation failed to result in something that could be monetized.
They were forced to rethink their early strategies. Instagram changed the types of data scientists they hired, moving away from academic backgrounds and toward product-focused, applied backgrounds. Facebook created new roles to support a team approach to AI. Data organizations were rebuilt in a CoE model.
They had to go back and start with the data. Infrastructure had to be rebuilt. Meta recently restructured their data organization and broke it up into units embedded in the departments they support. They need data professionals to work closer to value creation and outcomes.
Google’s public message is they are restructuring to improve productivity by 20%. Behind the scenes, they are auditing projects and redirecting resources to revenue generators. Innovation is critical for Big Tech, but as Apple knows, it must result in products.
Amazon and Microsoft got as close to “the right strategy” as possible. Talk to people in both companies; there were mistakes, but they built the foundations first. Both had strong cloud strategies for obvious reasons. They built their data, analytics, and AI strategies on solid foundations. After starting too fast, they slowed down and focused on outcomes.
TikTok is an emerging AI success story. Their strategy to build better content recommendations using AI kept innovation aligned with a single value proposition. Now they are the biggest competitive threat to social media incumbents. They use AI as a lever for competitive advantage.
If you’re building strategy and foundational pieces first, you’re moving at the right speed. Any business that leaps from data to AI is in for hard lessons. Don’t believe their hype. Watch for results.
Data, analytics, and AI support new ways to create and deliver value. That means each one needs a strategy that informs decision-making about the technology across the enterprise. The purpose is alignment.
Without a strategy, each technology is siloed within the technology organization. An informal strategy emerges that the rest of the business never sees. The silo prevents value and domain expertise from getting into the technical team. The implications don’t make it to external organizations. They need the big picture to understand what must change in their organization.
Technical strategies must be a part of the strategy planning process at the C-level to keep initiatives aligned with value. Create a single vision for any technology that supports the business or operating model. Build a technology model to define the vision and purpose. Adding a top-level strategic artifact for technical strategy is the starting point for alignment.
Why does technology get an artifact, but other business units don’t? Nothing else touches every corner of the business like technology does.
Businesses look at technology from a rearview mirror perspective. The firm has never relied on this much technology or managed this much technical complexity. Businesses need a forward-looking mindset.
Today is the lowest technical complexity and dependence the business will ever see again. It will never be this easy again. Companies will never move this slowly again. Customers will never have this level of simplistic needs again.
Transformation is a continuous process, and technical maturity is a progression. Data strategy sets up for analytics and AI. Each strategy must inform decisions that support near-term value creation and how the next technology wave will create value.
Without a long-term view of transformation, what is built today could become a barrier to adopting a new technology tomorrow. It is also easier to justify the costs of doing it right the first time with a long-term view. The current year’s projects rarely justify significant investments. Take a 5-year perspective, and the justification is apparent.
Continuous transformation is unsustainable without near-term returns. The current transformation must get to break even and become cash-positive. Technologies and transformation must be revenue generators, not cost centers. And it cannot always be about the big payday that’s 5 years down the road.
Maximize returns and growth on the current wave before going all in on the next one. Getting good at monetizing data sets the table for monetizing analytics.
The value of technology no one uses is always 0. Having the capabilities to build it or the money to buy it is only half of the consideration. Is the business ready to use it? Will customers adopt it?
Can it be monetized? Can it be put into production and made accessible for everyone who needs it? These questions are just as crucial as, can we build it?
Business units transform incrementally. Customers adopt as their needs mature. When products follow value, it feels slow, but the results materialize sooner. Break-even and profitability happen faster. Some of the new growth can be used to fund the next transformation phase.
The current push is to make CEOs more technical. Like the rest of the organization, CEOs need data and model literacy training. They don’t need to become software engineers and data scientists. Filling the C-suite with a single perspective has never worked.
An overfocus on technical capabilities drags strategic leaders into tactics. CEOs need someone who acts as a translation layer. A technical strategist can translate technology into the opportunities it presents and the value it can create. They can also translate business needs into technical initiatives.
Each C-level leader needs to focus on their domain. Having one foot in technical workflows becomes a distraction. Leadership needs to be able to focus on how their business domain generates value.
Technology starts to take over strategy when it takes over innovation. That’s where businesses lose control. Innovation must be defined in business value and connected to opportunities.
Objectives should be the starting point for discussing innovation. If technology starts the conversation, it’s harder to connect innovation back to value. Here’s how both sound.
“Here’s what is possible with data, analytics, and AI. We believe these are the best innovation opportunities.”
“How can the business use data, analytics, and AI to achieve our goals more effectively?”
The objectives approach allows the business to define innovation and connect it to business value. Technology is explored in a supporting role rather than a defining role.
I will conclude with a point that usually gets lost in the race to technical capabilities. There is already a C-level leader who stays close to the technical team. Their perspective is essential, but the people closest to business outcomes are users and customers.
They have the most valuable perspectives on how well new technologies meet their needs. Trust them to be the judges of quality and utility.
Delegate the technical strategy, leadership, and execution to others. Getting closer to the technical team can pull CEOs away from their customers and all the other talented people who make the business successful. Technology receives enough of the spotlight.
