Your AI Transformation Needs a Different Leadership Approach

AI isn’t just changing the way businesses operate. Successful AI navigation requires a change in the relationship between strategy and execution and between the C-suite and the rest of the organization. A firm’s ability to adapt along these lines will make or break its AI transformation.

We reached this conclusion after helping companies set and implement their AI strategies and upon completing a multi-region survey of 150 C-level executives spanning a diverse set of industries and roles. To some extent, all the executives we surveyed have used AI to improve employee productivity, handle customer service inquiries and generate data-driven insights that support the creation of new value.

But some companies (35%) have shown earlier signs of AI success and have a more optimistic view on the value they’ll create. Compared with everyone else, they’ve had more success integrating AI across business functions. They’re further along in creating AI-enabled products and solutions. They also report their AI talent and coordination as more effective than their peers’, underscoring the importance of people and culture in AI transformation.

These results are meaningful because they highlight how different approaches lead to very different business outcomes as companies navigate the “AI Delta” — the performance gap between value creation from successful AI adoption and value erosion from poor AI strategy and execution. The valuation impacts can be significant, even existential. They’re also asymmetrical, with the potential for gain even greater than the potential for loss. 

The AI Delta is fraught with unknowns, making it a high-stakes strategic challenge for businesses in nearly every industry. Still, the contours of an effective AI transformation strategy are taking shape. To begin with, in our work helping companies execute their AI transformation, we’ve found that those who successfully bridge the AI Delta exhibit three leadership attributes:

• A dominant strategic vision. Companies with a robust AI strategy know what problems they want AI to solve, which lets them make calculated choices about where to invest time and capital and keeps them from getting lost in a forest of thousands of potential AI use cases.
• Meaningful involvement from the CEO and chief financial officer (CFO). In successful organizations, these two chiefs are visibly committed to the strategic vision for AI and actively involved with driving transformation success, even if execution still resides with a digital executive.
• Greater collaboration between technology and functional leaders. Successful deployment of AI requires both technical knowledge and a mindset for practical solutions to business problems. So, while technical leaders need to have a seat at the table for setting the AI strategy, they also need to collaborate closely with other functions to ensure business relevance and scalability. 

A dominant strategic vision

Not all companies are prepared to transform themselves with AI. Some might not have the right data, talent or technology infrastructure. Others aren’t sure what the business cases would be, or they just don’t have the budget for it. Regulations, partnerships and even organizational culture can all get in the way.

But addressing these problems should take a back seat to setting the strategic vision for AI. A strategic vision provides focus and clarity. Once you know what it is you want out of AI, the scope of problem-solving narrows and becomes more manageable, enabling companies to build a critical mass of effort and progress into solving specific problems. Recognizing this, most (57%) of the companies finding early success with AI adoption identify strategic vision as a top factor influencing their readiness for AI transformation. Only 34% of other companies do the same (see Figure 1).

This raises the question of what goes into a strategic vision for AI. A strategic vision states, in plain language, what the organization aims to achieve (and does not aim to achieve). At one consumer firm, the vision is to drive top-line growth. “It’s about leveraging technology to drive value through innovation, which is a different objective than just saving money,” a company executive explains. The result is far greater clarity on which efforts to prioritize, which use cases to invest in and, ultimately, how to measure success.

A strategic vision also should be compelling enough to motivate employees and clear enough to be articulated to all stakeholders — internal and external. The idea is to get everyone pulling in the same direction.

To drive business value, a strategic vision needs to tie into the broader business objectives and AI investments. That’s the case with 62% of the survey respondents who have a defined AI strategy, rising to 80% among those who have successfully integrated AI. In fact, the executives we work with state that business alignment matters more than other AI investment priorities, like data accessibility, technical feasibility and security concerns.

All of this makes sense conceptually. But companies also need to take practical measures for their strategic vision to take hold. We’ve found several measures that are particularly effective at keeping an AI strategy on track:

• Executive leadership involvement. Senior executives have an ongoing responsibility to advocate for the strategy and foster an AI-driven culture. As one IT executive of a global consumer company puts it, “The CEO has positional power, a strong network and the ability to motivate people by reiterating the importance of AI to them.”
• Cross-functional teams. AI transformation affects nearly every aspect of an organization, requiring collaboration across different teams. “We’ve formed several working groups to gather existing efforts,” a chief information officer (CIO) in healthcare says, “with the express purpose of pulling together an understanding of the business levers that will drive value and the technology needed to fulfill the strategic plan.”
• Pragmatic governance and execution. An orderly AI transformation sidesteps potential pitfalls while boosting the credibility of the overarching strategy. “Our organization’s AI transformation strategy is supported by a robust AI governance framework,” says a chief technology officer (CTO) in healthcare. “Within that framework, we created the AI transformation roadmap, steering committee and project management committee.”
• A focused approach to applying AI. AI can support a wide array of objectives, from new productivity and competitive advantages to product innovation. Focusing development on areas tied to the strategic vision brings discipline and speed to the process. Make sure the use case is clear and aligned to the strategic vision, a CTO told us. “I approach the AI revolution with a bit of a skeptical eye. I don’t want to be the first out of the gate — I want to ensure that there’s a clear use case that ties to our strategy and that it doesn’t confuse users.”

A methodical approach to AI transformation gives organizations room to prioritize high-impact opportunities while staying aligned with the strategic vision. It also helps to avoid error and waste, which can have a surprisingly negative impact on the transformation’s perception within an organization. Ensuring that the organization remains committed to strategic vision and plan is imperative for success. A chief data executive working in financial services stresses the importance of playing the long game, saying, “while it is easy to be distracted by new technology and use cases, it’s important to remember that the AI transformation is going to be at least a decade-long trend, if not multi-decade and you need stay the path.”

“We need to be careful in selecting the best use cases based on ROI,” a data executive at a large consumer company says. “Focus on innovations that truly align with your business objectives, scale them gradually and ensure they deliver real value before expanding further. This approach allows for measured growth and mitigates the risks of overextending too quickly.”

Meaningful involvement from the CEO and CFO

The approach that some organizations have taken to AI is to set a policy and then encourage employees to explore and use the technology as they see fit. However, that kind of bottom-up strategy is too disorganized for a successful AI transformation. A top-down strategy (with a structured plan and implementation guidelines set by leadership) or hybrid strategy (a combination of bottom-up initiatives and top-down directives) will yield better outcomes.

“It won’t work unless leadership owns it,” a consumer industry IT executive says. “You can’t delegate it to someone else. That clearly communicates to the team that the boss isn’t interested.” Most of our survey respondents came to the same conclusion — 84% are pursuing a top-down or hybrid AI transformation strategy today.

So why aren’t more companies closer to realizing AI’s full value? Is strategic vision the only missing element? We don’t think so. Besides a strong vision, companies need a new type of leadership to get AI transformation right. And that leadership starts at the top with the CEO and CFO.

The involvement of these two executives signals the importance of AI transformation. They bring the weight of resources and accountability to the effort while becoming active participants themselves. Their advocacy is especially critical given the hefty investment and time to value that AI transformation often calls for, says the CTO of a large consumer company. The CEO and CFO are the ones who can “go to the board and get the investment in AI.”

They’re also the ones who can align the rest of the C-suite behind the strategic vision. This matters because AI transformation requires a significant degree of cross-functional collaboration, something that most companies don’t seem to be doing. Where it’s working, we see CEOs taking a much more active and directive role. In fact, firms achieving greater success with their AI efforts are 1.4 times more likely to have their CEO highly or fully involved with leading AI transformation (see Figure 2). 

The peripheral engagement that many CEOs and CFOs appear to have with AI transformation suggests a siloed perspective in the C-suite that’s at odds with how the role of technology executives is trending.

Tech leaders can’t afford to go it alone. Their success with AI depends on the relevance of the initiatives they’re driving. Giving what each business unit or function needs can affect workflows in other areas. There’s also the reality that where AI is concerned, delivering on any given business need requires some degree of data sharing across the organization.

That’s just the beginning. Once a solution is identified, development becomes an iterative test- and-tweak process. The feedback that business users provide at this stage helps technology executives understand how to scale the solution across the organization and sustain it amid ongoing — and sometimes dramatic — shifts in the business environment. Then there’s change management, which is just as important as solution design and development, because adoption is essential to achieving the value creation potential.

These conditions exist with other technologies too. The difference with AI is that success or failure is magnified. Shareholders are watching what companies do with AI, and if they see an edge in strategic value, the impact could be significant.

Against that backdrop, it’s not surprising that 59% of information technology and digital chiefs say their scope of responsibilities has expanded over the past year due to their company’s efforts with AI. Common new tasks include AI strategy development, AI project oversight and digital transformation leadership.

And partnering within the C-suite? While 38% of tech leaders have recently seen an increase in collaboration on AI with their executive colleagues, momentum is building. Fifty-one percent expect a shift in the year ahead (see Figure 3).

“One business norm that’s really changing,” a CTO told us, “is the amount of collaboration needed across the C-suite” — especially for earlier AI transformations. Being part of the executive leadership team, adds the chief data officer (CDO) of a consumer company, creates the opportunity to “drive and own AI transformation as thought partners instead of order takers.” 

Rewriting the AI transformation playbook

Transformation is hard enough without having to contemplate the sorts of strategic and cultural shifts we outline here. Still, the basic playbook isn’t all that complicated:

• Understand your key business value drivers and how you can use AI to enhance them
• Identify the use cases that align with your value drivers
• Define the infrastructure to support the business where it is, including making the necessary investments in data and talent  
• Build the use cases in collaboration with colleagues throughout the organization
• Commit to and enable change so that new, AI-enabled ways of working can take hold
• Get wins on the board that you can measure and scale
• Communicate what you’re doing to internal and external stakeholders 

The AI revolution is here, but businesses are struggling to transform. Although no one has all the answers on what’s likely to be a long-term shift, the most successful companies have arrived at similar approaches to transformation that have started to yield gains in performance and competitiveness and new revenue streams.

That’s welcome insight for those seeking a holistic, actionable AI strategy. It goes to the defining strategic problem of the near to medium term, which is determining the size of a company’s AI Delta and how to bridge it, bending the company’s growth trajectory upward. A strategic vision for AI, backed by an engaged CEO and CFO and enabled through a collaboration of technology and business leaders, will help companies break out of the experimentation phase and set their AI transformation up for success. 

For more information, please contact us.

The authors would like to acknowledge L.E.K.’s Digital Practice members Hunter Reynolds, Lena Shapiro and Tess Wrigley for their support and effort in authoring the survey and study.

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