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We brought together CEOs from a range of geographies and industries, including consumer products, IT software and services, and financial services, to hear how they were actively piloting, adopting, and implementing AI technology. Here is what we learned.
Three key takeaways from our conversation with 100 CEOs who are actively piloting, adopting, and implementing AI technology.
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Adopting a learning mindset will incentivize and accelerate innovation |
It takes a village—the CEO needs to empower the C-suite to work together on AI |
The board has a critical role to play, but director understanding is mixed |
Without the functions working in a network manner, there can be no meaningful transformation. The role of the CEO is to facilitate those connections. It takes team building, trust, and connectivity to ensure C-suite leaders move away from self-promotion to team promotion. The mission needs to be more rewarding than personal success.”
CEO attendee
RRA x HBS AI CEO Labs
How CEOs are adopting AI
01. To create business value. One CEO describes AI as a “game-changer” for their business. They said the AI tools they have adopted, including Gen-AI, predictive analytics, natural language processing, and robotic process automation are creating value through new revenue streams and increased pricing.
02. To provide better customer service. CEOs are harnessing AI’s predictive powers to provide a better experience for customers. This includes using AI tools to analyze customer information and signpost them to relevant products or services. Some are using AI chatbots for customer support, while others are using AI to optimize pricing to drive sales.
03. To optimize business processes. CEOs are adopting AI to boost productivity and efficiency across their businesses and supply chains. Some use AI for inventory management, others for workforce management, or to improve service effectiveness for customers.
04. Sustainability. Attendees are also using AI to support sustainability goals, from tracking KPIs, to digitizing supply chains and sourcing reusable materials. These AI-driven business models emphasize circularity, sustainability, and advanced customer personalization.
Where AI thrives—and stumbles
In 2023, Karim Lakhani, a professor at Harvard Business School, launched a joint research study with Boston Consulting Group that looked at whether AI could tangibly improve the productivity and efficiency of elite knowledge workers. The study of 758 BCG consultants found that:
- Consultants who used AI were 25% faster and completed 12% more tasks than those who weren’t using AI tools. The impact wasn’t just felt from a speed perspective—the study also found that 40% of the group using AI also yielded higher-quality results.
- Consultants who were deemed below-average performers benefited most from AI augmentation—with an increase of 43% in performance. However, above-average performers also noticed a 17% boost in performance by using AI.
- There was a fall in performance when it came to using AI to complete complex tasks, such as the triangulation of financial data in a spreadsheet that contained errors or analyzing a retail strategy using interview notes. In these scenarios, consultants were 20 percentage points less likely to produce correct solutions compared to those who were not using AI.
Key lessons from CEOs actively adopting AI
Our attendees shared the opportunities, challenges, and lessons they had faced when implementing AI across their organizations.
Lesson 1: Adopt a learning mindset to accelerate AI innovation
The group discussed how CEOs need to personally invest time learning about AI if they are to differentiate hype from reality. There was agreement that AI creates a brave new world for CEOs, requiring them to step up and more deeply engage with both internal technology functions as well as clients and customers on complex tech-driven solutions and products. Key issues included prioritizing initiatives amidst abundant opportunities, necessitating a focus on capital allocation, and adopting rapid deployment while embedding responsible AI practices.
One CEO said, “The AI wave and Gen-AI wave are much more complicated for leaders to comprehend than any other tech cycle in the past 30 years. With this in mind, we need to be part of the discovery—seeing what works and what doesn’t, and what to continue investing in, as well as paying attention to the experiences of teams inside the company.” They advised treating innovation like a startup. “You need to understand how to think differently—and carry thousands of people on the learning journey,” they said.
One CEO who helms an organization where AI touches 50% of revenues shared how adopting a Centre of Excellence dedicated to AI was helping to ensure rapid deployment of the technology across the business. “The amount of creativity and the number of people embracing AI is unbelievably fast,” they said.
Another CEO advocated actively embracing pilots as a way to both experiment and understand which use cases hold the most promise. “My advice would be to try multiple things concurrently; the world is changing too fast to put all your eggs in one basket,” they said. “Not every experiment will work. But we’re trying to learn quickly about use cases to focus on, which technologies we should consider using, and what change management is required to get the biggest bang for our buck. Running these experiments gives us a lot of unvarnished data about what’s going on and allows me to have more confidence going forward.”
Another shared how looking at AI through the value creation lens was a useful way of prioritizing AI experiments. “Instead of applying AI as a scattershot across the business, we’re trying to be very surgical. First, we identified the value creation drivers for our business over the next three to five years. Then we asked: ‘How could we use AI to facilitate that.’”
The AI Factory
An excerpt from Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World by Marco Iansiti and Karim R. Lakhani.
The AI factory is the scalable decision engine that powers the digital operating model of the twenty-first-century firm. Managerial decisions are increasingly embedded in software, which digitizes many processes that have traditionally been carried out by employees. No human auctioneer gets involved in the millions of daily search-ad auctions at Google or Baidu. Dispatchers do not decide which car is chosen on DiDi, Grab, Lyft, or Uber. Sports retailers do not set daily prices on golf apparel at Amazon. Bankers do not approve every loan at Ant Financial.
Instead, these processes are digitized and enabled by an AI factory that treats decision making as an industrial process.
Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide or even automate a variety of operational actions. This is what enables the superior scale, scope, and learning capacity of the digital firm.
Lesson 2: It’s up to the CEO to empower the C-suite around AI
The discussion highlighted the importance of the C-suite's role in navigating AI's rapid evolution, as led top-down by an AI-informed CEO. This is not easy. Some CEOs shared how their efforts have been hamstrung by C-suite leaders who continued to advocate for their functions or business units, rather than adopting the enterprise mindset necessary to spearhead large-scale transformation. It's critical CEOs solve this challenge.
As one CEO said: “In a time of radical transformation and invention of different business and operational models, you need your C-suite working cross-functionally. Without the functions working in a network manner, there can be no meaningful transformation. The role of the CEO is to facilitate those connections. It takes team building, trust, and connectivity to ensure C-suite leaders move away from self-promotion to team promotion. The mission needs to be more rewarding than personal success. Collaborations, in the age of transformations like AI, require digital people, operational people, business people, and analytics to come together. That’s how you move forward.”
Another CEO shared how they empowered teams one level below the C-suite in order to get more cross-functional working. “This cannot be an IT-led project. It needs to be a business leader-led project, with people who are able to execute on this,” they shared.
Attendees also discussed that while CEOs and senior leaders need to spearhead transformation, it makes best sense for them to ensure innovation flourishes across the whole organization, rather than over-centralizing AI adoption. As Harpreet Khurana, RRA’s Chief Digital and Data Officer, shared, “It is important to find the equilibrium between centralization of innovation and unfettered, unchecked mass innovation across the organization.” One CEO shared how their organization had worked to address privacy and security considerations, before decentralizing AI experimentation to yield ideas and prioritizing projects to progress. “In a world of Gen-AI and AI, privacy and security will take a lot more attention. We saw that as being very important to sort out first. Then the team was able to scale in a multi-functional way,” they said.
The 30% Rule
An excerpt from The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI by Paul Leonardi and Tsedal Neeley.
A digital mindset is the set of approaches we use to make sense of, and make use of, data and technology. This set of attitudes and behaviors enable people and organizations to see new possibilities and chart a path for the future.
And here’s the good news: you only need about 30 percent fluency in a handful of technical topics to develop your digital mindset. We call this the 30 percent rule.
To understand the 30 percent rule, think about learning a foreign language. To demonstrate mastery of the English language, a nonnative speaker must acquire roughly 12,000 vocabulary words. But to be able to communicate and interact effectively with other people in the workplace, all they need is about 3,500 to 4,000 words—about 30 percent of what it takes to achieve mastery.
In practical terms, a nonnative speaker does not need to master the English language to work effectively with others. Similarly, to work effectively with a digital mindset, you don’t need to master coding or become a data scientist. But you do need to understand what computer programmers and data scientists do, and to have proficient understanding of how machine learning works, how to make use of A/B tests, how to interpret statistical models, and how to get an AI-based chatbot to do what you need it to do.
Lesson 3: The board has a critical role to play, but director understanding is mixed
The group agreed that the revolution caused by AI was absolutely a board-level issue, and if it was not represented in the boardroom, AI would not typically get addressed in the organization. Here, it was agreed that the board needs to play a bigger role, helping the organization strike a balance between long-term transformation and short-term performance. Those that can achieve this will reap dividends. One Harvard-led study showed a strong correlation between the technology intensity of an organization and its performance in the stock market—specifically those organizations with the strongest technology architecture and innovation processes outperformed their peers.
CEOs shared how their boards were looking to actively upskill in the area of AI. But this doesn’t always mean bringing in AI experts. Some boards are adding members who are able to think more generally about how they can use technology to transform their business models.
The group also discussed the dynamics of board engagement with AI, noting a range of understanding and enthusiasm among board members. The dialogue revealed a desire for more AI education and strategic guidance from the board, with some CEOs saying their boards had appointed digital and technology experts to navigate AI's complexities and ensure responsible governance. One shared: “Our board is looking for more education. They’re looking for help and defining what their role is in overseeing AI.” Another added: “It’s tricky because many of the board members are learning, and probably know less than the management team in many ways. We have some people who are quite knowledgeable, others who are less so. It’s important the board gets the right level of knowledge and insights to first and foremost be able to govern what we’re doing, and second, to help lead us and push us to do more.”