Session 4: How APAC CEOs Are Deploying AI—and Their Top Lessons for Success

RRA AI CEO Lab

 

Update introduction to: We brought together CEOs from across Asia Pacific across a range of industries, including financial services, luxury, consumer, and IT software and 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 CEOs who are actively piloting, adopting, and implementing AI technology.

Icon

Imagination and learning velocity are key differentiators in AI adoption.

Icon

Culture and poor data are the biggest blockers to AI transformation.

Icon

AI is a board-level issue, and many are bringing in tech-savvy members.

 

 

quote

Whoever learns [AI] fastest wins.”

CEO attendee
RRA x HBS AI CEO Labs

 

 

How CEOs are adopting AI

01. For transformation: CEOs share how AI will usher in vast changes for organizations, and whole business models will need to be re-imagined. CEOs did mention, however, that an organization needs to have gone through digital transformation and have a connected data house to reap the benefits of AI. It was advised that leaders adopt centralized data stores that are accessible across the organization. But many companies are yet to consolidate their data or leverage the cloud sufficiently to make this happen.

02. To boost productivity and innovation: All attendees were focused on how AI could execute mundane or routine tasks, including admin tasks, customer service, or software development. One CEO shared that, thanks to GenAI, the productivity of their coding team was four times what it previously was, adding that they use GenAI to help write code, as well as check it, optimize it, and trial the result. Optimizing supply chains, investment theses, and strengthening connections with customers were all mentioned as specific use cases.

03. To stay ahead of the competition: Attendees also referenced the risk of being radically disrupted, outperformed, or falling behind the competition, noting that the opportunity available to them through AI was also available to their competitors. One CEO spoke of the most competitive companies being those that learn the fastest. “Whoever learns [AI] fastest wins,” they said.

 

 

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 CEO actively adopting AI

Our attendees shared the opportunities, challenges, and lessons they had faced when implementing AI across their organizations.

 

Lesson 1: Imagination and learning velocity are key differentiators

There was agreement among participants that the CEO should be the chief transformer of organizations. As one CEO stated, “If you are serious about transformation and you're not taking it on as your full-time job, nobody will do it.”

One  attendee spoke about the importance of imagination and understanding the art of the possible with AI. “If you look at your internal business processes, a corporation is nothing more than an aggregate of processes: you take inputs, you do something to them, and you produce an output. Well, if  you think about it that way, you can use AI to do a lot of business processes. The question becomes: Can you imagine the use case? One of the impediments to deploying AI is [a lack of] imagination, because you have to rethink your business processes through the lens of what the technology is capable of.”

Attendees also discussed the importance of “learning velocity”—how quickly CEOs can imagine possibilities, experiment, learn, and iterate. One CEO said CEOs need to play the role of both Chief Technology Officer and Chief Learning Officer, before adding: “Everything should be an experiment. “Why run two [pilots]? Why not run five or 10? You don’t need to run one every minute, but the idea is that you’re getting data. You farm hypotheses, test those hypotheses, and get an empirical result. You’ve got to do that at scale. You’ve got to do it at speed. But you can’t go faster than you’re capable of learning. So learning is really the critical path on everything.”

 

 

The 30 Percent 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 2: Data and culture are the bigger blockers to transformation

Attendees also discussed the specific role CEOs can play in driving transformation, with three clear themes emerging.

First, ensuring that systems are in place to mine and interpret data—and that you have the right foundational capabilities across the enterprise to harness it. As one CEO said, “Organizations have blocks of data everywhere and you’ve often got duplication of data. We need to solve for that collectively first. It’s going to be incredibly difficult but it’s the journey that we need to get to.”

Another CEO shared, “The age of digital transformation really comes down to data  and  how  you  exploit  data.  I  describe  data  as  the  lifeblood  of  the  modern  enterprise  that  carries  information  and  nutrients  to  all  functions. If we  get  the  data  right,  and we  have  a  system  in  place  where  we  can  learn  from  that  data  through  a  variety  of  mechanisms, everything  works  out  well. Then  it's  just  velocity  after  that.” They added, however, that people are still part of the equation. “We need to be asking, ‘Do we have people who can collaborate with algorithms? Do we have people who can ask the right questions, interpret the results, and then ask the obvious next question and drive that dialogue forward iteratively?’”

The CEO added that diversity of thought is even-more critical when building teams in an AI world. “It’s important to find people who are not wed to a particular process or particular way of doing things… My team is very diverse. I have historians, I have scientists, I have economists, I have math people. And conversations can go all over the place, but they tend to be interesting.”

 

 

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.

 

 

Second, CEOs have a critical role to play in setting the right environment for innovation to thrive. The group shared how culture was often one of the largest blockers to transformation. As Marco Iansiti and Karim R. Lakhani share in their book, “We emphasize that good leaders of digital firms must also understand the softer issues. They still need to master the human side and understand the critical issues that inevitably come up as workers interact with increasingly digital operating models. Managers need to have a feeling for the inspiration, capabilities, and culture needed to drive continued, ongoing evolution.”

The conversation then touched on the transformation of leadership roles and the importance of CEOs building trust and connectivity within the executive team to facilitate meaningful change. Attendees also discussed how leaders needed to pay constant attention to their teams’ experiences as they interact with AI in their roles and accept that although AI may help increase productivity, employees’ cognitive load won’t necessarily decrease.

There was a shared consensus that all employees needed the tools and skills to utilize AI—for innovation to flourish and to create versatility in the workforce. Targeted education and training initiatives will help ensure the workforce becomes agile, informed, and capable of navigating the complexities of AI-driven business landscapes.

 

 

The Four Types of Employee Responses to Digital Transformation

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.

We recommend a deceptively simple adoption framework that we have used to help leaders at many companies to influence people’s behaviors so that they are motivated to engage with a change program that requires learning new skills:

Do I have buy-in such that people believe digital transformation will be beneficial for them and the organization? And can I learn what I need to in order to succeed in this transformed organization?

Mapping the answers to these two questions produces four types of responses you’ll typically see and suggests what you need to do to make transformation successful.

The good news is that leaders and managers can move individuals from one quadrant to another.

To shift people from oppressed or indifferent to inspired, you first must increase buy-in by helping everyone believe that learning digital competencies is good for them and their organization.

Three factors are crucial to promote buy-in:

  • Increase messaging from leadership that stresses the importance of digital transformation as a new and critical frontier for the company.
  • Launch internal marketing campaigns to help employees imagine the positive potential of a company powered by digital technology.
  • Promote a shift in people’s identities; that is, encourage people to view themselves as contributing members of a digital organization.

After establishing buy-in, leaders can shift individuals from frustrated to inspired by boosting confidence in their capacity. Three factors contribute to people’s confidence in learning digital skills:

 

 

Lesson 3: AI is a board-level issue—and many are bringing in tech-savvy members

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. As Professor Karim Lakhani at Harvard Business School explained: “With AI, the board starts to play a much bigger role. The curation of the board becomes super important; their role is to create a healthy tension between optimizing the current state, and making your quarterly numbers for Wall Steet, and investing and spending time for the future state. That tension is tough and it’s going to be a tension that will be ongoing.” Professor Lakhani added, however, that a 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. “We see clear evidence that leading  companies  that  are  looking  to  transform  themselves are adding board  members  that  aren’t necessarily AI experts but are future leading in terms of thinking  about  how  they  can  use  technology  or  the  company  can  use  technology  to  think  about  their  business  model,” he said.

Several attendees mentioned aims to bring in more tech-savvy board members when succession planning was needed in an attempt to modernize the expertise the board could bring. The group discussed that no single figure should be loaded with the entire tech and disruption conversation, though boards must regularly focus dually on both the broader business implications and relevant technological aspects.

 


 

View the previous session

Back to RRA AI CEO Lab main page

View the next session