Decoding the Future: The RRA Systems View on Leading Through AI Transformation

The rise of AI isn’t just about implementing new tools—we are in the realm of full business model transformation, fundamentally re-imagining the way we operate, go to market, and lead.

As AI brings a seismic shift to the way we work, leaders need to rethink how their organizations fundamentally operate. Recently, McKinsey reported a 341% increase in generative AI (GenAI) roles over the last two years, with demand for AI talent outpacing availability by 12x. From automotive manufacturing to autonomous mobility, retail to ecommerce, media to streaming, and banking to fintech, leaders across industries are re-designing their business model around technology and AI, with large corporations transforming at a pace increasingly similar to fast-moving disruptors. AI has raised $122 billion of equity investment in 2023 alone, developing use cases that have the potential to generate an annual value of $2.6 – $4.4 trillion.¹

These changes have massive implications for organizations and the people who lead them. To keep pace with the changing needs of our clients, Russell Reynolds Associates hosted over 50 group CEOs and CHROs in our AI Leadership Labs roundtable series. In partnership with Harvard Business School, we discussed GenAI’s increasing impact on organizations and the leadership challenges that acceleration presents.

Read on to uncover how trailblazing CEOs are developing strategies to tackle the AI opportunity head-on, and RRA’s comprehensive view on leading your organization through the AI transformation.

Why is now a unique moment in time?

Leaders have grappled with transformation for decades now. First, the advent of the internet allowed access to information and communication at never-seen-before seen speeds. Then, digital transformation created efficiencies internally and externally, from supply chain to customer experience to online sales platforms. The next wave involved large traditional corporates competing with—and looking ever more similar to—new powerful digital entrants to the market.

Now, we have the advent of GenAI. If the internet reduced the cost of information transition to 0, GenAI is applying those same reductions to cognition and problem-solving.

Figure 1: The accelerating pace of technological change in organizations

With every new technology, the pace of change increases. And while GenAI is another technology in the arsenal of tech-change weaponry, it also represents an inflection point—the likes of which we haven’t seen since the popularization of the internet. Between this inflection point and tech’s exponential acceleration, there has never been such opportunity for fundamental business model transformation.

What AI-related challenges are keeping our clients up at night?

Leaking Value

In the age of digital transformation, new digital initiatives required a large team of tech experts to make change happen. In the age of GenAI, change and innovation is accessible to anyone—even those beyond your organization. Suddenly, two people in a home garage will have the same power to build a digital platform as a large corporation – or, as one CEO neatly put it: “What we do can now be replicated; that’s the biggest threat.”

During our AI Leadership Labs series, many CEOs shared concerns of value leakage. For organizations with a platform model or that are built on information/data, it may become challenging to stay relevant. This has started a trend towards disintermediation, in which traditional players can bypass smaller tech/digital players that they may have previously relied upon for technology-heavy services (analytics, website design, cyber security, engineering, AI, etc.), building them in-house instead.

Unprecedented Pace

For leaders, the question should not be: “Do we have time to learn and transform?” When it comes to AI, organizations have no choice.

That’s what one Harvard professor said when discussing this unprecedented pace of transformation. A CHRO attendee added: “The pace and rate of change has never moved as fast as it is now; the things we discussed 12 months ago are already irrelevant, and the change curve is exponential.”

Instead, the question should be: “What steps can we take today to ensure we’re preparing our organization and the people within it for the challenges of tomorrow?"

The majority of leaders globally have taken at least an initial step to implement GenAI (Figure 2). We have observed CEOs of smaller tech-driven businesses transform their entire tech stack overnight through an AI-enabled “cloning” process, which involves rebuilding technology from scratch, leveraging a team of outsourced engineers, then reintegrating it into their businesses – thereby bypassing years of change and disruption.

Figure 2: Where organizations are on their GenAI journey

^{Source: Russell Reynolds Associates H1 2024 Global Leadership Monitor, n= 1,382}

Despite moving at this breakneck speed, these CEOs are simultaneously weighing the long-term impacts of their organization’s AI decisions – or as one Labs attendee put it, weighing the paranoia of getting it wrong against the immense pressure to do something quickly.

^{Source: Russell Reynolds Associates H1 2024 Global Leadership Monitor, n=1,728}

Managing change and culture shifts

Transforming businesses and keeping pace with the AI revolution necessitates a culture of innovation, curiosity, and learning, with a shame free try-fast/fail-fast mentality. But getting to that point will be difficult.

Leaders should prepare for an element of shame—both from employees who do not yet understand the technology or opportunities, as well as from those hiding their AI skills (lest they look indolent).

Trust will also be key. Without it, employees won’t support the transformation goals, as they may fear they’re training their AI replacements.

Finally, this change will require constant upskilling and reskilling. The continually accelerating pace of change will mean that employees will have to learn more quickly and frequently than ever before.

The Long Road to ROI

As with all new technology (most recently VR and blockchain), hype and optimism can often far precede any real change or return on investment, causing frustration and dejection. Before the tech is ready for prime-time, organizations have to find use cases, willing participants, and budget to scale—all while countering resistance to change, upskilling needs, and demanding transformation management. Those doing this best are concurrently running organic and inorganic innovation via internal crowdsourcing and access to new technology, also creating centralized innovation hubs of tech and business talent.

New leadership requirements

The leadership profile and remit across all functions, at every level, will soon start to look very different. First, skillsets and competencies including change management, systems thinking, adaptability, rapid learning, and resilience will come to the fore. Second, the leadership team will have to build an enterprise mindset, collaborating and supporting each other’s goals, focusing on the success of the organization and mission, rather than of the individual.

AI Leadership Labs attendees reiterated that, when it comes to AI transformation, CEOs will have to work many levels down in the organization and in close partnership with those who understand AI, building a cabinet of advisors around them from consultants to technology experts to board directors.

^{Source: Russell Reynolds Associates H1 2024 Global Leadership Monitor, n=1,728}

So how can leaders effectively tackle what’s next? A systems view for AI leadership transformation

Our AI Leadership Labs series has helped us create, test, and revise the RRA Systems View on AI Leadership Transformation.

While acknowledging that AI is the next weapon in a well-stocked tech-transformation arsenal, it does differ in one key respect: while digital transformation required daunting costs, scaling, and armies of technologists to implement and integrate digital technologies across vast and complex organizations, much of the GenAI opportunity is in the hands of the entire organization from day one. The most powerful approach to transformation equips the entire organization with the tools and leadership to drive change across the business at every level.

^{Source: Russell Reynolds Associates’ AI Leadership Labs proprietary analysis, 2024}

Re-imagine the business

Now is the time for radical change, rather than simply upgrading the edges of the business.

Take Blockbuster, a famed loser of digital transformation. Blockbuster refused to transform and found themselves losing out to the likes of Netflix. Less known are the myriad of companies who did transform, but still fell short: for example, Barnes and Noble built an ecommerce platform and invested in technology, but fundamentally kept its core business the same.

We advise leaders to scenario plan for how AI connects to their broader business transformation, and use that to imagine an AI-enabled future over a long term horizon, with radical product innovation.

CEO enablement

CEOs looking to get smart on AI have created a “cabinet” of AI advisors around them, either informally or via a formal advisory board. Members of this AI advisory cabinet may include transformation experts (AI experts, general managers, and consultants); those who bring technical or visionary experience (entrepreneurs, technologists, and academics); or even strategists specializing in acquisition. This makes it easier to incorporate AI into strategic and operating discussions across the board and leadership team.

A great example of a successful AI cabinet occurred at the GenAI powerhouse Microsoft, where CEO Satya Nadella has surrounded himself with AI experts, partnering with OpenAI and proselytizing a “learn-it-all,” (rather than “know-it-all”) culture.² Labs attendees discussed emulating tech giants like Microsoft, Amazon, and Google, while also working closely with technology talent across their organizations.

^{Source: Russell Reynolds Associates H1 2024 Global Leadership Monitor, n=1,662}

C-suite redesign

Core to AI enablement is an ‘enterprise mindset’ across the C-suite, in which leaders focus on the success of the team and the company, rather than on their specific function’s prowess.

First, leaders must design a future-facing success profile for every function and role. Then, assess current capabilities against the leadership needs of the future. Following this, encourage each function to support other functions in their missions, and underpin them all with technology/digital capabilities (Figure 3).

We spoke to one consumer retail company CEO who had personally been facilitating connections across functions, fostering team unity over self-promotion in an enterprise mindset push. We also spoke to a consumer digital platform leader who standardized the technology that was available to each corporate function (e.g., data visualization platforms), so that teams could learn and upskill together. We also spoke to an education charity who enhanced cross functional teaming by ensuring that enterprise technology transformation projects were led by business leaders, rather than technology functional leaders.

Figure 3: Shifting from an expertise focus to an AI-enabled enterprise mindset

^{Source: Russell Reynolds Associates H1 2024 Global Leadership Monitor, n=1,383}

Board composition

The last decade has seen a dramatic increase in technical board directors, from CEO and GM technology talent, to strategy and M&A advisors, and now increasingly to CIO, CTO, CDO and equivalent talent. Organizations should appoint an AI systems thinker with relevant industry context, while also accelerating upskilling and training for existing board members around AI. Boards should aim for strategic and technological balance, not over-indexing on tech visionaries who can’t also bring business experience.

There are countless good examples of successful organizations appointing strong digital board talent, often not stopping at one or two technology experts (Figure 4). Walmart, for example, has appointed board members with experience from PayPal, AT&T, Univision Communications, Nextdoor Holdings, Altaba, and Google. Airbus recently appointed Dr Feiyu Xu, a leading figure in the world of AI, to their board. Not all companies have opted for such a direct solution; one gaming company has invested heavily in developing certain board members’ AI understanding, rather than engaging in the process of creating a space on the board; and one global information services organization is extending the charter of their audit committee to include responsible AI, while also appointing product & infrastructure talent to their board.

Figure 4: AI-savvy board directors can focus on multiple areas

^{Source: Russell Reynolds Associates H1 2024 Global Leadership Monitor, n=1,676}

AI enablement

Good data is the bedrock of functional AI. But currently, many organizations’ data is disconnected, with functions and business units picking from it as needed. For AI to be effective, organizations need a central data store, with silos being left to the last possible moment of analysis.

As the majority of industry leaders still lack confidence in their employees’ technical AI skills (Figure 5), immediately establishing the right data infrastructure and expertise is key.

Figure 5: Leaders’ confidence in their employees’ technical AI skills by industry

% leaders agreeing that their organization has the right technical skills or people in-house required to implement generative AI solutions

^{Source: Russell Reynolds Associates H1 2024 Global Leadership Monitor, n=1,676}

First, leaders must upgrade their data infrastructure, centralizing it and elevating their data governance. Then, they should build the AI capability and innovate around agreed use cases, which thereafter will maximize a partnership with AI platform organizations to dramatically increase capability and scale.

One particularly interesting story came from a small MedTech company CEO, who discussed how he had hired a team of outsourced AI developers to re-build their tech stack from scratch. Once built, they re-integrated it into their existing business, therefore saving years of arduous transformation and change management. At the other end of the spectrum, we spoke to a $50 billion pharmaceutical company who had moved 80% of their data to the cloud, integrating the data and re-engineering their platforms to embed AI, then hiring a senior AI leader to oversee the transformation.

How our clients are tackling the AI challenge

^{Source: Russell Reynods Associates’ AI Leadership Labs proprietary analysis, 2024}

Rethink culture and approach to learning

Develop a culture of innovation, free from shame or fear of failure. This can be achieved by giving all employees the tools needed to get to a basic understanding of AI and the opportunities it provides. Find champions of AI in the organization and make them more visible. These can include senior leadership, who themselves must demonstrate leadership commitment to AI by leveraging tools at the top. A successful AI and digital transformation strategy must be holistic, integrating technology with cultural and learning initiatives to build a resilient and innovative workforce. This involves continuous investment in talent development, fostering a culture of experimentation and agility, and ensuring that ethical considerations are at the forefront of AI deployment.

We spoke to an information services company who launched AI-powered products within their organization, and opened an innovation pilot to 4000 employees. We also spoke to several organizations who instituted mandatory AI trainings across the organization, from the board of directors down to the shop floor. One AI consultant discussed bringing the employee base on board by identifying willing and energized AI champions to make more visible in the organization, finding quick wins leveraging AI to tackle boring or tough areas of the role, and over-communicating the benefits and long term opportunities at every step.

A phased approach to AI business transformation

Many CEOs in our AI Leadership Labs shared the overwhelming scale of the AI challenge. And with other pressing topics from sustainability to DEI to macro-economic challenges all competing for air-time, it’s easy to see how decision paralysis can arise.

Those who have managed AI transformations best noted tackling the challenge incrementally, starting by supporting the CEO in leading the AI conversation, then integrating AI into all strategic discussions. This is then supplemented by bringing in AI-savvy leadership—from the board of directors down to the factory floor, all with access to AI tools and the requisite training to be successful, and then publicly championing and showcasing their success.

How we are helping our clients engage with AI’s opportunities:

• AI Transformation: AI-led transformation advisory, supporting boards and CEOs as they reimagine their business and associated leadership challenges
• AI Functional Leadership: Partnering with organizations to find pivotal AI leaders who can strategize with and implement AI technologies
• AI Infrastructure & Platforms: Supporting the growth and scaling of platform, data, and technology companies underpinning the rise of AI technology

Authors

Fawad Bajwa leads Russell Reynolds Associates’ AI, Analytics & Data Practice globally. He is based in Toronto and New York.
Leah Christianson is a member of Russell Reynolds Associates’ Center for Leadership Insight. She is based in San Francisco.
George Head leads Russell Reynolds Associates’ Technology Knowledge team. He is based in London.
Tristan Jervis leads Russell Reynolds Associates’ Technology practice. He is based in London.
Harpreet Khurana is the Chief Digital and Data Analytics Officer at Russell Reynolds Associates. He is based in New York.
Tuck Rickards is a senior member and former leader of Russell Reynolds Associates’ Technology practice. He is based in San Francisco.
Amy Scissons is the Chief Marketing and Communications Officer at Russell Reynolds Associates. She is based in New York.