Much has been written recently about the impact that AI will have on global organisations — companies, governments, non-profits – and how organisations must prepare to manage these responsibilities. Artificial Intelligence is arriving rapidly, and organisations will be forced to adapt, whether they choose to or not. Mustafa Suleyman, author of The Coming Wave argues, “Within the next few years, AI will become as ubiquitous as the Internet”. JP Morgan’s Jamie Dimon calls AI “critical to our company’s future success” and adds that AI will eventually “be used in almost every job” at JP Morgan. Like it or not, the arrival of AI is inevitable, and organisations must find a pathway to adoption that fits their mission, capabilities, and culture.
As an advisor on data and AI to leading global organisation for 30+ years, it is clear to me that the successful adoption of AI will depend on a few critical factors. First, great AI will depend upon great data. Mustafa Suleyman notes, “Eighteen million gigabytes of data are added to the global sum every single minute of every day”. Those organisations that establish a strong data foundation will be best positioned to leverage the benefits of AI. Second, organisations must be honest about the cultural challenges that they face, including their willingness and readiness to undertake transformational efforts that will alter processes and require new skill sets. Finally, organisations must be realistic about past experience and learn from previous efforts to build data, analytics, and AI leadership functions.
History can teach us. Organisations need to recognise and appreciate their mixed record in building the data foundation and culture that is a prerequisite for maximising organisational success. One long-running survey of leading global organisations has shown that progress on data, analytics, and AI has come slowly for most. Even as organisations have embraced the arrival of Generative AI within the past year, half of organisations still report that they are not competing on data and analytics, not managing data as an asset, have not created a data-driven organisation, and have not established a data, analytics, and AI culture. Further, more than three-quarters of organisations continue to cite cultural obstacles as the greatest barrier to data, analytics, and AI success.
Past efforts to establish data leadership roles and responsibilities have met with mixed results. Although a few organisations had dabbled with the Chief Data Officer (CDO) role prior to 2008-2009, it was largely in response to the financial crisis of this period that the role was formally established. By 2012, only 12% of leading companies reported had established the CDO role within their organisations, and it was not until 2017 that this number surpassed 50%. Over time, the responsibilities of the role expanded within many organisations to include analytics and AI. Although 83% of leading organisations now report having a Chief Data and Analytics Officer (CDAO), nearly half say that the role is still not successful and well-established, and continues to be characterised by a brief, uncertain tenure, with 6% saying that the role has been an outright failure.
With nearly two-thirds of leading organisations now reporting that Generative AI has the potential to be the most transformative technology in a generation, many organisations are reassessing whether there is a need for a separate and distinct AI leadership function. Although 61% of organisations report that Generative AI falls within the responsibilities of the CDAO, and 79% argue that it should be situated there, recent stories suggest that 11% of organisations have gone ahead and created a new role — the Chief Artificial Intelligence Officer (CAIO); 21% of organisations report that they are actively recruiting for the CAIO position. The New York Times published a recent lead story, Hottest Job in Corporate America? The Executive in Charge of AI. Boston Consulting Group (BCG) has weighed in with their perspective in a thought piece entitled, Every C-Suite Member Is Now a Chief AI Officer. MarketWatch has added a further perspective in their story, Chief AI Officer: A necessity for companies or an expensive impediment? We are at a critical juncture.
Global organisations are confronted with a potentially once-in-a-generation challenge, one with great opportunity as well as equivalent risks. Will organisations realise exponential productivity gains, elevate knowledge workers from mundane tasks, and improve customer satisfaction, or be hampered by threats of misinformation, ethical bias, and job displacement? What kind of data and AI executive leadership will be required to seize upon the opportunities, navigate the challenges, risks, and threats, implement the prerequisite safeguards and guardrails, and deliver transformational value to their organisations? I offer 3 suggestions.
First, make data and AI a business responsibility. Organisational reporting relationships continue to be a topic of ongoing debate. When the CDO/CDAO role was first established, it was mostly a defensive role focused on risk mitigation and compliance – ensuring that regulatory data reporting was accurate and complete. Over time the role evolved, with a greater focus on business outcomes such as revenue and customer growth. The success of the role and of its incumbents increasingly depended upon strong integration into organisational and business processes, strong partnership and collaboration with organisational and business leaders, and delivery of quantifiably measurable results. As the CDO/CDAO role has evolved, it has increasingly moved from being seen as primarily a technology and infrastructure role reporting to the Chief Information Officer (CIO), to a critical business role reporting to the CEO or COO, a pattern that has worked well for many of the most successful organisations.
Second, educate corporate boards on the opportunities and risks associated with AI. It has been noted that while over 95 percent of board members believe in the need for AI, just 28% of companies have made realistic progress. Further, there have been reports of misperceptions and misunderstanding by board members of their understanding of AI, its implications, and inherent risks. Organisations owe it to themselves to ensure that data and AI executive leaders are on the agenda to present regularly at board meetings — to educate the board, track progress, and highlight ongoing risks. It should be appreciated that AI continues to evolve at an accelerated pace and in this respect, we are all learning together. Board members, organisational leadership and data and AI leadership are in the same boat, where patience, commitment, and adaptability will be necessary to achieve the best outcome.
Third, plan for an AI future. Should organisations establish a Chief AI Officer role or situate AI responsibilities under the Chief Data Officer? It is likely too early to tell, and what works for one organisation may not function best for another. What is less subject to debate is that for organisations to succeed in an AI future, they need to establish strong data and AI leadership quickly, in whatever form works best for that organisation. The great news for data and AI leaders is that the demand for their skills and expertise will only increase. Although half of organisations continue to struggle, half of organisations have demonstrated success in integrating data and AI capabilities into their organisational processes and operations.
Mustafa Suleyman concludes, “We are going to live in an epoch when the majority of our daily interactions are not with other people but with AIs”. Jamie Dimon echoes this sentiment in his comments. This is what technological transformations have looked like throughout human history. Transformation brings disruption, and disruption can foster resentment and resistance. This is a challenge that data and AI leaders, and the organisations that they are a part of, can expect to face. We might not look forward to, or feel prepared for, an AI future. We might not like it. We might even resist it. But, like it or not, an AI future is coming. It’s not a case of if but a case of when. The arrival and enormous transformational impact of AI is inevitable. Now more than ever, global organisations will need strong data and AI executive leadership to navigate this future. Organisations best prepare.
Originally published in Forbes on March 7, 2024. Republished here with permission of the author Randy Bean.
Randy Bean
Randy Bean has been an advisor to Fortune 1000 organizations on data and AI leadership for nearly 4 decades. He is a Senior Advisor, Author, Speaker, Founder, CEO, Board Member, and Innovation Fellow.
Randy is the bestselling author of “Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI”, and a regular contributor to Forbes, Harvard Business Review, and MIT Sloan Management Review on Data & AI leadership.
He was previously Founder and CEO of NewVantage Partners (NVP), a data and AI leadership advisory firm to Fortune 1000 clients, which he founded in 2001. NVP was acquired by Wavestone, a Paris-based global consultancy, in 2021.
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