How is AI and data leadership at large organizations being transformed by the accelerating pace of AI adoption? Do these leaders’ mandates need to change? And should overseeing AI and data be viewed as a business or a technology role? (View Highlight)
We have had a front row seat over the past three decades to how data, analytics, and now, AI, can transform businesses. As a Chief Data and Analytics Officer with AI responsibility for two Fortune 150 companies, as an author of groundbreaking books on competing with analytics and AI in business, and as a participant and advisor on data, analytics, and AI leadership to Fortune 1000 companies, we regularly counsel leading organizations on how they must structure their executive leadership to achieve the maximum business benefit possible from these tools. So, based on our collective first-hand experience, our research and survey data, and our advisory roles with these organizations, we can state with confidence that it almost always makes the most sense to have a single leader responsible for data, analytics, and AI. While many organizations currently have several C-level tech executives, we believe that a proliferation of roles is unnecessary and ultimately unproductive. (View Highlight)
CDAIOs Must be Evangelists and Realists
Before the 2008–09 financial crisis, data and analytics were widely seen as back-office functions, often relegated to the sidelines of corporate decision making. The crisis was a wakeup call to the absolute need for reliable data, the lack of which was seen by many as a precipitating factor of the financial crisis. In its wake, data and analytics became a C-suite function. Initially formed as a defensive function focused on risk and compliance, the Chief Data Officer (CDO) has evolved in the years since its establishment, as a growing number of firms repositioned these roles as Chief Data and Analytics Officers (CDAOs). Organizations that expanded the CDAO mandate saw an opportunity to move beyond traditional risk and compliance safeguards to focus on offense-related activities intending to use data and analytics as a tool for business growth. (View Highlight)
Once again, the role seems to be undergoing rapid change, according to forthcoming data of an annual survey that one of us (Bean) has conducted since 2012. With the rapid proliferation of AI, 53% of companies report having appointed a Chief AI Officer (or equivalent), believe that one is needed, or are expanding the CDO/CDAO mandate to include AI. AI is also leading to a greater focus and investment in data, according to 93% of respondents (View Highlight)
These periods of evolution can be confusing to both CDAIOs and their broader organizations. Responsibilities, reporting relationships, priorities, and demands can change rapidly—as can the skills needed to do the job right. In this particular case, the massive surge in interest in AI has driven organizations to invest heavily in piloting various AI concepts. (Perhaps too frequently.) These AI initiatives have grown rapidly—and often without coordination—and leaders have been asked to orchestrate AI strategy, training data, governance, and execution across the enterprise. (View Highlight)
In most mid-to-large enterprises, data and AI touch revenue, cost, product differentiation, and risk. If trends continue, the coming decade will see systematic embedding of AI into products, processes, and customer interactions. The role of the CDAIO is to act as orchestrator of enterprise value while managing emerging risks. A single leader with a clear business mandate and close relationships with key stakeholders is essential to lead this transformation. (View Highlight)
Based on successful AI transformations we’ve observed, organizations today must entrust their CDAIOs with a mandate that includes the following:
• Owning the AI strategy. To bring about any AI-enabled transformation, a single organizational leader must define the company’s “AI thesis”—how AI creates value—along with the corresponding roadmap and ROI hypothesis. The strategy needs to be sold to and endorsed by the senior executive team and the board.
• Preparing for a new class of risks. AI introduces safety, privacy, IP, and regulatory risks that require unified governance beyond traditional policies. CDAIOs should normally partner with Chief Compliance or Legal Officers to manage this mandate.
• Developing the AI technology stack for the company. Fragmentation and inconsistent management of tools and technology can add expense and reduce the likelihood of successful use case development. CDAIOs need the power to follow through on their vision for the adoption and development of tools and technologies that are right for the organization, providing secure “AI platforms as products” that teams can use with minimal friction.
• Ensuring the company’s data is ready for AI. This is particularly critical for generative AI, which primarily uses unstructured data such as text and images. Most companies have focused only on structured numerical data in the recent past. The data quality approaches for unstructured data are both critical to success with generative AI and quite different from those for structured data.
• Creating an AI-ready culture. Companies with the best AI tech might not be the long-term winners; the race will be won by those with a culture of AI adoption and effective use that maximizes value creation. CDAIOs should in most cases partner with CHROs to accomplish this objective.
• Developing internal talent and external partner ecosystems. It’s essential to develop a strong talent pipeline by recruiting externally as well as upskilling internal talent. This requires building strategic alliances with technology partners and academic institutions to accelerate innovation and implementation.
• Generating significant ROI for the company. At the end of the day, CDAIOs need to drive measurable business outcomes—such as revenue growth, operational efficiency, and innovation velocity—by prioritizing AI initiatives tied to clear financial and strategic KPIs. They serve as the bridge between experimentation and enterprise-scale value creation. (View Highlight)
As important as what CDAIOs are being empowered to do, is how they’re positioned in an organization to do it. Companies are adopting different models for where the CDAIO reports within the organization. While some CDAIOs report into the IT organization, others report directly to the CEO or to business area leaders. At its core, the primary role of the CDAIO is to drive business value through data, analytics, and AI, owning responsibility for business outcomes such as revenue lift and cost reduction. While AI technology enablement is a key part of the role, it is only one component of CDAIO’s broader mandate of value creation. (View Highlight)
Given the emphasis on business value creation, we believe that in most cases CDAIOs should be positioned closer to business functions than to technology operations. Early evidence suggests that only a small fraction of organizations report positive P&L impact from gen AI, a fact that underscores the need for business-first AI leadership. While we have seen successful examples of CDAIOs reporting into a technology function, this is only when the leader of that function (typically a “supertech” Chief Information Officer) is focused on technology-enabled business transformation. (View Highlight)
Whatever the reporting relationship for CDAIOs, their bosses often don’t fully understand this relatively new role and what to expect of it. To ensure success of the CDAIO role, executives to whom a CDAIO reports should maintain a checklist of the organization’s AI ambitions and CDAIO mandate. Key questions include:
• Do I have a single accountable leader for AI value, technology, data, risk and talent?
• Are AI and data roadmaps funded sufficiently against business outcomes?
• Are our AI risk and ethics guardrails strong enough move ahead quickly?
• Are we measuring AI KPIs quarterly at minimum and pivoting as needed?
• Are we creating measurable and sustainable value and competitive advantage with AI? (View Highlight)
