Summary.    With the explosion of generative AI and other powerful new data tools, chief data and artificial intelligence officers (CDAIOs) are more important than ever as companies navigate this new landscape. Too many, however, are set up to fail by poor alignment, lack of...

This year, the already tenuous roles of the chief data, analytics, and AI officer (CDO/CDAO/CDAIO) have become even more precarious. Many companies have seen departures and recalibration of corporate data and AI leadership responsibilities.

These roles are still relatively new. The CDO job was established at big banks in response to the financial crises of 2008–2009, and was subsequently extended to industries as diverse as pharmaceuticals, health care, consumer goods, entertainment, and the Federal government. Between 2012 and 2023, according to survey data, companies having appointed a CDO grew from just 12.0% to 82.6%, with an expansion of responsibilities over time to include analytics (CDAO) and AI (CDAIO). However, only 35.5% of major companies report that the role is successful and well established, and just 40.5% say that the CDAIO role is well understood within their company. Clearly, something is not working.

For some data and analytics leaders, 2023 has felt like a return to the earliest unglamorous days of the role: financial turbulence and the explosion of generative AI has forced them to focus on defensive risk and regulatory tasks rather than forward-looking initiatives focused on growth, customer acquisition, and the creation of new products and services. Corporate leadership is placing demands on CDAIOs to deploy the potentially transformative capabilities of GenAI while avoiding harm — a high-pressure balancing act with a technology that offers huge risks and opportunities.

In a moment when more companies want and need CDAIOs, the role is as challenging as ever — and often set up for failure. Here are five steps companies can take to fix it.

What’s Wrong with the CDAIO Job?

As coauthors we have been firsthand witnesses of and participants in the rise and evolution of the CDAIO role. Randy has served as an advisor to leading companies on the use of data and analytics for over two decades. Allison served as an industry CDO for five years and is currently advising CDAIOs and companies on how to deliver business value. While we both agree that the role can feel impossible, we also believe the current iterations include the foundations for a better, more effective version of the job.

The first generation of chief data officers were often hired into large companies in regulated industries such as health care and finance. Initially, the role was understood as a defensive one that focused on control and risk rather than a business one, even though both functions use the same data and analytical skills. Transaction data used by banks to detect fraud patterns is also used to uncover existing or latent customer needs, but companies invested in the former and not the latter. As the focus shifted to commercialization of data, companies too often viewed this as a technical and talent problem, rather than as a business problem. They invested heavily in technology and people, building out data infrastructures and teams of data engineers and data scientists, but didn’t focus enough on the importance of business relationships and the most critical business questions.

As a result, companies didn’t end up getting what they wanted from their data programs. While 91.9% of companies report that they have achieved some measurable value from their investments in data and analytics, just a dismal 23.9% say they have created a data-driven organization, and an even more paltry 20.6% of firms report having established a data culture. The CDAIO has been left on the hook for major projects that require huge investment and reach into all corners of the company, but often fail to deliver measurable benefit. Even when they’ve done exactly what was asked of them, it can be hard to argue they’ve succeeded.

We believe that two factors largely led to this situation: the wrong focus and a lack of trust.

Rather than technology and infrastructure problems, the focus of the role should have been on business outcomes: Identifying the problem that you are trying to solve for your customers, prioritizing use cases with the highest business return, and cross-pollinating capabilities, whether the goal is commercial, risk control, or both. “The hardest part of the job is knowing what problem you are trying to solve for your customers,” says Cassie Kozyrkov, chief decision scientist at Google, and a pioneer in the field of decision intelligence.

Lack of trust has been an equivalent factor. Business leaders need to trust that the investments that they are making in data, analytics, and AI are delivering a business return — that they’re money well spent. If business value isn’t clearly being delivered, that trust erodes and business leaders will be reluctant to make further investments. CDAIOs, especially in large companies, have built out governance infrastructure of people, policy, processes, and stewardship models in efforts to federate ownership of trust in data throughout the organization. These efforts are complicated, often unpopular, and the benefits are difficult to quantify. Since virtually every problem in a digital economy can be described as a data problem, victory is hard to achieve unless there are agreed-upon metrics against which progress can be measured.

How to Fix It

Progress in any new era of innovation comes in fits and starts and can be hard to measure. It is fair to argue that data and business strategy have been misaligned, have not been a C-level and board priority, that governance efforts have been too clunky for widespread adoption and measurement, and that discipline has been incomplete. The emergence of generative AI has magnified these issues, and raised new issues of trust, quality, and ethics that are in the news and are commanding executive and board attention.

Companies can and must fix how they manage data, analytics, and AI, and set the CDAIO role up for success. This requirement will only expand, especially when 83.9% of companies plan to increase their investments in data, analytics, and AI next year. Here are a few concrete recommendations companies can undertake today to repair the CDAIO role and deliver business value from their data, analytics, and AI investments:

Make data everyone’s business.

While CDAIOs have long been promoting the importance of data literacy, there has been inconsistent adoption of practices such as strong governance, policies, and standards. The areas with the most maturity and discipline in data are typically the finance and compliance-related functions. Success in these areas, reinforced with C-level and board involvement, can serve as models for the enterprise.

At Schneider Electric, a global energy management and digital automation leader, Philippe Rambach, chief artificial intelligence officer, speaks of how Schneider has built a corporate culture that’s everyone’s business:

Being serious about data management requires a dedicated organization. To support this ambition, we decided to carve out data from IT, focusing data on a governance, business, and performance agenda across the company. We then decided to create two roles of chief data officer and chief AI officer. What’s key in the data-focused journey is to strive to have a single source of truth in the company, and to make high-quality data easily accessible across the company to all decision makers.

Make business leaders champions for data projects.

Business leaders need to become the champions and advocates for investment in data and analytics. Successful data leaders are critical partners to business leaders, who come to rely on them as righthand lieutenants providing critical data and decision-points that can drive successful business outcomes. CDAIOs should not seek to impose an agenda (“data and AI are great; we should be doing so much more”), no matter how well intentioned. Seek out business leaders who are ready to champion data and AI within their business lines and become trusted partners through results that build credibility.

Review all data and AI investments to make sure funds are well spent.

Distinguish between “nice to have” versus “need to have” investments. Continue only with those that are delivering measurable business value to the organization today or can demonstrate a quick path to value in the near term. Companies must refocus their investments on those capabilities that are essential and are needed to grow and compete. Data analytics and AI leadership requires time, attention, clear and effective communication, and the storytelling skills to articulate the need, establish realistic expectations, and elicit buy-in.

Shift to an ecosystem mindset.

To make the most of data and AI, cultivating partnerships and collaboration with vendors, universities, and other partners is important. Schneider’s Rambach adds:

The new nature of competition is really not about technology; AI technology moves too fast for that. It’s about the value you deliver to customers. And whatever value you deliver, it can be augmented through partnerships. We open up our IoT platform for third-party innovations so partners can use our software development kits to develop new applications to innovate for better efficiency and sustainability of buildings.

Proceed with caution.

While generative AI offers game-changing opportunities, Rambach reiterates the importance of understanding risks and proceeding with caution, as these models:

…expose companies to new types of vulnerabilities, notably by giving easier and faster access to larger quantities and diversity of data to more users. Now is the time to establish data governance and cybersecurity measures to use these new capabilities responsibly. Companies and users should always approach generative AI with caution and focus on confidentiality, like users should not upload any confidential information on publicly accessible AI chatbot platforms. Secured/private versions of LLMs should be preferred.

Many CDAIOs lead enterprise committees with leaders from risk, finance, technology, cybersecurity, legal/ethics, privacy, HR, and the business lines. These teams need heightened stature and accountability. Companies should also add data, analytics, and AI expertise to their corporate boards. Just 23.8% say that the industry is doing enough to address data and AI ethics. Issues of AI and data privacy, governance, and ethics will pose a threat to companies if not managed responsibly and effectively.

At a time when many companies are taking a hard look at the CDAIO function, now is the time when data and AI leaders must step forward to show how they are contributing to the business value of the company. Those companies that have a clear vision for how they will deliver business value from their data and AI investments will be the companies that are most likely to prevail in the coming decade and beyond.