CONTRIBUTOR
CEO and Founder,
Data Kinetic

The excitement is palpable, isn’t it? In recent months, it seems like every leader in the Global 2000 and beyond has been pondering: “What can ChatGPT or any of the latest AI technologies do for our organization?” Or, just as likely: “How safe are these technologies, really?

 

These are crucial questions, yet they are seldom addressed with a practical perspective focused on applied AI outcomes. The traditional tools and processes we’ve employed to introduce technology have given us insights into managing risk, establishing committees and fostering collaboration among teams.

While these efforts often result in impressive slide decks, they tend to prioritize observation and risk management over moving the business forward with new technologies. To truly harness the potential of AI, enterprise leaders must rethink how they evaluate and implement AI in practice.

Looking Closely at AI Leadership 

Currently, the responsibility for AI is often delegated to IT, InfoSec, HR and other horizontally oriented teams. This is understandable because AI can be nerdy, presents risks and is complex to comprehend. It doesn’t help that technology companies that explain and sell AI often struggle to start with a business-oriented approach; instead, they quickly steer the conversation toward selling their platforms.

It’s safe to say that no CEO has ever declared, “I’m thrilled with our perfect AI platform! Our strategy is complete!”

AI leadership must distance itself from purely focusing on technology and, instead, explore new avenues for delivering tangible value. The key lies in identifying transformational use cases that begin with a clear business statement.

The true role of AI leadership entails an intricate understanding of the most critical business challenges, translating them into a portfolio of AI initiatives for the organization, securing C-Suite support and alignment and continuously monitoring the performance of each investment.

At this point, you may be wondering why we need a new C-suite role for this purpose. Couldn’t a CFO, CIO, CTO, or another existing role handle it effectively? Perhaps, but despite AI being on the executive radar for the past decade, a 2022 Deloitte report revealed that 50% of AI projects lack executive buy-in

Moreover, we’ve all heard statistics about how only a fraction of AI projects make it to production. This may be attributed to organizations adhering to traditional methods for assessing and advancing AI adoption. Organizational dynamics may also play a role, with Conway’s law suggesting that company-wide transformations are destined to fail from the outset. To catalyze AI transformation, the Chief AI Officer (CAIO) must report directly to the CEO and be responsible for uniting all senior leaders in a unified plan.

So, what’s next? You’ve decided to introduce the CAIO role, but what should they do? As a leader, how do you measure the success of this role? There are four essential dimensions to track in order to gauge the success and impact of adding a CAIO to your organization:

  1. Company-wide AI Planning: Aligning the entire organization is no easy feat. Typically, the process of creating an AI roadmap begins with a fancy conference room, some muffins, a slew of ideas, a PowerPoint deck and a presentation six weeks later to a mostly agreeable audience. This approach often falls short. Instead, invest in a roadmap that encompasses educational transformation programs and AI projects. Make it the framework for aligning all teams, establish principles for prioritizing and assessing the impact of initiatives, and communicate, educate and backlog new projects as needed. Make roadmap reviews and ideation a regular occurrence.

Without clear principles for making changes and approving new projects, multiple, conflicting company-wide AI transformational roadmaps are likely to emerge from various teams that didn’t get what they wanted initially.

  1. Stay Accountable and Track Performance: Starting an AI project is straightforward, but completing one is challenging. A proficient CAIO sets clear impact goals from the outset and continually guides the organization toward achieving those outcomes in a production setting. An outstanding CAIO continuously tracks the actual impact of each project and keeps the business informed about its performance over time.

Equally important is measuring the evolution of the organization as it adapts to the initiatives. Do not underestimate the resistance to change from those who prefer the status quo. Displaying both project progress and organizational change management progress will provide valuable insights in both directions. Remember that there’s often a reason for the old way of doing things, and by understanding resistance at the ground level, you may discover ways to enhance your AI endeavors.

  1. Look at the Competitive Market: This dimension is frequently overlooked and underestimated, as our colleagues in Mountain View can attest. As a member of the C-suite, the CAIO is expected not only to drive transformation and impact but also to understand and communicate effectively how competitors might use AI to disrupt your business or introduce new competitive pressures.

A successful CAIO can provide insights into competitors’ potential initiatives and devise countermeasures or contingency plans if necessary. The goal is not necessarily to preempt every scenario but to recognize and respond to potential threats in the future.

  1. Manage that Program: AI program management is the primary focus of AI transformation today. We all recognize the importance of managing bias, ensuring fairness, and auditability in program management. However, successful AI program management, when led by a CAIO, should expand throughout the organization. 

This means providing regular program updates in board meetings and keeping the broader organization informed through regular team updates. Ensuring company-wide transparency on initiatives and implementing an education program at all levels of the business is crucial.

Expanding program management to demystify how AI programs support the business and its teams not only accelerates progress but also increases the likelihood of discovering your next major AI breakthrough during those company-wide training sessions.

Ultimately, it may not be an easy journey, but it’s worth it. While you can wait for the easy button, consider this: What’s stopping someone else from figuring it out before you do?