How AI is Redefining the Role of Analytics Leadership

When intelligence becomes the new oil.

Photo by Stacy on Unsplash

Background

This article is a continuation of my now-declared series on “Intelligence is the new oil”. Two articles back, I wrote about the observation that intelligence is replacing data as the new oil. And against that backdrop, the possible rise of a new class of knowledge workers, whom I labeled as Intelligence Engineers.

And then in my last article, I wrote about what I would keep and what I would change if I could restart my corporate life as a Chief Analytics Officer.

And so, my 134th article combines both these topics. It’s an outline of how I see the role of the Chief Analytics Officer and related Analytics leadership being impacted, and transmuting, in this new Age of AI.

(I write a weekly series of articles where I challenge or shed new light on the practice of data analytics / data science which you can find here.)

Capabilities Building

In my article, I defined intelligence as it relates to the Age of AI — “the ability to process information and achieve goals”. This is a computational perspective of intelligence. The CAO and Analytics leaders operate at the organisational level, and in that broader perspective, “intelligence” can be defined as the collective capacity of an organisation to sense, interpret, and respond to its environment in a way that creates a sustainable advantage.

To understand the impact of the paradigm shift towards intelligence on the role of the CAO, we need to first understand what it is that a CAO does. We need to think in terms of capabilities building. At the C-suite, the CFO’s (chief financial officer) role is to build and shape the capability of the organisation to make good short-term and long-term financial decisions. The CRO’s (chief risk officer) role is to build the capability to actively manage the right risk profile for the organisation. For the CAO, the expectation is to develop the capability to leverage data to improve the efficiency and effectiveness of decision-making across the organisation. I would argue that this CAO objective remains valid in the Age of AI. The outcome remains the same — improving the efficiency and effectiveness of decision-making. The ubiquity and democratisation of AI plays naturally into the efficiency domain, with the activity of data processing becoming increasingly easier. But decision effectiveness is dominated by the ability to see the right signals, the ability to generate insights.

The New CAO

Back in September 2024, in my 54th article, I wrote about what it takes to become a chief analytics officer. This was of course in the earlier days of AI proliferation. In that article, I set down 3 defining characteristics or competencies for getting that top job. They are:

1. The ability to problem-find and problem-define, rather than problem-solve.
2. Acquisition of T-shaped skills — broad experiences across many data analytics / data science sub-domains and very deep skills in a few of them. (You can’t code your way to the top; specialists don’t rise to become leaders.)
3. The ability to manage complexity instead of just complications — operate as a “decision scientist” rather than a data scientist.

In the new world, these 3 competencies will still be relevant, but dialled into the new reality in slightly different ways. Two key responsibilities will define the role of the new CAO — insight generation and knowledge management.

Let’s tackle insight generation. This has always been the cornerstone of data analytics / data science. In the new world, the CAO will be battling with the CFO and COO (chief operations officer) to go beyond the simple and obvious substitution strategy. Both the CFO and COO have little control over top-line earnings, and they affect organisation outcomes largely through cost-cutting and productivity improvements. Their go-to solutions will always be automation, replacement, substitution. They play the efficiency game. Replace headcount with AI. The new CAO, however, sees effectiveness in decision-making as the real prize in the Age of AI. Focus on augmentation of human capabilities; augmentation of decisioning abilities. Where 1+1=3. The new CAO will need to dial up their ability to problem-find / problem-define. They need to be able to influence the CEO and the management team to invest in the right kinds of AI capability that will allow the organisation to stay in the game, to win in the game. They need to direct where should AI play. That’s all about insight generation.

Next, the world in which intelligence is the new input (i.e. oil) instead of data, the environment is going to be extremely noisy. Lots of armchair experts. Lots of hyperbole. Thinking fast but not necessarily thinking smart. The new CAO will be doing battle in this environment. Pushing for good intelligence vs bad intelligence. In this new world, knowledge management will be THE key to creating competitive capability. Balancing institutional knowledge vs personal (employee) knowledge. Explicit (encoded) vs tacit (experience-based) knowledge. The new CAO must thrive in managing complexity; reigning in the beast by actively shaping how an organisation captures and inventories its vast knowledge. The new CAO needs to operate like a decision scientist because knowledge lives in decision ecosystems.

Conclusion

The new CAO is going to be a progressive evolution of the old CAO. I don’t believe there is a need to create roles like Chief AI Officer or Chief Intelligence Officer. I am particularly allergic to bullshit C-suite roles that are linked with technology shifts, like AI Transformation Officer or Digital Transformation Officer. Organisational objectives don’t change. What changes is their environmental adaptation. The old C-suite roles have already been set up with the right objectives. They just need to evolve and accommodate. A good CAO already has the necessary ingredients to lead in the Age of AI. They just need to recognise where the boundary spaces are being redefined — that AI is a shift-right moment, where we can finally de-focus on data process and focus on quality insight generation and improving the effectiveness of decision-making through robust knowledge management.