GenAI First Steps — Use Case Selection is Key. How to Pick a Winner.

 GenAI has been on my mind lately, as it has for many data leaders. This piece struck a chord with me in the way it considered the early steps toward GenAI adoption.

I particularly like the approach of describing those early moves as experimentation and controlled experimentation at that. That’s a powerful way of putting it; it implicitly conveys the need to proceed with due caution to reap the benefits. It is indeed a balance between risk and reward, between caution and speed.

These early steps toward GenAI are a delicate balancing act—not just for organisations but also for the reputations of those who lead their data functions.

Generated with AI. September 19 2024 11:26PM

For me, a well-considered choice of use cases is critical. Pick the right few to start the journey:

• Ones that don’t reach too far and won’t see the light of day without a squadron of consultants
• Ones that recognise and can take advantage of the (actual) state of readiness of your data estate (and associated tech)
• Ones that don’t carry too much downside risk if things go wrong
• But, equally, don’t choose those that only deliver benefits so small (or easily achievable by other means) that the outcome is seen as inconsequential

You need to signal you’re moving to keep key stakeholders happy and stop a raft of parallel fragmented activity. But more than that, there’s also an excellent opportunity to act to control a risk that is emerging (already manifesting) across a much larger stakeholder cohort. That’s staff rushing to embrace the productivity boost they see GenAI can give them but not thinking about / knowing about / caring about the risk that may bring. Find an early use case that helps staff do the right thing rather than feeling their only choice is between reaching outside or doing nothing.

Choosing the first GenAI use cases well can also offer a means to deal with this risk. Before corporate (or customers’) data goes outside organisational walls or a hallucination is blindly acted upon unchecked.

And then there’s the question of AI governance.

- When should we introduce guide rails vs. guiding principles vs. hard and fast standards and robust, comprehensive frameworks?

- How much is too much? And how will we know when that point has been eclipsed?

- Can this governance development work as a parallel activity rather than something that needs to be landed first?

Pragmatically, I see the need to implement “enough” governance while actively watching for unchecked behaviour or people seeming mired and unable to move. We must also be prepared to learn, iterate, and adjust quickly.

This brings us back to one more criteria for use case choice—getting those right (or at least right enough) is critical!

The Rise of the Chief AI Officer: A Strategic Imperative for Data-Driven Leadership

 

As we stand on the precipice of a new digital dawn, the role of the Chief AI Officer (CAIO) emerges as a beacon of innovation. This role is not just another title in the executive suite; it is a clarion call for a transformative approach to leadership in the age of artificial intelligence. The CAIO is envisioned not as an isolated figure but as a unifying force, seamlessly integrating into the very core of an organisation’s strategy.

 

Hold on there! I didn’t write that, AI did. Sounds compelling though, doesn’t it? Either that or it sounds like the first paragraph of a novel in which our hero, CAIO, saves the day.

Image generated with AI, August 25 2024 at 6.19pm

I came across this article (The Changing C-Suite: Chief AI Officer In, Chief Diversity Officer Out (itprotoday.com)) which put forward different points of view about the potential for the emergence of a Chief AI Officer and where the role might fit within the broader C-Suite. It got me thinking and I dropped a few quick thoughts on LinkedIn.  Now that I’ve mulled some more and my friendly Co-Pilot has grabbed your attention with his (its?) prose, here’s a little more reflection…. (This bit actually written by a human!)

 

The corporate world is abuzz right now with the potential of artificial intelligence and the lure of its promise to redefine the boundaries of innovation and efficiency. But the success of AI, to my way of thinking at least, in many cases can’t be separated from the quality of the data that fuels it. Well-managed, accurate, trusted and understood data is the cornerstone upon which AI systems should be built. Without this critical foundation, AI projects are at risk of underperforming, leaving a chasm between the expected value and what’s actually delivered.

 

Beyond the Silo: The Collaborative Paradigm for Sustainable AI Integration

I’d suggest that if Chief AI Officers do appear as a mainstream role, the successful amongst them will be those who embraced (and were allowed to embrace) the multifaceted nature of the role. A leader who is solely focused on AI and what it can do for the organisation isn’t setup for success. He or she is potentially isolated from key enablers. At best, isolated in their own process, they lack the influence to attract the necessary consideration and support from others. At worst, they’re blind to the things that could erode the value AI brings or cause serious organisational harm springing from an unseen implication or missed consideration.  Without an holistic view of the organisation’s data and all its nuances, they may well find themselves at a disadvantage. This narrow focus can lead to a short-sighted approach chasing quick perceived value, but potentially overlooking the broader implications on risk and governance.

A siloed Chief AI Officer may find themselves navigating a labyrinthine path, fraught with challenges, short-lived victories, not to mention their own short tenure!

The sustainable integration of AI into business practices needs a leader who is not only doggedly chasing the value from AI but also has a deep understanding of the business landscape. The Chief AI Officer must not just be a collaborative master personally, but also build a culture of collaboration, working hand-in-hand with both line of business leaders and data leaders.  Anything else may well fail to ensure that AI initiatives are more than a momentary shining star.

 

Chief Data Officer: Sidekick or hero in this story?

The Chief Data Officer, already attuned to the intricacies of managing both defensive and offensive with regard to data assets, provides a complementary perspective to the Chief AI Officer. By joining forces, either as a single role or a collaborative partnership, the CAIO and CDO can create a synergistic relationship that uses AI for its transformative power and new ways to deliver value while also building upon and strengthening pre-existing data imperatives.

My view is that early integration is a good thing. Sure, find the box canyons and the gotchas the hard way. Innovate and potentially fail before trying again. But, wouldn’t things be easier with an ally to help point out the pitfalls ahead of time?

Balancing offensive gains with a defensive awareness and building from a solid base, gives our hero half a chance to move from just starring in a quickly forgotten short story to become the feature act across a box set of novels.

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