Effective Generative AI & Digital Transformation needs deeper Organizational Surgery — McKinsey

McKinsey has a thought-provoking narrative that should resonate deeply with those seeking to leverage the potential of Generative AI. Let me summarize the five ideas (and a couple of quotes) that stood out for me.

I am not a huge fan of McKinsey, but found some kindred souls on this one !

You should definitely read the blog [here], alongside additional insightful readings — a book [here] and the “Rewired In Action Case Collection” [here].

I have started reading the Rewired book — plan to write a blog summarizing the ideas w.r.t Digital Transformation & Generative AI - A dynamic duo !

1. Generative AI has enormous potential, … but the payoff may only come when companies do deeper organizational surgery on their business

• As I had written “Hallucination as a Feature not a Bug” [Here], only by employing Generative AI natively can we extract it’s value.

Opportunistic applications (where Generative AI is used now) are not an optimal sweet spot for Generative AI

While “faster & cheaper and with less people” is worth pursuing, the focus should be on “doing things better to create a competitive advantage” — which Generative AI can do, once these fantastic beasts are tamed properly !!

• For example, “Customer service is a commodity capability, not part of the core business for most companies. While Generative AI might help with productivity in such cases, it won’t create a competitive advantage.”

2. Launching pilots is (relatively) easy; getting pilots to scale & create meaningful value is hard because they require a broad set of changes to the way work actually gets done

• This another important point. Developing a prototype is easy because the foundation models do a lot of the work — so we can quickly show interesting results.
• But deploying them in production requires lot more effort — Guardrails, Red teaming, Observability, Risk Evaluation, Governance and Compliance are all things that take time and thought. They can’t be solved by prompt engineering or RAG !

For my new readers I have blogs on the guardrails [here] and Red Teaming [here]

• Organizations need to set up the technology architecture to scale — composability, orthogonal extensibility are all important architectural pragmas

3. Up-skill the talent you have but be clear about the Generative AI-specific skills you need

• But they aren’t the normal coders or data scientists. The best Generative AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts and strong forensic skills to figure out causes of breakdowns

4. Form a centralized team to establish standards that enable responsible scaling

• I am a big proponent of a centralized cross-functional Generative AI WG that is in the middle of all the activities (see figure below)

To facilitate responsible scaling, this centralized cross-functional Generative AI Working Group (WG) emerges as a strategic necessity. This team serves as the linchpin, developing protocols and standards to support scalable deployment while mitigating risks effectively.

5. Keep in mind that the domain is still very nascent

As Sam Altman mentions in his interview with Gates [here], the current models are at their stupidest and we are on a very steep improvement curve.

“In some ways, this article is premature — so much is changing that we’ll likely have a profoundly different understanding of Generative AI and its capabilities in a year’s time”

Finally, couple of interesting quotes heard at the Authors talk video about the genesis of the book [here]

• “They’re (Generative AI) amazing. They pass the Arthur C. Clarke test of technology that’s indistinguishable from magic” — Rodney Zemmel
• “One use case never does the job. It is a family of many that needs to be strong together to actually create meaningful impact and change experiences” — Eric Lamarre
• “There is a difference doing digital vs. being digital — an old IT culture vs. the new digital culture” — Rodney Zemmel