GM Readers! ☀️

Welcome to the 2nd issue of Evolving Internet Insights: Brainfood — a weekly deep dive into a relevant emerging tech topic.

This week we are covering how consulting giants, McKinsey and PwC are using AI to super charge themselves.

Thanks for reading!

Liang and Dan 🙌

🧠 Brain Food

One focus topic to feed your brain.

AI Disrupts Knowledge Work 👔

It seems like every study on how Generative (Gen) AI will disrupt the existing job market and the future of work points out that Gen AI has knowledge work in its “crosshairs”.

For example, a recent study by McKinsey highlighted:

• “Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities.”

• “The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.”

• “Our updated adoption scenarios … lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates.”

Historically, the headlines around job automation by technology have largely focused on physical and manual labor. But, as reports like the one above point out, Gen AI bucks this trend and has the potential to disrupt all types of knowledge work.

This puts the onus on professional services firms, whose entire business is based on knowledge work, to act urgently and strategically.

Consulting Firms Fight For First Mover Advantage 🏃🏽‍♀️

To get ahead of the coming AI disruption, McKinsey and PwC, two consulting powerhouses, released internal Gen AI tools to “supercharge” their internal workforce. Rather than just talk about Gen AI and the coming disruption that they themselves have called out, they are acting on it, using the technology to their advantage.

McKinsey released Lilli, a generative AI powered research tool to augment its consultants in their workflow. Lilli is trained on Mckinsey’s proprietary data and intellectual property, which includes over 100,000 documents and interview transcripts from internal and third-party content. Lilli helps augment a key part of a consultant’s job, research and finding information. As former management consultants, we remember these fact-finding and context-setting missions well. They usually included a lot of desktop research, scouring internal databases, reaching out to fellow consultants at the firm, etc. What we would have done as young analysts to have automated these tasks! 🙃

With the additional time, consultants can spend more time on the strategic parts of their job or working directly with clients. It’s as if every analyst gets an AI powered analyst who has all the knowledge from all the engagements in their brain. “In just the last two weeks, Lilli has answered 50,000 questions,” and “66% of users are returning to it multiple times per week,” said Erik Roth, senior partner who led Lilli’s development.

Similarly, PwC, the global accounting and consulting firm, released ChatPwC to help its employees answer accounting and regulation questions. ChatPwC is part of the firm’s $1B investment into AI over the next three years. PwC also has a treasure trove of analysis and learnings from its client engagements to better train and tailor ChatPwC to its use cases. With ChatPwC, the firm is looking to use AI to upskill its global workforce of ~320K employees across 150 offices worldwide. Talk about “supercharging” a workforce! ⚡

So What? 🤔

At a high-level, we can think of an AI platform as having two key components: technology (e.g. software, code, models) and data.

With a fledgling open source ecosystem, from big companies like Meta open sourcing Llama, their large language model (LLM), to open source models like Vicuna, we expect a future where anyone can pick and choose which LLMs to deploy. In this world, proprietary data becomes an even more critical source of competitive advantage.

McKinsey and PwC are leveraging their proprietary data to get ahead and be the “Corporate Vanguards” in their respective industries as far as embracing artificial intelligence goes. By deploying their tools internally and “drinking their own champagne,” they can also begin to extrapolate what types of similar solutions might work for their clients.

Even from just the initial pilot, not only are they making their consultants more efficient and productive, but they are also ironing out the kinks internally first. Everything from technology issues, to user experience, to data security, to change management. Of course, beyond the short term internal benefits, there are also long term strategic benefits.

McKinsey and PwC together probably advise close to 100% of the Fortune 500. Given the initial success of both McKinsey’s Lilli and PwC’s ChatPwC (though, the naming of the latter could use some work in our humble opinion 🫶), their clients might look to their leadership in thinking through how to implement AI.

Most companies are just getting their feet wet in AI right now, but eventually they will need to make a decision on how to deploy AI, not whether to deploy it or not.

When they are ready to deploy, they will probably call familiar advisers. This positions McKinsey and PwC, two firms heavily based on knowledge work, to fight The Innovator's Dilemma and be disrupters of their own industry. And to be fair, other consulting firms are all thinking about and investing in AI (see examples by Accenture and Deloitte here and here). Though, with the McKinsey and PwC launch, they are trying to get ahead of the curve – putting pressure on other players in the industry to act.

To borrow some video game lingo, this is an S-Tier move by McKinsey and PwC. 👏👏👏

As we enter the new normal in AI, companies might not gain a large competitive advantage in AI, but they might get penalized for not using AI. With the pace of innovation in AI and its potential to reshape entire industries, we think acting on AI falls in the upper left quadrant of the Eisenhower Prioritization matrix (as ex-consultants, we had to include this matrix–a “2x2” in consultant speak 🤓).

The growing ubiquity and rapid pace of AI signals “Do it Now” because it is important and urgent to all players in any industry.

Additional Readings: Here Here Here Here

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DISCLAIMER: This post is provided strictly for educational and informational purposes only. Nothing written in this post should be taken as financial advice or advice of any kind. The content of this post are the opinions of the authors and not representative of other parties. Empower yourself, DYOR (do your own research).