In our insightful conversation with Alex Singla, Senior Partner at McKinsey & Company and Global Leader of QuantumBlack, AI by McKinsey, we tackle the most commonly asked questions about generative AI and how to implement it effectively. 

Listen in as Alex sheds light on identifying company-specific opportunities, organizing and governing AI tools, balancing risk and value creation, and navigating the future of talent and tech stacks. We also take a look at the journey of getting started and learning quickly to harness the power of generative AI. We explore the art of creating cost-efficient, scalable solutions that drive adoption and navigating the learning process to maximize speed, cost structure, and reusable code. 

Our conversation focuses on the delicate balance between moving too fast and too slow in the competitive AI space. We also weigh in on the role of large language models in achieving industry-specific solutions and how to optimize them for efficiency. Further into our discussion, we address the complexities of building and leveraging large language models, focusing on the costs, pros and cons of in-house building, and the importance of data privacy and IP protection. We then examine the skills needed to run these models, the learning curve, and the economic value derived from this process. 

The discussion concludes with a look at how generative AI can be used to improve customer experience and how to implement safeguards to avoid unethical behavior. Join us for this informative and thought-provoking episode with Alex Singla!

Key Quotes: 

• In many worlds, speed itself is a strategy. If you and I were doing exactly the same thing, but I'm just faster, every time I'm faster, I will continuously beat you, right? And so, speed itself is a strategy. What I don't mean by that is like reckless speed, I'm not saying that. I'm saying in general, speed is a strategy.
• Some of what you learn is not just what you build, like what solution you build, but how you build it matters a lot. Why the how matters is because if you build things in a way that get to the better answer faster, you ping off the cloud less often, and therefore you have less consumption. If you provide better results on your first query versus your fifth query, your employees or your customers get a better response faster. And so all these things make, have impact on scalability and cost. 
• You don't want to be the last person in the race when your industry is taking off.Now, at the same time,in my conversations with clients, it's actually funny.I get framed two things with CEOs often, which is, I don't want to be the fastest person out there who blows up my organization because I made a bunch of mistakes. And I don't want to be the last guy who was waiting so long that everyone overtook me and I became irrelevant.
• Those who customize, add some things on top and make it more specific, and leverage their own proprietary data and insights, I think that's how you win.I think that's how you create differentiation and strategic distance.
• How you build matters a lot. There isn't a company that I don't talk to that worries about IP, data, privacy. It's actually quite inspiring to hear every, every CEO, every top team thinking about IP and data privacy and protecting their customers' data. And basically not making sure their information doesn't just make the LLM better, which then makes everybody better. And so the how, I think, is a structured way in terms of, you know, do you bring those models in-house within your four walls and then only run your proprietary information on those, on those models? That's what I see most companies are doing, right? They're leveraging the large language, the external large language models for things that are completely not competitive. 
• As people build these tools, it's so critical they build them into the day-to-day workflow of how someone acts, right, and someone behaves.

Time stamps:

• (01:09) - The biggest questions customers have when it comes to AI
• (06:53) - How speed has become a business strategy
• (11:36) - Converging on large LLMs
• (16:02) - How companies are leveraging large, public LLMs, to protect customers
• (22:29) - Automation and Human Involvement in AI

Links:

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