Are you a Centaur or a Cyborg?

If you're still skeptical about AI's role in the workplace, a recent BCG study is here to shatter your doubts. Consultants using ChatGPT-4 didn't just outperform their peers—they crushed them. We're talking a 12% uptick in completed tasks, a 25% speed boost, and a 40% leap in quality.

How? The study observed two innovative approaches to successful AI use: Centaur Practices, where the human divides and delegates tasks between themselves and the AI, and Cyborg Practices, where AI is fully integrated into every aspect of the workflow.

Interestingly, it was the consultants with below average performance who improved the most.

One hitch: If the topic was outside the scope of GPT-4’s capabilities, the consultants actually performed worse than the control group.

So, what's the secret sauce for companies eager to leverage LLMs?

Data is a precious resource, and companies like Morgan Stanley, McKinsey, and IBM are all swimming in it. That being said, success in the space isn’t just about novelty anymore—especially as AI tools become a dime a dozen. It’s also not just about using AI as a time-saver, the real game-changer is in AI’s ability to make the once-impossible, possible.

The formula boils down to three key ingredients: a laser-focused initial approach, robust feedback loops, and the creation of unique data assets.

But, while we’re neck-deep in optimizing workflows, who's steering the ship.

Industry titans like Elon Musk and Bill Gates have been busy debating AI's potential to either doom humanity or solve world hunger. The consensus? Regulation is long overdue—but the road to Global AI Governance is anything but smooth.

CHART OF THE WEEK

AI Adoption within the Enterprise

A recent survey of 1,000 enterprise AI leaders reveals some eye-opening stats, like the fact that a staggering 50% of leaders estimate that between 26-50% of their total workforce will be using generative AI in their day-to-day work within the next two years.

Despite AI being hailed as the next big revenue engine—with 57% of enterprise leaders reporting that their boards are expecting a double-digit increase in revenue from AI/ML investments in the coming fiscal year—more than half of all C-level execs admit they're under-resourced to meet the sky-high expectations for generative AI innovation.

Strategic talent in the AI space is scarce, so how do they plan to meet demand? 48% of enterprise leaders plan to beef up their existing AI/ML teams with more data scientists and engineers and 22% are looking to bring on an entirely new team for their generative AI efforts.

The takeaway? The enterprise AI landscape is buzzing with ambition, but there's palpable tension between what's expected and what's actually feasible. As generative AI accelerates, the challenge isn't just about adopting the technology—it's about aligning resources, expectations, and reality.

EVENTS

The Enterprise Roadmap to AI in Finserv & Insurance

Are you drowning in data but starved for insights? Maybe you’re feeling the heat to show immediate wins in AI?

As investment in Generative AI is slated to multiply 4x in the coming years, the question isn't whether to adopt it, but how to do it right.

On October 11th, we’re gathering three enterprise leaders to demystify the biggest use cases and toughest challenges in implementing Generative AI in Enterprise Finserv and Insurance.

Sign Up for the Webinar

AI DISCOVERY ZONE

Need a visual aid to go with that data? ChartGen AI lets you upload any data set to generate AI-powered charts.

MISSION MUST-READS

• ‍How Startups Can Leverage AI Without Getting Caught in the Hype Cycle‍
• Zen and the Art of Building a "Self-Managing" Team

PARTING MEME

Missed last week’s issue? Read it here.