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Are we overestimating AI's capabilities?

ChatGPT is now so advanced that it's been likened to a human therapist. The irony? She’s never actually been to therapy.

ChatGPT is now so advanced that it's been likened to a human therapist by an OpenAI senior employee. The irony? She’s never actually been to therapy. So, are we overestimating AI's capabilities or underestimating our own?

A recent survey reveals that 78% of executives inflate their AI skills, a trend that—to the surprise of no one—is more pronounced among men. Considering women make up only 26% of data and AI positions in the workforce, it's more than just a skills gap that’s the problem. There’s an integrity gap that intersects with gender disparities in tech, and it’s widening faster than you can say "gender bias.”

But skills are just one piece of the puzzle for successful AI adoption, according to a recent HBR article: Judgment is the competitive edge in an AI-saturated market. Translation? The future of work is not just top-down or bottom-up. With the rise of gen AI tools, the scope of judgment expands beyond decision-makers—it's now a collaborative process between human and machine.

This collaborative future is echoed by Microsoft's Jared Spataro, who sees AI as a tool that is helping us find the signal amid the noise—transforming communication, and evolving meetings from time-bound events to "knowledge objects." It's not about less communication, but smarter, more focused interaction.

On the investor side, Kristina Shen of a16z has a similar bet when it comes to the future of AI for SaaS and the importance of human involvement—especially for that tricky last 5-20% of nuanced decision-making.

So, will we be architects or just passengers along for the ride in the inevitable AI-driven future?

It's a delicate balance between human and machine, skill and judgment, and control and agility. As we chart this course, the only thing we know for certain is that the stakes—ethical, practical, and existential—have never been higher, and according to the experts, humans need to remain an essential part of the equation.


There’s a Massive AI Skills Gap in Enterprise

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In a wake-up call for CTOs, CDOs, and CIOs who are looking to generative AI as a catalyst for workplace transformation, Forbes recently dropped a bombshell: 70% to 95% of enterprises are failing in their digital transformation efforts, and a significant skills gap is to blame.

Workera's study found that only 28% of enterprise teams already have the necessary competencies in generative AI skill sets. That gap directly affects their ability to innovate. But for those who have integrated generative AI tools like LLMs into their workflow, the payoff is substantial—an average 168% increase in productivity with task completion time reduced by over 150%.

So, what's the roadmap for skills transformation? Enterprises need to answer three fundamental questions: What skills are essential for each role? What level of proficiency is required in these skills? And in what sequence should these skills be acquired?

Navigating the AI skills gap is not just about identifying deficiencies; it's about creating a strategic plan to turn those gaps into opportunities for growth and innovation.


Balancing Innovation and Data Safety Will Make or Break Your AI Strategy

How Balancing Innovation and Data Safety Can Make or Break Your AI Strategy

Generative AI can mine treasures from vast troves of unstructured data at a previously unfathomable scale. But something else is lurking in those data sets: risk. The same AI tools that unlock value are also capable of destroying that value if the data safety risks aren’t mitigated.

“If you built your solution with bad data, you basically violated the law but haven't been caught yet,” said Michael Rispin, General Counsel at Sprout Social.

To highlight the biggest risks facing AI practitioners, A.Team assembled a panel of experts that also included Ander Steele, Data Scientist and Head of AI at Tonic.AI, and Anjana Harve, Global Chief Information Officer at BJ’s Wholesale Club and A.Team CxO.

So, what should come first in AI development: safety or innovation?

We put this question to the audience during the webinar and they voted near-even split—53% for safety, 47% for innovation.

“Risk and innovation are flip sides of the same coin,” said Harve. “AI puts a spotlight on challenges that we’ve had for a long time.”

Namely this: A company’s success and failure ultimately boils down to the quality of their data.

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Canva announced that it will pay $200 million over the next three years to designers who consent to having their content used to train the company’s AI models. Paying skilled users to help train a model is a step forward in the complicated ethics around making new AI tools powerful, safe, and effective.



Are your sunglasses stupid? Do you wish they would smarten up? Ray Ban and Meta have partnered to launch their second attempt at smart glasses, for seeing, recording, listening, and even holding a conversation. Yay!?


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