Welcome back to the AI Daily Brief. Today we are once again doing a special

Operators Cut edition of the AI Daily Brief, and it is part two of the agent readiness

series that we started last week. You might remember the inspiration for this was that I

recently did an episode that was all about everything that we've learned as part of the

thousands of surveys we've done across just an absolute boatload of agent readiness audits as part of Superintelligent. People were super interested in some of the learnings there, and so I invited Super Intelligence head of research, Newfar, onto the show, to go a little bit deeper on some of the key topics. Last week, in the kickoff of the three-part series, we talked about culture and what it takes to make an AI-ready culture in your organization. This week, we're plumbing into the single biggest barrier that we see, which is around data and technology. Once again, we're trying to make this extremely practical, useful, and applicable, and I hope it's super useful for you. If you want to dig in deeper and figure out how to discover what parts of this are relevant for your organization and how you might think about your AI plans going forward, shoot me a note at NLW at B-Supert.aI and I will get you in touch with the right people. For now, let's take into part two of the Agent Readiness series. Newfar, welcome back to the show for part two of this Agent Readiness Series. Thanks for having me. You know, in the previous session of this series, we discussed the cultural readiness for agent

adoption, and we emphasize that it's one of the most important and often under-treated

aspect of agent readiness.

This time, we will focus on the data and the technology readiness for agent adoption.

Our data shows that there is one clear universal truth, and that

is that motivation and ideas and desire for AI outpace the willingness to fix the underlying

infrastructure, meaning that the technical readiness scores are frequently the lowest across all

the dimensions of our audit. The question is why most companies get stuck. And sometimes

it's on motivation, ideas, and FOMO that gets them not to take the right infrastructure actions,

both from data and technology perspective. And we try to categorize the main archetypes of companies or executives getting these decisions wrong.

And the first archetype that we identify is the magpie.

These are companies that are, after all the fun and games of building agents.

They want to brag it in marketing and social media.

They just can't be bothered with the drudge work of sorting

through data and systems.

And this is where companies get stuck in pilot hell.

And unfortunately, there are many, many companies that fall into this category.

The other archetype that we're sometimes saying, those are the one that look at all the

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