AI hype follows a worn path. During the DOT-Com Giek we were promised that the internet would bring an overnight revolution. Although it was revolutionary, a few changes arrived quickly, but most of the time, as much as marked by failures and false starts as by permanent breakthroughs.
I was in Bound in San Francisco last week and the AI Hype was overwhelming. Conversations were either only aimed at the tactical use of AI or, interesting enough, on reformulating AI in our thoughts.
Oh the new operating system
AI quickly becomes the infrastructure layer of modern things.
In his keynote, Dario Amodei, CEO of Anthropic, called it an operating system, just like Cloud Computing. Adoption is no longer theoretical. The use is widespread over roles and industries. In pharmaceutical, legal and production, AI reduces friction on a scale that people cannot match. For example, innovative manufacturers now use AI to detect the context of users to automatically operate different experiences and accelerate their use of AI to mines for insights.
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Claude and other assistants reform all the productivity of organizations, accelerate code and making content through automated drafts and suggestions. But lessons from the past sound loudly in my memory. Not every shiny demo is ready for Prime Time. How many incredible demos and keynotes have resulted in less than Stellar Real -World applications and functions?
An operating system must be reliable before it can be transforming.
Curiosity makes AI valuable
AI is powerful, but curiosity makes it valuable. AI offers a scale, but curiosity offers depth.
Araavind Srinivas, CEO of Pertlexity, reminded us that progress often starts with the questions that nobody dares to ask. Eric Bailey, president of Bailey Strategic Innovation Group, pushed further and shows how our brains mislead us with certainty when what we need instead professionally non-know, a disciplined willingness to lead through research instead of assumption. Beth Dunn, head of producer experience at agent.ai (a network for AI agents), revived curiosity as an action of empathy, a way to listen to the connection.
Curiosity is not just a characteristic. It is the basis for learning, trust and innovation.
Human truths and creative courage
Curiosity is not abstract. It appears in how teams deal with fear, failure and resistance.
Amy Poehler spoke about thanking fear before he passed by in her closing carriages. Failure, she argued, is not just a setback. It creates the conditions that make learning possible. The best teams appreciate large fluctuations about safe bets.
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In my session “Stop fighting against resistance – start using it”, I saw curiosity to help groups to overcome resistance and build up Momentum. One participant worked through a simple framework that enabled her to reformulate her resistance to updating old website. Another realized that their resistance came from something that was not related to the work, and reformulating the problem led to a breakthrough in thinking.
Without curiosity, AI is a stochastic parrot. The only thing it can do is find the most likely used word, number or color for a certain situation.
A necessary caution
AI alone produces speed without meaning, and only curiosity generates meaning without momentum. Many leaders will be standard automation or reflection for various reasons: FOMO (fear of missing AI), pressure from the management/board to do something instead of nothing, personal comfort and/or skills in talent. Each of these late value on the table or leads to missed opportunities. AI without curiosity leads to irrelevant sound and curiosity without AI leads to brilliant ideas that never scale.
Senior marketers are told to move, automate and scale quickly. But speed without depth is empty. Curiosity makes scale meaningful. The companies that bloom will not be those who use each AI tool. They are those who for both designs: optimizing stacks for efficiency while cultures cultures reward curiosity.
Recommendations for senior marketers
- Efficiency of each other when applying for: Build AI and Automation Roadmaps in addition to a portfolio of open questions that teams will explore. Think of brainstorming sessions, shared collaboration tools, hackathons.
- Define KPIs and reward curiosity again: Balance transit and engagement statistics with learning statistics such as new customer insights surfaced or tested hypotheses. Make experiments and ask difficult questions part of performance reviews. Consider an “failed experiment number” or shared fast libraries and examples.
- Design for identity and reliability: Treat AI as an infrastructure and avoid scaling unstable tools. Switch from channel hacks to identity -conscious strategies, using what you know about individuals to shape communication and trust. A way to achieve this is to shift the focus from constant searching for new tools to building shared momentum and language around a set of standardized platforms. You must choose these platforms for their ability to make personalization and adjustment possible.
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