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Every company today says it’s data-driven.

Billions are spent on analytics. AI pilots are everywhere. Dashboards glow with real-time metrics.

And yet, only a small fraction of organizations actually transform.

In this episode of FUTUREPROOF., I sit down with Sebastian Wernicke — author of DATA INSPIRED: Building an Organizational Culture of Inquiry for Lasting Transformation—to unpack why.

Sebastian argues that the problem isn’t a lack of data. It’s a lack of inquiry.

Most companies use data to optimize what already exists. Few use it to question assumptions, rethink business models, or challenge leadership narratives. That’s the difference between being data-driven and being data-inspired.

We explore:

• Why data doesn’t “speak for itself”
• How organizations become excellent at staying the same
• The dangers of data-resistant minds
• Why psychological safety is foundational for real AI success
• What “radical data integrity” actually requires
• And how to navigate AI’s “jagged frontier,” where human judgment still matters

This isn’t a conversation about tools; it’s about whether your culture is equipped to learn — especially when the evidence is uncomfortable.

Because AI won’t transform your company. It will amplify whatever culture you already have.

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We talk a lot about AI reshaping the future.

We talk less about who gets to participate in it.

In this episode of FUTUREPROOF., I sit down with Corbb O’Connor, who leads accessibility advocacy at Level Access. Corbb is blind. He’s spent years consulting enterprise teams — from financial institutions to global brands — helping them design digital experiences that are actually usable by people with disabilities.

This isn’t a compliance conversation.

It’s a systems conversation.

As AI systems increasingly generate interfaces, content, decisions, and workflows at scale, accessibility can no longer be an afterthought. If accessibility isn’t embedded upstream — in product design, in data pipelines, in AI outputs — exclusion compounds just as quickly as innovation.

Corbb argues that inclusion is not a moral add-on. It’s infrastructure. It’s economics. It’s risk management. And increasingly, it’s competitive advantage.

We explore:

• Why accessibility should be treated like cybersecurity — a non-negotiable requirement, not a retroactive fix
• The difference between “AI for accessibility” and “accessible AI”
• Why automated scanning tools can’t replace human testing
• How poor product design quietly excludes users without teams even realizing it
• Why psychological safety and culture matter just as much as tooling
• And whether AI will widen or narrow accessibility gaps over the next five years

If digital products define access to banking, healthcare, employment, and civic life, then accessibility isn’t a feature.

It’s participation.

And as AI becomes core infrastructure, the question becomes sharper:

Are we scaling inclusion — or scaling exclusion?

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We talk constantly about the future of work — AI agents, automation, leaner teams, productivity gains.

But what if the real drag on performance isn’t technology — it’s coordination?

Unproductive and unnecessary meetings cost companies up to $1.4 trillion every year. Seventy-one percent of senior leaders say meetings are inefficient. The average knowledge worker now spends around 11 hours a week in meetings. And nearly half admit to faking excuses to avoid them.

This isn’t a scheduling issue.

It’s a systems issue.

Dr. Rebecca Hinds — founder of the Work Innovation Lab at Asana, the Work AI Institute at Glean, and author of YOUR BEST MEETING EVER: 7 Principles for Designing Meetings That Get Things Done — argues that meetings are organizational “junk drawers.” Instead of asking whether a meeting is necessary, companies simply default to adding another recurring invite.

Her solution is radical in its simplicity: treat meetings like products.

Define the user. Clarify the outcome. Design the experience. Measure performance. Iterate.

In this episode, we zoom out beyond tactics and ask deeper questions:

Why are humans so inefficient at coordinating with one another?
What do broken meetings reveal about incentives, trust, and accountability?
Does AI meaningfully solve meeting dysfunction — or simply automate it?
And in a world pushing toward automation, what is the human role in collaboration?

If coordination is broken, no productivity tool can save us.

And if meetings are the canary in the coal mine, we should probably pay attention.