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We're living through the fastest technological acceleration in human history.

Every week brings a new AI model, a new productivity tool, and a new prediction that everything is about to change forever.

And yet somehow, most people feel less focused, less certain, and more overwhelmed than ever.

In this episode of FUTUREPROOF., I sit down with Fredric Marshall, author of THRIVE: The Antidote to Future Shock, to explore a possibility we don't talk about enough:

What if the biggest risk of AI isn't that it replaces humans?

What if it's that it exhausts them?

Fred has spent decades helping organizations like Apple, Pfizer, and Genentech navigate periods of intense change. His argument is that most organizations aren't suffering from a technology problem. They're suffering from an attention problem.

We're surrounded by tools designed to save time, yet nobody seems to have any.

We have more information than ever, yet many leaders feel less certain.

And we keep calling it a productivity problem when it may actually be a human capacity problem.

We discuss:

• Why "future shock" may be the defining leadership challenge of the AI era
• How AI can reduce friction—or quietly create more of it
• Why burnout is often a systems problem disguised as a personal one
• The hidden cost of constant context switching
• Why clarity may become more valuable than speed
• How leaders can separate signal from noise
• What it actually means to thrive during exponential change

Because if every problem gets solved with another app, another dashboard, or another AI assistant...

At some point, someone has to manage all those solutions.

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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.

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We live in a moment where disagreement feels dangerous.

Politics is polarized. Social media amplifies outrage. Inside companies, dissent is often muted — not because people agree, but because they assume speaking up will damage relationships or reputations.

But what if most of that fear is wrong?

Julia Minson, decision scientist at Harvard Kennedy School, studies the psychology of disagreement. Her research on “conversational receptiveness” reveals something counterintuitive: people systematically overestimate how much disagreement will harm a relationship and underestimate how much thoughtful dissent earns respect.

That miscalculation has consequences.

When leaders avoid disagreement, bad ideas survive. When teams confuse persuasion with understanding, trust erodes. When we treat conflict as a character flaw rather than a cognitive process, we weaken our institutions.

In this episode, we explore why humans are wired to assume they’re objectively right, how subtle language shifts can dramatically increase receptiveness, and why polarization may be less about ideology and more about judgment errors.

And in an era where AI systems increasingly summarize, mediate, and even “assist” in conflict, what happens if our tools inherit our biases? And if healthy disagreement is essential to good decision-making, how do we preserve it inside organizations that prize alignment over friction?

This isn’t a conversation about compromise.

It’s about whether we still know how to disagree in ways that make us smarter.

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Everyone says they’re “AI-first.”

Very few organizations are AI-ready.

In this episode of FUTUREPROOF., we sit down with Ben Tasker, who is leading one of the largest workforce-scale AI education efforts in the public utility sector — upskilling 36,000 employees while advising global organizations on certification and governance.

Ben calls this moment the “AI Between Times.” The tools are evolving rapidly, but the AI-driven economy they promise hasn’t fully stabilized. That gap creates risk — and opportunity.

We unpack what actually breaks when companies try to move beyond pilot projects:

• Why buying AI tools is easy — and building internal capability isn’t
• The tension between augmentation and displacement
• What the 70/30 rule means in cost-constrained environments
• Why governance must precede implementation
• And how AI fluency is quietly becoming a new form of institutional power

Ben argues that AI strategy lives or dies at the human level. Not because technology isn’t powerful, but because incentives, culture, and leadership determine whether that power compounds or fractures an organization.

This conversation isn’t about hype cycles.

It’s about whether institutions can transform fast enough — without breaking trust in the process.

Because the future of work won’t be defined by who bought the best tools.

It will be defined by who prepared their people.

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In this episode of FUTUREPROOF., we sit down with Kevin Ashton—the technologist who coined the term Internet of Things and helped usher in the smartphone era—to talk about something even more foundational than AI.

Stories.

In his new book, The Story of Stories, Kevin traces a million-year arc—from the first fires where early humans gathered, to the invention of writing and printing, to electricity, electronics, and the smartphone. His thesis is provocative: language did not create stories. Stories created language.

Every major storytelling revolution has followed a simple pattern: it increases the number of people who can tell stories—and the number of people who can hear them.

For the first time in history, anyone can tell stories to everyone.

But there’s a catch.

While AI cannot understand meaning, algorithms now determine which stories we see, amplifying bias, shaping belief, and influencing behavior at scale. The power of storytelling has never been more democratized—or more intermediated.

We explore:

• Why storytelling is innate, not cultural
• The eight great revolutions of human communication
• Why machines can generate content but not meaning
• The risks of algorithmic amplification
• The role of critical thinking in a post-scarcity information world
• Whether the next storytelling revolution is technological—or cognitive

This conversation isn’t about nostalgia.
It’s about understanding the oldest human technology in a moment when the newest one is accelerating everything.

If we think in stories—and we always will—the question becomes:
Who shapes the stories that shape us?

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For years, organizations have poured millions into DEI training.

And yet most employees still report discrimination. Promotion gaps persist. Trust remains uneven.

So what’s going on?

In this episode of FUTUREPROOF., I sit down with Lily Zheng — strategist and author of Fixing Fairness — to interrogate a hard truth: much of what we call DEI doesn’t work. Not because fairness is unpopular. Not because inclusion is misguided. But because we keep trying to fix people instead of fixing systems.

Lily introduces the FAIR framework — Fairness, Access, Inclusion, and Representation — and argues that the real leverage isn’t in workshops. It’s in incentives, evaluation criteria, hiring processes, and executive accountability.

We explore:

• Why standalone DEI training can backfire
• The “missing stair” metaphor — and how organizations normalize dysfunction
• The Cobra Effect of poorly designed diversity incentives
• Why representation is ultimately about trust, not optics
• What meritocracy gets wrong about itself
• And why rebranding DEI won’t solve structural problems

At a moment when DEI faces political backlash and corporate retrenchment, Lily makes a counterintuitive claim: the future of workplace inclusion will be more rigorous, more measured, and more accountable — not less.

This is a systems conversation.

Not about slogans.
Not about performative commitments.
About incentives, power, and what actually moves outcomes.

If you care about leadership, governance, and the second-order effects of institutional design, this episode will challenge you.