The Readiness Problem: Two Rooms, One Verdict

Your next AI pilot will not fix an operating model that cannot scale it.

Two rooms. Two cities. One verdict. In this solo episode of Scouting for Growth, I connect the conversations from AI Horizon 2030 in Las Vegas and Majesco’s Frontier Customer Summit in New Orleans, where two very different groups arrived independently at the same conclusion: AI capability is increasingly available. The operating model required to turn it into measurable results is not.

The evidence reinforces the point. Research conducted with my students at Bayes Business School found that 26 of 30 leading carriers have yet to cross the halfway mark on both the People and Operating Model transformation axes. In Las Vegas, executives rated their own readiness to run agents in production at just over three out of five. The technology is moving quickly. Most organizations remain stuck in the middle.

But readiness is not simply about becoming more technologically sophisticated. Boston Mutual moved from nine-day policy issuance to same-day issuance by redesigning the work around its platform. Manulife transformed a heavily customized model into something repeatable and scalable, while increasing straight-through processing for digital claims from 80% to 98%. What matters is not another isolated capability. It is whether the organization can redesign workflows around the outcomes intelligence now makes possible.

I frame the practical response around three moves: name the accountable owner, choose the operating model before the model, and make trust demonstrable. Every agentic initiative needs a named executive with authority over the outcome. AI needs to sit inside an end-to-end workflow rather than beside it. And organizations need to be able to explain who authorized an action, whether it was permissible, what produced the decision, and which human ultimately carries accountability.

Because when intelligence becomes widely available, access to intelligence stops being the differentiator. The advantage moves to the organization around it: how decisions are made, how humans and agents share work, how trust is governed, and how quickly a promising experiment can become a repeatable operating model.

KEY TAKEAWAYS

I want to challenge the assumption that another AI pilot will move an organization closer to becoming AI-ready. The conversations in Las Vegas and New Orleans pointed to the opposite conclusion. Most insurers do not appear to be constrained primarily by access to models, vendors, or technical capability. They are constrained by how work is organized around those capabilities. When intelligence becomes increasingly inexpensive and widely accessible, the more important question moves from “Where can we use AI?” to “How must the work change?” That shift starts with the customer workflow and works backwards.

This is why accountability, workflow design, and trust have to be treated as part of the operating model rather than as implementation details. Every agentic initiative needs a named owner with the authority to change what happens next. The AI itself needs to operate inside a redesigned end-to-end process, supported by clear identity, entitlements, decision rights, and auditability. And trust must be demonstrable. If an agent takes an action, leaders should be able to explain who authorized it, whether the action was permissible, which data and model informed it, and which human carries accountability. For me, the principle is increasingly human in the lead, not simply human in the loop.

The practical implication is deliberately simple: start with one owner, one process, and trust you can prove. Boston Mutual and Manulife show why this matters. Their progress did not come from layering intelligence over unchanged workflows. They redesigned the work and connected operational improvement to a better customer or member outcome. Readiness is therefore not a product an enterprise can purchase. It is a leadership choice about how the organization will operate when intelligence is everywhere. The firms that make that choice deliberately will move from isolated frontier moves toward becoming frontier firms.

BEST MOMENTS

“Insurance does not have an AI problem. It has an operating model problem.” – Sabine VanderLinden [01:56]

“When intelligence is nearly free, you stop competing on accessing it because everyone has access to it. You start competing on the operating model wrapped around it.” – Sabine VanderLinden [07:02]

“You can launch an MVP for one client. You cannot scale with one.” – Sabine VanderLinden [09:08]

“Readiness is not something you buy. It is something you design.” – Sabine VanderLinden [10:42]

“On top and beside is a chatbot with a budget line. Inside, the workflow is a redesigned business process where humans and agents share the work.” – Sabine VanderLinden [12:46]

“When intelligence is free, what exactly is your operating model for?” – Sabine VanderLinden [16:53]

ABOUT THE HOST

Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.

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