I Was Using AI Wrong

Like many emergency managers and business continuity professionals, I initially saw AI as a productivity tool. A way to speed up the tedious parts of our work: extracting key details from lengthy plans, generating realistic exercise scenarios, drafting after-action reports. And yes, AI does these things remarkably well.

But I was missing the point entirely.

We don't have a content creation problem in emergency management and business continuity. We create plenty of content—plans, procedures, reports, presentations. Our shelves (digital and physical) are full of them. The real problems run deeper: our content often lacks the quality and utility it should have. More critically, it frequently fails to represent the diverse knowledge and perspectives across our communities and business ecosystems.

I've always maintained that emergency managers and business continuity professionals are systems engineers, not content managers. Yet I was using our most powerful new tool as if we were exactly that—content managers looking for a faster typewriter.

A Timely Validation

I recently started reading Sangeet Paul Choudary's Reshuffle: Who Wins When AI Restacks the Knowledge Economy, and found myself nodding throughout. Choudary articulates what I'd come to understand through practice and had already begun taking action on: that AI's transformative power lies not in performing individual tasks better, but in fundamentally restructuring the systems in which work happens.

His framework helped crystallize something I'd been grappling with in emergency management—why using AI for content generation felt hollow despite being efficient. The real opportunity wasn't to produce more content faster. It was to address the coordination and engagement challenges that have always limited our effectiveness.

From Tasks to Systems: Three Shifts in Preparedness

Applying this systems-level thinking to emergency management and business continuity reveals new possibilities:

Shift 1: From Extracting Details to Enabling Coordination

The old approach: Use AI to mine existing plans for buried details, making it easier to find what we need when we need it.

The systems approach: Use AI to fundamentally change how we coordinate and collaborate in creating and engaging with those plans in the first place.

Much of an organization's or community's most valuable preparedness knowledge is tacit—buried in the heads of individual stakeholders, locked in departmental silos, scattered across fragmented conversations. What if AI could help convert this tacit knowledge into explicit, actionable insights accessible across the entire system? Not reading plans faster, but changing who contributes to plans and how that knowledge flows through our organizations and communities.

Shift 2: From Crafting Scenarios to Surfacing Collective Intelligence

The old approach: Use AI to generate compelling exercise scenarios and injects, making our tabletop exercises more realistic and engaging.

The systems approach: Use AI to capture, surface, and apply the tacit knowledge from stakeholders across entire communities and business ecosystems during the exercise design and execution process.

Traditional exercise planning often involves a small team making assumptions about what various stakeholders would know, need, or do. What if we enabled a different model—one where diverse stakeholders contribute their actual knowledge, perspectives, and concerns throughout the process? The exercise becomes not just a simulation of a crisis, but a mechanism for forming collective intelligence, revealing what we actually know as a system.

Shift 3: From Writing Reports to Enabling Continuous Improvement

The old approach: Use AI to draft after-action report paragraphs, turning rough notes into polished prose.

The systems approach: Use AI to empower granular insight sharing, sense-making, improvement planning, and accountability tracking across all participants.

The traditional after-action report is a static document capturing what central authorities observed and chose to include. 

But the most valuable insights often remain unshared—in the heads of participants who noticed something important but never had a structured way to contribute it. What if AI made it easy for everyone to share granular observations, then helped synthesize these into collective sense-making? More importantly, what if it tracked improvement commitments and created accountability loops that kept insights from disappearing into the usual "report shelf"?

Performance Over Appearance

The distinction matters because it shifts our focus from "Does AI think like we do?" to "Does it effectively address our actual challenges?"

In emergency management, our challenges aren't primarily about content generation speed. They're about:

• Engaging diverse stakeholders who possess critical knowledge

• Converting individual expertise into collective capability

• Creating actionable insights that drive actual improvement

• Building coordination systems that work under pressure

AI's value lies in its ability to restructure how we address these challenges—to transform the system, not just speed up tasks within it.

Prepp Collaborate: A First Step Toward Systems Change

This realization shaped how we built Prepp Collaborate. It's not designed to simply produce better tabletop exercises faster. Instead, it transforms the system around exercises—surfacing key knowledge and insights from stakeholders who are often not fully engaged in traditional planning processes.

We're creating a platform that:

• Captures tacit knowledge from diverse stakeholders before, during, and after exercises

• Forms collective intelligence by connecting insights across different perspectives and organizations

• Enables continuous sense-making rather than one-time reports

• Creates accountability for improvement actions identified through the process

The tabletop exercise is just the visible output. The real transformation happens in how we coordinate, how we learn together, and how we convert individual expertise into collective preparedness capability.

The Question Before Us

The question isn't whether AI can help emergency managers and business continuity work faster. It can. The question is whether we'll use it to address the fundamental coordination and engagement challenges that have long limited our effectiveness.

Will we use AI to produce more content, or to build better systems?

Will we use it to speed up individual tasks, or to transform how we think, decide, and coordinate as communities and organizations?

Emergency managers and business continuity professionals have always been systems engineers. It's time our tools—and how we use them—reflected that reality.

For more on how AI restructures knowledge systems, see Sangeet Paul Choudary's "Reshuffle: Who Wins When AI Restacks the Knowledge Economy"