SAMR – EduDataSci – Educating the world about data and leadership

Beyond Automation: Using SAMR to Explain AI Value in Property & Casualty Insurance Services

Most conversations about AI in property and casualty insurance start with the same promise: “faster, cheaper, smarter.” But in practice, the real question is where AI is being used.

Is it just doing the same work a little quicker… or is it changing the way underwriting, claims, and loss control actually run?

One of the cleanest ways to explain that difference is to borrow a framework from education technology: the SAMR model—Substitution, Augmentation, Modification, Redefinition—originally articulated by Ruben Puentedura. In SAMR, the first two levels are typically “enhancement” and the latter two are “transformation,” because they represent meaningful redesign (or reinvention) of the work itself.

In this post, I’ll map SAMR to the kinds of operational and strategic value AI can create across P&C insurance services (intake, underwriting, claims, fraud, and risk/loss services), staying away from customer chatbots and focusing instead on business process change that actually moves KPIs. Along the way, I’ll flag where AI and Insurance leaders tend to underestimate the “operating model” work required to reach the top of the SAMR ladder.

From Warehouses to Products: SAMR for Your Cloud Data Platform

Financial Services, Insurance, Wealth Management, and Professional Services have a gift—and a curse—when it comes to data.

The gift is that these industries know how to run critical systems with discipline. The curse is that we’re so good at controlling risk that we often rebuild the same constraints in every new platform we adopt.

That’s why so many “modern cloud data platforms” in these sectors end up feeling like the old data warehouse with a new hosting model: better infrastructure, familiar bottlenecks. Slow change. Long planning cycles. A platform measured by what it ingests, not what it enables.

In the previous post on AI and SAMR, the point wasn’t that AI is inherently transformative. The point was that without a disciplined framework, we use new tools to reproduce old workflows. The same is true for data.

Here’s what I’ll cover in this follow-up:

Why cloud platforms in regulated, risk-sensitive industries so often replicate warehouse-era behaviors.
How SAMR can be used as a simple truth-teller for your data platform strategy.
Why data products are the most practical “unit of redefinition,” and why you don’t need Data Mesh to benefit from them—though mesh and product thinking dramatically expands your toolset.
How ground-up design thinking keeps your platform anchored to real business goals, not elegant architectures

If your cloud platform still takes six months to change a definition, you didn’t modernize. You relocated.

From Substitution to Outcomes: How AI and SAMR Are Forcing a Rethink of Development Strategy

We like to say we’ve “transformed” how work gets done. But if you look closely at many enterprise systems, you still see the outline of a paper form hiding under a slick UI.

We replaced paper with terminals, terminals with web apps, web apps with SaaS—and then pointed automation at the whole stack. In too many places, we’ve simply substituted one medium for another, without asking whether the underlying process still makes sense.

In this post, I’ll do three things:

Introduce the SAMR framework as a way to think about how technology shapes work.
Show how many business processes sit on layers of substitution going all the way back to paper.
Explain how AI enables goal‑based processes, where we define the outcome and how we’ll know we’ve met it, and let AI figure out the path—without prescriptive step‑by‑step code.

And along the way, we’ll confront an uncomfortable idea: some of your “modern” processes may still be driven by the personal preferences of someone who retired more than fifty years ago.