Thin Slicing: Why Your AI Product Strategy Needs Less Theater, More Taste

How to Build AI Products That Actually Work

Here's a sobering statistic: 85% of AI projects fail. Not "underperform"—fail completely. They either never ship, ship to crickets, or get quietly deprecated within months.

I've seen this pattern play out across 34 AI product initiatives since 2009—long before ChatGPT made AI fashionable. Collectively, those projects generated over $500M in ROI. But the failures taught me more than the successes. After 16 years leading AI product development at companies like Cisco, GE Digital, and Aible, plus consulting with dozens of Fortune 500 teams, I can tell you exactly why most AI projects fail.

There's a pattern behind these failures playing out in boardrooms across the tech industry. A product team excitedly presents beautiful mockups of their new AI-powered feature. Stakeholders lean in, nodding approvingly at the sleek interface and ambitious vision. Everyone loves it. Roadmaps are drawn. Commitments are made. Resources are allocated.

Two years later, the product finally ships. And it tastes terrible.

The Innovation Theater Problem

Most companies approach AI product development by drawing a frame first. They create elaborate UI concepts, conduct design reviews, and build consensus around what the product looks like. It's innovation theater—a performance that feels like progress but lacks substance.

The fundamental flaw? They're asking people to evaluate a picture of sausage rather than tasting the actual sausage. And you can't taste a picture.

When these products finally launch, teams discover the key ingredients were missing all along. The data doesn't exist. The data won't hold together. The data is fake (tofu disguised as meat), stale, or hopelessly inconsistent. The beautiful UI becomes a shell around a fundamentally broken product.

The painful truth: no amount of design polish can compensate for missing or inadequate data infrastructure. Yet companies continue to invest millions in the presentation layer while the foundation crumbles beneath.

Introducing Thin Slicing

There's a better way, borrowed from an unlikely source: the butcher's counter.

Thin slicing is a product development technique that inverts the traditional approach. Instead of building elaborate interfaces and making grand promises, you create the absolute minimum UI necessary to hold the real ingredients together. Then you obsess over making sure every ingredient is present and of the highest possible quality.

A thin slice provides a taste, not a picture of the product. It's not much to look at, but it tells you everything you need to know. Too little salt? Not enough lean meat? Texture problems? You'll know immediately. Nothing can hide. There's no theater—just the meat.

How AI Product Sausage Actually Gets Made

Consider a real example from the cybersecurity world.

A team wanted to build an AI-powered threat detection system using RAG (Retrieval Augmented Generation) to convert common Sigma rules to their proprietary format for analyzing security events. The traditional approach would involve months of planning: comprehensive UI mockups, elaborate rule engines, multiple data source integrations, stakeholder and partner alignment sessions.

Instead, I helped them thin-slice: we focused on a single data source (CloudTrail logs), implemented just 50 RAG rules, and used a small ChromaDB instance (a few megabytes) to verify the conversion was feasible. The UI was ridiculously simple: a text area for pasting a Sigma rule, and below it, the converted output with a copy button. We built it in 2 days. The system returned results in seconds.

In the next 3 days, we ran six 30-minute customer interviews. The response? "I can use this right now—when can I have it?"

Could they have been more ambitious? Absolutely. They could have tackled 1,000 rules across multiple data sources—a project requiring months of effort. But that thin slice with 50 rules told them what they needed to know: the approach worked, the data was accessible, the performance was acceptable, and customers wanted it immediately.

We continued iterating on this thin slice, confirming functionality and desirability with real data (CloudTrail) at every feature addition. Two weeks after we started, we had a fully functional agentic system integrated into their existing application. It featured an AI agent that fetched real-time updates from security advisory blogs and converted them into rule suggestions, with human-in-the-loop review and approval. It was ready for hardening and shipping to customers.

However, it's equally important to note what we did NOT have:

• Fancy UI (minimal chrome—agents don't need decoration)

• 1,000-rule coverage (just 50 rules that proved the concept)

• Multiple data sources (just CloudTrail)

• Conversion from rule formats other than Sigma

• Advanced monitoring and alerting

• Enterprise SSO integration

We shipped the thin slice. Everything else came later, informed by actual usage data.

The Sharp Knife Principle

Knowing where to slice the data requires a sharp knife, a deep understanding of your domain, and ruthless prioritization. Ask yourself:

What is the absolute minimum scope that still demonstrates end-to-end value?

Not "what features do we want?" but "what is the thinnest possible slice that includes all the essential ingredients?"

For AI products specifically, this means:

• Real bespoke proprietary data (not “tofu/synthetic data”)

• Actual model responses

• Genuine integration points

• Measurable outcomes

The thin slice must be complete end-to-end, just radically scoped. The key is producing something your customers can test (and taste!) with their own requests.

This Isn't a New Concept

Amazon understood thin slicing from day one. Jeff Bezos didn't start by trying to sell everything to everyone. He started with books. Just books. Not refrigerators, not electronics, not clothing. Books were the thinnest viable slice of the e-commerce vision.

Why books? The ingredients were all there: large catalog (data quality), easy to ship (operational feasibility), passionate customers (demand validation), and low return rates (customer satisfaction). Books let Amazon prove their model worked before expanding to more complex categories.

Too many AI product teams try to build the refrigerator first—complex, expensive, and filled with unknown variables.

If you want to succeed today, start with books.

Ship Early or Take the Time? False Choice.

Leaders often frame product strategy as a binary decision: ship fast with compromises, or take the time to build it right.

Thin-slicing rejects this false dichotomy. The answer is: ship end-to-end, but smaller.

This is the land-and-expand strategy that successful AI products follow:

1. Land: Deliver a complete but tightly scoped solution that solves a real problem.

2. Learn: Gather actual usage data and feedback from production.

3. Expand: Systematically broaden scope based on validated learnings.

Each expansion is another thin slice, informed by the taste of the previous slice. You're never building blind.

The Uncomfortable Truth About POCs

Most "Proof of Concepts" in AI development aren't proofs at all—they're PowerPoints of Concepts, built on fake data, with vastly simplified and over-massaged data specifically designed to showcase the efficacy of the POC. Smoke and mirrors designed to secure funding, not validate assumptions.

That is not what thin-slicing is.

A real POC should be uncomfortable to present. It should look scrappy. It should work with real data, real models, and real infrastructure—even if the scope is deliberately minimal. It should force honest conversations about what's actually solving real customer problems versus what's theatrically impressive.

If your POC looks too polished, you're probably measuring the wrong things. A thin slice of salami has minimal UI or “casing” seen sideways, which practically hides it from view, whereas the sausage's internal contents and taste are fully exposed.

Making the Shift

Transitioning to thin slicing requires cultural and operational changes:

Stop rewarding presentation skills over execution discipline. The team that builds a working thin slice in two weeks should be celebrated over the team that produces beautiful mockups for a six-month project.

Redefine "demo-able." A demo should show functioning capabilities, not aspirational concepts. If you can't interact with real data in real-time, using your own queries, it's not a demo—it's a PowerPoint presentation.

Embrace the awkward middle. Thin slices often look unimpressive in stakeholder reviews. Resist the urge to add presentation polish before validating core functionality. Make it work, then make it pretty.

Measure cycle time from idea to production taste. How quickly can your team go from concept to something a real user can try? This metric matters more than feature counts or roadmap promises.

Taste Before You Scale

The AI product landscape is littered with failed initiatives that looked beautiful in planning but tasted terrible in production. The fundamental mistake is always the same: teams optimized for the picture instead of the taste.

Thin-slicing is a discipline of intellectual honesty. It forces you to confront reality early, when pivots are cheap and course corrections are simple. It turns product development from an act of faith into a series of empirical experiments.

The next time your team gathers to evaluate an AI product concept, don't ask "do we like how this looks?" Ask instead: "What is the thinnest slice we can build that will tell us if this actually tastes good?"

Then sharpen your knife, cut that slice, and find out.

Because in AI product development, you can't taste the picture. And a picture of sausage has never satisfied anyone's hunger.

Most Teams Can't Thin Slice on Their Own—Here's Why

The concept is simple. The execution is hard.

Thin slicing requires three things most teams don't have:

1. A facilitator who can see where to cut - You need someone who's done this 30+ times and knows where the high-value thin slices actually are.

2. A framework for building POCs in days, not months - Most organizations are stuck in waterfall processes that make rapid iteration impossible.

3. A process for testing slices with real customers - Without actual user feedback baked in throughout, you're just doing dangerous, expensive guessing.

The Snowball Design Sprint: Thin Slicing in Practice

Everything I just described—the thin slice approach, rapid customer testing, iterative expansion—is exactly how the Snowball Design Sprint works.

In 3 weeks, we help your team:

Week 1: Build the Thinnest Slice

• Frame the problem systematically and evaluate ROI and risk

• Identify the absolute minimum viable thin slice of data

• Create a functioning RAG system or agentic AI workflow

• Build a working prototype with your real data (not mockups)

Week 2: Test & Iterate

• Run customer interviews with the working thin slice POC

• Gather real feedback on what works and what doesn't

• Iterate based on actual usage data

Week 3: Expand & Harden

• Build out the validated concept into a near-production-ready POC

• Polish UX and content where needed

• Document architecture and prepare for engineering handoff

The outcome: A working AI POC that customers actually want, built in 3 weeks instead of 3 months—saving you $2M and 6 months typically wasted on projects that fail. More importantly: your team learns the framework and can thin-slice future projects without me.

Investment: $95,000

Not Ready for the Full Sprint?

Start with the AI Strategy Workshop (3 days, $65K)

If you're trying to choose between multiple AI opportunities and need to train internal facilitators, the workshop helps you:

• Evaluate 2-3 AI opportunities

• Identify the thinnest valuable slice for each

• Build working RAG-driven prototypes for each

• Choose which opportunity to pursue

Most workshop clients immediately book the Design Sprint to build the winning opportunity.

Book a 30-Minute Strategy Call

Want to discuss your specific AI roadmap challenges? I'll walk you through the Snowball Design framework and help you identify where to cut—even if you never hire me.

Book 30-Minute Call

I'm in Japan mid-November for workshop engagements. Limited early-November slots remain, or book for December when I'm back.

P.S. Not ready for a call yet? Reply with your biggest AI prioritization challenge, and I'll send you relevant resources.