Product Strategy

When Being Wrong Costs Someone Their Freedom

In public systems where an error changes the course of a person's life, the goal of applied AI is not to decide faster. It is to make expensive human judgment go further — with every step visible, contestable, and owned by a person.

In high-stakes public systems, a confident wrong answer is worse than no answer. Where applied AI belongs, where a human must stay in the loop, and why explainability is the whole product.

From Rigor to Tool: Applied AI Without Losing the Research

Rigorous research changes minds. A tool changes behavior. The gap between the two is where most research dies — and where applied AI is most tempting and most dangerous, because the easy version strips out the rigor that made the research worth trusting.

Turning research into a usable tool means putting findings in front of people who will act on them. Applied AI can close that gap — or quietly launder away the rigor. Where it fits, and how to keep the research intact.

The Paperwork Trap: How AI Can Free the Trades Back Office Without Losing the Owner

Small-business owners in the trades are drowning in invoices, permits, and compliance paperwork — not because they’re bad at business, but because the overhead was never designed for them. AI can change that, if it’s built right.

AI can cut the administrative drag crushing trades businesses — if it keeps the owner in control and shows its work.

Hiring for Capability: Applied AI and the Skills-First Bet

Skills-first hiring promises to judge people on what they can do, not the credentials they happen to hold. Doing that at scale means making fast judgments about capability — and that is exactly where applied AI helps, and exactly where it can do the most damage.

Skills-first hiring asks software to judge capability, not credentials. That is a judgment problem, not a filtering one — and the bias and explainability burden is the whole game. Where applied AI fits, and where a human still decides.

Reading the Law as It Moves: Applied AI for Legislative Intelligence

Legislation is a firehose of messy, fast-changing text where a single amended clause can flip what a bill means. Tracking it is a judgment problem, not a search problem — and that is exactly where applied AI helps, and exactly where a confident wrong reading can cost an advocacy campaign everything.

Legislative tracking is judgment over messy, fast-moving legal text, not keyword search. Where applied AI fits, why explainability is non-negotiable for advocacy decisions, and where a human still reads the clause.

Productizing the Insight: Applied AI for Innovation Consultancies

A consultancy's product is judgment that does not scale — senior strategists reasoning through a hard problem one engagement at a time. Applied AI can productize the scaffolding around that judgment. Confuse the two and you automate away the only thing clients were paying for.

Innovation consultancies sell judgment that resists scale. Applied AI can productize the repeatable scaffolding around it — without automating the strategic reasoning that can't be commoditized. Where the line goes, and how to draw it.

Cohorts, Employers, and Livelihoods: Applied AI for Workforce Operations at Scale

Run a workforce program at national scale and you're orchestrating two matching problems at once — people into cohorts, graduates into jobs — across thousands of lives. The operations are crushing and repetitive. The decisions inside them change whether someone gets a livelihood. Applied AI can carry the first without touching the second.

Workforce programs match people into cohorts and graduates into jobs, at scale, where a wrong call affects a livelihood. Here's how applied AI handles the operational load while humans keep the consequential decisions — plus a two-day way to start.

When Your Judgment Is the Product: Applied AI for Strategy and Policy Firms

For a strategy or policy firm, the asset isn't a process — it's the judgment of the people in the room. That makes "productize it so it scales" a dangerous instruction, because the obvious way to scale judgment is to dilute it. Applied AI offers a different path: scale everything around the judgment so the judgment itself can go further.

When the product is expertise and the moat is a community, scaling usually means diluting the very thing clients pay for. Here's how applied AI scales the work surrounding judgment without replacing it — and a two-day way to start.

Matching Help to Need: Applied AI in a Marketplace Where Being Wrong Has a Cost

A marketplace that connects people who need help with people who can give it has a brutal constraint most marketplaces don't: a bad match isn't a refund, it's a person who walked away worse off. Applied AI can do the triage and quality work at the speed the moment demands — if you build it knowing where being wrong is expensive.

In a help marketplace, latency and a bad match both have a human cost. Here's how to use applied AI for triage and quality — fast where speed is safe, human where being wrong is costly — and a two-day way to start.

Packaging a Proven Model: Applied AI Without Flattening What Made It Work

Organizations that spent two decades perfecting a method face a cruel paradox when they try to scale it: the moment you write the model down so others can run it, you risk losing the judgment that made it work. Applied AI can carry the procedure without flattening the practice — but only if you separate the two on purpose.

A model refined over twenty years is mostly tacit judgment. Productizing it usually flattens it into a checklist. Here's how to use applied AI to scale the procedure while keeping the judgment human — and a two-day way to start.

AI Adoption for Under-Resourced Organizations: Start Where It Costs You Most

A pragmatic playbook for thin-margin organizations that want to use AI without getting burned by hype.

Most under-resourced organizations don't need AI—they need better judgment at scale. Here's a two-day path to finding where AI actually helps.

The Navigation Problem: Why AI Alone Won't Get Patients to the Right Resources

Social-determinants navigation is one of the most tractable applied-AI problems in healthcare — and one of the most commonly botched. Here's how to do it right.

Matching patients to community resources sounds like a routing problem. It isn't. Here's the architecture that actually works.

From Marketplace to Operating System: Applied AI and the NIL Back Office

The first wave of NIL software solved discovery — match an athlete to a brand. The revenue-sharing era turned NIL into an ongoing operation someone has to run. Marketplaces don't run operations. Operating systems do, and that's where applied AI earns its keep.

Discovery in the NIL economy is solved. The unsolved, expensive part is the back office — deals, disclosures, compliance, payments — at scale. Here's where applied AI fits, and where judgment still has to win.

Applied AI in Sports Media: When Every Athlete Is a Distribution Channel

Teams and leagues are expected to produce thousands of personalized clips a week for athletes who each out-reach the old broadcast networks. The supply of moments hasn't changed; the demand for content cut from them has exploded. That gap is where applied AI earns its keep — or gets built badly.

The athlete became the distribution channel, and almost no one can feed that channel by hand. Here's where AI actually fits in sports media — and where judgment still has to win.

The Future of AI in College Athletics: What NIL Taught Us About Building for Chaos

NIL turned college sports into a fast-moving, lightly-governed market overnight. That's exactly the kind of mess AI is good at — and exactly the kind of mess that punishes software built without judgment. Here's where we think AI actually fits.

HARNESS: Turning AI Fog Into a Buildable Idea

AI made it easier to generate ideas. That is not the hard part anymore. The hard part is turning a fuzzy possibility into something a team can actually evaluate, scope, and build.

After Boulder Startup Week, the thing that kept sticking with me was not that teams need more AI ideas. They have plenty. What they need is a way to turn the fog into a real product shape. That is what the HARNESS Canvas is for.

Your App Idea Sucks

When anyone with a bit of time and credits can work with a code generation assistant to ship a functional, secure web application in a weekend, "ideas" are not valuable. If you’re still pitching “an app that does X,”...