claudeshoring: your company's best first AI project is the one it already solved

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claudeshoring: your company's best first AI project is the one it already solved<br>Don't build your AI process from scratch. Lift one you already offshored.

Michael Carroll<br>Jun 30, 2026

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Why enterprise AI projects are failing

Last year, 95% of enterprise AI projects failed.<br>The MIT report behind that number — The GenAI Divide: State of AI in Business 2025 — has its own theories, but a big part of it is simple: enterprises keep picking the wrong project.

At Coolhand Labs we work with clients across the whole maturity spectrum, and picking the right first project is much harder for an enterprise than for a startup. A startup can take an immature process and grow the AI up alongside it — building it the way you’d build any human process: block by block from zero.<br>Enterprises don’t have that luxury. Standing up a never-done-before process as your first AI project is a recipe for failure. Even something as simple as an internal chatbot drowns in questions before it ships:<br>What tools should it be able to access?

How much access do we give it?

Which team has ownership of the project?

What metrics prove it’s working?

+ 100s of other critical questions with no obvious upfront answer

For this reason, we’d mostly written off pursuing enterprise clients when we launched. But something has shifted over the last few months. A handful of enterprise clients found a cheatsheet — one that answers all of those questions before they’re asked and takes them straight to the only stage that matters: we’ve got something running, here’s the ROI, do we double down or kill it?<br>The secret? Something I’m calling claudeshoring .<br>The Everything Engineer newsletter is free — now & forever — but please subscribe. Subscribers are what give us the energy to keep sharing the things we are seeing in the fast-developing world of enterprise agents!

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What AI can’t do well

Before we get to what claudeshoring is and why it works, we need to talk about AI’s kryptonite: risk.<br>That deserves its own post, but here’s the short version. The core of any profitable business is taking on risk of failure in the hope of outsize reward. Do you spend on marketing or on production efficiency? Do you hire ahead of a forecasted surge in demand, or preserve capital and play it safe? There’s often no clear right answer.<br>AI can go through the motions of these decisions, but — as things stand — it can’t validate the decision on the fly in its session and never has to live with the consequences of failure. AI agents don’t have mortgages to pay, reputations to protect, or swimming pools they want to buy with that Christmas bonus. Agents’ appetite for risk extends only as far as their context windows.

Which means everything downstream of risk — owning an outcome, making judgment calls around ill-defined variables — is the stuff AI is worst at. It’ll make a convincing argument for what to do, sure. But it never has to face the partner across the kitchen table when the bonus evaporates — and the pool deposit goes with it.<br>So your best AI candidate is a process you’ve already stripped of both: the need for an owner, and the need for long-horizon judgments. Something you can hand off as a written process document, with failover built in — automated rubrics, managerial review — to double-check the work close to real time.<br>The best process to move to AI, in other words, is one you’ve already offshored.<br>That’s not just my read. That same MIT report found the biggest AI returns weren’t in the sales-and-marketing tools soaking up most of the budget — they were in back-office automation: cutting agency spend, streamlining operations, eliminating business process outsourcing. The highest-ROI place to point AI to the work you can claudeshore.<br>Offshore to Claudeshore: All you need is a map

If your company has offshored a process, it probably went through a checklist like this:<br>✅ define the process and the result(s) you want<br>✅ break it into steps and subprocesses<br>✅ build a way to measure the quality of the output at every point you can<br>✅ specify every tool (and level of access) the process touches<br>✅ write the whole thing up in a handbook<br>✅ put someone in charge… and properly incentivize them to keep the process aligned with company goals<br>Now ask what an AI process needs. Basically the same things, just named with new AI jargon:

If your offshore setup is genuinely buttoned-down, you can move the process to AI as a near-literal lift-and-shift:<br>take the machine image or virtual desktop you spin up for new offshore hires

run it on a server

provision the AI its own accounts, with the right access

load the training and reference materials onto the desktop in a searchable format

point a full system-control tool (OpenClaw, Hermes, Claude Code, the like) running a capable model at the machine

give it some basic prompt instructions with...