Stop Using AI to Make Garbage Faster: Redesign the Workflow Before You Automate

Enterprises keep pouring money into AI and wondering why the return feels thin. The uncomfortable answer is rarely "we picked the wrong model." More often, teams bolted a clever tool onto a broken process—and celebrated the speed.

That is making garbage faster. It is paving the cow path: asphalt over a winding trail that never should have been the road. If you want real AI adoption, redesign how the team works before you automate. Seat licenses are not a strategy. Workflow redesign is.

You're not under-tooled—you're over-automating broken work

Most organizations I meet already have ChatGPT, Copilot, Claude, or a handful of niche agents. The demo looked great. A pilot produced a shiny draft. Then the work hit the real system: approvals that bounce across three inboxes, data that does not reconcile, handoffs nobody owns, and a "process" that only works because one veteran quietly compensates for the gaps.

Speed that up with generative AI and you do not remove the dysfunction. You scale it. Rework arrives sooner. Exceptions multiply. Managers get more notifications, not more decisions. People blame the model. The model did exactly what you pointed it at.

An X News cluster from early September 2026 framed the pattern bluntly: companies repeating past mistakes by automating inefficient workflows—summarized in the trend story as "making garbage faster," and tied back to Michael Hammer's classic warning against "paving the cow paths." Treat that cluster as a signal of the conversation, not a peer-reviewed study. The business lesson underneath it is durable either way: generative AI for business workflows fails when the workflow itself is the product of accident and habit.

Deloitte's agentic AI readiness research puts numbers on the same gap. In its August 2026 survey work, only about 16% of leaders said their business processes were prepared for agentic adoption—and many organizations are still "layering" agents on top of existing processes instead of redesigning them. That is the ROI trap in one chart: short-term demos, long-term disappointment.

What the jobs boom does not fix

A second X News cluster amplified The Economist's analysis that AI has been associated with roughly a million net new U.S. jobs so far—more demand in infrastructure and AI-related white-collar roles than simple back-office wipeout narratives suggest. Useful macro context. It does not rescue a messy invoice path, a broken handoff between sales and delivery, or a reporting ritual that takes nine steps because "that's how we've always done it."

Hiring AI engineers, annotators, or "AI leads" while leaving the operating system of work unchanged is another version of buying tools. AI adoption is still a team redesign problem.

The 3-step adoption playbook

Here is the playbook I teach in corporate AI training. It is deliberately boring. That is why it works.

1. Map the broken step

Pick one painful workflow—not "the company," one path. Invoice-to-cash. Proposal-to-kickoff. Meeting-to-decision. Support triage. Campaign brief to publish.

Write the real steps, not the happy-path SOP on SharePoint. Who touches it? Where does work wait? Where does quality die? What gets retyped, re-asked, or re-approved every week? What only works because one person remembers the exception?

If you cannot name the broken step in one sentence, you are not ready to automate. You are ready to buy another dashboard.

2. Redesign the workflow

Before anyone opens an AI tool, ask the redesign questions:

• Which steps can disappear entirely?
• Which handoffs can collapse into one owner?
• Which decisions need a human, and which need a rule?
• What inputs must be clean before a model or agent touches them?
• What does "done" mean in a way a stranger could verify?

This is where teams get uncomfortable—and where the money is. Redesign is not a prompt. It is clarifying roles, removing zombie approvals, fixing the data definition, and deciding what good looks like. When Hammer talked about not paving cow paths, this was the point: change the path, then pave.

3. Then automate

Only after the path is worth traveling do you add AI: drafting, classification, extraction, routing, summarization, standing instructions inside Projects, light agents for the repeatable middle of the flow.

Automate the redesigned loop. Measure cycle time, rework rate, and exception volume—not "number of prompts" or "seats rolled out." If the first drafts still need a full human rewrite, you automated output theatre, not the workflow.

Punchline, locked: Adoption isn't buying tools—it's rebuilding how the team works.

Why "AI training" has to mean workflow training

A lot of corporate AI training still stops at clever prompting. That helps individuals. It does not fix a multi-step bureaucracy no single team owns.

Train people to:

1. Spot cow paths (speed without redesign).
2. Map one workflow with owners and wait states.
3. Redesign for fewer steps and clearer "done."
4. Encode the new way of working (SOPs, standing instructions, Project knowledge).
5. Automate the clean path and review exceptions on a schedule.

That sequence turns tool demos into AI adoption. Reverse it and you get expensive garbage with better fonts.

Soft next step

If your organization already spent the AI budget and still feels stuck, do not buy another seat pack hoping this time will be different. Map the broken step. Redesign the workflow. Then automate.

I offer a free discovery call to pressure-test where your team is paving cow paths—and which workflow is worth redesigning first for measurable AI adoption. This is a conversation and a prioritization pass, not a free full audit.