AI agents are quickly becoming one of the most attractive ideas in business. Founders want agents that can answer customers, summarize reports, create tasks, review work, update CRMs, check fulfillment, draft emails, and help the team operate with less manual effort. That interest makes sense because many growing companies are overloaded with repetitive work and too many decisions still trapped inside the founder’s head.
The mistake is assuming the agent itself is the system.
In Episode 6 of Follow the Yellow Brick Road, Emma Rainville and Mitch Barham talk about the Scarecrow Problem, which is what happens when the front end of the business looks intelligent but the backend is missing the structure to support growth. Their example is a company with strong ads, good ROAS, revenue coming in, and still no profit left at the end of the month. The business is not failing because nobody is buying. It is failing because the operations behind the sales are breaking.
That same principle applies to AI agents. An agent can only be useful inside a business if the business gives it a clear role, a real workflow, usable documentation, appropriate access, examples of good work, and a way to be reviewed. Without those pieces, the agent is not an AI workforce. It is another tool floating around in the chaos.
The Business Has to Know Where the Work Breaks
One of Mitch’s strongest recommendations in the episode is to whiteboard the problems. List what is breaking, look for the patterns, and find the common bottleneck. It may sound simple, but it is exactly what most companies skip when they start thinking about AI.
A founder may say they need an AI customer service agent, but the real issue may be that customers keep asking the same question because the onboarding experience is unclear. A team may want an AI reporting assistant, but the actual problem may be that nobody agrees on which numbers matter or what decision the report is supposed to support. A company may want an AI operations agent, but the real breakdown may be that tasks are created without owners, deadlines, or completion standards.
When a business skips diagnosis, it usually builds AI on top of symptoms instead of causes.
That is why the Scarecrow Problem is such a useful lens. If ads are driving customers into a broken business, the fix is not always better ads. If AI is being asked to support a broken workflow, the fix is not always a better prompt or a more expensive platform. Sometimes the fix is to understand the workflow, document it, clean up the handoffs, and decide who owns what.
AI Agents Need SOPs That a Person Could Follow First
Emma’s point about SOPs in the episode is blunt and important. An SOP should be detailed enough that someone new could follow it. That does not mean the person will become a strategic expert overnight, but they should be able to complete the mechanical steps of the task. They should know where to go, how to access the tool, what credentials or password system to use, what to click, what to check, and what the finished result should look like.
This level of documentation is not just helpful for training employees. It is the foundation for training AI inside the business.
If an SOP is vague, the AI has to fill in gaps. If the SOP does not explain exceptions, the AI cannot reliably handle exceptions. If the SOP does not include examples, the AI has no standard to imitate. If the SOP does not define escalation rules, the AI may try to answer when it should stop and involve a human.
This is especially important for customer service, fulfillment, QA, reporting, and internal operations because those areas often include judgment. A customer support agent cannot be told only to “answer customer questions.” It needs policies, approved language, refund rules, escalation paths, examples of good replies, and boundaries around what it should never promise. A QA agent cannot be told only to “check the work.” It needs the scorecard, the standard, the examples, and the definition of what counts as acceptable.
A good place to internally link this section is the older blog about AI agents and business context, especially if that article explains why agents need more than task completion: Read: What an AI Agent Actually Does.
Documentation Can Start With the Work Already Happening
A lot of companies avoid SOPs because they imagine documentation as a separate project that nobody has time for. Emma gives a much more realistic approach in the episode. If someone is already doing the task, they can record it with Loom, speak through what they are doing, and use the transcript as the raw material for the SOP. The person does not have to stop the business to write a polished manual from scratch. They can capture the real work while it happens.
That matters because the best SOPs usually come from reality, not theory. They include the awkward steps, the tool quirks, the places where people hesitate, the things that experienced team members forget are not obvious, and the decisions that happen quietly in someone’s head.
AI can help turn those recordings and transcripts into a draft, but the human still matters. The person doing the task needs to review the SOP, correct the gaps, add missing context, and keep it updated as the workflow changes. That is also where AI agents become easier to build later. The company is not inventing the process for the agent. It is documenting the process the business already uses and improving it enough that both humans and AI can follow it.
This is a much more practical path for a growing company than trying to build a complete AI workforce from scratch in one giant project. Start by capturing the work. Turn it into usable documentation. Improve the workflow. Then decide which parts can be assisted or automated.
Handoffs Matter More Than Most AI Demos Admit
Most AI demos focus on the task. The agent writes a reply, summarizes a meeting, drafts a report, generates a checklist, or pulls information from a document. That can look impressive, but business value often lives in the handoff. Who receives the output? What happens next? How is the work reviewed? Where does the final decision happen? What gets updated after the agent completes its part?
The episode touches this through the broader conversation about systems, execution, and the team rowing in the same direction. A business does not scale because one task happens faster. It scales when tasks connect cleanly to the next part of the workflow.
An AI agent that drafts a support reply is only useful if the team knows when to use it, when to review it, when to escalate, how to track recurring issues, and how to turn customer complaints into better operations or marketing. An AI reporting agent is only useful if the report goes to someone who knows what decisions the data should influence. An AI SOP assistant is only useful if the resulting documentation is stored, maintained, and actually used by the team.
This is why AI implementation belongs inside operations, not just software. The question is not whether the AI can perform the action. The question is whether the business has a place for that action to live.
Vision Keeps AI From Becoming More Noise
The episode becomes even more important when Emma and Mitch talk about vision. Emma explains that many companies either do not have a real vision or have one that the founder understands but the team cannot repeat in any meaningful way. If the vision is basically “make money,” the company has not given the team a real direction.
That problem gets worse with AI.
AI makes it easier to create. It makes it easier to generate plans, drafts, summaries, workflows, tickets, documents, and reports. But if the business does not know what it is building, more output can become more noise. Different team members may use AI in different ways, build disconnected workflows, automate low-value tasks, or create more work for each other without improving the company’s real priorities.
Emma’s canoe metaphor fits this perfectly. If everyone is rowing in different directions, adding AI just gives people stronger oars. The company may move faster, but not necessarily toward the same destination.
A clear vision gives AI implementation a filter. It helps the business decide which workflows matter, which agents should be built first, which tasks are worth automating, and which shiny objects should be ignored. It also helps the team understand why the systems are being built. The goal is not to use AI because AI is popular. The goal is to make the business better at delivering on its actual purpose.
The Best First AI Agent Is Usually Not the Flashiest One
For a business dealing with the Scarecrow Problem, the best first AI agent is rarely the most impressive demo. It is usually the one connected to a real bottleneck. That may be support triage, SOP creation, QA review, internal reporting, lead research, meeting summaries, or fulfillment checks.
The right starting point depends on where the business is already losing time, clarity, or money. If support tickets are overwhelming the team, start there. If tasks keep falling through the cracks, look at project handoffs. If the same person is always asked the same internal questions, turn that knowledge into documentation. If reports are created but not interpreted, build a workflow that helps the team understand what changed and what needs attention.
This approach is less glamorous than promising a fully autonomous AI company, but it is more likely to work. It treats AI like part of the operating system rather than a magic layer above the business.
If you are serious about building AI agents that support operations, customer service, fulfillment, QA, reporting, and delivery, you need to think in terms of workflows, SOPs, access, guardrails, and feedback loops. The agent is not the whole system. It is one part of the system.
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AI Agents Need Ongoing Management
Another lesson from the episode is that SOPs should not be created once and forgotten. Emma says they should be updated regularly because the business changes, tasks change, tools change, and more work can be automated over time. The same is true for AI agents.
An AI agent should have a feedback loop. The team should review its output, note recurring mistakes, improve the instructions, update the examples, and adjust the workflow as the business learns. If the agent is handling customer service drafts, someone should be reviewing accuracy, tone, escalation decisions, and whether customers are getting better answers. If the agent is assisting with reporting, someone should check whether the summaries are useful and whether the right issues are being flagged. If the agent is creating SOP drafts, someone should make sure the documentation is actually usable.
This is where many companies underestimate AI. They treat it like a one-time installation instead of an operational asset. But business-ready AI requires management in the same way any system requires management. It needs ownership, review, updates, and standards.
That does not make AI less powerful. It makes it more practical. A managed agent can improve over time. An unmanaged agent becomes another risk.
Final Takeaway
AI agents can absolutely help a business scale, but they are not a substitute for business structure. They need SOPs, handoffs, examples, data access, guardrails, ownership, and a clear place inside the workflow. They also need a company vision strong enough to keep the team from automating in random directions.
That is why the Scarecrow Problem matters beyond paid ads. A business can look smart on the surface while the backend is still missing the brain. Smart ads can expose that. AI can expose it too.
Before building more agents, look at the work. Whiteboard the bottlenecks. Capture the tasks with Loom. Turn transcripts into SOPs. Clean up the handoffs. Decide what the agent is allowed to do and where a human needs to review. Connect the AI work to the company’s actual focus.
That is how AI becomes useful inside the business. Not as a disconnected tool, but as part of the system that helps the team execute.