How to Fail Your Company Using AI Agents

It’s no wonder there is so much buzz about AI agents.

AI is becoming the corporate equivalent of a hammer looking for a nail. Leaders see impressive demonstrations of AI agents and immediately begin searching for processes to automate. But automation alone does not create value. In many cases, it simply accelerates existing problems.

An AI agent can qualify leads faster, write more emails, answer more customer inquiries, and process more requests than a human ever could. However, if the qualification criteria are flawed, the outreach strategy is ineffective, or the process itself is poorly designed, the agent merely scales those weaknesses. The question is no longer whether AI can automate a task. The more important question is whether that task should be automated in the first place.

However, many of the largest failure cases with AI are not related to a technical failure; they are primarily due to the fact that they did not properly define the supporting processes, decision criteria and business strategies associated with the particular problem to solve. The following pointers illustrate a common theme: automating uncertainty; scaling up poor performing processes; and replacing judgement that was never defined in the first place.

Using AI Agents to Automate the Chaos

An IT service company has 50 employees. Their lead intake process is totally disorganized as the leads can come in from email, phone, web form, LinkedIn DMs, etc. Creating an extra 2 hours of work for the sales team to organize leads every day has caused the founder to think: I can build an AI agent to triage all these leads.

The truth is their real problem is not triaging incoming leads, but that they do not have a way to determine what suitable leads look like or what the definition is of a bad lead, so if they bring in an agent to triage the leads coming in, they will be triaging incorrectly. They’ll be able to do it faster, but still incorrectly.

Before they build the agent, they need to answer these questions:

Once these questions are answered (through manual triaging), they could consider building an agent. They built the agent first in order to have the agent answer these questions for them, and the agent did not answer those questions. All the agent did was automatically process their confusion on a larger scale.

They made the mistake of thinking that the intake process is the issue when, in actuality, it was the improper qualification criteria. Once the agent began calling unqualified leads, they realized that they needed to address the qualification issue, but the agent hid that issue for 3 months while they burned through the time of their sales team.

Using AI Agents To Solve the Wrong Problem

A Sales Leader used Claude to send automated outreach email campaigns. As a result, he’s thinking: “Let’s create an AI agent that will handle the writing of cold outreach emails.” But does the capability exist? Yes. Is the economic component viable? Let’s look at it from a mathematical perspective. Service-based email outreach currently utilizes 2-3 people to create approximately 300 emails per week in total; an agent would be able to produce 3,000 emails on a weekly basis.

Now the question becomes: Sending 3,000 emails to whom? With only 500 qualified prospects on your prospect list.

Simply having an agent doesn’t create demand for cold email outreach. It results in an increase in output for something you’re already executing. If your manual method of outreach isn’t generating responses, in volume, type, or fit, then automating it won’t change that result; it will only happen faster.

The error this individual made was being lured by the agent’s capability of producing output quickly, rather than evaluating the economic feasibility of needing that volume. Although the two sales reps already owned and controlled the entire process, the issue they faced was not the capability of producing content rapidly; it was developing an effective prospecting strategy.

Prioritizing Automation over ROI

Proving that you’re addressing the right issue through AI agent development and implementation is critical. AI agents can be very expensive to develop – anywhere from $15,000 to $100,000 (or more), depending on the complexity of the task. You are investing in engineering, API calls, infrastructure, iterations, etc., and you have an expectation of return on your investment = replacing people or multiplying the amount of work performed.

When you automate the wrong thing, you have spent capital to create a scale (which is not good), and you now have 10x the volume of the wrong problem (and 10x the volume of bad data). Your metrics look good on the surface (for example: thousands of requests processed monthly) yet your bank account is thinner than before and your original problem has worsened.

It is ineffective to compare your AI agent cost versus a salary of a person supporting a task if the agent is solving the wrong issue. This is analogous to trying to determine whether or not a bulldozer is cheaper than a shovel when the bulldozer is improperly placed. The greatest loss incurred in this case is not just the cost of building and using an AI agent, but also finding out how long it will take you to recover from the wrong decision.

AI Agents Are Great at Following Rules. Not Creating Them.

Imagine you are a Delivery Lead for a product or services company. Projects continue to run into scope expansion issues. She thinks: “I’ll create an automated agent that will read all incoming change requests and automatically flag scope expansion.”

Benefit: Install and never touch again. The agent will identify any scope expansion before it can become truly at risk of expanding.

Challenge: There is primarily no defined scope expansion rule. What constitutes scope expansion; an ask for 10 hours when the budget was for 8 hours? A request for features not directly related? A client asks for something that should have been raised during discovery?

The company has never established a definition for this. Consequently, the agent will be forced to make judgement calls, many of which will be incorrect. The Delivery Lead will end up overriding the agent almost exclusively, “no, that actually falls within the original scope,” and the agent turns into a waste of time.

Agents can deliver against defined rule sets with accuracy but are incapable of developing rule sets. She should have spent 2 weeks with her team documenting the scope criteria prior to implementing the automated agent. If scope criteria is respected, the agent can assist with administering it.

Outsourcing Judgment to AI Agents With No Governance

A B2B service company may have a sales director who is looking for an AI agent to qualify leads, hot, warm, and cold, using high-intent, budget, and timeline signals. This could be done to speed up the sales process and save time. However, rather than creating a set of questions for the agent to use to qualify leads, the director simply gives it a general idea of how he wants leads qualified, intent, fit, and urgency, a broad command.

When the sales team starts using the new AI agent, they quickly realize that the leads the agent has qualified do not match their definition of qualified leads. There are too many false positives and too many false negatives, so the sales team stops using the agent altogether.

The director’s mistake was using an agent to qualify leads before determining his own method for qualifying leads. Had the director spent one week qualifying 50 leads and recording the reasons why he felt each lead was hot or cold, the agent could have mirrored the director’s logic. Instead, he wanted the agent to come up with the criteria itself.

Now that the agent has been used to qualify leads, the director is blaming the agent, not himself, for the leads being in the wrong category. However, the agent is working fine. The director’s lack of preparation for how to use the agent is the reason for its poor performance.

AI Agents Can Execute a Process. They Can’t Design One.

The founder of a service provider firm decided to develop an application that answers client inquiries, project status, invoice inquiries, and contract language, via an agent. He outsourced the development of the system and received a fully functioning product which was successfully implemented.

The automated inquiry handling worked well and the agent answered inquiries accurately. However, none of the parties considered the impact on client relationships caused by the change in how people would be communicating with each other.

When a client calls with a question and a staff member answers, that provides a connection between the two parties, builds rapport and trust, and creates opportunities to identify other issues the client might have, such as a weakness in an employee’s skill set.

By automating the inquiry process, the agency increased the speed at which a client gets their question answered but reduced the warmth, the human interaction, associated with receiving the answer from a staff member.

The error made was evaluating the situation as a technical question, can we automate this, rather than assessing the business impact and determining whether the process should be automated and what effect it would have on the client relationship. The implementation worked well but negatively affected the relationship with clients.

Conclusion

In summary, the commonality among these instances is that AI has not been the issue, nor will it serve as a remedy. Instead, leaders have looked to AI to compensate for a lack of strategy, undefined process, unclear or missing decision criteria, or an effective business design that was never built in the first place.

AI agents excel at executing rules, scaling existing best-practice workflows, and minimizing manual labor; but they are not capable of defining what constitutes a qualified lead, developing a prospecting strategy, creating scope boundaries, establishing governance, or determining whether the customer experience should remain human.

Organizations should ask “how can we automate this?” only after they ask themselves whether they have appropriately designed the process. The organizations that derive the most benefit from AI do not necessarily automate first; rather, they create clarity, document their decision making, and understand the impact of automating the work that they do. AI does not remove confusion from a process, it only magnifies it.

Therefore, if the processes are clear, AI will scale them; but if the processes are not clear, AI will simply help make mistakes at a faster rate.