Treating AI Like Software Leads To Failure. Treat It Like An Employee.

Most organizations are approaching AI the same way they approached every other technology initiative - there’s a requirements phase, a build phase, a launch, and then the team moves on to the next project.

That model is familiar. It’s also wrong.

AI is not another system you deploy and walk away from. It is not a CRM or an ERP … you can’t install it and then optimize when it’s convenient to while leaving certain stones unturned.

AI agents behave far more like employees than applications, and organizations that fail to recognize this are the same ones wondering why their AI investments are stalled, shelved, or quietly creating risk.

The AI mistake organizations keep making

When I talk to executives about AI, the conversation often starts with technology. Which model? Which platform? Which vendor?

It misses the point.

First, I'll sound like a broken record here ... data (which is fuel for your AI) is platform-agnostic and should be treated as such. If you're approaching this on a per-platform basis, you've already lost.

Second, AI doesn’t fail because the technology is immature; building AI agents is largely a commodity. Anyone can connect a model to an API and generate responses!

The reason AI initiatives fail is because organizations treat them like static systems instead of living actors inside the business.

AI agents are unmonitored speakers for your business

Once an AI agent is deployed, it begins speaking on behalf of your organization. It interprets your data. It reflects your policies. It responds to customers and employees using the language and information you have grounded it on.

And unlike a human, it does not pause to think before it speaks.

That alone should change how leaders think about deployment.

An AI agent is effectively an unmonitored endpoint for your organization’s knowledge, legal position, and customer information.

• It’s answering questions when you are not there.

• It’s creating impressions when leadership is not in the room.

Sound uncomfortable? It should!

Most have governance, training, and oversight processes for people because people represent the company. AI agents represent the company too, but many organizations deploy them without comparable controls.

The risk goes beyond hallucinations or incorrect answers - the real risk is misalignment.

An agent that makes sense in one department can contradict another department entirely.

That contradiction often shows up first in front of a customer, not internally.

Why AI behaves more like an employee than a platform

The most useful way I have found to explain agentic operations is to compare agents to employees.

When you hire someone, you don’t just give them access to systems and hope for the best, right?

You onboard them. You train them. You monitor performance. You have regular reviews, such as 1-on-1s and performance reviews. You help your employee learn and adapt when needs change or when what they’re providing drifts off-course from what’s needed.

AI agents require this same approach.

• Onboarding an agent is grounding it in the right data and information architecture.

• Training is prompt refinement, feedback loops, and reinforcement over time.

• Performance management is observability, monitoring, and tuning.

• Governance (and stewardship) is defining what the agent can say, what it cannot say, and what happens when it crosses a line.

Ignoring these steps does not make the agent faster or cheaper. It makes it unpredictable.

Drift should be expected. It’s not failure.

I see panic from leaders when an AI agent starts behaving differently than it did at launch. It’s assumed to be broken.

It’s usually not.

AI agents translate human language into mathematical patterns. You do not get the same answer twice … over time, as data changes and usage patterns evolve, behavior shifts.

This drift is expected, and it’s not the problem - it’s a lack of visibility into what’s driving this. I like to use a golf analogy:

• Early on, the agent hits consistently in the fairway

• Over time, without training, it starts hitting into the rough on either side … not ideal, but a manageable mistake

• Eventually, it starts careening the ball deep into the woods, the pond, or out of bounds. A mistake that’s painful.

Let me stress this again: nothing is broken! The agent is simply learning differently.

Without monitoring and adjustment, that learning happens in public instead of in controlled environments where you can course-correct before a customer or prospect runs into it.

Why most AI proofs of concept never reach production

Why do so many AI pilots stall before becoming real, scalable capabilities? After all, CIO Dive reporting shows over 40% of them being scrapped.

It’s because outcomes, data paths, and operational ownership were never clearly defined ... not that the models are weak. 

Organizations rush to prove something is possible instead of defining how it will live inside the business. They skip observability, feedback mechanisms, and governance. 

Then they’re surprised when the agent behaves in ways they can’t explain, which erodes trust and leads to a reduction in risk - pulling out entirely.

A proof of concept can tolerate ambiguity, but production can’t - it’s one reason why I stress data architecture so much to clients.

AI readiness can’t be deployed in a vacuum

One department launches an agent that makes perfect sense to them, but contradicts another team - think customer service going live with a chatbot but that offers different answers than what finance or legal might downstream.

AI is truly cross-functional because it’s speaking for your entire organization, not just for a single department. So your readiness isn’t about checking boxes, it’s about alignment.

Every function that will be affected by an agent’s inputs or outputs needs a voice before it goes live.

If one team deploys AI, the entire organization inherits the consequences.

What leaders should do differently about organizational AI Readiness

• First, just like any other initiative, start with your critical outcomes - not tools.

• Architect your data and information before layering in models. 

• Invest in resources for better observation and governance alongside licenses and development.

And most importantly, accept AI requires ongoing care like a full-time employee vs. a one-time delivery like a technology.

You’d never hire an employee and fail to check their work; don’t deploy an AI agent and walk away.

Get AI readiness consulting help from a trusted source that’s doing this today for companies just like you. We’re ready when you are.