Companies are betting big on AI, but very few are seeing the returns they expect. This leads to the inevitable question: why? Is the technology not good enough?
The problem often isn’t the technology itself, but a fundamental misunderstanding of what it takes to make it successful. We’re conditioned to see new technology as a tool to be installed, a button to be pushed. But Generative AI is different. The small fraction of companies that are succeeding have adopted a different mindset: they treat their AI not like a vending machine, but like a new, incredibly smart employee who needs to be onboarded.
The Paradox No One Talks About
Here’s what makes AI uniquely frustrating: it’s simultaneously overhyped and underhyped. There is a massive expectation that AI can do anything: write brilliant narratives, analyze complex data, solve business problems. And technically, it can. But making it do the specific thing your business needs? That’s not trivial at all. An AI will write you an impressive analysis with perfect confidence, whether the facts are right or completely fabricated. This paradox is why the onboarding mindset isn’t just helpful, it’s essential.
The “Smart Kid Off the Street” Problem
Imagine you hire a brilliant new graduate, a genius in their field, and on their first day, you drop them into a complex role with no guidance and tell them to “get productive”. They wouldn’t know where to start. They’d need an email account, access to the right systems, and time to understand the company’s hidden network: the unwritten rules and nuanced processes that dictate how work actually gets done. Even for a super smart person, it takes time to absorb all this context before they can be truly effective.
An LLM is exactly like that smart kid off the street. It possesses incredible general intelligence, but it has zero context about your specific business, your customers, your data, or your unique way of operating. The companies that succeed are the ones that focus on systematically providing the AI with the right context so it can apply its intelligence effectively.
A Manager’s Onboarding Checklist for AI
If you want to avoid becoming part of the failing majority, stop thinking like an IT department and start thinking like a manager onboarding a new hire. Set aside the technical jargon and follow a simple onboarding plan.
- Define a Clear Job Role. First, ask yourself, what do you want this AI to do? Do you want it to process invoices, optimize your supply chain, or analyze product performance? A clear job description is the essential first step. Here are our recommendations on where to look for these potential AI-powered use cases.
- Provide On-the-Job Training. You would show a new employee examples of what a successful project looks like. Do the same for your AI. Give it examples of the task done correctly so it can learn the pattern of success. These samples of good outputs allow the AI to learn the desired pattern, format, and nuances of success for the specific task at hand.
- Give It the Right Tools & Data. You wouldn’t give a new marketing hire access to the company’s entire financial database—that would be distracting and risky. Similarly, give your AI focused access only to the data and tools it needs for its specific job. But remember, it’s not just about access. You need to build the scaffolding that helps AI understand what to do with that information. Think of it as creating guardrails and guidance systems. AI can only react to the context it’s provided. Wrong context guarantees poor solutions.
- Conduct Regular Check-ins and Give Feedback. The MIT study on AI failures mentioned “learning” over 20 times, and for good reason. But here’s what most companies miss: learning doesn’t happen automatically. You have to engineer it into your AI system. It’s not something you get for free. This means monitoring performance, providing corrective feedback when the AI deviates from goals, and building systematic ways for it to understand when it’s right or wrong. Just as you would with any new employee, you need to actively teach your AI what success looks like in your specific context through continuous iteration and feedback loops.
The Non-Linear Path to Value
Let’s be honest about something most vendors won’t tell you: the journey from AI implementation to increased profits is rarely a straight line. It’s a meandering path of discovery. Unless you can map out exactly how AI will drive your end goal, you’re embarking on an exploration. Some processes that seem perfect for automation will surprise you with their complexity. Others you hadn’t considered will become obvious wins.
This is why sitting on the sidelines waiting for the perfect AI solution is a surefire disaster. Your competitors are already learning, iterating, and discovering what works. The companies winning with AI aren’t the ones waiting for perfect technology; they’re the ones who started the onboarding process today, knowing they’ll adjust as they go.
The Payoff: Infinite Scale
Why go through all this managerial effort? Because the payoff is unlike anything you can get from a human employee. Once you have properly trained your AI and it has learned its role, context, and tools, it can perform its job with flawless consistency.
It doesn’t get tired or distracted. And most importantly, its ability to scale is virtually infinite. An AI that has mastered invoice processing can handle ten thousand invoices as easily as it can handle ten. The upfront investment in training pays off with unparalleled and scalable productivity.
The companies that are struggling with AI are the ones still looking for a simple technology fix. The ones winning are those who have realized they’re in the business of management and training. They’re the ones who treat the AI like what it is: the most powerful new hire they’ve ever made…one that just needs the right onboarding to unlock its potential.
