Why You Should Treat AI Like A Junior Employee

Today, Chad will show you how easy it is to drive growth with AI. 

In 2021, crypto entrepreneurs were a dime a dozen. Today, many of them are flocking to the next hot thing — AI-driven outbound lead generation. Login to LinkedIn and you’ll see a flood of slick demos that invariably send the same message: Personalized outreach is as simple as inserting a three-line prompt into an AI agent!

But here’s the catch: crypto bros-turned-AI-experts only show you the results they get with a handful of giant enterprises. Thanks to their size and resources, these companies all have perfectly sales-friendly websites with immaculate HTML. So when you try to apply the same formulas to real-world conditions — 100+ rows of data from less successful companies — your accuracy will plummet to 50-70%. 

As it turns out, AI won’t do your thinking for you. To get the responses you need, you’ll have to do your part. 

AI Needs a Great Manager

People often think that AI is a magical solution, but it’s more like a junior employee that needs coaching, guidance, and patience. AI isn’t some savior — it’s an assistant that’s only as good as your ability to manage it. Just as a fresh hire requires onboarding, feedback, and regular goal alignment, AI models thrive on fine-tuning and clear direction. 

For example, large language models (LLMs) will show you mountains of information, but they’re only useful if you know exactly what you’re looking for. Vague prompts yield vague answers. Without proper instructions and guardrails, you'll get surface-level insights instead of meaningful results.

In AI as in life, hard work beats talent. Just like hiring a senior executive with no industry experience can backfire, using an advanced model without specific fine-tuning is often useless. The best AI systems have practical, relevant experience baked in through training and user feedback.

Multi-Agent Systems: Think Smart Teams, Not Big Teams

The key to unlocking AI’s potential is collaboration—not just between human teams but between multiple AI models or “agents” working together. Like people, AI systems work better together, combining perspectives to generate stronger outcomes. And just like with people, there’s a balance to be struck. Assemble too many agents, and you’ll just get noise — much like an overstaffed project.

Getting a Competitive Edge is Hard—Keeping it Is Even Harder

There simply is no substitute for hard work. When an 18-year-old ex-crypto "guru" shows you how to craft three-sentence prompts, remember this: If you copy their methods, you’re already playing catch-up. To get ahead, you need to put in work like no one else is.

Just consider how hard it is to actually achieve business success. According to research from Harvard Business School, over 95% of new products fail. It’s simply always been hard to get a competitive edge, and AI won't change that — especially when your competition is already using it too. 

Taking a Custom Approach to AI 

Rather than relying on vague prompts and single-agent systems, you need proper evaluation (and quite a bit of data to cover all the edge cases) if you want to stay ahead of the competition. Niching down and building finely tuned AI workflows is the name of the game. Generalist solutions might look impressive on social media, but real results come from bespoke systems built to meet your specific needs.

Treat AI like an employee. Be patient. Train it well. Set clear goals. Identify the edge cases. The companies that figure this out will be the ones that truly unlock AI’s full potential—and keep their competitive edge for the long haul.

If you’d like to have autonomous AI forecasts that align with your specific business needs, let’s see how AlgOps can help you stay ahead of the crypto bros with custom, multi-agent forecasting.