The DeMicco Digest
Grab your headphones and enjoy a mini-podcast version of this blog. Sit back and listen while we walk you through the key points!
Most people think of artificial intelligence tools as plug-and-play, perfect replacements ready to drop into daily work. The reality couldn’t be further from the truth. Behind the scenes, there’s a massive amount of development, iteration, and discipline going into these systems. Just as we saw in the early days of the internet, when competing technologies battled for traction, we’re watching the same process unfold with AI.
What’s encouraging is how seriously the major players are taking this responsibility. OpenAI’s recent GDPval study is a great example. Instead of testing models on trivia or synthetic tasks, they evaluated 1,320 real-world assignments-spreadsheets, documents, and presentations-across 44 occupations in 9 industries central to U.S. GDP. These tasks were designed by professionals with an average of 14 years’ experience.
The results? Even in “high reasoning mode,” human experts still outperformed GPT-5 nearly two-thirds of the time. Instead of glossing over that fact, OpenAI published it openly. That honesty matters, because it shows they know long-term success depends on delivering durable solutions to real audiences.
Meanwhile, Anthropic’s Claude Opus 4.1 nearly matched human experts head-to-head in the same tasks. That’s impressive progress-and one reason I often turn to Claude, alongside Perplexity, when I’m doing research or building efficiencies into client workflows. These are some of my go-to tools, not because they’re flawless, but because they’re advancing quickly in the right kinds of applications and proving their value in real-world use cases.
However, here’s the key point I always emphasize: you can’t simply trust the system and walk away.
AI models may have incredible base-layer technology, but adapting them for your specific needs, whether for client-facing tools, research assistants, or process automations, means taking on an ongoing responsibility to maintain, train, and nurture them. Training and fine-tuning are not “one-off” projects. They’re living processes. Just as businesses evolve, markets shift, and regulations change, your AI implementation has to keep pace.
And the GDPval study reinforces this. The abstract highlights that more reasoning effort, richer context, and stronger scaffolding (structured guidance) all significantly improve performance. In other words, success isn’t automatic…it comes from applying discipline, strategy, and continuous refinement.
This lesson applies not only at the model level but also to the applications companies are building on top of these systems. We’ve worked with clients who developed proprietary manuals and industry reference guides, assuming that once those documents were uploaded into an AI agent, the system would “just work.” The challenge comes when they realize that’s not the case.
- Segmentation and structure matter. Manuals must be broken into logical chunks so the AI can retrieve relevant sections accurately.
- Metadata and organization matter. Without proper tagging and indexing, you risk drift—where the model pulls in unrelated content or mixes contexts.
- User behavior matters most. What seems straightforward in a written manual often shifts dramatically when real users ask questions in unpredictable, messy ways.
That’s why implementation can’t be treated like a finished project. What separates success from disappointment is monitoring real interactions over time, retraining where needed, and nurturing the system as it evolves.
You wouldn’t hand a child a book titled How to Grow and then walk away. You nurture, guide, and adapt along the way. AI systems are no different.
Discipline Separates Hype from Strategic Success
Anyone who knows me knows that my team and I invest time in thoroughly testing these tools before rolling them out internally or to clients. That’s what separates real strategic use (and the success that comes from it) from surface-level hype. It takes work, patience, and a willingness to keep refining.
❝ Success is the result of good judgment. Good judgment is the result of experience. Experience is usually the result of bad judgment.❞
– Tony Robbins
I’ve always loved this saying, and in this context, it’s a reminder that to become wise with AI, you’ll go through mistakes, experiments, and recalibrations-and that’s exactly how we guide clients through this process.
So yes, these tools are impressive. Yes, they’re advancing fast. But they aren’t replacements-they’re instruments…and in the right hands, they can transform the way we work.
Joseph DeMicco brings over 30 years of experience to his roles as founder and CEO of Amplify Industrial Marketing + Guidance, founder of Industrial Web Search, and instructor for the Goldman Sachs 10,000 Small Businesses program, specializing in data-driven marketing strategies.
