The people I talk to who have “tried AI” and decided it's overrated are not wrong about what they experienced. They're wrong about why.
The most common version of the story: they tried ChatGPT or Claude for a few weeks, found the outputs generic, stopped trusting it, and filed it away as a tool that's probably useful for someone else. That experience is real and the outputs they got were probably as generic as they remember. The issue wasn't the model. It was the setup, or more accurately, the complete absence of one.
There's a real difference between casual use and configured use, and the gap between them is larger than most evaluations account for.
Casual use looks like this: open a chat window, explain your situation from scratch, ask for help, get an answer that could apply to anyone in a similar position, close the tab. Maybe useful, maybe not. Either way, the model knows nothing about you, has no memory of what you've decided, no sense of your voice or priorities, and no context on the people you work with. The next session starts from zero.
Configured use looks different. The system already knows your role, your current priorities, and the relationships that matter. When you ask it to draft something, the first version sounds like you on a good day. When you ask for input on a decision, it steelmans the opposing position before it agrees with you. When a meeting ends, it processes the notes, captures commitments, and queues the follow-up. When you're heading into a call, the context on that relationship is already loaded.
The gap between those two experiences isn't a prompting problem. What's missing is infrastructure.
The work that actually consumes executive time sits in an awkward middle zone: judgment-adjacent tasks that require enough context to do well, but not enough judgment that they strictly need you. Prospect research before a call, follow-ups in your actual voice, synthesizing what a scattered week of meetings produced, tracking what you've committed to and what others have committed to you. These are the things that eat an hour here and ninety minutes there, and that add up to a week's worth of overhead if you don't have a system for them.
Most people are using AI like a text editor with a capable autocomplete: useful, occasionally impressive, and completely stateless. The issue is that a stateless tool with no context about you can't produce personalized, high-quality output consistently. It's like hiring a contractor for the day, every day. They're capable, but every morning you spend 20 minutes briefing them: who the players are, what happened last week, what you're trying to accomplish. They do solid work, then they're gone. Tomorrow, same contractor, same briefing. A configured system is closer to a long-tenured EA who already knows who Sarah is before you ask them to draft her something.
The analogy that keeps coming back to me is the CRM versus the spreadsheet. Both can hold the same data. The CRM is useful over time because it has structure, relationships between records, and a protocol for how information flows in and out. A spreadsheet is useful once and becomes a liability as it grows. Most people are running AI like a spreadsheet: flat, ad hoc, with no continuity between sessions.
I've watched people try to solve the voice problem by opening every session with a paragraph describing their own writing style. “I write in short sentences. I'm direct. I prefer active voice.” The model nods along and produces something generic with slightly shorter sentences. That's not voice calibration; it's a description of voice calibration, and neither party really knows what to do with it. A new hire handed a bullet list of your personality traits couldn't impersonate you either.
Closing that gap requires a context system that holds your decisions, your relationships, your preferences. A voice calibration layer learned from your actual output. Workflows designed around how you actually work. This takes real setup time. In my experience, you're looking at weeks of daily use before the system has enough context to produce outputs that genuinely feel like leverage. Which is, honestly, why AI doesn't stick for most people who try it: they put in a few hours, hit the floor of generic outputs, and stop before the context has accumulated enough to make the ceiling visible.
The demo always shows the ceiling; the onboarding experience shows the floor. And for most people, the floor is discouraging enough that they stop there.
When I was building the system I now use to run Sidekick, I searched pretty hard for something pre-built that covered the actual work: sales workflows, meeting intelligence, communications, relationship tracking, strategic analysis. An actual system, configured and ready to run, for exec and GTM functions. I couldn't find one. So I built it, distilling years of experience working with LLMs into the structure. The version I use today reflects months of optimization against industry and expert best practices, and hundreds of hours of real application.
Better infrastructure around the model you already have is what moves the output from generic to genuinely useful. Building that from scratch is a real project. If you want to skip the research phase, that's what Sidekick Solo is for.