By Robert Kirkpatrick | TotalValue Group LLC
I caught myself doing it again last week.
I had a question about cash flow timing for a project I'm starting. Without thinking, I typed it into ChatGPT the exact same way I would have typed it into Google three years ago. Short. Keyword-heavy. No context. Just: "cash flow timing small business project."
Got an answer. Fine answer. Generic, applicable to anyone, useful to no one in particular.
Then I stopped and actually thought about what I had just done. I had treated a system that could know my business, my industry, my goals, my risk tolerance, and my current project constraints... like a search bar.
That's the problem I want to talk about.
Google Trained You. And It Trained You Well.
Spend two decades using a tool and you stop thinking about how you're using it. Google taught an entire generation to compress their actual question into five words and scan the results for something close enough to helpful. We got good at it. We learned the grammar of the search box, how to phrase things so the algorithm would understand, how to skim a results page in four seconds and find what we needed.
That skill is real. It's also completely wrong for AI.
Gartner is projecting that traditional search engine volume will drop twenty-five percent by 2026, as users shift toward generative AI tools. That projection isn't based on wishful thinking. It's based on what people are already doing. Thirty-seven percent of consumers now start searches with an AI tool instead of Google, according to a recent study cited in Search Engine Land. Twenty-nine percent of all ChatGPT conversations fall into the "practical guidance" category, meaning people are asking it how to make decisions, not just what something means.
The behavior is shifting. What isn't shifting, yet, is the mental model most people bring to the table.
There's a Difference Between Searching and Asking
Here's the simplest way I can explain it.
When you search, you're querying a database. You want the database to find you the closest match to your words. The better you are at choosing your words, the better your results. The database doesn't care who you are. It doesn't remember your last session. It has no idea what you're trying to build or why you're asking.
When you ask, you're starting a relationship. You can give context. You can say: here's what I'm working on, here's what I've already tried, here's the constraint I'm working around, here's what matters to me. The system can hold that context and actually use it. The answer changes based on who you are and what situation you're in.
That gap, between a generic answer and a contextual one, is the entire ballgame.
Eighty-two percent of Gen Z users, according to Adobe's 2026 data, prefer AI tools that give direct answers over traditional search. That's not just a preference for faster results. That's a preference for answers that feel relevant to them specifically. They're not searching. They're asking.
What One-Off Questions Actually Cost You
Most people are leaving most of the value on the table.
They fire a question at ChatGPT or Claude. They get an answer. They close the tab. Tomorrow they come back with a new question, starting from scratch again, with no context carried forward.
Every session they're essentially meeting the AI for the first time.
Think about the difference between asking a random stranger for business advice and asking your accountant who's known you for three years. The stranger might give you technically correct information. Your accountant gives you an answer calibrated to your situation, your history, your actual numbers. The information content might overlap, but the usefulness does not.
The people who are actually winning with AI right now have stopped treating it like a search box and started treating it like a standing advisor. They give it context once. They build on that context over time. They don't ask one-off questions, they run the same advisors on every relevant problem.
That is a completely different workflow. And it produces completely different results.
What an Advisory Layer Actually Looks Like
This is where things get concrete, because "treat AI like an advisor" is easy to say and hard to actually implement without a structure.
An advisory layer has three things a search box doesn't.
Persistent context. The AI knows who you are before you ask anything. Your business type, your goals, what you've tried, what's worked, what's off the table. You give this once, in a system prompt or a structured setup, and you don't repeat yourself every session.
Defined roles. Not every question needs the same kind of answer. A financial question needs a different analytical frame than a marketing question. An editorial question needs a different voice than a technical one. A well-built advisory layer assigns specific roles to specific domains, so the AI isn't trying to be everything at once.
Checkpoints. This is the one most people skip. An advisor doesn't just answer your question and disappear. A good one pushes back, asks what you're actually trying to accomplish, flags things you might not have considered. You can build that behavior in. You can instruct the AI to challenge assumptions before giving a recommendation, or to surface risks alongside solutions.
When those three things are in place, you stop getting generic answers to context-free questions. You start getting output that sounds like it was built for your situation, because it was.
That's what the CORE Operating System is built around. It's a structured prompt system that gives you the persistent context layer, the role definitions, and the checkpoint behaviors out of the box. You don't have to engineer it from scratch. The architecture is already there.
The Other Side of This Equation
Here's something most articles about AI search don't mention, because they're focused on the user side.
If people are asking AI for recommendations instead of Googling for options, the question isn't just "how do I use AI better." It's also "how does AI know to recommend me?"
McKinsey's data says forty-four percent of consumers now prefer AI search for buying decisions. That's a massive chunk of decisions being filtered through a layer that doesn't work the way Google works. There are no blue links. There are no ads. The AI synthesizes what it knows and surfaces what it thinks is the best answer.
If your business isn't part of what AI knows, you're not even in the running.
This is the parallel problem. Building an advisory layer for yourself makes you more effective. But if you're running a business, you also need to build the layer that makes AI recommend you to others.
Make AI Recommend You is a prompt-based system I built specifically for this. It trains you to feed AI the kind of structured, specific, credibility-building information that makes it more likely to surface your business, your work, or your expertise when someone asks a relevant question. The search-to-advisor shift isn't just changing how you ask. It's changing who gets found.
Why This Shift Matters More Than People Realize
I'm not a futurist. I don't make predictions about what AI will do in ten years. I'm a data analyst who pays attention to what's already happening.
What's already happening is this: people are asking AI what to buy, who to hire, what to read, how to fix things, and what decisions to make. Twenty-nine percent of all ChatGPT conversations are about practical guidance. The platform has nine hundred million weekly active users as of early 2026 and processes two billion queries a day.
The behavior is there. The volume is there. The question is whether you're using it like a search bar or like an advisor. And whether, if you're running something, you're showing up when people ask.
The infrastructure for both of those things is smaller and simpler than most people expect. It's not an enterprise AI project. It's not six months of implementation. It's a set of structured prompts that you build once and actually use.
Go back to that cash flow question I mentioned at the start. Here's what it looks like when I ask it right:
"I run TotalValue Group LLC, a digital products company focused on AI prompt systems. I'm starting a project with a three-to-four month runway before revenue. My current concern is whether to front-load expenses in month one or spread them across the quarter. Given that I'm bootstrapped and want to preserve runway, what cash flow sequencing would you recommend?"
Same underlying question. Completely different answer. Because that answer is built for me, not for anyone who could have typed those five words.
That's the shift. It's not complicated. It just requires you to stop treating AI like Google.
Try It Before You Build It
If you want to start somewhere, the free version of the AI Signature Scrub is a fast way to see what your current AI output actually looks like through an analytical lens. It's not the advisory layer tool, it's the output quality layer. But it gives you a concrete sense of what structured AI use produces versus what unstructured use produces.
The full toolkit, including the CORE System, is at kirkpatrick3.gumroad.com.
The website for TotalValue Group is here if you want to see what we're building.
Robert Kirkpatrick is the founder of TotalValue Group LLC and builds AI prompt systems that replace work you'd normally pay a consultant to do. He's a data analyst by trade who got tired of watching people fight AI tools that were designed to help them.
Top comments (0)