I want to be honest about something that most advisors will not say out loud: I used AI to do work that would traditionally require a team, weeks of billable hours, and a price tag north of $20,000. And the result was, in most respects, better than what that team would have produced.
That is not a comfortable statement for someone who has spent thirty years in advisory work. But it is an accurate one, and if you are a business owner evaluating whether to hire a consultant, you deserve the full picture.
The Engagement
The scope was familiar territory for me. A growth-stage business with around $4 million in revenue needed a complete strategic package: a SWOT analysis, competitive landscape assessment, financial model with scenario analysis, and a pitch deck for investor conversations. The kind of work I have done dozens of times across industries from food service to real estate to CPG.
In a traditional consulting engagement, this package involves two to three analysts working four to six weeks. Someone is running market research. Someone is building the financial model. Someone is designing the deck. A senior partner reviews everything, adds the strategic layer, and presents it to the client. The bill lands somewhere between $15,000 and $25,000, depending on the firm and the complexity.
I decided to run the entire engagement using AI tools alongside my own expertise and see what happened.
What I Used
The primary tool was Claude for research synthesis, strategic analysis, and document drafting. I used it the way I would use a talented junior analyst — give it the context, ask the right questions, push back on the output, and refine. For the financial model, I combined AI-generated structure with my own assumptions and stress-testing. The pitch deck content was AI-drafted, then rewritten with the kind of language that actually moves investors, which is different from the language that sounds impressive in a conference room.
I also used automated research tools to pull competitive intelligence, market sizing data, and industry benchmarks. The kind of data gathering that used to take an analyst three days of desk research.
What Worked
Research Speed
The competitive landscape analysis that would normally take a week was substantially complete in hours. Not a surface-level overview — a genuine analysis of positioning, pricing strategy, market share dynamics, and competitive moats. The AI synthesized data from multiple sources, identified patterns I would have eventually found manually, and organized the output in a structure that was immediately useful.
First-Draft Quality
The initial drafts of the SWOT analysis and strategic narrative were surprisingly strong. Not perfect, not ready for a client — but the kind of first draft that a good analyst produces after a week of immersion in the business. The AI had absorbed the context I fed it and produced analysis that reflected genuine understanding of the business dynamics.
Financial Model Structure
The AI built a model framework with revenue drivers, cost assumptions, and scenario toggles that was structurally sound. The formulas worked. The logic flowed. It was not a template — it was a purpose-built model that reflected the specific revenue streams and cost structure of this business. I have seen freelance modelers on Upwork charge $3,000 for work that was less rigorous.
Competitive Analysis Depth
This is where I was genuinely surprised. The AI identified competitive dynamics that I might have missed in a first pass — not because it was smarter than me, but because it processed a volume of information that no human can match in a comparable timeframe. It found patterns in competitor pricing, identified gaps in the market that were not obvious from the surface, and organized the analysis in a way that told a clear strategic story.
What Fell Short
Industry-Specific Nuance
The AI produced analysis that was directionally correct but occasionally missed the kind of nuance that comes from having operated in a space. When analyzing a food service concept, for example, it applied generic unit economics assumptions rather than the Denver-specific cost structures that I know from having opened restaurants in this market. The numbers were reasonable — they just were not sharp. And in a pitch deck going to sophisticated investors, "reasonable" is not good enough.
The Smell Test
There is a form of pattern recognition that comes from thirty years of building and advising businesses. I call it the smell test. When someone shows me a financial model, I can feel when something is off before I trace the formula. When I read a competitive analysis, I can sense when a conclusion is technically supported but strategically wrong. The AI cannot do this. It does not have scar tissue from deals that went sideways, from leases that looked great on paper but killed the business, from financial projections that hit every target and still resulted in a failed venture.
At Noodles & Company, I watched projections come to life and die in real time across dozens of new locations. At Zenco Capital, I built a $50 million venture from zero and learned firsthand how quickly assumptions break under operating pressure. When I sat in the room at Spicy Pickle and watched franchise economics that looked perfect on a spreadsheet collapse in practice, that taught me something no AI can replicate. Those scars inform every model I review, every competitive analysis I write, and every strategic recommendation I make.
Client Relationship Building
Strategy is not just about the document. It is about the conversation that happens around the document. It is about the business owner who tells you the real problem in minute forty-seven of a meeting, after spending the first forty-six minutes telling you the version they think you want to hear. It is about reading body language when you present a financial scenario that scares them. It is about knowing when to push and when to hold back. AI cannot do any of that.
The Honest Verdict
AI produced 80% of the output in 10% of the time. The last 20% — the judgment layer — is where the advisor earns their fee.
That ratio matters enormously. It means the economics of consulting have fundamentally changed, but the value of senior advisory has not. The 80% that AI handles is the heavy lifting: the research, the data synthesis, the first-draft analysis, the model framework. This is work that consulting firms have historically charged premium rates for because it required expensive human labor. That justification is gone.
The 20% that remains is judgment, experience, relationship, and the ability to say "this number is technically correct but it will never survive contact with reality." That layer is not going away. If anything, it is becoming more valuable because the analytical baseline has risen. When every advisor has access to AI-powered research and modeling, the differentiator is the quality of thinking that sits on top of it.
What This Means for the Consulting Industry
The industry is bifurcating, and it is happening faster than most firms want to admit.
On one side, you have firms that sell hours. They staff teams of analysts, bill for research time, charge for model-building labor. These firms are in serious trouble. Their cost structure cannot compete with an advisor who uses AI to do in hours what their teams do in weeks. The economics do not work, and no amount of brand prestige will sustain a 10x price premium for the same output.
On the other side, you have advisors who sell judgment. They use AI to handle the analytical heavy lifting, then apply decades of operating experience to refine, challenge, and contextualize the output. Their value proposition is not "we will research your market" — it is "we will tell you what the research means and what to do about it, based on having been in your seat."
I know which side I am on. When I advise a real estate developer on a deal structure, the value is not in the model — it is in knowing which assumptions will survive investor due diligence because I have raised capital for developments before. When I review a franchise expansion plan, the value is not in the competitive analysis — it is in knowing which franchise economics actually work in practice because I have built franchise systems from scratch, including the FDD, the operations manuals, and the unit-level P&L frameworks.
What This Means for Business Owners
If you are a business owner considering hiring a consultant or advisor, this shift creates both risk and opportunity.
The risk is paying $20,000 for work that an AI-equipped advisor can do for a fraction of the cost. If your consultant is primarily billing you for research hours and model-building time, you are overpaying. Ask them directly: what tools are you using, and what percentage of the deliverable reflects your personal expertise versus data processing?
The opportunity is getting better strategic work at a lower price point. The best advisors are using AI to elevate their output, not to cut corners. They are spending less time on data gathering and more time on strategic thinking. The deliverables are sharper because the advisor is focused on judgment rather than being buried in spreadsheets.
Why This Is How Vorsant Sprint Works
I built Vorsant Sprint around this exact model. AI handles the analytical heavy lifting — the research, the data synthesis, the structural framework. I add the strategic layer — the operating experience, the judgment calls, the "here is what this actually means for your business" context that only comes from having done it.
The result is consultant-grade strategy deliverables at a price point that makes sense for growth-stage businesses, delivered in 48 hours instead of six weeks. Not because the work is less rigorous, but because the process is fundamentally more efficient.
That is not a threat to good advisory. It is the future of it.
