In Issue #1, I introduced a three-bucket framework for thinking about AI business value:
Boosting Productivity - Making your teams work faster and smarter
Creating New Value - Enhancing products, services, and customer experiences (covered in Issue #2)
Driving Disruption - Reimagining business models, value chains, and market approaches
Today, we complete the framework by exploring the third bucket: Driving Disruption.
This is where thinking big becomes essential. We're not talking about optimizing existing processes or adding AI features. We're talking about fundamentally questioning how value gets created and captured in your industry.
The first two buckets build on what exists. Disruption asks: "If we were starting this business today, knowing what AI can do, what would we build?"
That's an uncomfortable question. It implies your current model might be obsolete. It suggests competitors might reimagine your industry before you do. And it demands you think beyond next quarter's results to imagine entirely different futures.
But that's exactly my point - AI doesn't just make things faster or better. It changes the fundamental economics of knowledge work. When something that took weeks can now take hours, when expertise that was scarce becomes abundant, when capabilities that required specialized teams become accessible through natural language - the entire value chain reshapes.
Force 1: Cost compression at scale
AI doesn't create 10% efficiency gains. It creates order-of-magnitude shifts. A $500,000 project becomes a $50,000 project. A six-week timeline becomes six days. A team of twenty becomes a team of five with AI augmentation.
This isn't just "doing more with less." It's a fundamental repricing of intellectual labor. When the cost of analysis, synthesis, and insight drops dramatically, everything built on those economics needs redesigning.
Force 2: The democratization of expertise
AI makes specialized knowledge abundant. The legal research that required a team of paralegals. The financial modeling that needed expert analysts. The strategic frameworks that justified premium consulting fees. All becoming accessible at a fraction of previous costs.
This doesn't eliminate the need for experts - it changes what expertise means. The value shifts from knowing the frameworks to knowing when to apply them, from executing analysis to interpreting it with business context, from having the answers to asking the right questions.
Force 3: New capabilities enable new business models
The most profound disruption isn't making old things cheaper. It's making impossible things possible. Capabilities that were economically unviable suddenly become practical. Business models that couldn't scale now can. Services that couldn't exist suddenly do.
This is where entirely new categories could emerge - not just better versions of what existed, but fundamentally different approaches the market hasn't seen before.
The professional services industry demonstrates these forces in action. And watching consulting's transformation reveals patterns that will repeat across every knowledge industry.
The headlines have been brutal. Accenture lost $60 billion in market cap in four months. The Wall Street Journal declared McKinsey's moment "existential." Fast Company, The Economist, Reuters - all running stories about consulting's AI-driven doom.
But consulting isn't actually dying. It's being forced to answer that uncomfortable question I mentioned earlier. And the answers reveal how disruption actually unfolds.
A clarity moment
AI makes what you're actually paying for crystal clear. Consultants historically bundled scarce expertise, analysis capabilities, strategic frameworks, decision validation, and brand cover-your-ass insurance into one offering. AI unbundles that.
The analysis and frameworks are now increasingly commoditized. The brand trust and political cover? Become even more valuable in an uncertain transformation moment. Companies still don't get fired for hiring McKinsey - maybe more so when AI makes everything feel riskier.
This pattern repeats across industries: AI reveals the different layers of value in your offering. Some layers get commoditized fast. Others become more valuable. Winners will understand which is which.
The cost reality
A major firm's biggest client recently told them they expected the same services next year at half the price. Not as a negotiation tactic. As the new baseline expectation when AI demonstrably reduces delivery costs.
You cannot fight this tide. When AI brings your cost of goods sold down, customers expect that passed to them. The only question is whether you redesign your business model proactively or have it forced on you.
Certain categories of consulting work are simply disappearing. Rote analysis, back-office functions, basic research synthesis - increasingly automated away.
But simultaneously, entirely new capabilities emerge. For example - interviewing everyone in a company used to be impossible - too expensive, too time-consuming. You had to choose between interviews (great context, terrible scale) or surveys (great scale, terrible context).
But now? AI voice agents can conduct those interviews at scale. You get both. What was economically impossible becomes practical. That's not optimizing the old model it's enabling entirely new approaches.
Here's another - four years ago, "AI transformation services" didn't exist as a consulting category. Now it's a major growth line for every firm. That's not efficiency - it's creation of new services.
The consulting example illustrates the framework. Now apply it to your context:
Question 1: What are your fundamental economics?
What would happen if your delivery costs dropped 50-80%? Not your profit margins - your actual cost to deliver value. How would that reshape your pricing, your target market, your competitive position?
Question 2: What expertise are you selling?
Which parts of your knowledge advantage are pattern recognition that AI can replicate? Which parts are contextual judgment that requires human understanding? The former gets commoditized. The latter becomes your moat.
Question 3: What becomes economically viable?
With AI, what could you do that was previously too expensive, too slow, or required too much specialized labor? Those impossible-made-possible capabilities are where new business models emerge.
Question 4: Where are you vulnerable to AI-native challengers?
Identify the 2-3 areas where someone building from scratch with AI-first assumptions could outcompete you. Those are your disruption risks - and your acquisition targets.
Question 5: What would you build if starting today?
This is the Think Big question. If you had no legacy systems, no existing customer expectations, no sacred cows - what would you design knowing what AI can do?
The gap between that answer and your current model is your disruption opportunity.
Disruption doesn't wait for you to be ready. The question isn't whether your industry faces these forces. It's whether you'll drive the disruption or be disrupted by someone who thinks bigger than you do.
That's what the third bucket is about: Not incremental improvements. Not even major enhancements. But fundamental reimagining of how value gets created in your industry.
And as consulting demonstrates, that reimagining is already underway. See the video below for the "Blueprint".