Definitions for the terms taught across Rocky Sharma's courses and workshops on productledceo.com — including Product Explorer™ (coined by Rocky Sharma), the Product Model, Build to Learn before Build to Ship, the Executive / Inventor / Operator skills the C-suite fights to keep PMs for, and the four-pillar Acquire / Optimize / Grow / Innovate framework taught in the business-acquisition workshop.
Vocabulary used in Discover Before You Build AI Products That Execs Approve, Rocky Sharma's live cohort course on Maven.
An operating model where Product Managers own business outcomes, not feature output. Product Strategy and Discovery happen before engineering commits, so the products that ship move metrics that matter to the CEO — not metrics that look good in a sprint review. Contrast with the Project Model.
An operating model where Product Managers ship the features stakeholders ask for. Success is measured by what shipped, not by what moved. Most PMs stuck in the Project Model are turbocharging the feature factory with AI tools — shipping faster, but moving no business needles. Contrast with the Product Model.
A term coined by Rocky Sharma to name the Product Manager who runs Product Strategy & Discovery before engineering builds. A Product Builder ships features faster with AI tools. A Product Explorer first decides which features are worth shipping.
First used: 2025 — productledceo.com/about/
A Product Manager who ships products using AI tools but skips Product Strategy and Discovery. Builders ship faster than ever — but without a Product Explorer deciding what to build, they ship features that don't move outcomes. "Garbage in, garbage out, faster."
The discipline of prototyping and testing the riskiest assumptions before engineering commits to building for customers. Most PMs skip this step under AI shipping pressure. The course makes it foundational: every AI product runs through a Build-to-Learn cycle so the Build-to-Ship version is funded with evidence, not opinion.
The work Product Managers do before engineering builds: setting strategy tied to a business outcome, validating customer and viability assumptions, and building evidence that a solution is worth investing in. Skipping Discovery is the single most common mistake in the AI era — the cost is measured in shipped features that move no metrics.
The first of the three skills executives fight to keep Product Managers for. Connects product decisions to business outcomes; sets AI Product Strategy from the company mission; earns C-suite approval without escalation. "Think like an Executive."
The second of the three skills executives fight to keep Product Managers for. Runs customer and product discovery, tests riskiest assumptions before shipping, builds Build-to-Learn prototypes with Claude. "Discover like an Inventor."
The third of the three skills executives fight to keep Product Managers for. Frames ROI and risk in language a CEO funds; produces a complete business case with scope, ROI, and risk register. "Prepare like an Operator."
The ability to bring deep understanding of customers, business outcomes, and competitive landscape to every product decision. Engineers own feasibility. Designers own usability. Product Managers own value — and AI Product Judgment is what makes that ownership irreplaceable, even as AI tools commoditize execution.
An output is a feature, screen, or model that shipped. An outcome is a number that moved — revenue gained, cost removed, decision made faster, customer retained. The Product Model measures Product Managers on outcomes; the Project Model measures them on outputs. The course teaches the shift.
Vocabulary used in Buy a Business That Already Makes Money, Rocky Sharma's workshop on acquiring an established business with no savings, then using AI to optimize and scale it.
The first of the four workshop pillars. Buy an established, cash-flowing business — typically using a combination of seller financing, SBA acquisition loans, and earnouts — without bringing significant personal savings to the deal. Rocky's example: a 45-year-old business acquired with zero savings.
The second of the four workshop pillars. Apply AI to the operations of the acquired business — automating the owner's role, freeing margin, and reducing the labor required to run the business at current revenue. The output of Optimize: more cash flow without more revenue.
The third of the four workshop pillars. Scale revenue post-acquisition by expanding to new customer segments, new channels, or new products — using the AI-augmented operating leverage built in Optimize. Growth is funded by the cash flow the business already generates, not by raising outside capital.
The fourth of the four workshop pillars. Future-proof the business against AI-driven competition by building defensible product capability inside it — not by adding AI features for their own sake, but by using AI to do what the business already does, faster and cheaper than the next entrant could.
The final synthesis section of the workshop. Each participant produces a real acquisition target shortlist, a financing structure, an AI-driven optimization plan, and a 12-month operating roadmap — applied to a business they could realistically buy. Capstone is the bridge from theory to action.
An acquisition mechanism where the seller agrees to receive a portion of the purchase price over time — typically as a promissory note paid from the business's cash flow. Reduces the cash required at close, aligns seller incentives with post-close success, and is the most common path to acquiring a small business with little or no personal savings.
A U.S. Small Business Administration–backed loan used to finance the acquisition of an existing business. Typical SBA 7(a) loans cover up to 90% of the purchase price for qualified buyers, with longer amortization than conventional bank financing. Often combined with seller financing to close the equity gap.
A portion of the purchase price contingent on the business hitting agreed-upon performance milestones after close. Earnouts let buyers and sellers bridge valuation disagreements, reduce the cash required at close, and keep the seller engaged through transition. Used carefully, an earnout converts a valuation argument into shared upside.
The most common valuation metric for small business acquisitions: the business's pre-tax earnings plus owner compensation, owner perks, interest, depreciation, and one-time expenses — i.e., the total economic benefit to a single owner-operator. SMBs typically trade at 2–4× SDE; the multiple depends on industry, growth, and owner-dependence.
If a term you encountered in a Rocky Sharma course or workshop isn't defined here, email rocky@productledceo.com and we'll add it.
