Create capabilities, build momentum, and foster teams and culture that allow you to Find, Sell, Serve and Retain customers consistently.
Achieving product–market fit unlocks the foundation for sustainable growth by ensuring your offering truly meets customer needs. It enables your business to build the right capabilities, processes, and culture to find, sell, serve, and retain customers consistently. With a validated market fit, teams gain clarity, confidence, and momentum—aligning efforts across product development, marketing, and customer success. This alignment fuels efficiency, strengthens brand trust, and creates a scalable path for expansion and long-term profitability.
Market Share Efficiency (MSE) should be a core objective when pursuing product–market fit and scaling your business. Setting growth targets must be grounded in a clear understanding of the regions and markets where you operate, ensuring goals are both ambitious and realistic. By accurately defining your TAM (Total Addressable Market), SAM (Serviceable Attainable Market), and SOM (Serviceable Obtainable Market), you can sharpen your product strategy and focus resources where they matter most.
Remember, achieving even a small share of your TAM—often around 2%—can already signal strong market traction and a successful product–market fit, laying the foundation for sustainable, efficient growth.
Strategic Direction
Before embarking on a Product-market fit strategy, ensure you understand your vision and which direction you need to take to succeed. Knowing the available paths, consciously choosing one, and committing to it are critical first steps toward establishing your product direction. It also establishes where your resources and innovation need to focus, while at the same time building one or more moats, to ensure you can compete and capture value long-term.
Corporations can leverage their resources, proven business models, and customer lifetime value to prioritise product-market fit that scales and complements existing cash sources. Startups, though often lacking experience and resources, can innovate more quickly and seize growth opportunities with speed. This agility can threaten established market participants and is frequently referred to as “disruption”. Despite this, startups or new corporate propositions pursuing “disruption” strategies must still consider new types of moats.
| Intellectual Property Strategy | Protect all proprietary innovations with patents and licenses to maintain control and become the essential tech component everyone needs. |
| Architectural Strategy | Develop a proprietary platform that dominates the new value chain, ensuring you can reach and keep your customers anywhere, anytime. |
| Value Chain Strategy | Leverage deep knowledge of existing value chains to build unique capabilities in a single “horizontal” layer and become an indispensable partner. |
| Disruption Strategy | Target a smaller, underserved customer segment to build traction, develop new technology, and eventually disrupt bigger incumbents. |
Build Moats
Startups—especially AI-native ones—must think about moats to avoid being competed into zero margins. But timing matters: first ship something people desperately want, fast. Early defensibility is often just speed of execution (daily sprints, relentless shipping). Once product-market fit is real, moats emerge through hard, unsexy work: deeply engineered agents for mission-critical workflows; forward-deployed engineering that embeds in customers; data + eval flywheels; and business model choices (e.g., charging for “work done” vs. seats).
AI also reshapes old moats: LLMs can lower legacy switching costs (data migration, schema mapping) while creating new ones (custom logic, memory, personalisation). Incumbents face counter-positioning traps (per-seat revenue cannibalised by automation). Brand still matters (consumer trust), and accurate scale mostly sits at the model/infra layer (training, crawling, distribution). Final advice: don’t over-optimise for a moat before you have something to defend—find painful problems, move fast, then layer moats.
7 Moat Types
- Process Power: Superior, hard-to-replicate operating know-how (e.g., finely-tuned AI agents with brutal reliability, CI/CD across thousands of integrations).
- Cornered Resource: Preferential access to scarce assets (proprietary data, regulatory clearances, unique distribution, or a specialised model fine-tuned for a domain).
- Switching Costs: Pain (time, money, retraining, workflow rewiring) that locks customers in; in AI, long pilots yield bespoke agent logic + memory that’s hard to abandon.
- Counter-Positioning: A model incumbents won’t copy without harming themselves (e.g., task-based pricing vs. per-seat; automation that shrinks their revenue base).
- Brand: Trusted default status—customers choose you even when products look similar (significant for consumer AI and sensitive enterprise use).
- Network Economies: Product gets better as usage grows; in AI, this is often data + eval loops—more interactions → better prompts, routing, and model behaviour.
- Scale Economies: Unit costs fall with size; prominent at the model/infra layer (training, inference, web crawling) and in high-fixed-cost agent platforms.
Also, never lose sight of your core advantage of Speed. In AI’s greenfield, shipping faster than labs and incumbents is the first—and sometimes decisive—advantage.