A moat is a lasting competitive advantage that protects a company from imitators, such as network effects, switching costs, a strong brand, exclusive data or deep integration into customer processes. The question is particularly relevant for AI offerings, because many products build on the same available models. Advantages then tend to come from domain expertise, closeness to customers, good processes and trust rather than from technology alone.
An AI-driven start-up is a young company whose product or business model is fundamentally based on artificial intelligence, such as specialised assistants for an industry, analytics tools or automated services. Such start-ups often build on existing foundation models and differentiate themselves through domain expertise, data, usability and integration. Challenges include dependence on model providers, cost per request and regulation. Example: a start-up that automatically prepares inspection reports for trade businesses.
A go-to-market (GTM) strategy sets out how a product or service will be launched successfully in a market: which target group has which problem, how the offer is positioned and priced, which channels it is sold through and which metrics measure success. A GTM plan links marketing, sales and product. Example: a software provider starts with one industry, one partner channel and a 90-day goal.
Return on AI describes the economic return of an AI deployment relative to its costs. A simple rough calculation is: hours saved per month × hourly rate, minus the costs of licences, model usage, set-up and support. If a team handling 200 requests needs 4 instead of 10 minutes per request, it saves 20 hours a month. Such calculations remain estimates; quality, error rates and onboarding time should also be taken into account.
