Productising AI Services

Custom consulting is a trap. You trade time for money, every engagement is unique, and your capacity ceiling is your personal hours. Productised services — fixed scope, fixed price, defined deliverable — break this model. Done right, they're better for clients too: clear expectations, predictable cost, faster decisions. The key insight from studying high-performing boutique consultancies is that productisation doesn't mean dumbing down your service. It means you've done the intellectual work upfront to define what a successful AI engagement looks like, and you package that repeatably. The client gets the benefit of every previous project you've done. For AI consulting specifically, there are three natural productisation points. First, the AI Audit — a time-boxed assessment of a client's current state, AI readiness, and highest-value opportunities. Typically 1–2 weeks, $8–15K, delivered as a report and presentation. Second, the AI Sprint — a focused build that implements one specific AI capability end-to-end. Typically 4–6 weeks, $25–60K, delivered as working software plus documentation. Third, the Retainer — ongoing AI advisory, implementation support, and iteration. Monthly fee, $5–15K/month, defined hours and scope. The power of this model: you can market it, price it publicly, scope it quickly, and deliver it repeatedly. Your first engagement in a new vertical is hard. By the fifth, you've got templates, playbooks, and dramatically lower delivery risk.

Scope creep kills margins on AI projects faster than any other consulting category, because the technology is evolving so quickly that clients constantly want to add 'just one more thing.' Preventing this starts at the proposal stage. The most effective technique is deliverable-based scoping rather than hours-based. Instead of '40 hours of AI consulting,' you sell 'a working lead scoring model integrated with your CRM, plus documentation and one training session.' The client knows exactly what they're getting. You know exactly what you need to build. Arguments about scope become rare because the contract is clear. A 14-day fixed-price sprint model works well for AI implementation. Structure it as: Days 1–2 discovery and technical spec, Days 3–10 build and iteration, Days 11–13 testing and refinement, Day 14 handover and training. You commit to a specific outcome within that window. Out-of-scope requests go into a Change Request that reprices the additional work. Govern your projects with a simple weekly status report (2 paragraphs max) that shows: what was done this week, what's next week, and any blockers or decisions needed from the client. This keeps communication tight, surfaces scope creep attempts early, and builds trust with stakeholders who need to justify the spend internally. Also critical: build discovery as a paid engagement. Never do substantial discovery for free. A paid discovery protects your time, filters out uncommitted clients, and means you're compensated for the intellectual work that shapes the entire project.