The AI Consultant's Window

aiconsultingsoftware

AI tools have collapsed the cost of building software. Anthropic shipped Cowork in 10 days. Cursor’s agents produced 3 million lines of code in a week. I built a 200,000-line TypeScript MVP in 7 weeks - work that would have taken a team 12-18 months before these tools existed.

If you can wield these tools, there’s an obvious opportunity. Businesses need custom software. They don’t have engineers who know how to use AI agents effectively. Consultants who can bridge that gap should find work.

The Uncertain Bet

Building a consulting practice takes years. You need clients, reputation, systems, trust. You need to survive the slow periods. By the time a practice is mature, will the tools be accessible enough that businesses don’t need a consultant?

This isn’t a victory lap for consultants - it’s an uncertain bet. The energy required to build a consulting business is substantial. Is it worth it for a window that might be 2 years? 5 years? 10?

The direction seems clear: these tools will eventually be usable by people without engineering backgrounds. The timeline is opaque. And the question for anyone considering this path is whether you’re building a career or riding a wave.

Greenfield vs. Migration

Not all custom builds are created equal, and this distinction matters for understanding where the window might stay open longest.

Greenfield is straightforward. No existing system, no data to migrate, no users with entrenched workflows. You’re building exactly what you need from scratch. This is where AI tools shine brightest - and where the consultant’s window is most likely to close first.

Migration is harder. Replacing existing SaaS means:

  • Data export and import - often messy, incomplete, or requiring transformation
  • Transition planning - running parallel systems, phased rollouts, fallback procedures
  • Change management - users who learned the old system need to learn the new one
  • Mapping legacy workflows - existing processes may not map cleanly to new designs

Here’s a concrete example: a multi-facility tennis and pickleball club I know is considering replacing their Club Automation membership system. The software handles court booking, membership billing, lesson scheduling, pro shop transactions - a typical SaaS tool doing too much, none of it particularly well.

Building a custom replacement is now feasible. The core functionality isn’t that complex. But the transition? Years of member data. Payment histories. Recurring billing relationships. Staff trained on specific workflows. The build might take weeks; the migration and transition planning could take months.

This is where consulting value persists longer. It’s not just “can you use AI to write code” - it’s “can you manage the organizational complexity of moving from one system to another.” That’s a different skill set, one that AI tools are further from automating.

The Portfolio Problem

There’s another dimension to this that’s worth considering: what happens after you build?

I’ve spent nine years building and maintaining a SaaS product. Once you reach maintenance mode - stable customers, predictable feature requests, infrastructure that mostly runs itself - it’s a good business. Recurring revenue, compounding familiarity with the codebase, known edge cases.

A consulting practice built on custom software is different. You’re not building one product for many customers. You’re building many products for many customers. Each client has their own codebase, their own infrastructure, their own quirks. The portfolio grows, and so does the maintenance burden.

This is more labor intensive by nature. Ten clients with ten custom applications means ten different contexts to hold in your head, ten deployment pipelines, ten sets of dependencies that need updating. The work compounds in a way that SaaS maintenance doesn’t.

Can AI help here? Almost certainly. The same tools that accelerate initial builds should accelerate maintenance - understanding unfamiliar codebases, writing migrations, updating dependencies, debugging production issues. The question is whether you can systematize this well enough to keep a growing portfolio manageable.

This might be the real skill that determines whether a consulting practice scales: not just building fast, but building in ways that stay maintainable across a diverse portfolio. Standardized stacks, consistent patterns, good documentation, automated testing - the same things that always mattered, but now with AI agents as part of the maintenance workflow.

The consultants who figure this out will have an advantage over those who just build fast and move on. The window isn’t just about initial builds - it’s about the long tail of maintenance that follows every project.

The Honest Assessment

Let’s not overclaim. AI will eventually handle migration work too. Data transformation, migration scripts, even change management playbooks - these are all learnable patterns. Portfolio maintenance will get easier as AI agents get better at context-switching between codebases. The timeline for automating this work is probably longer than pure greenfield builds, but it’s still finite.

The window exists. Betting on it is rational. Betting the farm on it is risky.

Migration work and organizational complexity seem more durable than pure code generation. So does the ability to systematize maintenance across a growing portfolio. The businesses that need the most help are ones replacing existing systems, not building from scratch. The skills that matter aren’t just technical - they’re about managing transitions and building in ways that stay maintainable.