The Most Expensive Mistake in Building an AI Practice May Be Hiring Before You Have a Business
October 17, 2026 · Keith Pasley
The Most Expensive Mistake in Building an AI Practice May Be Hiring Before You Have a Business.
A technology provider decides to launch an AI practice.
It hires an AI architect, invests in certifications, signs several vendor agreements and develops a services portfolio.
Six months later, the team is busy.
But the practice isn’t profitable.
The problem may not be the technology or the talent.
The company invested in delivery capacity before validating enough paying customer demand.
I’ve seen this pattern across technology services businesses long before AI.
Companies build capabilities around where they believe the market is going, then expect sales to catch up.
AI makes that decision particularly risky because the technology, customer requirements and delivery economics are changing quickly.
Consider two approaches.
Approach A: Build the practice, then find the customers.
Hire specialists. Develop offerings. Train salespeople. Launch marketing. Hope the pipeline develops quickly enough to cover the investment.
Approach B: Validate demand, then build the practice around it.
Start with existing customer relationships. Identify recurring business problems. Sell paid discovery. Use qualified delivery partners where necessary. Measure the economics. Invest in permanent capabilities as demand becomes predictable.
The second approach isn’t necessarily faster.
But it gives executives better evidence before committing capital.
I would use four investment gates:
1. Customer demand: Are customers paying to solve this problem, or simply expressing interest?
2. Delivery economics: Can we price and deliver the solution at an acceptable gross margin?
3. Repeatability: Can we deliver substantially the same service to additional customers without rebuilding everything?
4. Capacity: Does contracted work justify hiring, or would qualified partners provide better flexibility?
This doesn’t mean never investing ahead of demand.
Some strategic capabilities require early investment.
But those investments should be deliberate, funded and tied to clear milestones.
The objective isn’t to build the largest AI team. It’s to build the smallest capable organization that can consistently deliver profitable customer outcomes—and expand it when the business justifies doing so.
Before approving your next AI practice hire, ask:
What customer demand, expected gross profit and delivery workload justify making this a permanent expense?
Technology does not create business value. Better decisions do.
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