An AI Strategy Reset

Executives at software and services companies keep asking me,

“How do we add AI to our product or service?”

It feels logical.

And it’s wrong.

Most companies are skipping the part that creates the fastest ROI and the biggest competitive advantage. And they don’t realize it.

Teams rush to build AI features because it sounds strategic.

Meanwhile internal inefficiency is killing margins, client work is bottlenecked, teams are stretched thin and the “AI feature” build is a big learning curve and distraction- all while your competitors seem to be getting faster, leaner, and more profitable.

Software and services businesses live and die by efficiency. If your internal engine is slow, no amount of AI glitter will save you.

The costly mistake most leaders overlook:

Building AI for clients takes time (software projects are notorious for going over-time and over-budget) and it takes resources away from existing systems, services and products which are needed to keep current clients. There’s a long runway before you see returns- if you see them at all.

So What Actually Works?

For those who know how to leverage it, AI is a workforce multiplier.

Even when it doesn’t produce perfect work (and really, does it ever?), it produces usable work- fast. It’s almost always easier to refine AI‑generated output than to start from scratch.

Whether it saves 10 minutes or 10 hours, that time can be re-purposed- to client relationships, closing more deals, revenue and profit generating activities- even thinking…

The counterintuitive way forward?

Start with AI internally, not AI externally.

Adopting AI internally yields the quickest return on investment and the biggest productivity multiplier for your business because it enables people to produce more, better, faster.

And that frees people up to focus on the only things that matter:

sales and clients.

But adopting AI internally requires a mindset shift.

Your new accountant won’t (can’t) write marketing copy.

AI can. It can be your developer, analyst, designer, content creator, researcher- and excel at all of them, with the right users at the keyboard.

And it doesn’t sleep.

When you hire someone, you make sure they’re onboarded or at least given some context and expectations about their role. Then you keep an eye on them and give them feedback.

You need to do the same with AI.

The quality of AI’s output depends on context and prompts.

And still, sometimes it will fail- just like humans do. The difference? It still keeps you ahead of the competition.

A 12‑person SaaS team I worked with cut weeks off their release cycle by using AI to handle first‑pass QA tasks- generating test cases, running automated checks, and flagging likely failure points- which let them ship cleaner builds to clients faster.