In conversations with executives from software and services companies, I hear the same refrain: “We’re struggling to figure out how AI fits into our business.”
For many, the first instinct is to ask: “How do we incorporate AI into our products/services?”
My answer, which may seem counter-intuitive, is to start implementing AI internally.
By starting internally, you’ll see the fastest ROI on your AI investment while advancing your capabilities to deploy AI more broadly across the business.
You will not only capture the quickest return on your AI investment, but you will gain invaluable experience in its application.
For instance, use AI to direct your managers and staff away from administrative tasks and to revenue and profit generating tasks like landing more clients, faster.
Let’s envision how that can look.
It requires a shift in mindset. In this scenario, adopting AI is more like hiring a new employee than implementing a new technology. Actually, it can be like hiring many new employees.
Unlike a typical hire with a single skillset, e.g. your accountant won’t also be on the marketing team, AI can take on multiple roles: developer, marketer, content creator, analyst, designer. And it doesn’t sleep.
That said, just like a new employee needs training, guidance, and feedback, so do AI platforms.
You need to learn to give instructions that generate the output you’re seeking (“prompt engineering”) and understand it takes time to get results from AI platforms- just as it does with a new hire.
And just like with an employee, you must supervise. AI platforms deliver at unmatched speed but their output requires careful review.
Still, the time spent is a fraction of what would be spent building from whatever it is (code, content, data analysis) from scratch (and it’s usually easier to critique someone else’s output than your own).
Just like with an employee, you need also ensure the AI will uphold client confidentiality and protect intellectual property. But the effort required is more than made up for in the dramatic productivity speed-up.
The companies that survive won’t be the ones that ignore AI. They’ll be the ones whose teams learn how to use AI to produce more, better, and faster– in client proposals, product development, and internal efficiency.
For software and services companies, where margins depend on efficiency and client wins, that can mean the difference between lagging and leading.
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