Did you know, less than 10% of AI projects actually make it to ?
And it’s not because the technology isn’t ready.
Last week, I spoke to a CFO about the financial implications of software systems. I told him how we integrated AI into some of our workflows to cut SaaS bloat and how we’re seeing massive cost savings.
He said, “Many of my colleagues, me included, don’t think companies can build a solid AI business case”!
It caught me off guard.
Wherever you look, everyone is talking about AI and how it can streamline entire workflows and operations.
The” word on the street” is that if you don’t use AI, you’ll be left behind.
“Why do you say that?”, I asked.
What follows are insights I gathered from the CFO mixed with my opinions.
The Real Problem Isn’t Technology But How We’re Using It
Most people who are talking about AI are simply using ChatGPT, Gemini, Co-Pilot,Claude AI and the likes for very basic things. Like summarising documents, writing emails, creating content, some software testing etc.
The question to ask is, “Do these actions really move the needle for a business?”
Senior management is saying, “Show me the numbers.”
Productivity gains are hard to measure. Unless you’re replacing employees with AI and AI-powered tools!
In my experience, here’s the thing…
The business that are successfully integrating AI aren’t chasing flashy stuff like video generation and whatnot.
Messy Data
The biggest bottleneck for AI integration is that your data is scattered across different systems, poorly organised, and not ready for AI at scale.
It’s a catch-22. You need a solid data infrastructure to make AI work. On the other hand, you need AI wins to justify investing in that infrastructure!
Privacy and Data Protection
Employees are going rogue with AI tools. They’re signing up for AI platforms and feeding company information into public systems without realising the implications.
The CFO said, “We’ve had to shut down access to AI tools after discovering employees were unknowingly sharing sensitive data with public AI platforms.”
What’s the Way Forward?
Start small. Really small. Start with small experiments that can prove value quickly.
Bring together people from different departments and rapidly test AI use cases for operations, sales, marketing, finance etc.
The goal is moving from idea to proof of value in weeks, not months.
Once you prove something works, you can scale from there and build a solid business case.
Impress upon your employees that the real risk is not that AI will take their job(s). It’s that someone who knows how to use AI effectively might take it instead.
Today’s graduates are entering the workforce with AI skills baked in!
The organisations that are winning aren’t treating AI as a threat.
They focus on clear, measurable use cases, invest in solid data foundations, and take a measured approach to innovation. Plus, they’re making sure their people understand how to use these tools in their daily work.
The hype is at saturation point and AI is fast becoming the norm.
Your best bet is to do the groundwork and prove value one project at a time.