I still recall when “AI in business” sounded like a sci-fi dream or marketing fluff. Today it isn’t. The technical promise has crossed into real-world territory—which means if your company isn’t at least asking how AI will affect it, you’re already behind.
1. The Current State of Play
- According to the latest from McKinsey & Company, nearly nine in ten organisations report using AI in some way—but far fewer have embedded it deeply enough to drive enterprise-wide impact.
- At the same time, our understanding of where AI actually delivers is sharper. For example: algorithms predicting demand in supply chains, chatbots handling routine customer support, and decision-tools supporting marketing automation.
- Yet the reality: only a small fraction of firms are getting measurable financial returns or business transformation from AI.
In short: the tools have matured, the interest is high, yet the adoption gap remains large.
2. What It Means for Business
Here are the major shifts I’m seeing, from my vantage as a business engineer working across IT and digital transformation for decades:
a) Workflow is being redesigned
It’s no longer enough to bolt an AI tool onto an existing process. The firms that win are rewriting the process itself, so the AI becomes part of how things get done — not just a “nice-to-have”.
b) Competitive advantage is shrinking for non-adopters
If your competitors begin using AI to scan data faster, personalise at scale, automate decisions, or launch new business models, then inertia becomes a vulnerability. The upside of early adoption is harder to capture — the downside of lagging is bigger.
c) The human side matters more than ever
Data, algorithms and compute are only part of the equation. People still define success: leadership commitment, culture open to change, ethical guardrails, and skills to act on insights. The “AI revolution” will be won or lost through human choices.
d) Risk and regulation are real
Don’t assume the hype means “free for all”. AI has its dark corners: biases, regulatory scrutiny, lack of transparency, and integration failures abound.
3. What You Should Be Doing Today
If you’re in a business role (IT, operations, strategy, or C-suite), here’s a short checklist:
- Pick one sharp use-case — something with high value and clear metrics. Avoid trying to “do everything at once”.
- Redesign the process around the tool, not the other way round.
- Build your foundation: data, infrastructure, and governance. Without this, many AI projects stall or collapse.
- Upskill your people — tools don’t replace humans yet, they amplify or change them.
- Be disciplined about deployment — monitor, evaluate, iterate. Measure cost, risk, business impact.
- Stay ethical and compliant — possible future regulation means being on the front foot is better than scrambling.
4. The Big Picture
This isn’t just about automating tasks or reducing cost (though that still matters). It’s about rethinking how your business works, how you make decisions, how you compete, and how you serve your customers. For many companies, AI will be the difference between modernising and just maintaining.
Looking for deeper insights?
I’ve collected a number of articles and reflections on AI, IT and business solutions—feel free to explore them in my archive: