AI at work: More than just productivity, it’s a mindset shift
20 March 2025
Let’s be honest - most people using AI at work aren’t announcing it in team meetings. They’re quietly testing things out, using it to tidy up reports, summarise long emails, or reword an awkward sentence. And why not? AI can be a brilliant personal assistant.
But here’s the thing: when AI adoption stays at this level, it’s like having a secret superpower no one else benefits from.
AI in organisations moves through three distinct levels of productivity - Personal Productivity, Process Productivity, and Paradigm Productivity. Each stage tells us something about how people interact with AI, not just as a tool but as something that can shift the way we think about work altogether.
The three types of AI productivity (and why we need to talk about them)
1. Personal Productivity: the “let’s keep this between us” stage
This is where AI starts for most people - quietly experimenting, tweaking their own workflow, and keeping it to themselves.
Why? A few reasons:
They don’t want their boss to think they’re slacking off.
They’re not sure if AI is technically allowed at work.
They don’t know if what they’re doing will be useful to others, so they just focus on their own tasks.
At this stage, AI adoption is happening, but in silos. The problem? If everyone keeps AI use to themselves, the organisation misses out on the bigger productivity gains AI can offer.
2. Process Productivity: AI moves out of the shadows
This is when AI use gets official. Companies stop treating it as a quiet experiment and start integrating it into existing processes, usually in the form of:
AI-powered chatbots taking customer inquiries.
AI tools sorting through thousands of emails to prioritise urgent ones.
AI helping legal teams summarise cases instead of manually trawling through documents.
At this level, AI is finally being used at scale, but here’s where things can stall: many companies focus too much on efficiency and not enough on whether the whole process needs rethinking.
3. Paradigm Productivity: the big rethink
This is where AI stops being just an efficiency tool and starts shaping entire business models. Instead of just speeding up existing workflows, organisations redefine what the work is in the first place.
Think about the taxi industry. Traditional taxi companies have used AI for routing and pricing (Process Productivity), but Uber changed the whole game. Instead of asking, “How do we make taxis more efficient?”, Uber asked, “How do we help people get from A to B in the best way possible?”. That’s the difference between optimising a process and rethinking the entire system.
Most companies never get here - not because they can’t, but because it requires a shift in mindset, not just in technology.
To explore how AI can drive productivity, join me at Future Skills Organisation’s upcoming webinar. Register now to gain insights and practical tips on leveraging AI for innovative business strategies and how it's reshaping skills and training.
So, what’s holding us back?
AI is moving fast - faster than most organisations can keep up with. The real challenge isn’t just getting AI tools in place; it’s making sure people know how to use them well.
But here’s the tricky bit:
AI skills aren’t just technical. The real value comes from knowing how to ask the right questions, think critically, and use AI in a way that actually improves work.
Traditional training models can’t keep up. AI is evolving so quickly that by the time most training programs are rolled out, new tools have already replaced the old ones.
Companies don’t always value the right skills. The best AI adopters aren’t just the tech experts - they’re the ones who can combine IQ and EQ. They understand the technology but also know how to apply it in a way that makes sense for people, customers, and teams.
Specialists, generalists, and the “AI-ready” workforce
Here’s something that needs more attention: AI is making generalist skills more valuable than ever.
For years, organisations have been obsessed with specialists - hiring based on specific, technical expertise. But the rise of AI means that some of the most important skills aren’t the easiest to measure.
Things like:
Critical thinking - knowing when to trust AI and when to question it
Empathy - understanding what customers need, beyond what AI can predict
Adaptability - being able to shift with AI, instead of resisting it
But here’s the problem: employers aren’t always sure how to evaluate or reward these skills. It’s easy to measure technical ability, but much harder to assess how well someone understands customers or thinks creatively.
Yet, these are exactly the skills that will set people apart in an AI-driven world. AI can generate text, but a human decides if it makes sense. AI can process data, but people decide what to do with it. AI can automate processes, but it’s up to humans to rethink whether those processes should exist at all.
A new way of thinking about AI (and work itself)
So, where does this leave us? AI isn’t just another tool to master - it’s shifting the way we work, make decisions, and structure organisations.
For companies, this means rethinking how they measure skills, how they train employees, and how they encourage AI adoption. It means recognising that the most effective employees will be the ones who know how to use AI in a way that makes sense for people.
For education and training organisations, it means keeping up with AI in real-time, rather than trying to catch up years later. The old model of waiting to formalise training programs won’t work when AI is evolving every few months.
And for individuals? The best way to prepare for an AI-powered workplace isn’t to become a machine-learning expert. It’s to become the person who knows how to use AI in a way that makes work better, smarter, and more human.
Because AI isn’t here to replace people - it’s here to help us rethink what’s possible.
By Ray Fleming, Chief AI Officer, InnovateGPT
Published in Future Skills News, FSO’s LinkedIn newsletter here.