Harnessing AI for Continuous Improvement: A Pathway to Innovation

Is AI the Game-Changer for Continuous Improvement?

Let’s be honest: there’s a lot of chatter about artificial intelligence, and it’s not going anywhere anytime soon. The big question we should be asking ourselves is whether AI will become a genuine game-changer for continuous improvement. My take? It absolutely could, but only if we embrace the right mindset. The mantra is simple: test, learn, and adapt.

Start Small and Build Up

Recently, my working group at City Skills had a deep dive into how AI can mesh with our continuous improvement (CI) efforts. We’re all still figuring this out, and quite frankly, that’s perfectly fine. The magic often happens when we start small, rather than making grand leaps that risk losing us in the shuffle.

With AI, there’s no need for an all-or-nothing approach. Instead, let’s explore manageable projects that can incrementally improve our processes. It’s about being a bit brave and a tad experimental, don’t you think?

The PDSA Approach: A Perfect Match

Here’s a thought: the Plan-Do-Study-Act (PDSA) cycle aligns beautifully with responsible AI experimentation. The principles of PDSA—planning, engaging, studying results, and acting on findings—are a natural fit for testing AI capabilities.

For those who aren’t familiar, the PDSA cycle allows teams to test changes on a small scale before fully implementing them. It encourages a culture of learning and adaptability. Why wouldn’t we apply this framework to AI? It’s a fantastic way to ensure that we’re using AI not as a shiny new object, but as a genuine enhancer of what we already do. By adopting this mindset, we can safely push the envelope while minimising risks.

AI as a Thought Partner

Rather than treating AI just as a tool for answers, let’s use it to spark broader discussions. What if we could run simulations to predict various outcomes of our CI projects? Or analyse coaching transcripts to uncover hidden gems of learning? The possibilities are endless when we shift our perspective.

Low-Risk Use Cases for Quick Wins

So, where do we begin? Identifying low-risk use cases where AI can offer value without overwhelming our teams is key. Here are a few ideas worth exploring:

Sparking broader brainstorms by presenting data insights.
Analysing coaching transcripts to refine our communication techniques.
Enhancing improvement documentation with smarter tagging and search capabilities.

Each of these initiatives can serve as a stepping stone towards broader AI integration in our CI efforts. And importantly, they allow us to maintain the essential human touch in our processes. Remember, AI can’t visit the gemba or build the kind of trust that’s crucial for team dynamics—that’s our responsibility!

Embrace the Journey

As I reflect on this topic, I’m reminded of my early days as an entrepreneur when failure felt terrifying. But each setback brought essential lessons that shaped my approach. In many ways, the integration of AI into continuous improvement is similar. It’s a journey, one that demands we embrace both successes and failures as part of the learning process.

So here’s my call to action for you: if you’re bold enough to experiment with AI in your CI initiatives, I’d love to hear about it. What’s working for you, and what challenges have you faced? Let’s keep the conversation alive and learn from one another.

After all, continuous improvement doesn’t just rely on technology; it thrives on the connections we create and the wisdom we share.