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What to expect in a LEGO® Serious Play® workshop
If you’ve never experienced a LEGO® Serious Play® (LSP) workshop, you might be wondering what actually happens. Will I need to be creative? What if I’m not good at building? What will we achieve? These are common questions I hear from participants before their first session.
Why LEGO® Serious Play® works: Co-creating positive change
Throughout this series, we’ve explored why LEGO® Serious Play® works: the cognitive science that creates deep engagement, the communication mechanisms that transform dialogue, and the cultural conditions that create a sense of belonging. Now we arrive at the heart of the matter: what all these elements enable when they come together: co-creation.
Why LEGO® Serious Play® works: Building belonging through authenticity and psychological safety
In Parts 1 and 2, we explored the cognitive science that makes LEGO® Serious Play® effective for individual learning, and the communication mechanisms, metaphor and equal voice, that transform how groups interact. And something even more fundamental happens when these elements combine: they create the cultural conditions for genuine sense of belonging.
Why LEGO® Serious Play® works: The power of metaphor and equal voice
In Part 1, we explored the cognitive science behind LEGO® Serious Play®. How constructionist learning, embodied cognition, and flow states create conditions for deep individual engagement. The methodology’s power extends beyond how individuals think. It fundamentally transforms how groups communicate and collaborate.
Why LEGO® Serious Play® works: The cognitive foundations
Ever wonder why building with LEGO® bricks creates insights that hours of talking couldn’t reach? There’s actual neuroscience behind it. When your hands build, your brain thinks differently, accessing knowledge you didn’t even know you had. This is constructionism in action, and it’s transforming how teams solve complex problems.
The HEAR Framework: A practical guide to strategic listening
Strategic listening sounds compelling in theory but how do we actually do it? In Part 1, we explored why treating internal community management as organisational intelligence matters. Now let’s get practical. The HEAR Framework offers a systematic approach to turning everyday conversations into actionable insights: Hone what matters, Evaluate the context, Articulate the patterns, and Respond with action. Here’s how leading organisations are making strategic listening real.
Do you hear what I hear? From reactive to strategic listening in organisations
Most companies treat community management as housekeeping: moderate discussions, answer questions, keep things civil. But we’ve seen something different: organizations reimagining it as strategic listening, a systematic way to catch problems early, understand culture as it actually lives, and turn everyday conversations into intelligence that improves how work gets done.
AI is more than ChatGPT: time for mature AI conversations in communication
When AI comes up in communication circles, one name dominates the conversation within seconds: ChatGPT. But when AI equals ChatGPT in our minds, we’re not just limiting our toolkit, we’re limiting our thinking about what responsible, inclusive communication in the AI age actually means. This post explores why ChatGPT tunnel vision is problematic, what alternatives exist, and how communication professionals can think more broadly and strategically. From privacy risks to missed innovations, from vendor lock-in to ethical considerations: discover why a single tool isn’t enough and how to make conscious choices that align with your values and working style.
On change, trust, and conversations that matter
This episode marks my final one as host of the IABC EMENA podcast and I couldn’t have asked for a better guest. I sat down with Simon Cavendish, new Chair of IABC EMENA, to talk about the human side of leading change. From trust and transparency to the IKEA effect, we explored how internal comms shapes transformation and why involvement matters more than perfection. While I’m stepping away from this podcast, I’m not done podcasting. I’ll be continuing the conversation over at my own Good Comms podcast, where we’ll keep asking better questions about leadership, inclusion, and communication that makes a difference.
A practical framework for responsible AI in communication (Part 3 of 3)
How do you develop both layers needed to use AI responsibly in communication? This practical framework addresses foundational professional skills first, then layers on AI augmentation skills. The path forward is clear: either rush into adoption without support and watch work quality decline, or commit to strong foundations and use AI to amplify genuine expertise. Leading organizations like Amazon and IBM model this approach, ensuring baseline competence before advanced AI capabilities.
The baseline skills needed for responsible AI use (Part 2 of 3)
Most AI training programs overlook a fundamental truth: teaching prompt engineering won’t help people use AI responsibly without foundational skills to recognize what good communication looks like. AI operates like Michelangelo’s chisel. It didn’t make him a master sculptor, it allowed him to execute expertise he’d already developed. The principle is simple: strong baseline skills multiplied by AI equals enhanced productivity. Weak baseline skills multiplied by AI equals scaled problems. Effective AI upskilling requires two distinct layers: foundational professional skills first, then AI augmentation skills. You cannot skip Layer 1.
Responsible AI in communication starts with the right foundation (part 1 of 3)
As an inclusive leadership and communication consultant, I believe in “communication for good”. Communication that creates understanding, builds bridges, and breaks barriers. Workslop is the opposite. Recent research shows 40% of workers received low-quality AI content last month, costing nearly two hours per incident to fix. But we’re diagnosing the wrong disease. This isn’t about the tool. It’s about the skills gap. The real conversation we should be having isn’t “Is AI good or bad?” but “How do we support everyone in using it well?”