84% of executives say innovation is critical to their growth strategy, yet only 6% are satisfied with their innovation performance (Department of Innovation). I've personally seen innovation talked about but, in practice, it often doesn't happen. The structure isn't in place and the culture stymies risk taking, rather than encouraging it. 

Understand your risks before you go all in for AI. Successful AI implementation needs a clear business case, supported by strong data foundations, and a change management strategy that puts people at the heart.

Want to start an AI project but don't know whether to Buy, Partner or Build? The "Buy vs. Partner vs. Partner" dilemma is not a new one, but in the age of AI, it has become one of the most critical strategic decisions a leadership team can make. Before your data science and engineering teams default to "we'll build it ourselves", it is critical to recognise that this is more than just a technical preference, it's a decision that will fundamentally shape your project's scope,

Customer journey mapping, be it internal of external customers, is more than a fully CX exercise. It's an excellent project planning tool that spots problems before a line of code is even written. 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬 Map out every touchpoint your customer will have with what you're building. Do this upfront, before you've committed budget. This will let you see and structure processes and features early on, making sure you also spot issues that could blindside you after l

Building and launching successful products means understanding what success looks like in the first place. And a good ‘product project’ is more than just being on time and budget.

The team, the sponsors, and the stakeholder are all there. Scope, goals, and timelines are aligned. But the most critical voice is missing: the end-user.  Not the internal client. Not the budget holder. But the person who really matters, whose life or work we are supposed to be improving. When the customer isn't represented from day one, we risk delivering a project that's on time, on budget, BUT isn't fit for purpose.

Post-Mortems or Lessons Learned are useful if run properly, of course, BUT they won't save your project. We've normalised learning lessons after delivery, after the damage, after the failure. But what if there was a better way?

The most expensive thing in your project isn't what you think. It's a the endless one-hour meetings with either the wrong people, or the wrong agenda. And I've seen this everywhere, hell, I've definitely been guilty of it myself in the past!

Stop asking for "Lessons Learned". Start asking for "Lessons Applied". How many times have you sat in a post project meeting review, listing out all the "Lessons Learned", only see them filed away in a document that's never read again?

AI needs to be viewed through the Product lens, as it's never going to be a project, where you go live and walk away. If you treat AI like a project, your AI initiatives aren't going to deliver sustained value.

I've delivered both projects and products (from idea to market), and there are significant differences in how you need to approach them. Many "projects" are actually products in disguise, and that's why they fail to deliver long term value. 

Debunking a Persistent Project Management Myth: It's not just Time and Budget we manage. One of the biggest misconceptions in project management is that PMs are simply “deadline-and-budget enforcers.”  In reality, the best project managers know that success is measured by more than on-time delivery and cost control. While timelines and budgets are essential constraints, modern PMs balance a wider set of priorities, quality, stakeholder satisfaction, team morale, and business

What if your AI wins were hiding in your most boring problems? The real game-changers aren't necessarily the flash AI demos but, instead, the biggest wins could come from using AI to tackle everyday annoyances that quietly eat up your time. Companies that start by automating these boring tasks quickly build confidence and experience. Then, tackling bigger, more ambitious AI projects feels a lot more manageable.

Stop annoying your sponsors: 𝐌𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞 𝐀𝐫𝐭 𝐨𝐟 "𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐄𝐬𝐜𝐚𝐥𝐚𝐭𝐢𝐨𝐧". Escalate too often, and you look incapable. Escalate too late, and you become a liability. Sound familiar? Before even thinking about your sponsor, ask: "Can my team and I solve this without external help?" and "Is this issue significant enough to warrant executive attention?" This shows you respect your own authority and your sponsor's time.

Successful adoption of AI demands more than just technology. It requires the right structure, strategy and ongoing monitoring. I've written up the 10 steps I believe are key to lasting and scalable AI adoption, from choosing the right tasks and tools to managing risks and ensuring ethical alignment. These 10 steps help set the foundation for meaningful, sustainable impact.

Your project dashboard is a sea of green but you know something's off.  Your status updates say everything is on track, your weekly reports are all positive. But you know that it's not accurate. 𝐖𝐞𝐥𝐜𝐨𝐦𝐞 𝐭𝐨 𝐆𝐫𝐞𝐞𝐧 𝐋𝐢𝐠𝐡𝐭 𝐒𝐲𝐧𝐝𝐫𝐨𝐦𝐞!

There seem to be the same two patterns, companies either going all-in on AI (and crash) or avoid it entirely (and fall behind). The smart approach isn't necessarily revolution, instead it's a systematic evolution, experimenting without over-committing.  AI isn't always going to be a system replacement, but a capability enhancement.  Don't underestimate the hidden complexities of integration, data security, and the need for new team skills.

Successful Transformations need 3 distinct voices in the room from day one - Voice of the Customer, Voice of the Employee, Voice of the System.

Not sure how to measure ROI for AI Projects? Traditional ROI models don't work for AI projects. You can't always point to direct cost savings or revenue increases in year one. AI often delivers value in ways that don't fit into a standard business case template. The benefits are real but they are different.

onfused about the difference between Generative AI and Agentic AI? As AI continues to move fast and reshape the way we work, it's crucial to understand the difference. 𝐓𝐡𝐞 𝐊𝐞𝐲 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞? ✴️ Generative AI is reactive and collaborative. Agentic AI is proactive and independent.