She Writes Change

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.

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.

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.

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.

After years of managing technology transformations, I've learned that you cant treat AI like "just another IT project". There are critical key differences, including mindset and approach. AI projects aren't traditional tech projects with a new label. They operate under fundamentally different rules:

How do you build trust when AI isn't 100% certain? Your board expects certainty, BUT your AI delivers probability. AI adoption is different to SaaS platforms and their 'deterministic systems'. But people are used to systems that are 100% predictable (think ERP, CRM). AI just doesn't work that way. AI operates in probabilities, not guarantees. And that probabilistic nature isn't a bug to be fixed in the next release, it's the core of how these systems function. I've been resea

Whilst AI can handle more of the technical and admin work, the uniquely human aspects of project management become MORE critical to success, not less.

It's easy to think that 'digital natives' will adopt new AI tools easily. However, the reality is that training, communication and trust are just as critical, if not more so, for AI projects.

𝐅𝐫𝐚𝐦𝐞 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠: It’s a business tool, not a side experiment so apply the same strategic intent, and ROI focus you would to any core investment

Using AI, Project Managers and Leaders can create more compelling project Cost Benefit data.

The AI for project management market is predicted to hit $14.45 billion by 2034. Organisations betting on AI for scheduling, risk management, governance, and admin are seeing measurable gains in cost control, agility and oversight.

The right 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐬𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 gets funding, alignment and traction, and AI can help you write it in hours, not weeks.

Like any technology, AI comes with it's own set of risks, from emergence of unexpected capabilities, hallucinations, creative interpretations to unexpected real-world behaviours. 

Navigating AI Adoption: What's your biggest challenge and how do you solve it? Trying to harness AI can feel overwhelming. It's complicated and it's not SaaS, there are key challenges that need to be faced. From decision paralysis and a lack of skills to trying to grapple with infrastructure, ethics, data, security and change management.

If you're about to start leading AI transformation, or AI projects, or even sponsoring AI projects, then this is great tick list to get you started.

And honestly, most projects fail because of a lack of clarity and alignment, because people can't answer these questions:

BUT what I've learnt through my AI journey is that traditional business cases don't fit the realities of AI projects. 𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐀𝐈 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐚𝐬𝐞𝐬 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭? 𝐀𝐈 𝐯𝐚𝐥𝐮𝐞 𝐞𝐦𝐞𝐫𝐠𝐞𝐬 𝐨𝐯𝐞𝐫 𝐭𝐢𝐦𝐞