AI Changemakers
The timing of ChatGPT’s mainstream launch was serendipitous. It was one week into my planned 6-month career break, ironically to celebrate driving a successful digital transformation during the pandemic, a period I thoroughly enjoyed.
I always say the pandemic was a blueprint for the AI age. Upskilling teams, building new processes and focusing on achieving business value through new channels. What I learnt during that period has flowed neatly into taking on AI Transformation.
Understanding AI and realising what needed to be done became clear during that ‘break’. It formed a very clear mission that once I returned to Sydney, led me to take action immediately. That was the start of a journey that continues today.
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I could just see this not going well.
Not in a ‘robots will kill us’ sort of way, a more tangible, personal and immediate way.
My two core observations:
My experience inside large corporate organisations told me change would be slow - and not necessarily inclusive.
Technology seduces easily. I have seen tech companies treated like celebrities in the business world. There’s often awe and wonder, a seeming desire to want to be part of the cool squad. Tell me I’m wrong? This made me question what decisions could be made, who would be listened to, and what approach would be taken once the tech was in place.
There are many business and organisational problems where this phenomenal technology can be applied. But to know where and how takes a closer way of working between technology and business teams. Not many technology firms stay around for that bit.
The end goal that motivates me is the opportunity to transform an organisation weighed down by clunky and outdated ways of working. I see the AI age as helping businesses be the best they can be, driven largely by freeing up brilliant talent to get back to what they’re good at.
Disruption has been persistent since the pandemic, emerging channels have become a mainstay, supply chains have had to navigate the unimaginable, and the population has taken a new shape. Yet organisations still operate in pretty much the same way they did in 2019.
It’s the perfect time to take stock - what does our business do today, who does it service, where in the organisation has the most friction, and how can AI+ our brilliant people help us to transform?
In this economy we should also be addressing what commercial pain points we could solve more immediately thanks to AI aligned to clear objectives and the correct teams.
Where does AI help or hinder my business?
The biggest miss today is that neither the CEO nor total leadership appears to be having this conversation upfront. Often AI sits with the CTO quite simply because it’s a technology. I believe this has caused a frustrating delay when it comes to AI’s impact on the business. The tools may have been deployed but without a clear plan, they may not have been used to target specific business priorities.
The premise of AI Changemakers is that successful change and business value is best achieved by those already in the organisation. I’ve found through my own experience that results come quickly when internal experts are upskilled and combine their wealth of knowledge with fresh skills.
For AI Changemakers to succeed, there needs clear structure and support.
I structure my corporate programmes so that ‘Leaders go first’. This means exploring the landscape, the risks and the possibilities, the governance and the competitive threat of AI. It then includes the critical ‘hands on tools’ stage to see leaders benefit from AI use in their own day-to-day.
An AI-confident leadership team is one that is clear on where AI can play a role in the business and therefore doesn’t publish a vague AI mandate. AI-confident leaders can more easily support their teams as AI projects and experimentation begins.
Following the Leaders sessions, my programme focuses on Capability for Business Teams. A one day workshop soon turns into a session full of ideas for AI-use as teams connect the dots between the work they do and their understanding of AI. An AI hackathon then gives the practical opportunity to try things out and bring some of the ideas to life.
The final stage is a 90-Day Sprint. The long list of use cases from the capability session form the basis of this plan. I ensure AI experiments are aligned to business priorities and rank these by impact and complexity. This then becomes a realistic project plan for the duration of the sprint.
I’m amazed and genuinely disappointed by the limited number of AI success stories being reported. I believe quick wins achieved in 90-days is a simple first step that builds a results-focused AI-culture in an organisation. This is where skills are truly embedded and where potential soon becomes evident - both in people and for further projects. It’s an invaluable foundation that can lead smoothly to future projects.
AI Changemakers are the people that I see rise to the surface during these stages. Some through their questions, others in their comfort with the tools and others through their ability to connect the dots between AI capability and business value. Mindset wins over anything. AI Changemakers may sit in the technical or the business-side. The ability to be curious about one another's worlds, to experiment and problem solve means the objective remains central and AI exploration builds deeper knowledge and not just an AI solution.
A framework to stay on track
Back when I was in e-commerce during the pandemic at Unilever, I’d identify a business problem, put an activity in place and measure the result. Simple enough and incredibly impactful when you’re able to share results and mini case studies most weeks. During times of disruption and change, these insights help inform broader strategies as behaviours are still emerging.
This approach has evolved into an Enterprise AI Adoption Framework I’ve named SPARROW. SPARROW moves from Strategy to Execution in a structured, well-documented way. It puts the focus on the Business Strategy and forces a Problem Statement to be defined. It then moves to building an AI Action Plan that can test solutions that address the challenge. Realise Value is the critical go/no go stage where the business benefit is calculated. Once pilots cross this point the questions become more about scale: how does this adhere to Regulation and is it Responsible, how do we Optimise the approach and scale it? Finally, before it can launch, a commitment is required around the timeline to check the AI solution still delivers. This final stage is titled Work & Re-Work.
A well-defined and well-documented approach means every AI pilot is a learning. Even if the AI itself didn’t deliver to the objective, the AI Changemakers and cross-functional teams build the habit of connecting business problem to possible AI solution, and vice versa.
A People-First Approach is Smart (not just Nice)
I’ve spent too much time with energised teams, inspired leaders and curious communities to believe that AI’s success will be driven by anything other than the great people who adopt it. Drive, Domain Expertise and New Skills make for a killer combination.
When there’s vision, clarity and structure, people feel compelled to build and grow. Fear is often a lack of knowledge. Once more leadership teams make AI a central strategic question, I believe greater AI education will follow and that will translate to bolder visions for the future. I hope to see armies of AI Changemakers ready to turn their experience of quick wins into something truly transformative. I think AI can be the catalyst for a transformation that every sector would benefit from. But that success will be driven by the empowered people, not just technology.
As Jeff Bezos is quoted as saying “AI will only cost jobs in companies that lack new ideas”
The next AI Changemakers event is in Sydney on 24th September. Save your spot here.
