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Gen AI has entered the boardroom: Insights from the top

I enjoyed presenting to over 300 leaders and changemakers at a recent event in Melbourne dedicated to the future of technology, innovation and work. No doubt this is one of my favourite topics, especially as it relates to Generative AI and its impact on how work gets done. I’ve captured the main points from my keynote here, including the six major challenges faced by leaders, and the five ‘no regret’ moves they can make to overcome these. I also cover a topical example of how Gen AI is already disrupting industries as a reminder to all of us that we need to proactively explore this new tech so we can disrupt with it, rather than be disrupted by it.


AI is well and truly on the Executive agenda

One of the biggest changes I have seen in recent months is the significant shift in Executive sentiment: all of a sudden, with the unprecedented and rapid advent of Gen AI, we have leaders’ full attention. This has been a truly awesome moment in my career, which started over a decade ago in data analytics when data and technology weren’t yet a priority.

In my role, I am in the very privileged position to speak with and listen to lots of different Board members and Execs around the world about data, technology and most recently Gen AI. So, what is keeping leaders awake at night? 


A snapshot of how Gen AI is challenging leaders

  1. Acknowledging this is significant disruption: Firstly, there is a genuine acknowledgment that Gen AI will present a similar disruption to when the internet first emerged or when mobile phones arrived on the scene. And it is not just ‘what do we do with this technology?’, it’s about reflecting on what this technology fundamentally means for the strategic choices that need to be made, like ‘How do we win?’, and ‘ How do we compete?’.
  2. Considering the potential existential threat to business models: Enough said. This is, and should be, a real concern. If you’re not actively exploring Gen AI, your competitors are and you will be left behind.
  3. Realising white collar jobs are no longer immune to disruption: There are implications for the future of work and how the workforce needs to be redesigned to enhance productivity and achieve faster, better and healthier outcomes.
  4. Recognising the need to balance step-change growth and incremental productivity opportunities: Incremental step changes need to be made; and they are already happening. Take Microsoft Office, which is embedding Gen AI into its software suite. The question is, what will you place your bets on to make the right step changes as quickly as possible? This could mean anything from reinventing the contact centre experience to the back office finance function …
  5. Must balance the risk that comes with speed, without constraining innovation: How do you balance risks while you are moving fast and avoid constraining innovation? This is a genuine concern.
  6. Identifying where to differentiate and the role of data: Finally, differentiation. The role of data has suddenly become more important. Anyone can use Gen AI like ChatGPT, so it comes down to how you tune these models with your own proprietary data – or an ecosystem partner’s data – to truly help you differentiate.


From tech strategy to genuine business transformation

In the past, I’ve heard leaders say in response to emerging technology, ‘We need another tech strategy’, or ‘We need an AI strategy’, or a Gen AI strategy. These days, I have seen an exciting shift in mindset, with leaders wanting to understand how they can drive their business strategy transformation, fuelled by Gen AI. They are asking how they can use Gen AI to do things differently – cheaper and faster. And also how to do different things, whether that’s creative innovation, developing new business models or designing new products and services. Simply put, leaders want to know how they can better execute their strategic choices so that they can confidently become the business they want to be. 


New business models are both scary and exciting

Earlier this month, we saw how someone used Gen AI to create a song by Drake + THE WEEKND. A song that never existed before, but sounded just like something these rappers could have produced together. And it received 15 million views on TikTok almost instantly. It was incredible to witness – because it proffers a key moment in time that all industries can learn from.

The lesson is in thinking about what you would do. In this case, the song was taken down by streaming services because of copyright infringements. But the point is, what could you do differently with your business model with a little help from Gen AI?

I encourage you to think about whether there is a new business model you can unlock by using this new technology. What if Drake wants to collaborate with his fans, uploads a heap of sample songs, taught Gen AI key algo rhythms and co-created new music with his adoring public? It could create a new revenue stream. 

Again, the point is that we shouldn’t just think about protecting our existing operating models, we need to think about potential new ones. As I mentioned earlier, your competitors are probably already doing things differently. We need to think about what defines your unique value – is it your equivalent to the joy of consuming songs produced by Drake alone, or could it be similar to the joy fans may also get in co-creating new songs?


‘No regret’ moves you can make

AI won’t replace you, but an organisation that is using it will. On a closing note, here are some ‘no regret’ moves organisations can make to respond to the rapid advent of Gen AI:

  1. AI fluency of leadership teams – including Executive and business leaders: Leaders are the make or break of Gen AI failing or succeeding, they need to understand what Gen AI is and isn’t good at, its risks and how to tie it in to strategic value
  2. Set up an operating model to experiment and scale in a structured way: We know that Gen AI is always 100% confident, but not always 100% right. This makes experimentation and validation key to successfully scaling this exciting evolution of AI. Experiment, test, deploy, scale, identify the investment needed, partner if necessary. Organisations need to get this right up front
  3. Identify and prioritise a set of use cases that align to business strategy: Avoid getting distracted by passion projects and focus on that that link to the value pool
  4. Lay out a clear technology strategy that connects to value: Always make sure that the technology serves your business so it runs faster, more efficiently and can deliver what employees and customers value most
  5. Proactively engage your ecosystem of partners: Consider the value of doing things yourself, versus working with an ecosystem of partners to deliver the best outcomes.

On that note – where do you sit? Are you ready to be a disruptor with Gen AI, or will you risk being disrupted by it?