Guess what, we're standing on the precipice of a new digital era. I'm sure you've heard that a million times already in the last year or so - but it's true. Though AI isn't new by any stretch of the imagination, generative AI does seem to have heralded in a new era. But there are a lot of big promises out there, some accurate, some less so. So what can we learn from the path we've trodden to get here to help guide us going forwards? The path I'm talking about is that of cloud computing, in many ways the promises and the fears are the same. In this article I'm going to attempt to draw parallels between these two watershed moments in technological time and see if there's a blueprint for navigating the new generative AI landscape in front of us.
Start with the Strategy, Not the Technology
The allure of generative AI, with its capability to produce novel content and solve complex problems at a pace we've never seen before can easily overshadow an essential foundational step. Are we creating the right content and are we solving the right problems?
To answer these questions we need to make sure we're aligning the technology to our overarching business strategies. In many ways this mirrors the pitfalls most organisations found themselves falling into with cloud technology. The rush to the cloud was so fast people forgot to take the time and figure out if it was actually helping them achieve their long term goals or not. Everywhere I look I see the same with Generative AI.
So lets learn from our previous mistakes. Prioritise strategic alignment over implementing technology just so you can say you did it first. That way generative AI becomes a business enabler rather than a new cost centre for IT to manage.
Building on a Foundation of Data Governance
Cloud computing taught us the paramount importance of data governance — a lesson equally critical in the era of generative AI. As we venture into creating and leveraging AI-generated insights and content, the principles of data security, privacy, and ethical usage need to be our guiding stars. Implementing robust data governance frameworks ensures that generative AI operates within the bounds of ethical standards and regulatory requirements, securing the trust of customers and stakeholders in the process.
The Talent Transformation Required for AI
The cloud era highlighted a significant skills gap, propelling a wave of upskilling and reskilling initiatives across pretty much all industries. Similarly, generative AI calls for a talent transformation. I learned a new term the other day - Prompt Engineering. This is the ability to structure text in such a way that it can be interpreted and understood by a generative AI model. We can't just assume that people know how to do this without learning it first. Cultivating an environment of continuous learning and adaptability will be essential for success and this is going to take time and investment.
Scalability and Flexibility are Key
Again, cloud computing's legacy of scalability and flexibility offers a template for the infrastructure supporting generative AI. We've been given a new, almost limitless, way to scale solutions completely unrestricted by the infrastructure on which it sits. This opens up options which simply didn't exist yesterday, let alone 6 months ago. But don't let the paradox of choice cripple you. As I said above, establish a clear strategy and stick to the core values of it, but remain open minded to refine the course if need be. Bear in mind I said refine, not completely overhaul.
Conclusion: The Path Forward
As you chart your course forward, make sure generative AI is a tool in your armoury. But use the lessons learnt from cloud computing as guideposts. It's there to remind us that, yes our lives will be very different going forwards, but no, we probably won't be half bionic within 5 years. At its core, what matters the most is that you are solving your core business problems first, and that requires knowing what they are in the first place and what you want to achieve in the long term.
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