Leadership’s role in GenAI Adoption
The question isn’t whether to embrace it or not, it’s a matter of how and when.
The impact of artificial intelligence is already being felt across many industries, and the rate of adoption is only increasing.
AI is not the goal in itself. It’s the means to achieving business objectives
A common mistake is approaching AI as a technology problem rather than a business problem. This leads to a focus on the technical aspects of implementation, such as selecting the right algorithms and tools, rather than on the strategic goals and human components of the transformation.
When organizations focus too narrowly on the technical aspects of AI, they can lose sight of the bigger picture. I have seen this play out in my own experience - companies invest heavily in building out AI capabilities, only to struggle to find meaningful ways to apply those capabilities to real business problems. This can result in a significant waste of time and resources.
"A leader is one who knows the way, goes the way, and shows the way." - John C. Maxwell
Executive commitment, ownership and accountability
If leadership doesn’t have a clear idea of what they want to achieve with AI, and if they can’t clearly share that vision with the rest of their organization, the initiative is already doomed to fail.
Having a clear vision and strategy for AI is essential for success.
This includes not just a high-level vision, but also a detailed roadmap for implementation. Without this roadmap, it can be difficult to make progress and ensure that the organization is moving in the right direction.
But what if leaders don’t have a deep enough understanding of AI to know where to start?
A CEO and even their team usually aren't on the bleeding edge of what's out there and what is possible. And they don’t need to be. Their focus should be on the company’s corporate strategy, growth goals, and business model. Their opportunity is to bring in the right people to help them create a roadmap. Without sound advice from outside AI advisors who understand how to create a portfolio approach to AI initiatives, they don’t have enough evidence to bring about a complete vision.
The right mix of talent
Once leadership has set the vision, getting the right talent in place to execute an AI transformation is critical. This includes both technical talent, such as data scientists and engineers, as well as business leaders who can drive the transformation forward.
This talent and expertise may have to come from outside of the organization who can most effectively understand how ready the organization is for such a transformation. But external or internal, all talent has to understand the strategic goals of the transformation and ensure that the organization is aligned around those goals.
In “AI and Digital Transformation: A Comprehensive Guide,” author, Shah argues that AI transformations fundamentally differ from other types of transformations. That AI transformations require a different mindset and approach than other types of transformations.
While there are certainly unique challenges and considerations regarding AI, the fundamentals of successful transformation still apply. This includes having a clear vision and rollout strategy, building the right team, and ensuring the organization is aligned around the transformation through frequent and clear communication.
There are certainly examples of AI transformations that have gone wrong. One example that comes to mind is the case of Microsoft's chatbot, Tay. It was designed to learn from online conversations and become more human-like over time. However, within hours of its launch, Tay began spouting racist and offensive comments. Tay was designed to learn from the online conversations it was exposed to, and some users began intentionally feeding it offensive content.
Most recently, Samsung's semiconductor division started allowing engineers to use ChatGPT in their work. But they didn’t think through how this could impact their org. Three instances of secret information ended up being leaked to ChatGPT by team members. One employee requested the chatbot to inspect a sensitive database source code for errors, while another sought code optimization. A third employee fed a recorded meeting into ChatGPT and asked it to produce minutes.
I’m all for iterative experimentation. And permission to move quickly. Kudos to the division’s leadership for allowing employees the room to learn. But we have to take a thoughtful and careful approach to implementation and put safeguards in place that protect the company, its customers, and its employees.
AI Content Disclaimer: The human author who wrote this augmented the final post with Generative AI tools for transcription (Fireflies.AI), organization (ChatGPT 4), research (Perplexity AI) and image creation (Midjourney V5.2).
