Last week, I had the privilege of delivering a keynote presentation at Monday.com’s Elevate conference in London. The topic, “Leveraging data and artificial intelligence (AI) for organisational transformation,” allowed me to challenge some common misconceptions about AI adoption and share practical insights on harnessing the power of existing data. In this post, I outline the key themes discussed and provide some additional context.

Debunking the Perfect Data Myth

A central theme of my presentation was debunking the myth that organisations need perfect data to make a material impact with AI. This notion has held many businesses back from exploring the transformative potential of AI, leading to missed opportunities and stagnation in an increasingly data-driven world. I emphasised that waiting for “perfect” data is often a recipe for inaction, and that valuable insights can be gleaned from seemingly unsuitable or messy data sets.

To illustrate this point, I introduced the audience to a concept I call the Money For Old Rope Machine (MORM). This tool, which I first unveiled at an AWS private equity event, is designed to help organisations identify AI opportunities using their existing data. The MORM demonstrates how businesses can unlock value from data they already possess, even when it seems unsuitable for AI projects at first glance. By shifting focus from the idealised, synthetic data often associated with generative AI to the real-world data locked up in their businesses, companies can uncover a wealth of untapped potential.

Establishing an AI and Data Centre of Excellence

However, unlocking this potential requires more than just a change in perspective. It demands a structured approach to AI governance and implementation. To address this, I explored the concept of an AI and Data Centre of Excellence. This isn’t just another IT initiative or an extension of a cloud center of excellence. Instead, it should be a board-level endeavour, reflecting the strategic importance of AI and data in driving organisational transformation.

I outlined key responsibilities for the AI and Data Centre of Excellence:

• Human-AI collaboration and oversight
• Management of AI-specific vulnerabilities
• Prevention of misuse and harmful content
• Intellectual property protection
• Evaluation of AI business use cases
• Cost optimization and ROI assessment
• Data quality, governance, and management
• Accountability, transparency, and explainability
• Skill development and education (technical and non-technical)
• Bias identification and mitigation
• Privacy and data protection.
• Legal, regulatory, and corporate governance.

The AI and Data Centre of Excellence serves multiple crucial functions. It provides a framework for ethical AI use, ensures alignment between AI initiatives and broader business goals, and acts as a central hub for AI expertise within the organization. By establishing such a centre, companies can navigate the complex landscape of AI adoption more effectively, balancing innovation with responsible use.

Getting Started with AI Pilots

Of course, theory without practice has limited value. That’s why a significant portion of my presentation focused on how organisations can cost-effectively get started with AI pilots and quickly prove their value. The key is to start small but think big. By identifying low-hanging fruit – areas where existing data can be leveraged for quick wins – companies can build momentum and make a compelling case for broader AI adoption.

These pilot projects don’t need to be grandiose or resource-intensive. Often, the most impactful AI initiatives begin with simple applications of machine learning to existing business processes. For instance, a company might use historical sales data to improve demand forecasting, or apply natural language processing to customer service logs to identify common pain points. The goal is to demonstrate tangible value quickly, paving the way for more ambitious AI projects down the line.

Looking Beyond the Hype

Throughout the presentation, I emphasised the importance of looking beyond the hype, especially around generative AI. While tools like ChatGPT have captured the public imagination, the real value of AI often lies in less flashy but more immediately applicable use cases. By focusing on how AI can solve specific business problems using existing data, organisations can avoid getting caught up in the AI hype cycle and instead focus on driving real business value.

As we navigate this exciting landscape, it’s crucial for leaders to adopt a mindset of continuous learning and experimentation. The field of AI is evolving rapidly, and what seems cutting-edge today may be commonplace tomorrow. By fostering a culture of curiosity and innovation, organisations can stay ahead of the curve and continuously find new ways to leverage their data for competitive advantage.

Conclusion

In conclusion, the journey to AI-driven transformation doesn’t require perfect data or massive upfront investments. It begins with a willingness to explore the potential of the data you already have, coupled with a structured approach to governance and implementation. By starting small, proving value quickly, and scaling thoughtfully, organisations can unlock the transformative power of AI and position themselves for success in an increasingly data-driven world.

About the Author

Mario Thomas is a seasoned professional with over 25 years of experience in web technologies, cloud computing, and artificial intelligence. In his role as the Head of the Global Trainer Centre of Excellence and Press Spokesperson at Amazon Web Services (AWS), Mario develops executive training programs and AI sales enablement strategies worldwide. He is a Chartered Director and a Fellow of the Institute of Directors, providing valuable insights to Board Directors and senior executives on leveraging technology for organisational transformation.