Dynamics Matters Podcast Ep 106: The five pillars of successful AI adoption: pillars 1 and 2, data

With special guest Andrew Welch, CTO of Cloud Services, HSO.

We discuss:

✔ The definition of AI

✔ Why there is no AI without data

✔ The rise of commercial AI

Artificial Intelligence (AI) is no longer confined to the realm of science fiction. It's a reality that's transforming the way businesses operate. In a recent conversation, Andrew Welch, a seasoned expert in AI, provided insights into this ever-evolving field and a strategic roadmap for organisations looking to harness AI's potential.

Defining AI

To understand AI, Welch takes us on a historical journey. He suggests that the concept of AI can be traced back to Alan Turing's machine in the 1940s, a groundbreaking invention that, despite its lack of true intelligence, acted as an incredibly intelligent tool to enhance human intellect. For Welch, true AI emerges when machines can think independently of their creators and the predefined parameters. He acknowledges that the road to this form of AI has been a long one, with different phases and advancements.

Commercial acceptance of AI

Michael Lonnon points out the increasing commercialisation and mainstream acceptance of AI, highlighting that organisations are eager to explore its advantages. Despite this enthusiasm, he notes a lack of practical guidance for organisations on how to effectively leverage AI.

Welch emphasises the significance of commercialisation, asserting that while innovative technology is crucial, it's of little use without customers or users. He illustrates this with the example of Xerox's brilliant graphical user interface, which, despite its innovation, remained untapped until Apple commercialised it. This underscores the critical role of commercial viability in the tech world.

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The commercial imperative

Welch provides compelling statistics that reveal the stark productivity disparities between the most innovative firms and the rest. In the United Kingdom and Canada, the most productive companies have seen substantial productivity gains, while the least productive ones stagnated. Additionally, investments in modernisation and innovative technologies have led to significant returns for forward-thinking firms.

These disparities, Welch argues, underscore the urgent need for organisations to develop a coherent AI strategy. To succeed in the AI landscape, they must not only be technically proficient but also commercially adept.

The five pillars of an AI strategy

Welch elaborates on the five pillars of a comprehensive AI strategy that organisations should consider:

Data Consolidation: Most organisations have neglected their data over the years, resulting in chaotic and fragmented data ecosystems. The first step is to consolidate data into AI-accessible storage facilities.

Data Readiness: This involves ensuring data hygiene – governance, security, cataloging, cleanliness, and data lineage. Having clean, secure, and well-documented data is essential.

Incremental AI: This pillar involves implementing AI in everyday tasks, making them faster, more precise, efficient, and cost-effective. It could be as simple as enabling AI-driven tools that an organisation already possesses.

Differential AI: This pillar encourages organisations to explore uncharted territories where AI can perform tasks previously thought impossible for humans. It's about pushing the boundaries of what AI can do.

Scaling AI: Scaling AI across the organisation involves building a robust AI platform, integrating proper application lifecycle management, and DevOps practices. It also includes bringing shadow data into the fold.

Getting Started with AI

For organisations embarking on their AI journey, Welch recommends starting with data consolidation and data readiness. These steps are fundamental to enable AI to function effectively. After all, an AI model's performance relies heavily on the quality and quantity of the data it's trained on.

Once an organisation has these foundations in place, they can then move on to more advanced stages like incremental AI, differential AI, and ultimately, scaling AI across the organisation.

Conclusion

As AI becomes an integral part of business operations, it's essential for organisations to not only possess technical expertise but also a strong commercial mindset. The data-driven pillars of an AI strategy laid out by Welch offer a comprehensive roadmap for organisations to leverage AI effectively, enabling them to thrive in an increasingly AI-powered world.

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