Harnessing AI for organisational change led from the Board

I had an incredible afternoon as a guest lecturer at the London School of Economics and Political Science (LSE) where I got to discuss harnessing AI for organisational transformation led from the Board, at this summer’s Data Science and AI for Executives class.

It was an enriching experience, thanks to the insightful questions and valuable contributions from the audience. I am particularly grateful to Sabine Benoit, Kenneth Benoit and Edgar Whitley for their gracious invitation and support. The enthusiasm and curiosity about AI from senior executives from around the world were truly inspiring.

About the Lecture

The lecture focused on the critical role that AI can play in driving organisational change, especially when led from the Board. Here are the key areas covered:

The Hype: Navigating the Noise Around AI

Understanding the current buzz around AI and distinguishing between the hype and the real impact it can have on business. This section highlighted the importance of keeping a broader perspective on AI’s capabilities beyond the current poster child of AI, generative AI, and moved the audience to consider multiple types of AI depending on the use case. Key concepts included:

• How generative AI became the first mass consumerisation of AI: Generative AI, particularly models like ChatGPT, brought AI into the mainstream, significantly increasing public awareness and interest in AI technologies.
• How the noise around generative AI has drowned out other AI disciplines: The focus on generative AI has overshadowed other important AI fields such as computer vision, reinforcement learning, and natural language processing, which continue to deliver significant business value.
• How it took us a while to get beyond obvious use cases for generative AI: Initially, generative AI applications were limited to chatbots, content generation, and text summarisation. Over time, more innovative use cases have emerged.
• The importance of not losing sight of other AI disciplines: Executives should be aware of the seven other areas of AI—such as predictive analytics and computer vision systems—that are currently driving substantial business impact.

The Opportunity: Understanding the Value of AI

Exploring real-world statistics and use cases that showcased AI’s potential in transforming businesses. This included examples from various industries where AI has successfully driven innovation and efficiency. It also differentiated between innovation and transformation, explaining that incremental business change through AI can be as impactful as broad change. Key concepts included:

• A deep dive into multiple areas of transformation:
  • Increasing revenue margin and profit: AI can optimise pricing strategies, enhance sales forecasting, and personalise marketing efforts.
  • Catalysing innovation: AI enables businesses to enter new markets, develop new products, and improve existing offerings.
  • Enhancing customer experiences: AI provides deeper insights into customer needs and preferences, leading to more personalised and satisfying interactions.
  • Operational efficiency: AI streamlines operations, reduces costs, and improves decision-making processes.
  • Reducing organisational risk: AI helps in identifying and mitigating risks through predictive analytics and automated monitoring systems.
  • Increasing sustainability: AI contributes to sustainable practices by optimising resource usage and reducing waste.
• Key indicators of AI’s value and impact: Metrics and case studies from various industries highlighting the significant improvements achieved through AI.
• Example use cases from six industries: Detailed examples illustrating how AI has transformed sectors such as healthcare, finance, consumer packaged goods, manufacturing, and transportation.

The Pitfalls: Preparing yourself and your business for AI adoption

This section addressed the practicalities of AI implementation, focusing on potential risks, legal considerations, and the crucial role of Board oversight. It emphasized the need for a robust governance framework to manage AI adoption responsibly. It also touched on key questions I hear from executives about the implications of AI on their business as well as how to go about setting up an AI centre of excellence. Key concepts included:

• The need for governance of AI: The importance of Board ownership of AI governance, ensuring that AI initiatives align with the organisation’s strategic goals and ethical standards.
• Steps to create an AI centre of excellence: Establishing a dedicated team to drive AI projects, foster collaboration, and ensure best practices.
• Areas of concern for Boards: Issues such as data privacy, security, bias in AI models, and compliance with regulations.
• Key questions for AI project leaders: Critical questions Boards should ask to ensure effective oversight and accountability in AI initiatives.
• Addressing hallucinations, hypnosis, poison tokens, prompt injection, and bias:
  • Hallucinations: The phenomenon where AI generates incorrect or nonsensical information. Ensuring rigorous validation and verification processes can mitigate this risk.
  • Hypnosis: The subtle influence AI might exert on decision-making processes. Maintaining human oversight is crucial to avoid unintended manipulation.
  • Poison Tokens: Data points that can corrupt AI training data, leading to skewed or harmful outputs. Implementing robust data cleaning and monitoring practices is essential.
  • Bias: The inherent biases that AI models can perpetuate. Regular audits and diverse training data can help in reducing bias and ensuring fair AI outcomes.
  • Prompt Injection: A method of injecting harmful prompts into AI input to manipulate its responses, similar to SQL injection in databases. Ensuring secure and monitored inputs can prevent such attacks.

The Pilot: First moves with AI

Ending on a practical note, I provided the audience with tactical steps to evaluate potential pilot AI programs that were low friction, low cost, and high impact. This included guidance on identifying suitable pilot projects and the mechanisms to use to get them underway in their business. Key steps included:

• Identifying pilot projects: Criteria for selecting AI projects that offer quick wins and demonstrable value.
• Mechanisms for pilot implementation: Best practices for executing pilot projects, measuring their success, and scaling successful initiatives.

And Finally

I wrapped up with some thoughts around the question that comes up all the time, “Will AI take my job?” and my view that we may finally achieve the ‘3-hour’ work week (more on that in an upcoming blog post). I talked about the importance of the ‘human in the loop’, and offered my view on when we’ll achieve artificial general intelligence (AGI) — you’ll have to ask me directly to hear my answer!

About the Author

Mario Thomas is a transformational business leader with nearly three decades of experience driving operational excellence and revenue growth across global enterprises. As Head of Global Training and Press Spokesperson at [Amazon Web Services](https://aws.amazon.com) (AWS), he leads worldwide enablement delivery and operations for one of technology's largest sales forces during a pivotal era of AI innovation. A Chartered Director and Fellow of the [Institute of Directors](https://www.iod.com), Mario partners with Boards and C-suite leaders to deliver measurable business outcomes through strategic transformation. His frameworks and methodologies have generated over two-billion dollars in enterprise value through the effective adoption of AI, data, and cloud technologies.