Developing Your AI Business Strategy and Metrics

One of the main reasons why AI initiatives fail to deliver business value is down to a lack of clear business objectives. Leaders will need to consider carefuly the areas of the business where AI can have the greatest impact. To integrate AI into broader business and technology roadmaps, a clear understanding of success, embeded performance metrics and appropriate governance and compliance models.

AI Ethics, Regulation and Risk

Despite the excitement about the transformational capabilities of AI, concerns remain regarding trust, security, ethics and safety. This new era of generative AI raises particularly important questions about the responsible use and development of AI. Organisations will need to consider the risks of AI, fully understand their implications and explore how they mitigate them.

Scaling AI Initiatives

There’s no doubt that the value of AI is maximisied when at scale. However in practice, challenges arise when organisations attempt to operationalise AI. As AI is scaled, collaboration will be absolutely key, as will understanding how to structure teams and working practices that bring together different roles and skill sets. Beyond the soft skills, technology leaders must traverse the mindfield of tools and platforms that support and enable AI at scale and ensure these integrate with existing AI and technology infrastructure.

Exploring the Impact of AI on People and the Workplace

There’s no doubt that AI is going to have a revolutionary impact on the world of work, but its difficult to predict exactly what the future of work will look like. AI-enabled automation promises to free workers from monotonous tasks, leaving them free to innovate and disrupt. This will inevitably require the development of new working practices, job redesign and resource redeployment. But what does this mean for the low wage and skilled workforce? And how do you retain key talent required for the development and deployment of AI? Business leaders will need to consider now what the org chart of the future look like!

Evaluating the Disruptive Impact of Generative AI

Developments in generative AI are happening at a breakneck speed and early adopters are already incorporating the latest models and developing use cases that will provide competitive advantage. So what will be the impact on your industry? How should you go about identifying the best use cases for generative AI? How do you evaluate the risks in such a rapidly evolving environment? Which operating models will allow you to capitalise on the opportunities of generative AI? How should they go about experimenting with generative AI safely?

AI and Data Analytics: Making Better Insight-Driven Decisions

AI-enabled data analytics provides more granular insights which help inform decision-making and, ultimately, enable innovation. However, complex foundation models require vast amounts of data to learn and, crucially, data quality directly impacts the performance, accuracy, and reliability of AI models. To maximise value from AI, organisations will need to develop an AI-aligned data management strategy, encompassing data processing, analysis and storage, that enables them to collect enough high-quality data and make that data available to the right people at the right time!