The data leadership series part Five
Making senseof AI
- Approaches to artificial intelligence
- Understanding the core concepts
- Foundations for success
The data leadership series part Five
Each month, with the help of data experts on the panel, we will guide you and your teams through a relevant data leadership topic – with practical advice that you can follow.
Since ChatGPT burst onto the scene, business and technology leaders alike have been curious about how to exploit AI technologies. It’s on everybody’s radar, but poorly defined.
Starting with types of business outcomes leaders want from ‘generative’ vs ‘predictive’ AI use cases, we’re going to work backwards. We’re going to look at all the moving parts involved in the process, and most importantly, how to get value from AI.
Part 1 sets the stage for AI in the context of Enterprise Data Management. We’ll discuss the approaches that Data Leaders can take to assessing AI value use cases, the types of AI that are ‘enterprise ready’ right now and why ‘training’ is such an important topic.
Part 2 is about the moving parts of AI – we look to establish the critical components such as models, decision engines, neural networks, and so on. Each of these components comes with its own set of risks that can be mitigated, and opportunities to be unlocked.
Part 3 is about AI Readiness. As Data Leaders, we must separate the fear from fact, but also look optimistically to the value that AI can deliver when harnessed correctly. The AI revolution isn’t coming, it’s already here. What actionable steps can you take to ready your business?
Principal Consultant, Ortecha
Head of Information Architecture, BT
CEO, Ideas CruciblePhD in Artificial Intelligence
AI is transforming businesses rapidly. It’s crucial to balance innovation with risk management and adopt AI responsibly.
AI’s success relies on robust data management. Frameworks like DCAM & CDMC ensure effective AI in finance, healthcare, and beyond.
There are several ways to get ready for the oncoming AI (r)evolution. Unsurprisingly, most of them focus on improving the diet of data feeding AI.
