We started by considering how to enable innovation and drive value – whilst also keeping security high, especially where integration risk is a key concern.
Many players in the energy industry have large OT (operational technology) estates – which means that a complete migration to the cloud may not be feasible. Edge computing to share workloads between local networks and the cloud is therefore an important approach.
“We’re also seeing growing use of containerisation which increases portability and flexibility. Another important principle is to minimise customisation of SaaS platforms – copy best practice for maximum value.” - Rajesh Naik, Data and Analytics Leader, Energy and Utilities, IBM
Data security is obviously also critical – by understanding your data assets, categorising and classifying them and maximising visibility of any gaps which can then be remediated. Knowing your data is important in another sense too – many businesses are concerned about vendor lock-in.
“In our unit- and subscription-based economy, companies have options. Once you have the data, you can make good decisions. Go out and explore.” - Marlon Oliver, Senior Vice President, EMEA & APAC Operations, Flexera
Of course, it didn’t take long for AI to come up. AI is creating a fundamental shift with the prospect of hugely increased analytical capability, predictive modelling power and enhanced employee productivity. AI is becoming the co-pilot not just for people but for whole processes too.
“To harness AI, we need process-driven thinking. In partnership with technology providers, it’s about architecting solutions where AI is embedded into processes that are value-additive in nature but sustainable by design.” - Lewis Richards, Chief Sustainability Officer, Microsoft
All three panellists coalesced around the point that to drive successful AI adoption, culture is key – and this needs to be led from the top. At a leadership level, some degree of trust in AI is needed, within a clear governance framework, and staff must be encouraged to buy in. Investment in staff training and upskilling is also clearly essential. Meanwhile, data is critical to success as this is the foundation layer upon which AI works.