Earlier this month, I was invited to join the Energy Trading Operations and Technology (ETOT) conference where 200+ guests represented the UK energy trading market. In my keynote, I spoke about the impact of prosumers and electric vehicles on energy trading markets, and gave an outlook into how an AI-driven trading world could look like.
I firmly believe that data, the underpinning foundation for any Robotic Process Automation (RPA) and Artificial Intelligent (AI) solution, as well as the capabilities within an organisation can become either the grease (secret sauce) for monetising flexibility across assets (whether they be physical assets, flexible contracts or your customers) … or the sand that can bring a previously well-oiled machine to a halt. My conversations at the conference, as well as the many case studies shared over the two days confirmed this again and again.
Within the energy markets, RPA has had a focus on ‘efficiency improvements’ for a number of years including back office process automation (52% of ETOT participants said that active RPA solutions already support their BAU operations). However, and with the increased focus on short-term flexibility and speed to market, a number of organisations are now extending the application of RPA and AI into the revenue-generating side of the business, e.g. the automation of trading; the execution of exchange auction bidding; and forecasting improvements.
From my experience, automating and enhancing revenue-side processes is more challenging and whilst opportunities are harder to find, the rewards usually exceed those generated through back-office automation. Given that only 41% of ETOT participants said they have started looking into flexibility tools, RPA and AI to support their business strategy in this space, this seems to be an area where there are still a number of untapped opportunities.
Two messages were emphasised by all speakers, and it’s something I cannot repeat often enough:
- Effective data governance and management is an essential prerequisite to benefit longer term from the increasing prevalence of AI and data-heavy automation: establishing and implementing a clear data strategy, unique to your own business drivers and your strategy, will ensure businesses can effectively monetise their ‘data assets’. 55% of ETOT participants noted that their front office teams would materially benefit from improvements in data quality and implied that these changes would impact revenue generation as well as operational efficiency.
- Business “capability” can’t be turned around overnight, it requires a sustained level of focus: to develop these capabilities, organisations need to start a journey and adapt their processes and technology, and ensure they have the right people and skills to deliver in this constantly changing environment. It is not about a single one-off material financial outlay, but requires a clear view of what is important to the business, and a set of initiatives that incrementally improve capabilities in the same direction over time. It’s not sufficient to just list a set of activities on a path. Each incremental initiative should be return-led – or putting it another way, each should add incremental value as capabilities are built.
There are a number of organisations that are pursuing such an approach, where they are planning to develop effective data governance and organisational capabilities in parallel over time. One such example is to improve forecasting data and techniques and with each incremental change further the accuracy, technical capability and wider capabilities associated with that business need. By delivering incremental value with each cycle, the programme will continue to gain momentum over time, and will become a self-sustaining change activity.
Let me know if you are interested to discuss any of my observations and suggestions further.