能源和公用事业智能自动化

Introduction

The global energy and utilities sector is undergoing unprecedented change:

1.  The three “Ds” of decarbonization, deregulation and decentralization are having a significant impact. Currently only 10% of UK’s electricity comes from coal-fired generators, and in 2019 the National Grid has logged more than 1,000 hours of coal-free electricity.

2.  The sector is moving from its conservative, regulated past to a new future where innovation is key. Its consumer base, which used to be largely passive, has now moved to a world of ‘prosumers’ who expect a sophisticated, service-based industry.

3.  Digitalization will be critical to capitalizing on these shifts. Technologies, such as automation and artificial intelligence (AI), are playing a pivotal role in managing the balance between demand and supply, boosting efficiencies in all the entirety of the value chain, innovating the customer experience and transforming business models.

Executive summary – key takeaways

1.  The sector has under-estimated intelligent automation’s true potential

-    Nearly half of the respondents have under-estimated the benefits they derived from their intelligent automation initiatives.

-    We estimate that the sector can save between $237 billion to $813 billion from intelligent automation at scale.

2.  Scaling-up initiatives is still a critical issue, though significant progress has been made in the AI solutions

-    Like their peers from other sectors, the energy and utilities industry is facing considerable headwinds when trying to scale their automation initiatives. Currently only 15% have been able to deploy multiple use cases

-    The sector has made considerable headway in adopting AI solutions. In 2017, only 28% had a few or multiple AI use cases, but today this number stands at 52%. These use cases are primarily aimed at the core competencies.

3.  Organizations are missing out on critical use cases that can deliver outsized benefits

-    In core functions, only 18% of organizations are deploying quick-win use cases (by which we mean they are low on delivery complexity but high in terms of benefits achieved). Use cases such as forecasting, energy trading, yield optimization, grid behavior interfaces and complaints management fall under quick wins.

-    Support functions tend to utilize more robotic process automation (RPA) use cases, with quick wins emerging in order management, contract management, employee data management, and defect detection etc. Only 11% of the organizations are focusing on the quick wins in support functions.

4.  The road to scaling intelligent automation

-    Learning from the best practices followed by high-performing ‘Automation Frontrunners’, we have developed five recommendations for scaling intelligent automation.