The Road to Intelligent Automation in the Energy Sector


  • Sorin ANAGNOSTE Bucharest University of Economic Studies


With Robotic Process Automation (RPA) nearly at its peak in terms of awareness and capabilities, organizations are exploring what’s beyond it. The road to Intelligent Automation must include a cognitive roadmap that each vendor solution should consider it before developing. While RPA can cover between 10% and 40% of the processes of a business unit in each organization, intelligent automation can go much further – close to 100%. These solutions have learning capabilities attached to it and analyze decisions and act just like humans. Systems enhanced with these technologies can decide how to allocate the effort in an organization, it can stop performing some activities in order to allow performing other much more important. And while these may not seem something transformational, just knowing that a Power & Utilities (P&U) company works with this kind of robots to face the extraordinary energy requests especially during the peaks is a great thing. In this article, we will explore the new field of Intelligent Automation (IA) with a suggestive Case Study in P&U. 


Accenture (2017). Artificial Intelligence is the future of growth. Retrieved on August 19, 2018, from

Amardeep M. (2016). Artificial Intelligence: How far or how close. Retrieved on August 12, 2018, from

Anagnoste, S. (2017). Robotic automation process - The next major revolution in terms of back-office operations improvement. Proceedings of the International Conference on Business Excellence, 11(1), 676-686.

Anagnoste, S. (2018). Setting up a Robotic Process Automation Center of Excellence. Management Dynamics in the Knowledge Economy, 6(2), 307-322.

Andrew, N. (2017). Why AI is the new electricity. Retrieved on July 20, 2018, from

Aspara, G. (2018). Blue Prism Interview Questions and Answers. Retrieved on August 25, 2018, at

Bratianu, C. (2018). Intellectual capital research and practice: 7 myths and one golden rule. Management & Marketing. Challenges for the Knowledge Economy, 13(2), 859-879.

Bratianu, C., and Orzea, I. (2014). Emotional knowledge: the hidden part of the knowledge iceberg. Management Dynamics in the Knowledge Economy, 2(1), 41-56.

Bratianu, C., and Vasilache, S. (2010). A factorial analysis of the managerial linear thinking model. International Journal of Innovation and Learning, 8(4), 393-407.

Bratianu, C., and Vatamanescu, E.M. (2017). Students’ perception on developing conceptual generic skills for business: a knowledge-based approach. VINE Journal of Information and Knowledge Management Systems, 47(4), 490-505.

Bratianu, C., and Vatamanescu, E.M. (2018). The entropic knowledge dynamics as a driving force of the decision-making process. The Electronic Journal of Knowledge Management, 16(1), 1-12.

Forrester Research (2017). The Forrester Wave™: Robotic Process Automation, Q1 2017. Retrieved on August 14, 2018, from Q1+2017/-/E-RES131182.

Gartner (2018). Define Your Artificial Intelligence Strategy. Retrieved on August 24, 2018, from

Ghinea, V.M., and Bratianu, C. (2012). Organizational culture modeling. Management & Marketing. Challenges for the Knowledge Society, 7(2), 257-276.

HfS Research (2017). Enterprise Automation and AI will reach $10 billion in 2018 to engineer the OneOffice. Retrieved on August 17, 2018, from 417.

Market and Markets (2017). Robotic Process Automation Market worth 2,467.0 Million USD by 2022. Retrieved August 20, 2018, from

McKinsey & Co. (2017). Artificial Intelligence – The next digital frontier?. Retrieved on August 23, 2018, from

Russel, J. (2017). Google’s AlphaGo AI wins three-match series against the world’s best Go player. Retrieved on August 24, 2018, from




How to Cite

ANAGNOSTE, S. (2018). The Road to Intelligent Automation in the Energy Sector. Management Dynamics in the Knowledge Economy, 6(3), 489–502. Retrieved from