The human side of intelligent automation

When it comes to digital transformation, many organizations take the term literally – focusing on the technological aspect of change without considering how it will affect the business or the people in it. In fact, digital transformation depends as much on humans as it does on machines.

For energy and utilities organizations, this blending of technological capabilities and human intelligence is of the utmost importance. Our recent research, Intelligent Automation in Energy and Utilities, identified a multi-billion dollar savings opportunity for organizations that deploy intelligent automation at scale. Curiously, three of the five key research findings deal not with technology, but with people – their ability to change, their aversion to risk, and their propensity to think big.

Here, we explore three learnings from working with energy and utility organizations that have successfully adopted intelligent automation:

1. Vision: Think big, start small, scale fast.

As part of our research, our organization analyzed more than 80 use cases for intelligent automation. In so doing, we found that only a minority of efforts focus on so-called “quick wins” – areas where intelligent automation is relatively simple to deploy but also delivers a high-benefit return, both financially and in terms of building momentum for organizational change.

This appears to be a missed opportunity, as many business leads believe that only large-scale projects will generate the attention, credibility, and results needed to initiate broader change. While this approach may work for some organizations, we’ve found that having a big vision but starting with “quick wins” tends to be a more effective strategy. We advise clients to “think big, start small, and scale fast.” Proving value can be done with a minimal investment; building momentum starts with small, but meaningful, results.

For example, one of our water utilities clients in the UK started their AI journey with a big vision to build an AI capability. However, we worked with them to identify and execute a “quick win” that has a significant impact on the organization’s strategy: water leakage detection and location. Once we helped them solve this problem, business leaders could see the possibility of AI in multiple areas, which enabled the scalability of the AI initiative.

2. Sponsorship: Believe, embrace, instill

Deploying intelligent autonomation at scale requires an executive sponsor who believes in the potential of the technology to solve business challenges and can instill that belief in others. This person builds excitement, trust, and momentum throughout the organization for these capabilities and persuades senior leadership of the importance of investing in a cohesive intelligent automation strategy. This individual is also responsible for creating the vision for the organization, as it relates to intelligent automation.

In our UK water company example, the early initiative was tech-focused and lacked momentum. However, the new executive sponsor had a deep understanding of the business, passion for AI, and a vision for how AI could help solve key challenges within the organization. He instilled this vision in his colleagues, the business, IT, and other areas of the company and served as an advocate of intelligent automation, driving adoption across the organization. This type of sponsorship is an extremely important component to scaling intelligent automation efforts from one-off initiatives to an organization-wide strategy.

3. Experimentation: Moving beyond agile methodologies

Agile delivery models are common in executing intelligent automation initiatives, especially those that are iterative in nature, such as AI or machine learning (ML) projects. However, in order to scale intelligent automation applications, organizations must go beyond agile delivery models to create a culture of experimentation – one that builds agility and responsiveness into every aspect of the business and encourages collaboration throughout. By testing, iterating, and deploying on the micro-level, organizations can not only minimize the time and cost of initiatives, but also enhance results by focusing on continuous improvement.

In our water leakage example, our initial experiment identified leaks 17 days sooner than the previous approach. Despite this significant improvement, the tool did not substantially impact operations. Upon further review, we realized that the core user issue was not detecting the leak sooner, but locating it within the network faster. This prompted our team to shift the nature of the assignment to focus on leak location. This revised solution prompted yet another issue: field engineers needed a simple user experience to help them do their jobs better. We changed that too, developing a more geographical-based visualization of pipes that used color-coded leakage scores to make the output more understandable. By embracing this culture of experimentation, we were able to continuously improve and solve problems in real time, as opposed to implementing predetermined solutions.

For many companies, it may come as a surprise that technology itself will not be the most challenging aspect of transformation. Rather, it is winning the hearts and minds of the people expected to adopt it. By focusing on these three areas – vision, sponsorship, and experimentation – it is possible for organizations to anticipate some of the most common questions that people have in times of change: Why? How? When? With any luck, the momentum established through “quick wins” and the passion displayed by leadership will leave people with a different kind of question: what’s next?

For more information about our Intelligent Automation capabilities and how we can help you on your journey, please reach out to me directly and read my last blog on the three ways energy and utilities organizations can harness the power of AI, ML, and big data – now and in the future