Business Transformation Requires Transformational Leaders

The Industrial Agile Framework is a framework for applying Agile to physical product delivery. It pulls together everything that’s needed to design and mass produce a product, beginning with an idea and including design, components, supplier considerations, manufacturing, and everything in between. With Industrial Agile, you can change directions while working on product development and you don’t have to go back to square one. And, as with Agile for software, inspecting early and often means finding and fixing errors before they become excessively costly. At the end of their recent webinar on the Industrial Agile Framework, Cutter Consortium Senior Consultants Hubert Smits and Peter Borsella responded to some questions that you may be wondering about as well.

Established risk management methodologies and approaches tend to be static in nature and lead to models that are backward-looking. During the COVID-19 crisis, many companies have found their decision-making tools and dashboards for crisis management and business continuity to be inadequate given the geographic scale of the disruption. New risk models look ahead by utilizing AI and ML and can be continually updated as more data becomes available. In the first in a series of webinars, Tom Teixeira, Carl Bate, and Craig Wylie answered some questions about what risk management looks like in this changing business landscape.

Daniel J. Power, Ciara Heavin, and Shashidhar Kaparthi argue that a better governance mechanism is necessary to minimize the dangers of rushing to adopt AI and automation without due consideration of the risks. They present a governance framework for intelligent automation that includes all key stakeholders and offer policy prescriptions and guidelines for successful intelligent automation.

Tad Gonsalves and Bhuvan Unhelkar argue that while machine intelligence facilitates smart automation and autonomous operations, yielding benefits, it cannot handle decisions that need to account for subjective factors, such as satisfaction, perceived quality, or joy, which cannot be parameterized in an ML algorithm. The authors recommend judicious superimposition of human natural intelligence (NI) on machine intelligence as a better way to facilitate business decisions that factor in customer value. In their discussion of how to achieve this goal, they also present a few use cases that embrace this hybrid intelligence.

In most enterprises, business processes are automated in isolation, creating “automation silos” — a major barrier to realizing the fuller potential of enterprise-wide integrated automation. In their article, Aravind Ajad Yarra and Danesh Zaki address this issue. They differentiate between first- and second-generation smart automation and identify key imperatives to ensure desired integration across an entire business process. Furthermore, they present a detailed architecture for, and a pathway toward, smart automation 2.0, which enterprises can adopt to enable their automation bots to cooperate across the value chain

Namratha Rao and Jagdish Bhandarkar outline the concept of intelligent auto­mation using AI, ML, and RPA. A case study from the financial sector highlights the benefits gained through RPA. The authors explain how an intelligent bot can be trained and deployed over a period of a few months, and they emphasize establishing a roadmap, applying the right security measures, and setting up robust governance as three key tenets for scaling automation.

Joseph Byrum describes an intelligent enterprise as one that embraces AI to guide all its functions and decisions, small or large. However, this business is not run by the all-knowing, utopian artificial general intelligence (AGI) that science fiction writers and some commentators envision, which is a distant dream. Rather, it is an enterprise run by augmented intelligence — humans using AI and decision support tools that are enriched to the extent that is currently realistic and feasible. Byrum discusses the advantages of enterprises embracing augmented intelligence but cautions that making the entire enterprise “intelligent” requires concerted effort.

We present in this issue of CBTJ a set of five articles that provide actionable insights on topics of current interest to professionals and executives. We hope the articles inspire and encourage you to harness advanced automation in your domain of interest.