Business Transformation Requires Transformational Leaders

As we explore the idea of “making a digital shift,” it’s important to examine the ways to keep up the momentum and stay on track in managerial, not technical, terms. The premise is that, as with a paint job, meticulous preparation is essential to success. From the earliest days, partial successes and outright failures litter the history of digital shifts, with write-offs running into 10 figures on some government projects.

The myth surrounding Agile projects goes something like this: a small team of developers who can handle any coding task (database, business logic, user interface, middleware, etc.) works hand-in-hand with the end user who talks with the development team about the details of the work requirements. The small-team-filled-with-generalists model may work for some small projects, but it doesn’t scale. The problem has been with confusing two parts of the traditional development problem: collaboration and specialized skills.

In this Advisor, we take a closer look at another type of important COVID-19 data: secondary data, which can help with future pandemic predictions.

This edition of The Cutter Edge discusses why disruption is needed to keep agile alive and relevant, identifies five key factors essential to delivering value and realizing a digital shift, and more.

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.