Digital Strategy, Operating Models & Technology Implementation Insight

Expert guidance in business technology strategy, leadership, and implementation in response to digitally-driven disruption of traditional business models. From emerging new operating models to strategies that put data at the heart of your business; overcoming cultural hurdles to what makes a digital leader; achieving enterprise agility to creating a culture that supports continuous experimentation — you’ll be on the cutting edge of the factors that are critical to successful digital transformation.

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A major player in the transportation and logistics industry recently conducted a digital shift over an extended period. The program was initiated by the IT department, with the clear goal of making the organization more digital and finding its digital equilibrium. This introduces a key question: is the IT department really the best place to start when it comes to digitalization?
Although many organizations have developed digital strategies, far fewer have managed to implement them successfully. As we explore in this Executive Update, creating a “sense of urgency” is often seen as a top challenge for digital transformation due to general unawareness of the opportunities and threats to to the core business. Furthermore, many organizations consider a lack of skills and competencies as major challenges on their digitalization journey.
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.
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.