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 the core business. Furthermore, many organizations consider a lack of skills and competencies as major challenges on their digitalization journey.

In a recent survey, Cutter Consortium asked organizations whether their customer experience (CX) practices and technologies are meeting or beating company expectations. We also sought to answer the following question: can organizations that have deployed these CX practices and technologies deliver a better customer experience and journey?

Business architecture is a critical — and typically missing — bridge between strategy and execution. Organizations should leverage it to translate strategies and other business direction and collectively architect, prioritize, and plan actions to be taken from a business-driven, enterprise-wide perspective. Indeed, business architecture and business architects contribute unique value across the strategy execution lifecycle, as well as connect other teams and help them be more effective.

Transforming all the data we generate into insights requires many steps. For someone to have the confidence level to use the resulting information and insights, these data manipulations must be trusted. In this Advisor, we review these steps.

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.

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.

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.

The volume of data available within an enterprise — and externally to it — is phenomenal. As a consequence, the role of information architecture has evolved, from the passive structuring and managing of data to a smarter, more active role of information effectiveness.

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.

M&As can be rife with challenges. But with a relentless focus on using enterprise architecture (EA) to catalyze acquisitions, the challenges can be mitigated. In this webinar, Gustav Toppenberg and Stefan Henningsson look at the problem of acquisitive growth and reveal how advances in EA practices not only overcome the obstacles, but also enable value creation.

Researchers at hospitals, universities, and technical institutes are teaming up to apply artificial intelligence, machine learning, and analytics to help determine and predict COVID-19 patients’ hospitalization paths and medical needs.

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.

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.

This month's issue of Cutter Business Technology Journal (CBTJ) examines the new face of automation and explores novel ways to address the various issues and challenges encountered.

In my coaching, it is incredible how much pushback I receive on the idea of demoing your architecture. It seems Agile teams are comfortable demoing end-user functionality, but incredibly uncomfortable when you ask them to demo architectural elements.

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.

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.

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

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.