AI’s disruption has yet to be felt in the workplace, but there are waves of changes coming our way that will alter the way we work as well as the type of work we do.

Many EA efforts start with an exercise to create a vision and mission statement for enterprise architecture. These sessions often consume considerable time with a group of people thinking about the definition of EA and discussing details of frameworks and practices. The result is typically called an EA charter. While some of these efforts are useful, others are not. In this Advisor, we consider the value of the EA charter.

We believe that at the heart of the ability to manage an ongoing and multilayered organizational transformation rests a sophisticated enterprise architecture capability with a specific charter to act as a transformation engine connecting strategic intent and execution excellence.

Designing cognitive computing systems (CCSs) requires a strong case for the investment into those systems. Organizations must not only be able to justify the initial investment into developing a CCS, but also think through the investments that will be needed to ensure it can be refined and enhanced over time.

I’ve had the good fortune for decades to work with project managers in companies ranging across many industries. From these experiences, the best project managers I’ve worked with seem to have the following traits.

There is perhaps only one thing more crucial to secure than money: information. The heavy burdens associated with securing the authenticity and history of data are well-known to several sectors.

Establishing business architecture within an organization takes passion, persistence, and patience. Inspired by over a decade and a half of helping organizations to mature their practices — combined with personal mountaineering experiences — this Advisor shares a few lessons for conquering the “business architecture summit” using mountains as metaphor.

In this issue, we examine some of these questions along with the drivers of AI global trends and their impli­cations — now and in the future. Our contributing authors provide insights on key opportunities, strategies, and approaches for realizing AI’s potential and discuss emerging issues and concerns, including how AI may impact jobs and businesses.

Based on responses so far, an ongoing Cutter Consortium survey on the adoption and application of AI technology provides some insight into the issue of enterprise AI adoption trends.

“The purpose of a system is what it does” — referring to “system” as the company as a whole — means that a company’s statements of intent (“we are an innovative, digital native company”), or even its market analysis or the initiatives it has undertaken, are secondary to what a company actually does.

The industry is ripe for transformation in areas including customer service and marketing, claims management and fraud detection, and underwriting.

This Advisor explores one approach that has been particularly effective in making enterprise architects understand the realities of organizational change in their own context.

Digital transformation is commonplace in today’s economy. Digital transformation leaders and enterprise architects have a choice to make in developing their digital backbone. The digital backbone can be an asset in ensuring that digital transformation efforts are carried out in such a way that they are in align­ment with the enterprise and its approach to transformation. 

A strategic approach around big data not only includes the multiple analytical, architectural, project, and technical elements in a synergistic manner, but also pays due attention to the financial and people aspects, resulting in business value.

In this Advisor, we consider what leaders are expected to do within Agile organizations, then see how these duties translate to a set of desired skills and personality traits.

The existence of a digital backbone in an organization means that anyone aspiring and planning to transform different parts of the enterprise will be able to leverage the digital backbone in a consistent and sustainable way, ensuring that each effort connects to and leverages a common platform. 

Even radical approaches to change management, such as business process engineering or Lean, assume there is a desirable endpoint (however temporary) toward which change is directed. However, what if change happens so continuously that no fixed endpoint exists for any initiative, but instead organizations must constantly change, not just to succeed, but even to survive?

As you think of the possibilities for using AI to redesign auto business processes, remember this about today’s AI systems: they use techniques that analyze large amounts of data, looking for patterns. With new techniques, they can increase the data available for use, but they need data to gain experience.

Governments are starting to embrace the possibility of creating their own cryptocurrencies.

As a second-order effect of the Internet, digital transformation can be broken down across the three major areas that it will transform: customer experience, operational processes, and business models. Although transforming the customer experience is the most visible manifestation of digital transformation, as I discuss in this Advisor, transforming internal processes through digitization, worker enablement, and performance management can also show great benefits.

Both the explosive growth of Agile adoption and the return to collaborative and experiential learning are part of a larger global transformation whereby people are seeking to cast off authority in exchange for autonomy and peer collaboration.

There is an ingrained optimistic spirit infusing the enterprise, creating a bias toward action, toward change, toward better things. And, a resulting need for speed: speed to market, shorter cycles, quicker turnaround, and more throughput. Faster, faster, faster! More Agile. But speed kills. Even avid practitioners of Agile — if we pay attention to some of the current conversations — are generally of the view that Agile is not merely about speed, and that breakneck speed can break necks and more, if “Agile” is simply an excuse to hurtle mindlessly into space.

Volume, value, and velocity are all essential in digital transformation. This Advisor looks at this V-cube service delivery model.

This Advisor discusses the risks and challenges of implementing prescriptive analytics in the context of machine learning.

In many market sectors, the journey to become more Agile is a strategic imperative. This Advisor looks at some of the reasons some organizations find this transition difficult. These challenges point to the practices, processes, and cultures of the past as potential hurdles.