While issues around data and information governance are starting to get the attention they deserve, business and technology leaders still need help finding their way through all the conflicting demands. We invited several authors to present their perspectives and recommendations on this complex web of issues. We hoped for a wide range of ideas, and we were not disappointed.

Companies are turning to machine learning, computer vision, robotics, and other AI technologies to revitalize the retail shopping experience and boost customer experience and business benefits — both online and offline. But it is the new, cutting-edge, AI-driven applications under development that are most interesting — holding the promise of opening up new business models and possibly disrupting the retail sector.

In the first Advisor in this series, we explored enablers that we have found to be critical in driving successful digital transformation efforts. We believe these enablers can be unified into a powerful toolkit to facilitate successful transformation when assessed against three specific personas and observed through three distinct lenses. In this Advisor, we introduce these personas and lenses, offering additional insight into the human side of digital transformation.

If there is one measure that can drive an organization toward agility, it is the shift to deadline-driven smaller projects. Simply put, we recommend defining smaller projects (that can relate to each other to build a bigger project) and focusing on the deadline — not just on the results. This is part of the skill of project management with a drive to flexibility. The key idea: it is better to have 90% ready this month, on time, than 100% ready next month, which is too late.

Generally, business transformation results in designing a target business operating model (BOM), or in other words, a new “business design.” To implement the target BOM is to operationalize the business strategy. With each transformation cycle and new target BOM, there is a shift in decision making depending on changes in the organizational hierarchy and ways of working. To make sure that this shift occurs smoothly and is working as expected, it is imperative to continuously measure the BOM’s effectiveness both in terms of current and predicted performance.

The security industry has heavily embraced artificial intelligence by applying machine learning and behavior analytics in order to add real-time threat detection, automated analysis, behavior analytics, and adaptive modeling capabilities to IT security environments, targeting a variety of security scenarios, including threat detection and analysis, spear phishing attacks, endpoint protection, and virtual hacking. 

Fog computing can create a digital twin of difficult-to-replicate process. Let me explain, through a hypothetical example of a craft brewery. Unlike industrial production, food and beverage manufacturers work with natural ingredients, where the quality of raw materials can vary, making it more difficult to create a uniform product without waste. Producing a consistent quality product is critical to building cus­tomer loyalty. When it comes to products of nature, in particular, manufacturers face unique challenges.

This Advisor — one in a series of “Agile Team Tips” — describes the benefits of backlogs and backlog grooming.

When it comes to delivering effective digital transformations, human behavior is often overlooked in favor of a focus on technology. In this series of Advisors, we outline how organizations can truly engage their people by understanding their behaviors and, consequently, ensure that they undergo successful digital change.

The critical role of a business architect is to understand the business needs and design the fundamental business elements that can be configured in many ways to realize what the business wants.

Business analysts (especially those responsible for process modeling) study each business func­tion and the corresponding systems supporting that function. In this Advisor, we look at key business functions that undergo process transformation when big data analytics are embedded.

Over the last 12-16 months, we have seen more than a dozen banks and financial institutions worldwide introduce (or announce) virtual banking assistants and bots. Such apps are now available to millions of personal banking customers, and banks are also starting to introduce AI-powered digital assistants for managing corporate customers’ accounts. This Advisor explores some examples of intelligent virtual assistants and bankbots companies have introduced over the past year or so.

Data centers that support today’s cloud systems must implement extensible networks based on technology that allows them to scale in an Agile, cost-effective fashion. Market trends include customer microsegmentation analytics supported by communications service providers that leverage microsegmentation analytics to improve customer retention, revenue generation, and customer experience.

Commercial edge computing products — including hardware solutions featuring embedded analytic and AI technologies — are just now starting to become available or will be soon. Despite these hindrances, already we are seeing examples of companies developing edge computing applications.

End-to-end strategy realization requires many people to work together seamlessly across five stages; this includes teams centered on strategy, customer experience, architecture, product management portfolio management, program and project planning, business analysis, business process, organizational design, and execution. Business architecture is a relatively new addition to the ecosystem of strategy realization, but has a valuable role in all five stages.

Healthcare and medicine are ripe for applying artificial intelligence (AI), and we are seeing many projects and commercial efforts underway pertaining to using AI, including for conducting research studies along with using medical and clinical data to uncover trends in efforts to develop tailored treatment plans for patients suffering from rare diseases. 

The “one size fits all” era, where RDBMSs were used in nearly any data and processing context, seems to have come to an end.

How does a utility company connect better with its customers? Utility companies must consider strategic investments in all available tools — including soft­ware dedicated to the purpose — to increase customer engagement in and control of their products and ser­vices.

If you are with a conventional company (probably one established before 2010), it’s very likely that you have a command-and-control structure with a board dominated by shareholder representatives and operationalized starting with the CEO. There may be many levels of command, down to section- or unit-level supervisors. This structure is time-tested, beginning with the Pharaohs of Egypt! It has the advantage of clearly delineating authorities, responsibilities, decision-making roles, and accountabilities.

We call out the hardware, the software, the applications, the information, and even the business processes, as we visualize the different layers of the enterprise. However, we stop just short of defining the enterprise stack fully; what is noticeable by its absence is the most important part of the enterprise: the people.

According to the principle of yin and yang, all things exist as inseparable and contradictory opposites. In this issue of Cutter Business Technology Journal (CBTJ), we explore the relationship between architecture and organizational agility as a powerful paradox: architecture is the way to agility.

Banking and financial services companies were among the first to apply artificial intelligence (AI) in strategic applications. Initially, this took place in the mid- to late 1980s in the form of expert and knowledge-based systems for credit and loan approval and mortgage processing, and so on, and then in the early to mid-1990s, when neural network-based applications for credit and bank card fraud detection, and profitability management, began to be deployed.

Given the risk/reward tradeoff inherent in disruptive technology adoption, this Advisor aims to identify the motives, pressures, and efforts that influence continued adoption intention and usage of a disruptive technology after the initial adoption stage.

An organization is likely to base its digital disruption upon the markets and geographies in which it already operates, and the existing workforce understands the customers and needs of both the current market and the current geographies. It is therefore essential that leaders utilize their existing resource pool by providing employees with the learning and tailored development to facilitate their transition to the digital era.

In this Advisor, Sheila Cox describes how effective delegation requires determining the ability to perform the specific task at hand and using the appropriate leadership style.