Governance is about what decisions need to be made, who gets to make them, how they are made, and the supporting management processes, structures, information, and tools to ensure that decisions are effectively implemented, complied with, and achieving the desired levels of performance. This requires that the accountabilities and responsibilities be well understood and clearly and unambiguously assigned, the reward system be aligned, and relevant performance metrics be in place.

There are major differences in approach and philosophy between the project and Agile “no project” management factions. However, organizations can use both tactics in harmony. Congruence can be achieved when an effective program/product management infrastructure is put into place and it is architected with Agile methods in mind. Such an infrastructure melds the two approaches and philosophies so that teams can work in such a way that both flexibility and control can be exerted as development proceeds and software products are released. 

Artificial intelligence (AI) is moving into public sector agencies — at both the state and federal levels — for use in such general applications as compliance, records management, community development, and finance, among others. Governments are also applying, or researching the use of, AI for other applications, including optimizing access to services and analyzing and predicting the likelihood of environmental disasters. This Advisor provides some examples of some of the many government research efforts underway that are intended to advance AI’s capabilities beyond its current limitations.

In this, the final Advisor in a four-part series, we explore the third lens of organizational behavior — capability: We “have the ability” to do it. We also examine the importance of language and its impact on culture in the digital transformation transformation.

Compared to software, industrial delivery takes longer, is more complex, and requires a broader set of skills. This series on industrial Agile opens with an overview of a framework for industrial agility, and considers these questions:

What is new in industry when Agile principles are applied?

How do the different frameworks of Lean, Agile, Scrum, and Six Sigma fit together?

Systematic, repeated breakthrough innovation often poses a challenge to large companies because it is inherently risky and frequently requires competencies and approaches that divert from the mainstream of the organization. In many large companies, internal bureaucracy and red tape tend to stifle the required creativity, and internal R&D teams may struggle to think sufficiently “outside the box.” Creating a stand-alone, semi-independent breakthrough team focused on step-out/adjacent opportunities or grand challenges is a common first step that companies take to address these barriers.

In this Advisor, the authors explore how two fundamentally different paradigms — Agile architecture and architectural agility — can reinforce one another. Based on a real-world case study from a US technology company, the authors describe how one organization has combined architecture with Agile thinking and methods to break through the anti-patterns and improve its results.

An awareness of granularity and context in analytics is vital for creating value to the business. In this Advisor, I focus on understanding the degree of granularity of analysis and how organizations can incorporate granularity in their analytical solutions.

In this Advisor, we examine the organizational behavior lens called pathway: We know “how” we need to do it. We also explore the importance of applying Agile to manage day-to-day activities, and transition states to manage the longer term and ensure delivery of the broader transformation objective.

There are more people who work on improving organizational agility than people who know why this should be required. How can you work on something when you do not know why you should do it? So, before working on transforming our organizations to become more agile, maybe we should discuss the business environment and make sure everybody in the organization sees the same one. Then we should look at our internal organizational environment and make sure everyone has the same expectations and the same perceptions of it.

In this Advisor, we propose that by architecting for antifragility, businesses can gain real agility and deliver systems with a higher level of quality.

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.