As with any new technology, there are numerous myths about robotic process automation (RPA). The most prominent myth — the one that has created fear among the modern workforce — is that RPA will replace humans. This myth does not hold true; RPA implementation actually works toward the betterment of the workforce and workplaces.

Although some jobs will disappear during the AI transformation, AI was not created to eliminate jobs but to automate mundane or time-consuming tasks. This will complement and empower humans to be more effective and efficient at jobs that require human insights and judgment. Organizations that fail to pay attention to the AI digital transformation are putting themselves at risk. Employees need to understand this reality and view this required personal transformation as an opportunity to grow and tackle new opportunities. Today it is no longer a matter of what you know, but how fast you can learn and adapt.

One of the techniques people in the Agile community argue for is retrospectives. A retrospective refers to a meeting held at the end of an iteration where teammates reflect on what they experienced and recommend improvements. It is an important tool because it allows the team to take advantage of their lessons learned. The bottom line is that organizations need to put processes in place to facilitate sharing. Besides being easy to use, developers need to be motivated to use these processes, or else the databases that are provided will remain unused.

Architecture stories are important to articulate so they gain visibility. For example, if you have a feature idea that you think a user would value, you might define a minimum viable product for it and whip up a quick/cheap prototype before making a final implementation decision. If the feedback isn’t positive, then you’d quickly pivot in another direction. The point is that I want the same level of thoughtful planning to occur for architecture as for features.

How can we identify and verify the often hidden working cultures that describe the rules of engagement used by stakeholders in all parts of the organization on a daily basis? This Advisor provides some answers.

To meet increasingly elevated customer expectations, organizations are implementing detailed strategies for distributing and standardizing customer experience (CX) practices and technologies across their various lines of business. In this Advisor, we explore the five most significant challenges organizations face in implementing CX strategies and supporting technologies.

Nowadays, there is a huge popular demand for Agile as a means to enable change and accelerate value. Popularity, however, is something other than reality; for most companies, the introduction of Agile requires a significant mindset shift. This almost always meets resistance from several directions in the organization. In addition, Agile adoption is often accompanied by some element of inefficiency and chaos if left unguided. The authors of this Advisor describe some specific ways organizations have combined architecture with Agile thinking and methods to improve their results.

Fintech firms are bringing to market a variety of technologies to introduce new products and compete with existing firms. However, the financial markets have a long history of regulation, meant to protect depositors and stakeholders and maintain the safety and soundness of the financial system. The industry must begin by creating an experimental space (a small market), where technology and its related products can be introduced. This is known as a “sandbox.”

Most organizations do not begin creating and leveraging business architecture until they have reached a certain size and start to encounter challenges such as poor customer experience, complexity within the business and technology environment, or ineffective strategy execution. However, if organizations instead employed business architecture from the very beginning — even in a very lightweight manner — they could likely avoid these challenges as they scale.

The year 2019 will be a time of perpetual technological innovation. A plethora of technologies will continue to evolve, improve, and become more widely adopted, with a growing set of new applications strategically applied across industries. We hope that this issue of CBTJ will help you prioritize your business technology objectives and chart your journey forward in today’s digitally competitive world.

Today’s chief data officer not only needs to rethink the relationship between data producer and data consumer but must become intimately familiar with the new requirements for predictive modeling (to unravel scenarios and identify patterns), advanced query (to follow an idea into discovery), and data visualization (to understand interconnections) — the big three for data analytics.

The technologies of this era — the postnormal era, the fourth industrial revolution, or any of its other synonyms — have accelerated society to such an extent that we have been, in effect, colonized by the future. Work is being futured at a pace driven by the advance of our fastest technologies and the consequent blurring among digital, physical, biological, and even cognitive systems. 

With twice the product in half the time™ being a generic goal for industry, what exactly is going wrong in today’s industrial environment? What stops or delays improvements in product delivery, despite embracing practices from Lean and Six Sigma? Why does it take years to get a new product out of the manufacturing plant? This Advisor seeks to demystify one of the myths that surrounds Agile product development: the myth that one way to optimize people’s time is to have them work on large batches: design the whole product, build a full prototype, test a full prototype, and design manufacturing only when the prototype passes all tests. The thinking is that this will reduce task-switching, eliminate mistakes, and achieve the desired high utilization of people. By planning the work in phases, we believe we can prevent problems in the phases that follow.

Combining the fundamentally different paradigms of Agile architecture and architectural agility can prove to reinforce one another. In a real-world case study from the consumer sector, this Advisor describes one company’s experience combining Agile and EA.

As part of a Cutter Consortium survey, we wanted to gain a better understanding of how organizations are currently designing and implementing their artificial intelligence (AI) applications: whether they are designing and building their applications in-house or whether they are also turning to AI consultancies to assist them with their efforts.

Architectures that combine blockchain and AI offer some exciting possibilities, not only for DRM, but for other applications as well — like supply chain, financial, and trading systems. These include those systems that need to provide responses to events that require a higher form of reasoning than can be practically performed using blockchain’s smart contract functionality alone. Researchers are now working out the particulars of such applications, and we hope to see commercial platforms emerging over the next few years.

In this Advisor, the authors use the case of a fictitious financial company to take a detailed look at fintech and marketing and how a company can leverage big data, analytics, marketing, and financial services. 

One of the keys to a solid architecture is considering how your organization will test it. This is true not only from an end-to-end perspective, but also when considering areas like usability, performance, security, reliability, and resilience. You must make these investments in quality and testing transparent to your stakeholders and help them realize their value proposition.

When people speak of capability assessments, they are referring to capturing and maintaining various metrics about the capabilities in their capability map, which can then be assessed to identify improvement opportunities and make such decisions as where to invest. Capturing these attributes provides a more complete view of each capability, beyond mere definitional information, making capabilities much more useful in practice. Organizations can use capability assessment information in a variety of proactive or reactive scenarios.

It’s human nature for people to do whatever they can to avoid threats to their well-being. This constant vigil includes changing organizational culture to fit within personal comfort zones. Consequently, the stakeholders in an organization do certain things in certain ways to meet what they believe to be their job responsibilities. Their actions are based on their perceptions of the best results for them within their framework of reference with as little effort as possible. These “in practice” rules of engagement are difficult to determine and subsequently manage.

One of the key reasons organizations invoke for staying away from public clouds is security, and specifically the risk that unauthorized parties will access data. From a technology perspective, placing data in a public cloud may actually improve security rather than weaken it. We explore the four simple reasons for this in this Advisor.

We cannot measure organizational agility from a simple definition. We must break down this definition into some measurable elements and build a measurement tool around those elements. Luckily for us, many scholars and others have worked on developing such tools. In this Advisor, we explore one of them.

Several factors are leading the fintech revolution and affecting technological innovations. These include cryptocurrencies, tokens, initial coin offerings, blockchain, and other security advancements (i.e., multifactor and biometric authentication). The ability of the financial services industry to experiment with these new technologies responsibly is an essential component of their future success and sustainability. The disruption affecting innovation in the financial industry is also driven by the pervasive connectivity that characterizes today’s telecommunications sector, enabled by the diffusion of Internet access worldwide and the integration of the Internet with other sensor-based technologies, forming the Internet of Things (IoT) infrastructure.

The range of potential fintech applications includes all the vertical industries and nearly every business model and process that enables them. No industry or process is safe from the disruptive impact that fintech will have in the next five years. This month's Cutter Business Technology Journal demonstrates fintech’s importance and explores the different levels of fintech technologies, experimentation, and applications.

We have learned that the cloud has enabled a shadow IT to emerge. That sounds scary (mostly to the IT people), right? But we have also learned that shadow IT is not totally a bad thing, as long as there is communication, coordination, architecture, and governance. We discuss this shadow IT more specifically in this Advisor.