Financial institutions have digitally transformed their business processes and products, creating vast sources of structured and unstructured data. AI offers the means to complete this transformation in radical ways — across the front, middle, and back offices, while also addressing the big data problem. In addition, AI is also shaping the fintech and regulation (“regtech”) landscapes, particularly in addressing what has become known as “Big Regulation.” However, AI’s promise must be balanced with current limitations to the application of enabling technologies like machine learning (ML) and natural language processing (NLP). This Advisor looks at the promise, potential, and limitations of AI in the financial industry.

Being able to show that your organization actively considers and cares about its impact on the world is sure to become one of the most important levers for talent and investment attraction and retention. Expectations now go beyond having great-sounding values on your website; people want to practice those values every day and be proud of what they are creating. Investors want to know that they can count on pro­active mitigation. Users want to see those values in the products they use. Consequence scanning requires that participants know what the product or service is intended to do, be able to determine whether the potential consequences of the product or service are positive or negative, and know what their organization considers positive.

Scientific-based modeling systems contain extraord­inary amounts of data and produce mountains of output. These systems offer almost limitless options for providing the user with informed knowledge. This Advisor seeks to use the concept of technology embeddedness to delineate factors that are prohibiting emergency managers from harnessing the capability of scientific modeling systems when responding to disasters.

This Advisor explores the complexities and challenges of big data–based analytical processing and storage.

Leveraging visuals is particularly important for a business architecture practice. Visuals matter, and for a business architecture practice to be effective, it must connect with people on a human level and in ways that build true under­stand­ing. Graphic recording is the visual live capture of content for an event or meeting, which acts as a visual record of the session. With this technique, there is little to no interaction between the graphic recorder and the speaker and participants. Graphic facilitation also includes visual live capture of the content for an event or meeting, but the graphic facilitator serves as a guide throughout the entire meeting process. These visual techniques bring people together to co-create around a specific topic, challenge, or opportunity. They allow people to “see” their thinking and shape concepts together. 

The field of robotics has benefited considerably from advances in various artificial intelligence (AI) technologies — most notably deep learning neural networks, computer vision, intelligent guidance and control systems, and voice and speech recognition. The biggest advances are being realized from developments in deep learning algorithms and machine vision technologies, which are allowing the creation of robots featuring advanced autonomous navigation and intelligent object recognition capabilities.

The growing prevalence of the Internet of Things, together with plummeting component costs, has made it possible to connect just about anything, from the very simple to the very complex, and to offer remote access, sensing, control, and monitoring. These technologies make it possible for healthcare providers and patients to work together to improve health in novel ways that were previously unimaginable. A critical element in this new model is that focusing on what is happening with the patient when they are not in front of a health pro­fessional, using sensors to deliver remote monitoring and a more complete picture of an individual’s health, is more likely to have an impact than focusing on the brief amount of time spent during in-person medical visits.

Large, non-software companies introducing Agile to their organizations tend to suffer from a cognitive dissonance of sorts: we would like to have the same look and feel across the entire company, delivering stellar-quality products, yet we want to enable high-performing, self-organizing, self-managed, and self-empowered teams to deliver (or demo) at the end of each sprint. In this Advisor, we summarize one area where this conflict becomes especially evident for large companies, particularly with non-software teams: process misalignment. We also share a potential solution that we’ve seen work in industrial practice.

In complex technology projects, dissent involves pushing back against biases, oversimplifications, and the need for certainty that will inform many proposed solu­tions. The role of dissent is to harden and strengthen these proposals and to identify the right course of action among them. Dissent is what provides another view and forces a team to step back and consider another reality; the more often a team presents dissent, the more likely it will explore the complex interdependencies that define modern enterprise technology projects.

In this Advisor, I have chosen the simple word “change” as a starting point for thinking about architectural change, rather than many of the others in rampant use — words such as “transformation,” “permutation,” “enhancement,” and so on. This choice seems appropriate because “change” is an abstract, neutral, and descriptive word without the deeper connotations that might impede the nuancing of this notion into something of a practical, if rudimentary, taxonomy that will help us grapple better with the different kinds of changes that we are compelled to deal with.

Although recent advances in computing user interfaces for decision support tools make the tools much easier to learn, understand, and manipulate, some decision makers may be reluctant to adopt and use a new decision support tool. Potential users with greater IT knowledge and expertise often find it easier to learn new systems than those who are infrequent users and hence lack knowledge and expertise. Thus, developers should strive to build a decision support capability that targets potential users, matching the design to user needs, abilities, and skills.

In statistical project management (SPM), we simplify the project management approach by eliminating many concepts that the dominant project management methodologies consider central. Objects represent a repeatable thing that non-IT people can wrap their minds around. They are supposed to be concrete, like a balance sheet report in an accounting system or an employee demographics data-entry Web form. Since objects are supposed to be repeatable, project managers and the IT organization would find it very helpful to know how long, on average, it takes teams to create and operate related objects. Thus, objects become an important list of deliverables and one that is crucial to estimate accurately. Objects represent, from the user’s perspective, the list of things that are delivered to them — a kind of a bill of materials.

The financial industry is currently focusing on AI solutions using ML and NLP technologies. Software, fintech, and regtech vendors are developing and improving models using data and supervised (training) and unsupervised learning. This proves more effective than previous AI approaches. This Advisor explores the current state of practice in the financial industry’s application of AI.

Agile is spreading and changing at such a rate that we have devoted a second issue of Cutter Business Technology Journal (CBTJ) to the topic.

Software architecture requires balance. During the 20 years I’ve been leading technology organizations to build products, mostly via Agile, I’ve learned some rules that have helped me — and my teams — successfully strike the right balance. These aren’t technically focused rules; they’re more generic, so they apply to monolithic, layered, service-oriented, and microservice architectures equally well. One of these rules is the subject of this Advisor.

According to our latest research, one of biggest issues impeding organizations from carrying out their customer experience (CX) management initiatives is a lack of CX professionals within the organization. So how are organizations meeting or planning to meet their CX implementation needs?

Design thinking is an elegant framing of problem-finding and -solving with a strong focus on delightful outcomes for the customer, while Agile practices focus on delivering the value envisioned in the design phase. This implies the two are equally essential to the team’s creative process. This Advisor describes the four steps in creative problem-solving that comprise the building blocks of innovation.

Many companies are unsatisfied with their innovation efforts and part of this is undoubtedly due to challenges around ideation and idea management. The required contribution of breakthrough innovation is ever-increasing, adding to this pressure. However, it is clear that some companies have developed strong processes and are reaping the rewards. The best practices outlined in this Advisor can help.

Recent research has shown that creating an Agile approach to designing for the con­sequences of technology is the best way to start building successful businesses that use responsible business practices to contribute to a better world. This Advisor explores a new Agile event called “consequence scanning.”

Smart automation is most effective when humans and machines work together to deliver desired outcomes. Effective design for automation is not only about how much can be automated but should also consider how automation works together with humans to deliver value. Several fallacies observed in smart automation initiatives across indus­tries can lead to failed initiatives that never see the light of day or that never deliver the promised outcomes. One fallacy that has led to such failures is the belief that all human activities can be automated.

An ongoing challenge with big data and other secondary analytics initiatives is getting access to data. This Advisor describes a risk-based approach to de-identification, in which the data is transformed and administrative and technical controls are put in place.

The road to automating software development is long and full of twists and turns. No doubt, there will be potholes and detours along the way. However, getting to the end goal now seems possible, if we travel a short distance at a time.

In this Advisor, we describe some of the more popular cognitive development platforms and services that are currently available. Although some cognitive development platforms are available for on-premise deployment, the ongoing trend is for providers to offer their cognitive products in the form of cloud-based environments that give companies the opportunity to license various API-based cognitive services for use in their own enterprise applications or commercial products.

This Advisor provides an overview of the importance of using visual techniques as part of a business architecture practice and highlights the use of visual design.

For most organizations that have deployed customer experience (CX) practices and technologies, it is still too early to tell if their efforts have actually allowed them to deliver a better customer experience. However, for approximately 31% of the organizations that have deployed CX practices and technologies, their initial efforts appear to be paying off. These findings come from the preliminary results of an ongoing CX management survey we are conducting.