While standard security practices still apply for automation initiatives, there are specific security practices to keep in mind for automation design.

Two major challenges hindering organizations from successfully implementing their customer experience (CX) management initiatives include difficulty defining and mapping just what the customer journey/experience should be and an inability to provide a seamless CX across their different channels. To tackle these issues, organizations are adopting customer journey mapping tools and service blueprints — two different but complementary practices.

APIs have moved beyond system-to-system integration and utility. They have now expanded to focus on solving problems for customers, the workforce, and suppliers/partners. So how do enterprises begin to evolve their API platform?

We are increasingly hearing about the role of the CCO — the chief customer officer — who has the authority to ensure that the organization provides a unified and seamless customer journey/experience across all customer channels and guides the adoption and dissemination of CX practices and technologies. But just how standard is the role of the CCO among organizations?

No matter where you are in your journey or what your stance is on digital architecture (is it real or just story?), this issue of CBTJ will challenge your worldview and challenge each of us “to work as the scientist rather than the consumer of the scientist’s work,” building and testing our own theories in the laboratory of our own experience.

Complex technology projects require a pushback 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.

What is the new new thing in artificial intelligence (AI)? What new methods or approaches have been invented? Based on my research, it appears that the real news in AI is processing: advances in computer speed, more solid-state technology, small embedded neural networks, and multilayer neural networks. So what does it all mean?

As artificial intelligence (AI) becomes more visible as a corporate strategic tool, organizations will have to incorporate issues surrounding AI as part of corporate strategy. In this Advisor, the authors examine some best practices for the successful implementation of AI initiatives.

In this Advisor, the authors examine prevailing concepts of trust in the context of AI applications and human-computer interaction. They emphasize that trust building is a dynamic proc­ess and outline how to build initial trust in AI systems.

Data democratization means providing equal access to everyone — level­ing the playing field between parts of the organization so that all parties can get access to the data. Data democratization is also a recognition that today’s economy is truly an information one, filled with information workers — and information workers need information.

This Advisor is a call to pause — to ponder where architecture has been and where it is and to provide a framework within which to do that.

Emotion recognition is an evolving technology that is still in the very earliest stages of enterprise adoption. However, commercial providers now offer platforms and services utilizing emotion recognition, and we are seeing end-user organizations express growing interest in using the technology.

This Advisor describes what an organization needs to develop and implement the organizational skills and capabilities associated with big data.

To incorporate a software delivery capability, your team should thrive on adopting new technology, practices, and processes. There are three primary ways companies can do this effectively, as we explore in this Advisor.

Organizations are interested in adopting several technologies to support their customer experience initiatives. This Advisor considers two specific areas: personalization and omnichannel customer engagement and marketing platforms.

Similar to the semantics of user stories, which always express change, it is beneficial to describe architecture as a series of changes — structural changes through time. The smaller the gap between the architecture representation of the architecture and its realization, both in expressiveness and timing, the more relevant the architectural changes.

Artificial intelligence (AI) and machine learning (ML) has vulnerabilities that can create serious consequences for lives and property. In this Advisor, we investigate AI’s vulnerabilities, examining where problems can occur in development and application.

In this Advisor, we discuss the social and emotional cognitive aspects of proj­ects.

While the capabilities of artificial intelligence (AI) are incredibly useful and offer business and government the opportunity to solve knotty problems, a common question is, “Does this capability pose threats to humans?” Are the sci-fi scenarios of malevolent robots or killer software pro­grams a real concern?

In order to implement continuous integration and testing concepts, consider the seven Agile approaches presented in this Advisor.

The emerging nature of smart automation technologies requires a holistic architecture-centric design. The business must address design considerations such as trust, transparency, security, and bias to create successful smart automation. In this Advisor, we explore efficiency and continuous learning considerations as well as compliance issues associated with the use of smart automation technologies.

The ultimate success of artificial intelligence (AI) will depend on how well society is prepared for its widespread application. This, of course, raises a slew of social, economic, legal, privacy, and ethical issues and challenges that must be seriously studied and addressed.

Sustainability means bringing new functionality to the market while keeping your underlying software assets healthy and nimble. To achieve such balance requires a form of data-driven design and decision making that balances commercial and technical con­siderations. In this Advisor, we review what is needed in terms of data, design, and decisions.

In this Advisor, we discuss several steps senior leaders and managers need to take to avoid the curse of ignoring the need for transformation that afflicted incumbents like Borders and Blockbuster.

Cognitive computing provides the underlying “engine” that, when coupled with the business knowledgebase, serves as the technology enabler behind the evolution of the cognitive enterprise.