Strategic advice to leverage new technologies

Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.

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Mark Greville proposes an alternative to the command-and-control theater that is governance (particularly technology governance) in most large organizations. He offers examples of business-model-assassinating decisions from previous generations and lays out a path toward a scalable, sustainable, useful governance approach that avoids the bureaucracy typically associated with governance. The article explores decision dynamics and proposes the method of public self-governance to break up complex governance structures, eliminate governance body queues, accelerate change, and drive accountability and transparency via a modern, decentralized approach.

Dinesh Kumar comes at digital architecture from the perspective of business capability maturity: the readiness of any organization is a function of the maturity of a set of digital business capabilities. He goes on to describe the DigitalCMF, including the business capability domains, the digital business capabilities, and various assessments and tools within the framework. He outlines a roadmap using capability engineering as a way forward to assist organizations on the journey to a digital future.

Barry M. O’Reilly calls on us to rise above the hype, myth, and storytelling that have created the concept we call “digital architecture.” He proposes that the concept is part of an ongoing storytelling process that we as humans use to understand and navigate our world; digital architecture isn’t a real thing, it’s just part of a story to help us find our path. O’Reilly cautions against adherence to dogma and the slavish belief that copy-and-paste frameworks can solve our problems. He counsels that we should recognize that we are in an infinitely repeating cycle of hype.

Simon Field integrates business capability modeling into SARM, a formal method for developing and evaluating competing designs for solution architectures. In this article, he shows how this technique can be used to build competing designs for “digital services.” SARM focuses on architecturally significant requirements, as these are most likely to be difficult (and expensive) to change once enshrined in the architecture. The framework uses business capabilities as a way of expressing functional suitability, which introduces a layer of abstraction difficult to achieve through other means.

John Murphy proposes some practical steps to resolve the communication difficulties that still plague transformation programs. He proposes business capability modeling as a way to create shared understanding and bridge the worlds of business, process, and technology information encapsulated in business capabilities.

There appears to be a new school emerging when it comes to digital architecture; one that embraces the complex and the uncertain. Some of our authors in this issue of CBTJ would certainly identify with that school. Proponents of this new school are building in areas of common concern — systemic resil­ience, critical thinking, and mental models — and are introducing variety, design tooling, and governance models. Each is attacking a systemic issue via experimentation, letting reality be the judge of what’s useful and what should survive.

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.