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.
Recently Published
This issue of CBTJ looks at three previous AI "waves" and helps us to get up to speed with the current state of AI and to think about some of the issues raised when we look beyond systems that appear to work as intended.
Given the distributed nature and the potential uncertainty of the elements contained in any blockchain event, validation is an essential component. In this Advisor, we discuss how blockchain could improve trust in, and dependability of, the business process under automation.
This Executive Update offers an approach for calculating ROBAI to help business architecture teams better demonstrate their value and impact.
In this Advisor, we examine the three conventional essential security requirements — confidentiality, integrity, and availability — which present somewhat different issues in the age of Industry 4.0.
Cutter Consortium Senior Consultant Paul Clermont describes some of the impact that AI has had at the boundaries of commercial organizations and public policy in an article aptly entitled, “Who Knew THAT Would Happen?” Those of us who have experienced unintended consequences of other technologies will want to answer “anybody” but should remind ourselves that some may not have the memory of prior years, and that hindsight is perfect. Clermont explores how to identify possible unintended consequences in advance and proposes countermeasures to negative unintended consequences in the form of design principles and public policies.
Cutter Consortium Fellow Lynne Ellyn recounts her experiences with AI technology in the real world, surveys the current landscape, and identifies key nontechnical issues that companies are likely to face when deploying AI-based systems.
As AI becomes more visible as a corporate strategic tool, organizations will have to incorporate issues surrounding AI as part of corporate strategy. Pavankumar Mulgund and Sam Marrazzo help us by providing a framework for developing an AI strategy. The authors discuss the “minimum viable model” approach to the development of the underlying AI/ML models, along with the platform on which these models run and the inevitable tradeoffs. They conclude their piece by examining some best practices for the successful implementation of AI initiatives.
One way of getting an off-course system (or person) back on track is by nudging. This concept can be particularly useful in goal-directed systems. But, to reiterate, errors will occur. In his article, Richard Veryard describes technologically mediated nudging; the possible unintended consequences; and the need to consider the planning, design and testing, and operation of the system for robust and responsible nudging.