Digital Strategy, Operating Models & Technology Implementation Insight

The growing availability of product, service, and ven­dor choices across the board means that organizations need a balance of metrics to provide accurate, timely insight for decision making and continuous improve­ment.

As a fast-evolving area, AI presents innumerable opportunities and applications that we haven’t even imagined yet. This issue of Cutter Business Technology Journal discusses the current factors and considerations surrounding AI today and take a look at where trends might be heading in the future.

Jayashree Arunkumar outlines how five AI trends are being slotted into real-world use, including graph-accelerated ML, generative AI, edge AI, artificial general intelligence, and coding. Arunkumar then examines how AI is helping the environment by accelerating the pace of delivering on the United Nation’s Sustainability Development Goals and how it might apply similar tactics to help improve world health. The article closes with four of the most recent AI developments.

This article looks at potential applications and impacts of AI on education. AI can help students receive personalized lessons, pro­vide educators with deep insights into students’ learning styles, revolutionize skills improvement for professionals, and lower the cost of education. The authors present the AI technologies being applied in education and then describe the platforms and applications now available.

Michael Jastram outlines the four trends driving product complexity and explains how AI has the potential to help us overcome the limitations of current develop­ment approaches. Both systems engineering and Agile struggle to keep up with today’s exponential growth in complexity. Model-based systems engineering (MBSE) was built to address complexity but requires a large up-front investment and frequently meets with cultural resistance. Jastram advocates for AI-based solutions that offer some of the benefits of MBSE without the need for long, expensive training processes. Regardless of the exact path, he’s excited for the coming years, saying ready-to-use solutions like IBM’s Watson barely scratch the surface of what’s possible.

Paul Clermont dives straight into the three overarching issues related to AI: unintended consequences, unintended bias, and privacy. Clermont offers no-nonsense advice for dealing with these issues, advocating for laws that make organizations responsible for the algorithms they use (whether bought or built) and prohibit unexplainable AI in applications that could harm people physically or affect their lives in significant ways.

This article presents the keys to achieving trust in AI. The first step is building cross-disciplinary teams. Then we must impart AI with emotional intelligence, which involves not only trans­parency, but also explainability and accountability. Eliminating bias and ensuring fairness must, of course, be in the mix.