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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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.
This Advisor tells the story of two similar organizations with initiatives to derive and define data models. The first organization had a business architecture in place, which included a well-defined capability map, value streams, and information map. The second organization did not.
Making an organization more data-driven doesn’t always entail a large transformation program, but it does require a clarity of vision and pragmatic joined-up thinking. To achieve all or some aspects of vision, there are four dimensions that need to be addressed in your data strategy: reach, richness, agility, and assurance. In this Advisor, we share some questions that Veryard answered at the end of the webinar.
Here in Part VI of this Executive Update series, we examine the remaining five technologies organizations are interested in adopting to support their enterprise IPA efforts.