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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If the ultimate goal of artificial intelligence (AI) is to efficiently replicate and exceed human thought for the good of humanity, then building trust requires that AI incorporate the multitude of sound human decision capabilities. Just reading this statement makes it clear that the journey toward AI ethics is no easy road.
In this Advisor, we explore three critical technologies that organizations are interested in using to support their enterprise IPA initiatives: natural language processing (NLP), intelligent virtual assistants/smartbots, and intelligent optical character recognition (iOCR). This data is based on a survey recently conducted by Cutter Consortium of how organizations are adopting or planning to adopt IPA.
As industries have become completely unpredictable, modern strategic planning requires distinct skills to determine competitive environments. This Advisor explores how troubleshooting can help you identify the competitive environment for your industry, how strategy is the design of your solution, and how your enterprise is the implementation of that solution.
Ben Porter uses several case studies to show how organizations have made progress in amplifying the value of analytics by demonstrating three actions: recognizing how value is created, focusing on delivering that value, and understanding the changes that must be adopted to ensure long-term value. He describes the four fundamental requirements for successful analytics projects (sponsor, tools, team, and project/problem) and closes with the critical assertion that value creation from analytics requires teamwork between IT, business, and analytics professionals.
Michael Papadopoulos and Philippe Monnot take a deep dive into ML projects. They address the “very powerful tendency to anthropomorphize ML and AI, imbuing it with human characteristics.” As we increasingly describe them in human terms, we often fail to make a critical distinction in the way humans and machines interpret the world.
In this Advisor, we discuss how AI impacts the teaching and learning experience and the quality of education. We also briefly explore a number of AI technologies that are being applied in education to achieve these advances.
The authors look at how to achieve robust analytics through four lenses. They begin by identifying effectiveness as the first hallmark, echoing the previous critical objective around finding an important business decision to be solved in order to deliver relevant value. They describe efficiency as the next critical element. The authors' third criteria for robust analytics is around minimizing risk by monitoring threats and opportunities in both internal and external environments. Finally, they describe ethics as the fourth tenet of robust analytics.
Frank Contrepois discusses human emotions and our connections to data. He shares how some companies manage to convert data into money and how other organizations can learn from and potentially replicate the approach (or at least the outcome). But this approach comes with some warnings, which he outlines. Contrepois closes by supporting this point: focus on a defined business problem and act by building an analytical model that improves decision-making confidence.