The AI Compass: The Success of Balancing Tension, Scalability, Consistency, and Complexity

Research and innovation are frequently viewed as similar if not identical—they are uniquely different.  Moving from AI pilots to production scale demands iterative, building block roadmaps driven by requirements and altered by unprecedented rapid-cycle products and services. 

There are two words that elicit dozens of definitions and spark action across corporations and academia—research and innovation.  In our continuously iterating and rapid cycling of artificial intelligence (AI) these terms and their characteristics are used interchangeably driven by the unprecedented advancements across all spectrums of AI.  Clearly understandable given the last 18 months of prototyping and piloting AI defined industry solutions, yet when defining use cases for production-ready solutions does it matter? 

The impacts of AI across economies and the employment markets will be profound.  The IMF believes that up to 60% of labor markets in developed economies will be materially impacted—that is wages, responsibilities, advancements, positions available, and “humans-in-the-loop.”   However, across the nascent AI ecosystems of commerce and solutions, where does research end and innovation begin?  Why should we care given the rapid-cycle AI advancements announced every week?

For enterprises seeking to leverage AI the distinction may appear to be an esoteric distinction.  As discussed in “AI Reality, Meet Organizational Enthusiasm” this could prove terminal given the unique differences between traditional research and innovation approaches, objectives, measurements, and inclusions.  To understand the discrete differences, industry leaders need to do what they do best—to address their opportunities and challenges in the context of a holistic roadmap.

Yet when it comes to AI, leadership personnel are working with unfamiliar touchpoints, operating parameters, fragmented capabilities, and complex skills.  Stated differently, the old has become obsolete, and the new AI landscape is still evolving and opaque.  To put AI (and its use cases) into the proper context, leaders need to find a heading as part of a “compass” design to include AI not just for short-term benefits, but for long-term adaptability, auditability, and inclusion. 

Figure 1 illustrates a conceptual compass analogy framework that distinguishes between three major taxonomies of AI purpose and impacts: 1) external, large scale macro trends, 2) internal, strategic, and operational characteristics, and 3) the more common discrete and mandatory AI solutions that currently captivate vendors and researchers within the enterprise.

To read the complete, published article complete with graphics and tables, please see Thomson Reuters Institute website at The AI compass: The success of balancing tension, scalability, consistency & complexity – Thomson Reuters Institute.