Introducing the Robotics Quotient: A new means of measuring organizational readiness for automation and AI

Forrester's RQ assesses readiness for working side-by-side with robots and AI.
Written by Forrester Research, Contributor

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Automation technologies -- from AI to robotic process automation (RPA), physical robotics, and more -- are transforming business processes and operating models. But most companies don't have the competencies to implement automation technologies successfully. And so we created RQ -- the robotics quotient -- to help digital and technology leaders make better investments in the prerequisites to success with automation, AI, and robotics.

Read also: Your next coworker could be a robot

RQ measures the ability of individuals and organizations to learn from, adapt to, collaborate with, trust, and generate business results from automated entities, including software like RPA, AI, physical robotics, and related systems. Across more than nine months of research, what Forrester learned from enterprise organizations is this: People, leaders, and organizations must all bring something to the table when preparing to deploy AI or automation. And the competencies of those people, leaders, and organizations must be refracted through the lens of trust, which varies by technology. We call this the PLOT framework.

The PLOT framework is key to self-assessing areas for improvement. In our self-scoring tool (which is an Excel spreadsheet embedded inside the report), clients can assess RQ across 39 different dimensions, scoring a current state and a desired (yet plausible within 6 to 12 months) state. Using this tool, you can identify the areas that need most improvement and the organizational competencies you need to acquire in order to succeed with RPA, AI, DPA, physical robotics, and the like.

People require emotional, logical, and technical skills. Our people evaluation derives not only from interviews and data but from 30 years of research into emotional intelligence (EQ) as applied to human-machine interactions. People high in RQ possess the ability to engage in sophisticated information processing and task completion by understanding, adapting to, collaborating with, and exchanging data and insights with intelligent machines.

Leaders must balance vision with adaptability and trust. Not only must leaders cultivate the right skills and inclinations among their employees (people), they must change their leadership style to suit the era of AI and automation. For instance, willingness to change -- while not acting whimsically or on an ad hoc basis -- is crucial to automation technology success. Often, goals and measurements will shift midproject, and leaders must find an effective way to adapt.

Organizations need new roles, superior processes, and training. We find that informal, underfunded initiatives don't work as well as formalized, clear changes to organizations. For example, there are new roles and skills that must be introduced into nearly every organization that deploys automation and AI -- and these roles and skills are even common to the deployment of physical robotics.

Trust varies by technology. For all their many commonalities, disparate automation technologies present different challenges and success factors -- most importantly as it relates to building trust. Depending on how transparent (or opaque) and how deterministic (or probabilistic) the software system is, humans will bring a different level of instinctive trust to the interaction.

Read also: How to implement robotic process automation: 3 tips (TechRepublic)

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