The latest HFS survey (December 2019) on the state of integrated automation, which covered the Triple-A Trifecta of AI, automation, and analytics, showed that 45% of enterprise leaders (see Exhibit 1) have different centers of excellence (COEs) for AI, analytics, and automation. An integrated CoE across these technologies is imperative for success, and enterprise automation leaders must ensure they address people, skills, IT, and business domain knowledge issues—not only technology know-how around AI, analytics, and automation.

Exhibit 1: Nearly 50% Enterprise leaders have isolated CoE’s for AI, analytics and automation

Has your company deployed a CoE for managing analytics, AI, and automation initiatives?

Sample: Global 2000 Enterprise leaders = 317

Source: HFS Research, State of Integrated Automation 2019

Design thinking enables sharing best practices and creating a unified view of governance across AI and integrated automation initiatives

Simply put, AI, analytics, and automation are connected at the hip—with two critical hinges, data and processes. It is vital, therefore, that Design thinking is used to enable an integrated CoE, with cross-team collaborations.

We saw this in action in a recent enterprise where the leaders of AI and automation initiatives at a global high-tech company were sharing the challenges they had encountered. Specifically, how it was for them to establish a unified governance framework and an integrated CoE to enable the whole organization to take a consistent and uniform approach toward adoption of the Triple-A Trifecta technologies. Different process and technology teams had completely different views of how the information security, data access, resource utilization, skills utilization of these initiatives should be run. Using a design thinking approach to combine approaches delivered the most realistic outcomes possible within the existing organizational realities of conflicts and constraints with the transformational technologies.

Only 20% Enterprise leaders have global, integrated CoEs for AI, automation, and analytics: Use design thinking to build an integrated skill-map

As exhibit 1 shows, only 20% of enterprise leaders surveyed claimed that they have integrated global CoE’s for AI, analytics and automation. Design thinking should be the glue that helps you choose a cohesive team with diverse backgrounds and skillsets [Refer Exhibit 2. These CoE members must be trained on basic design thinking skills such as conflict management and resolution, cross-team collaboration techniques, and viewpoint engineering.

Exhibit 2: Use design thinking to build an integrated map of roles and skills for an integrated AI, automation, and analytics CoE

To use this skill-roles map to leverage design thinking in AI, automation, and analytics integrated CoEs, enterprise leaders can take the actions Exhibit 3 suggests.

Exhibit 3: Action items for next Monday morning

The Bottom Line: Leaders must leverage design thinking to build integrated CoEs for AI, analytics, and automation

Design thinking enables a “one team” approach that helps evolve an optimized and integrated CoE across tech teams and process teams. To achieve transformational outcomes from AI, analytics, and automation, enterprise leaders must use design thinking to build integrated CoEs.