In 2018 there was a fourfold increase in the number of times that CEOs mentioned A.I. in their earnings call, the AI Index Research Consortium observed. This indicates that we are on the eve of a wave of investment.
Successful A.I. adopters take three coordinated and parallel steps to build momentum:
They accelerate a small set of lighthouse initiatives
They develop three leadership roles
They build a central A.I. capability
Accelerate a small set of lighthouse initiatives
Building an A.I. requires significant investment. Combining new technologies, generating or gathering the data, training the algorithms and deploying the solutions requires a coordinated effort. Most companies select a small portfolio of initiatives for acceleration.
To learn: by doing a few small projects, all involved will learn how to achieve results. Teams need to figure out how to build solutions despite the organizational and technical constraints. They will uncover what new processes and procedures need to be put in place, how to collaborate across different parts of the business, and how to ensure the value is eventually delivered to the users.
To build traction: the first few success stories will prove the value potential, build creative confidence, and convince the next parts of the business. Often these first projects are pre-existing analytics projects that already have some momentum but can be accelerated.
To create value: the first initiatives should focus on high-value use cases that can be captured fast. In these first projects, senior sponsorship is essential. They need to protect the projects from institutional inertia, facilitate coordination across the organization, ensure the value gets captured, and tell the story once it does.
Develop three leadership roles
To drive value from these initiatives in a responsible way, executive leaders build cross-functional teams where domain experts (“Translators”) and A.I. Talent work together. The three roles that need to be played well are:
Executive leaders. Executive leaders and their teams take charge on this complex topic, even when the potential may still be unclear. They prioritize opportunities: cast the teams and coordinate across the business. This is a hands-on activity, with active involvement in the teams to learn and understand both the business and technology aspects. They lead the people through the change – both those driving as well as those impacted. Successful people in this role are collaborative rather than authoritarian. They discover opportunity rather than seek certainty, and they work creatively and iteratively. And they actively organize for (value) co-creation rather than pursuing win-lose relationships. Last but not least, it is the executive leaders’ responsibility to ensure that A.I. is used ethically, meaning that the algorithms are fair, transparent, respectful of individuals’ privacy and other rights, and not institutionalizing historic bias.
Translators. To ensure that A.I. solutions are useful and trusted, domain experts (often called “Translators”) work with engineers during development and deployment. They “know the problem to solve,” and they can imagine innovative propositions or processes, bringing deep expertise and a fresh perspective. Just like the executive leaders, Translators are collaborative, opportunity seeking, and creative. And they combine practical business experience with a sound conceptual understanding of, and enthusiasm for, A.I and its potential.
A.I. Talent. Instead of working with a “suppliers” mindset, A.I. Talent work on equal footing with Translators. They are trusted peers who co-own the business objectives and who help discover opportunity. They champion responsible development. Successful A.I. Talent marry deep technology expertise with soft skills such as listening, empathy, influencing, and building trust with users, leadership, and external stakeholders.
Build a central A.I. capability
A select few “digital native” companies (like Google, Pinterest or Alibaba) have the maturity to have A.I. engineers embedded widely across the business. Other organizations might have pockets of advanced analytics capabilities, isolated in silos. Advanced players in this group are building a central A.I. capability: one organizational unit, concentrated in one location, where engineers work together with the business. This helps:
Attract talent: the required A.I. talent is scarce. A sizable team with senior engineers to learn from, access to data, exciting use cases, and the right location are essential ingredients for a competitive employee value proposition.
Support the business where needed: most parts of your organization don’t require constant A.I. expertise; not all parts of your organization will be ready. A central team enables you to move where the most promising projects are.
Standardize your approach: the central team will learn the best way to implement A.I. in your organization. This helps build partnerships, enhance approaches to data acquisition and management, and avoid proliferation of technology platforms.
Enable data access: advanced players not only concentrate their talent; they also centralize their data as much as they can within ethical, technical, and legal constraints. In February 2019, Booking.com announced in an email to their users that it would start sharing data across their service lines. Google has been doing so for years. Making high-quality data easily accessible and available is a pre-requisite for the A.I. journey.
The deployment of A.I. in industry and government is rapidly accelerating. Leaders can take charge by prioritizing the opportunity and getting started with the first wave of initiatives, developing their people and organization along the way.
A Dutch version of this article originally appeared in NRC Live.