A Better Approach to Enabling AI

An exclusive interview with Robb Wilson, founder, lead designer, and chief technologist behind OneReach.ai, the highest-scoring company in Gartner’s first Critical Capabilities for Enterprise Conversational AI Platforms report. Wilson is also the author of Age of Invisible Machines: A Practical Guide to Creating a Hyperautomated Ecosystem of Intelligent Digital Workers.

Wilson has spent more than two decades applying his deep understanding of user-centric design to unlocking hyperautomation. At OneReach he is helping to democratize two of today’s most transformative innovations — digital communications and machine learning — in hopes of “accelerating society to the inevitable moment wherein humans can interface with any system, the way that we innately do with each other.”

You have observed that many organizations are implementing AI inefficiently. Can you share some examples?

I don’t want to call companies out by name, but there are plenty of short-sighted orgs focusing solely on the cost-cutting benefits of AI. This kind of activity might appease stakeholders, but it sets organizations back in a moment where it’s critical to move forward, and quickly.

Getting AI right requires things that stakeholders are typically averse to:

• A willingness to fail
• An acceptance that ROI will come more slowly
• A commitment to bettering the organization for both employees and customers ahead of shareholder desires

A real-world example is call centers scrambling to replace human agents with crude AI rather than using the technology to improve the quality of interactions with customers. There are some customer requests that AI can handle rather nimbly, like providing directions or store hours. There are other requests that humans are more adept at handling, like security breaches.

Humans can provide an even better experience to a customer in need (and in this case, one that might already be in a panicked state) with AI working as an ally in the background, providing a synopsis of  the customer’s history and suggesting relevant prompts or actions.

Creating a seamless version of the latter approach takes a comprehensive strategy, a willingness to fail forward, and the patience to see the journey through. Many of the things required to move companies into the future with conversational AI are in direct opposition to the way many modern businesses are structured and maintained. This will have to change.

Too many organizations, and enterprises in particular, are used to having a protective moat around them — one built in part by legacy technologies. Unfortunately for them, we've entered a world where moats no longer offer protection. Particularly when technology providers lock organizations into their solutions or suite of tools, the moat becomes a prison. The technology that runs an organization will need to be part of an open ecosystem that is fully capable of integrating with new technologies and that can communicate with other organizations’ technology ecosystems.

Can you outline a better approach?

Hyperautomation is not an initiative you can put solely in the hands of any one group in your organization. You need a core team of specialists instituting a process that involves members from every department in your organization. That core team can come from within your organization or the outside world, but its members must be adept enablers. As they work their own skill-specific roles, they need to always be evangelizing the potential for anyone with a good idea to contribute to the design of automated solutions. This is an all-hands-on-deck journey.

Equally important — don't look to automate what you're already doing; automate what you should do.

Finally, you must start now. Don't start big and don't start with customer experiences. Get practice internally, while improving employee experiences. Orgs that wait to see what their competitors do will get smoked, becoming the Blockbusters or Kodaks of their industries.

Some project managers are excited about the ways AI can help them, while others are worried about their future viability. Can you share your thoughts on the impact AI will have on project work?

At the moment, there's actually a complementary relationship emerging between humans and AI. Humans aren’t great at doing repetitive tasks without error. Machines, on the other hand, are quite capable of this. Machines aren’t great at establishing context and coming up with creative solutions to problems. This is where humans excel. We should be deliberate when creating these advanced systems to use the strengths of machines to elevate humans, and vice versa. Human-in-the-loop is a critical part of this process.

As AI applications continue to be refined and become even more advanced, what skills will keep humans in the loop?

I think it will be critical to have lots of humans actively involved in designing the first wave of automation. This will help to ensure that people are an ever-present link in the decision-chain and will enable the creation of automation that surpasses what humans alone are capable of. As machines continue to improve in their abilities to automate increasingly sophisticated processes humans will still need to provide our skills as creative problem solvers. That said, on a long enough timeline (and it might be a relatively short one) machines are likely to take over most of the work being done in the world.

That isn’t to say that humans won’t be involved. We will still want people making key decisions and designing the experiences people have with technology, but there might be less to do. How you feel about this will have a lot to do with your relationship with work. We’re part of a society in which status and identity are deeply intertwined with job titles and roles. In the future, when someone asks what you do, the first answer might be “parent,” or “painter,” or “partner.” AI might give us the freedom to abandon our often-unhealthy relationship with productivity. Whether we can handle that newfound freedom is open to debate.

Any other insights or lessons you can share when it comes to using AI in a collaborative team context?

Conversational AI gives us a unique opportunity to bring humans closer together around technology. I’ve described opportunities to design experiences that are better than what humans alone can offer. These most often come about when a cross-disciplinary group comes together around a specific use case (or set of use cases).

For example, the people who work in the call center bring an in-depth understanding of what customers expect and are commonly looking for. Call center managers will know about common pain points, both external and internal. Data managers can provide a thorough overview of the knowledge that’s available in existing databases. Experience designers can look for ways to thread the insights these different groups bring into workable experiences. Prompt engineers can help unlock an organization's unstructured data as well as helping to design automated experiences that meet users where they are, armed with context.

I’ve seen firsthand how exciting this process can be, and the work often has multiple benefits. As the tedious elements of roles are automated, project leaders are free to focus more time on creative problem solving and interfacing directly with stakeholders. This kind of co-creation not only unifies team members across an organization around a shared vision for technology, it also creates the foundation for future-proofing an organization.

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