Four Evolutionary Phases Of Intelligent Digital Workers That Can Improve HR Outcomes

Understanding and implementing the elements of a conversational AI ecosystem

Most organizations approach the adoption of conversational AI with chatbots in mind. Business leaders imagine how these bots might automate processes that are a huge time-suck for teams across their organization—freeing them up to focus on growing the organization instead of maintaining the status quo. Many think it will be easy. But the ice-bath reality is that a collection of bots operating independently have a very low ceiling. The real power in conversational AI comes through intelligent digital workers (IDWs) sharing resources across an ecosystem of orchestrated technologies. This requires far more than just a few machines thrown at a few different problem areas. It’s a rather herculean undertaking, but the payoff is huge. When applied internally, for example, it can represent a large leap forward in employee productivity, engagement, and job satisfaction.
 

Leaders who’ve still got chills from the first attempt at AI-enabled automation might be hesitant to reboot or even restart the process. But every business is already in this race whether they want to run it or not. (Gartner calls this kind of activity “hyperautomation” and they see it as an inevitable market state.) Even for organizations that already have a collection of machines running, they likely aren’t functioning within an ecosystem—but are the result of random acts of chatbotting. By understanding the four evolutionary phases that an IDW can move through as it becomes better at completing tasks, businesses can better understand and implement the conceptual and practical elements of a well-functioning conversational AI ecosystem.
 
Now let’s trace an example skill—helping employees to request paid time off—through the four evolutionary stages. The following depicts what it might be like for our user, Jo, to interact with the same skill at various phases in its evolution.

The Four Evolution Phases of an IDW

Literacy

During the literacy phase, the IDW consumes and transforms numbers and characters into information. This can be either raw data, such as an integer that can be decoded into a date, or the formatted version of that integer into a date. An IDW in this phase is a primitive digital worker.
 

Jo: “Hey, can you help me book vacation time?”

IDW: “Hello, I think you want help making a vacation request. If it’s straightforward, you can request using www.apc.com, or I can get help from someone in HR, and one of us will get back to you.”

Jo: “Oh, that’s right . . . I can do that online.” Good to know I can always get help. I’d like to know how many days off I have left.

Knowledge

In the knowledge phase the IDW gains an understanding of the context of its information—knowing how and why information matters. For example, “comprehending” that date is someone’s date of birth. An IDW with this capacity can be described as a basic intelligent digital worker.
 

Jo: “Hey, can you help me book vacation time?”

IDW: “Okay, Jo, I can help you submit a vacation request. BTW, don’t forget that you need to get your shift covered. It looks like you have three PTO days left. How many would you like to use?”

Jo: Cool that it knows that. (It would be cooler if it helped cover my workload.) Glad I asked before I booked a 5-day vacation.

Intelligence

During the intelligence phase, the IDW develops an understanding of how to use or act on knowledge and information. So for our date example that would mean understanding the relevance of the date of birth in different contexts, such as: “I hope you have a great 21st tomorrow!” or “I just sent you a gift certificate for your 21st.” This capability is the hallmark of an IDW that is ready to function in an ecosystem.
 

Jo: “Hey, can you help me book vacation time?”

IDW: “Okay, Jo. I can help with that. I’ll work on getting your request approved. I’ll need to know the dates you’ll be away and how many PTO days you’d like to use—it looks like you have three left. If you give me that information, I’ll start looking for colleagues who might cover your workload and will follow up tomorrow.”

Jo: “Okay, Jo. I can help with that. I’ll work on getting your request approved. I’ll need to know the dates you’ll be away and how many PTO days you’d like to use—it looks like you have three left. If you give me that information, I’ll start looking for colleagues who might cover your workload and will follow up tomorrow.”

Wisdom

During the wisdom phase, the IDW learns how to use the richness of experience to inform a decision. As IDWs develop the ability to tailor solutions to individuals based on the context of past interactions and stored data, they become more like personal assistant, adding exponential value to their users and the organization. So now the DOB datum translates to the IDW offering: “Happy birthday! I see you’ve got dinner plans tonight and a workout scheduled with your trainer for tomorrow morning. If you think you might be out celebrating late, I can cancel the training session for you.” Here we have a personal intelligent digital worker or digital personal assistant.
 

IDW: Jo, I know you haven’t requested your vacation dates yet, but Georges just requested PTO during your usual vacation dates. He normally covers your workload. Do you want me to see if he can move his time off or find someone else to cover for you?

Jo: “Yes, please. You rock.” Phew! I’m glad my IDW was looking out for me. My family would have never forgiven me if I missed the reunion.

 As IDWs gain wisdom, friction begins to disappear for the user. But it’s important to remember that the evolution of the end user’s experience has an inverse relationship to the amount of orchestration required behind the scenes. Creating IDWs is really a matter of providing simplicity for users by finding ways to solve increasingly complex problems within a given ecosystem.

As the components within an organization’s ecosystem evolve, IDWs start to feel more like colleagues. They have skills that are helpful in collaboration or independently, and they can get things done. Think of an IDW as a new colleague in training. Imagine the IDW is training with Yvonne, an HR department supervisor, to become an expert in managing the approval processes and scheduling for paid time off. This is a complex task given the amount of context the IDW needs to decipher:
 
●    How much remaining PTO does the employee have?
●    How many shifts will need to be covered?
●    Who is available to cover these shifts?
●    Are there any conflicts with the dates requested?
 
Yvonne is an expert in these areas, so she trains the IDW to follow an ideal process. This often-overlooked factor in creating force multipliers is crucial. Greater impact comes from not just automating tasks to be done but actually improving the process you’re empowered to design- processes that can be tackled by an unlimited number of workers.
 
Once the IDW is operational, one of the ways it can continually contribute to its own evolution is by making use of human-in-the-loop (HitL), meaning it can reach out to Yvonne or her colleagues for help whenever it gets stumped.

Co-creation is the key to getting IDWs closer to the wisdom phase—co-creation between people as the IDWs skills are developed, and co-creation between humans and IDWs as those skills are improved. The example above shows how this co-creation can lead to better people management and employee satisfaction. And the same strategy can be applied to all facets of HR: from onboarding to training, to development.
 
This all points to a very fertile future for organizations that are willing to take the plunge … intelligently.  

Author Bios

	Robb Wilson and Josh Tyson are the CEO/Founder and Director of Creative Content respectively at OneReach.ai. Connect Robb Wilson Connect Josh Tyson