Author: Kim Bartkowski, Design Principal, iX, Global Business Services, IBM A/NZ
In my last post, I introduced a new blueprint framework our teams in the IBM Garage are using to create Cognitive Enterprises using AI. In this article, I will be talking about a new design practice and complimentary artefact to a Cognitive Enterprise Blueprint called Intelligent Workflows. These workflows are how our teams identify opportunities for AI, automation, robotics and IoT to help make business processes more efficient and more useful.
In this post we will focus on micro-moments in a user experience:
To choreograph an enterprise to deliver on the moments that matter for its customers;
And to help identify the right scenario for AI to deliver on its purpose, value and trust to the human it’s serving.
Whether you are improving a current state or creating a future state, an Intelligent Workflow uses many common design thinking and Lean UX practices. For example, we use Personas and Value Prop canvases to identify people’s needs and the new value we can create by solving those needs. In an Intelligent Workflow, our teams take a Persona canvas and use that to create the role of the digital worker. This ensures that the design of the human/machine relationship is transparent to the team and stakeholders.
What is a digital worker?
Digital workers are small machines embedded in digital workflows. We call them assistants because they support and augment the human in charge, by doing small, discreet tasks like automating an email to a supervisor or helping to integrate data from one system to another system.
These assistants are enabled by AI and are triggered by the sensors on an IoT device. I’ve included an example below of a user story about a digital worker that supports a manager in the area of team rostering for an energy company.
Consider all the impacts when a person calls in sick:
The gap in the value chain or workflow from the skills provided by this person
The leading indicators of sickness, like the amount of work coming up and the lagging indicators of how many people have been sick in the last week, and
The redistribution of work that needs to happen so the team can still deliver to plan with minimal impact to energy production.
This multiple-step analysis and synthesis would take a person, hours to perform and the day would be half over. In this user story, the work is done in minutes.
The use case and user story maps below are some of the artefacts our teams in the IBM Garage use to describe the organisation and patterns of data that need to flow through the AI systems to identify, understand, and apply knowledge and reason to produce an outcome.
Our product teams of engineers, designers and business product owners work to understand the data that sits underneath the variables to determine the fabric of the intelligence they will be building. They also look at the tasks a human would perform in the scenario and match the intents. Then they can collectively design the AI into the experience, interactions and conversations that will take place in the workflow between the humans and the systems. It may sound complicated but we’ve developed fun tools that help to remove the complexity and fear of doing something that is quite new.
Our Design for AI workshops are made so that all people can participate with all types of skills and backgrounds. We’ve included a set of cognitive playing cards with capability pictures on them that relate to the skills people use to perform common tasks. Often, it’s not one card that is selected from the pile, but a combination of these cards that helps our teams determine how the AI will engage with the human. Once we understand all this, then we can design the features and functions of the intelligence in the workflow.
Often these types of Intelligent Workflows are best communicated and understood by stakeholders through a set of common design artefacts:
A pictorial story or journey that communicates the experience for both the human and the AI. A customer journey map or Golden Thread is often used for this.
A sample set of human and AI user stories that simulate the human/machine interactions and how the relationship will build over time, like the ones shown above.
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