Editors Note: DBizInstitute is excited to share this article, written by Dr. Setrag Khoshafian, with our community and in advance of his new book release. Keep an eye on our website as we share additional articles in the coming months written by Setrag, as well as a pending Meet the Author webcast to discuss his new book ‘How to Alleviate Digital Transformation Debt’ expected to air Fall 2021. Be sure to pre-register for the webcast! This article was originally published on CognitiveWorld.com on December 22, 2019. This is a two-part article; Part I can be found here.
Digitized Value Streams
The most impactful aspect of AI can be realized in the context of the end-to-end digitization of value streams (a.k.a. “value chains”) involving multiple participants, business units, or partners. A value stream is associated with a business measure (a Key Performance Indicator (KPI)) and typically involves multiple participants across the digital enterprise. Referring a compelling perspective from the Theory of Constraints: “a chain is no stronger than its weakest link.”
Let’s face it: most organizations are siloed. Organizations are arranged vertically. However, responsiveness to business value generation typically involves multiple business units or participants. Value streams go horizontally across the digital enterprise. It is interesting that this aspect of digital transformation and the use of AI to optimize responsiveness for cases involving multiple business units is often overlooked.
The considerable business return and benefits of AI will come from services that connect the customer to the rest of the extended digital enterprise. What does the digitization of value streams mean for a digital enterprise? How does it impact digital enterprises that are attempting to evolve, improve, and transform the customer experience through AI? Well, it means customer interactions leverage AI for targeted, contextual, and optimal interactions towards timely, effective, and efficient resolution of their cases.
The digital transformation of the customer experience goes beyond the call center or self-serving customer channel interactions (e.g. Web or Mobile or IVR or Bots or Customer Service representatives (CSRs) or increasingly Intelligent Virtual Assistants). Optimizing the front-end channel customer interaction is of course critical and important. AI plays a huge role in optimizing the customer touchpoint. However, the customer promotion scores will depend on the aggregation of tasks that involve multiple business units to resolve the customer request. The task for the customer needs to be routed to the best resource, in the specific context or situation of the customer. Essentially, managing the end-to-end value stream work needs AI. For instance, fixing a broken appliance might involve an intelligent chat-Bot, a CSR, the Service department, the Field Service, the Supplier, and the Warranty department. As noted above, even in this modern era of digitization, most organizations are siloed. It is not enough to just focus on the responsiveness of the Virtual Assistant, the CSR, or the elegance of the Web or Mobile app for the customer. These are, of course, important but they are part of the end-to-end value stream that needs to be strong and responsive through all the stages of the value chain. Each business unit needs to leverage AI to optimize the resolution of the customer’s end-to-end case.
Here are some of the AI capabilities that can be leveraged in end-to-end digitized value streams:
- Virtual assistants and increasingly intelligent chat-Bots are becoming one of the most important categories of AI optimizations for the customer experience. There are a number of key AI enablers for this domain including Natural Language Processing. The interaction of an AI-enabled virtual assistant could be text-based, voice-based, or both. The often referenced “Turing Test” is particularly relevant here–as virtual assistants become more intelligent, it becomes more difficult to distinguish them from human intelligence.
- The Best Action in a context: Customer situations, customer context, and customer sentiments are in constant flux. The aggregation and fusion of customer interactions and customer behavior with their connected IoT Devices and sentiments through social channels need to be mined and then manifested in a prioritized list of actions: write-off a dispute, make an offer of a new product or service, offer free upgrade, or offer a discount on a service.
- Robotic Process Automation: AI techniques can also be used to analyze the performance and effectiveness of customer service agents, the applications they are using for customer interactions, and the processes in responding to customer requests. Unlike more daunting process re-engineering efforts that often require extensive changes to underlying IT technologies, RPA leverages the existing enterprise application landscape and uses AI with software robotics to eliminate human involvement in especially repetitive tasks.
- Intelligent Skill Based Assignment of work: Often, a lot of waste and customer frustration emanates from inefficient (a.k.a. “dumb”) processes that route work to the wrong resource. Handling a customer case will involve participants who could be humans or intelligent virtual assistants. The AI techniques can also be used to route the work to the best or most skilled resource for the specific customer request.
AI-Assisted Work
Digitization and especially work automation trends are changing and even disrupting entire industries. According to the Economic World Forum, nearly 50% of current jobs could be automated by 2055 with AI and automation as the main culprits. Even now, automation is having a huge impact on the workforce.
The spectrum of workers begins with clerical or manufacturing workers: repetitive and predictable work that can easily be automated. Increasingly, automation with software and robots are taking over this repetitive category of work. This of course includes physical Robots as well as Robotic Process Automation. AI is increasingly playing an important role in optimizing work processing and understanding where the potential bottlenecks for improvement are.
At the other end of the spectrum of work and worker types is the knowledge (a.k.a. cognitive) worker. Knowledge workers think for a living. They are often the retiring expert that has a lot of the know-how in their mind. They innovate and often come up with ideas for new products as well as the policies and procedures in the organization. At best, their knowledge needs to be captured and digitized to drive work automation. At the very least, these cognitive workers need to be made readily available and participate in social interactions in the context of digitized processes.
Between these two, you have the most important category that represents the majority of workers: the AI Assisted Worker. The medium of the assistance could be an elegant UX, Voice, Text, or Visual–including Video. It could be in the form of suggestions, recommendations, or observations. It is like having an always “on” Virtual Assistant or an AI enable UX that tries to help you with your assigned tasks in a particular context. The AI assistant continuously learns and improves itself with a feedback loop from the data that captures the results or the performance of the end-to-end value streams.
Therefor, in essence the “Action” part of insight-to-action is the shift in which AI-Assisted automation from the left to the right. More and more of our routine and not-so-routine work will be automated. The Virtual Assistants will be increasingly sophisticated and will start to take over work–freeing us to do more of what we are very good at: cognitive and creative work.
