Few topics garner more headlines today than artificial intelligence (AI), and for good reason. Despite the fact tech luminaries can’t seem to agree if it’s the world’s greatest existential threat or divine savior, there is little debate about its ability to fundamentally transform everyday life and the business models that support it. As Dr. Anastassia Lauterbach, board member of Dun & Bradstreet, recently put it, “The internet disrupted 20% of all business models. Artificial intelligence will disrupt the remaining 80%.”
Harnessing This Great Potential
Applying artificial intelligence to the business domain promises outstanding automation and collaboration benefits for workers and the enterprise alike. Achieving these benefits requires knowledge about state-of-the-art AI capabilities and their business use cases.
This “AI knowledge for business” enables existing BX roles (e.g., business process analysts, business process consultants, business architects, etc.) to perform all opportunity and value identification activities without the involvement of a technical resource like a data scientist or architect. Thus making existing planning much easier and less invasive for teams looking to get projects jump-started within their functional areas.
To help propel the adoption and drive of the business community to lead more of this “AI thinking,” our experts at Infosys Consulting have developed a comprehensive cognitive methodology – the Coblox framework. The intent is to help organizations from the inside create sustainable innovation using a simple AI methodology.
‘Coblox’ consists of three core components:
• 33 cognitive building blocks
• The mapping of all major business capabilities (finance, HR, commercial, supply chain, etc.) to their respective cognitive blocks
• A set of combination patterns of cognitive blocks to fully leverage various technologies for specific business use cases
Using the Coblox framework below, it covers all aspects of an enterprise: automating business processes, improving routine decisions, leveraging expert knowledge and fast-tracking exploration tasks.
Each building block in the framework above represents a specific cognitive capability. For example:
• Information extraction- can recognize structured data inside unstructured texts or images
• Recommendation- can advise a user on which item fits his interest profile best
• Diagnosis- can find out the most likely root cause which induced a certain observable symptom
• Similarity search- can find texts and images which have a similar meaning as a query text
• Frame-based conversation- can lead a dialogue in natural language to accomplish a specific task.
In strong analogy to human skill sets, the right combination of artificial cognitive capabilities is key to providing an end-to-end-AI solution for a specific business challenge, which clearly goes beyond pure mechanical automation attempts like RPA.
In my next article, I will explain in more detail how a customer complaint process for an organization can be largely automated by the combination of several cognitive building blocks. Thanks for reading, and I look forward to your thoughts.
Click here for part 2.
Associate Partner, Infosys Consulting
Dr. Harald Gunia is an enterprise architecture and artificial intelligence expert. He has more than 29 years of experience in all major AI technologies, including, machine learning (ML) and robotic process automation (RPA). He was worked in more than 10 industries and has deep expertise in digital capabilities and large-scale business transformations. Harald holds an M.Sc in computer science and a PhD in artificial intelligence.
Interesting article Harald. Looking forward to the example you mention about implementing AI in customer complaint processes.