Cognitive computing is emerging as a significant part of the next generation of computing. Because it is early days in this new generation of computing, there is still no widespread understanding of what it is and how it differs from some of its relatives: AI, internet of things, machine learning, conversational systems, bots, or NLP. We see in both the US and in Europe that companies are very interested, but are mostly still at the experimentation and proof of concept stage. We will be tracking some of these projects as they develop their cognitive applications and roll them out more broadly. There is no question, though, that interest is high, and that the ability to augment and assist users, as well as to move from static to dynamic systems has great appeal.
I recently had the opportunity to attend a focus group, sponsored by SAS Institute, on cognitive computing adoption outside the US. Attendees came from Denmark, Japan, Finland, Serbia, Netherlands, Sweden, Switzerland, India and Ireland. They represented financial services, telecom, consumer product manufacturers, government agencies, and airline companies. Here are some gleanings from their wide-ranging discussion.
How are you using or how do you expect to use cognitive computing?
- Automatically revise and evolve rules to expedite adaptation
- Uncover and improve best business practices and processes
- Detect patterns of behavior. Detect abnormalities. Identify risks.
Augment human agents who can’t handle the current workload by automating the more predictable aspects of the job.
Why move to cognitive computing?
- Handle large amounts of data with many more variables. Especially textual data.
- Reduce need for adding manpower. People just don’t scale.
- Stay ahead of competitors
- Uncover surprises. (This was a side benefit to a demonstration project that was originally designed to augment the human workforce)
- Curious to see what benefits might derive from cognitive computing that we can’t get now
- Get rid of silos
- Automate predictable or repeatable work
- Augment human work by developing digital assistants
Examples of uses:
Speech-to-speech product sales. One firm’s innovation lab is experimenting with building an App that will be personalized; will use machine learning to replace hundreds of business rules and 20 predictive models; and the machine learning will allow the models to evolve and help to revise rules faster.
- One firm is experimenting with discovering and extracting patterns of best business practices to establish KPI’s worldwide from hundreds of business managers’ individual knowledge. Need to understand what practices work and why.
- Expedite transactional processing. Moving from rules-based processes to teaching a system how to assess issues and minimize delays.
- Another firm is tracking and analyzing invoices using rules and econometric models. Their goal is to teach a system to automate model development and modification for tracking and analyzing invoices. Extending beyond rules and econometric models they want to add sentiment from incoming, non-English communications.
- Recognize patterns of behavior to find anomalies and predict risk in order to more thoroughly assess people and goods.
- Automate responses to customers, but on a more individual level. Part of a project to analyze customer opinions—a big data project.
In all cases, augmenting existing applications but seeking net new benefit from the use of cognitive computing systems was a consistent goal amongst all participants. Completely new product development or drastic changes to business processes from cognitive computing applications wasn’t seen to provide the palatable business benefit needed to embrace adoption. In all cases, however, changes and improvements to existing business practices were expected.
In Part 2 of this post, I take up some of the challenges that are confronting today’s experiments and experimenters.Share