Of Cognitive Science and Fidget Spinners
The State of Cognitive/AI Technologies in Central and Eastern Europe and Beyond… and Some Advice
When recently checking a popular tech website’s page on cognitive science, the lead article for that day was about fidget spinners. For those of you who don’t know what a fidget spinner is, chances are you don’t have children. I was informed of their existence by my son who saw one somewhere online and became fascinated with them. I now have one of my own, at the insistence of my son. It sits idle on my desk in the office. Their stated purpose is to give natural “fidgeters” an outlet for their anxious energy. Teens and office dwellers are their primary audience.
The scientific and medical communities are predictably divided on the alleged benefits of these spinners for people who hope they will help them focus. Yet many look to these objects as something that may allow them be more efficient and productive.
Cognitive technologies seek to do the same for knowledge workers, who are bombarded daily with ever-increasing volumes of information. If information and data are good, when does too much data become a distraction and something that keeps us from discovering its most important messages rather than guiding us to them?
The Very Formal Description
Cognitive/artificial intelligence systems are a class of technologies that has emerged to facilitate the discovery, use, and collaboration of information in analysis and decision making. These technologies use information curation, information retrieval, knowledge graphs, and numerous other components to help workers answer questions, predict future events, provide recommendations, and take actions to fix issues. These technologies use a wide range of information access processes combined with deep learning and machine learning to provide expert assistance in the workplace. Cognitive/AI systems are beginning to be combined with collaborative technologies to provide the next generation of tools necessary to get work done. IDC expects that these cognitive and collaboration tools will become ubiquitous over the next five years and will fuel a massive reengineering of the workplace, making it more responsive, agile, and able to facilitate data-driven decision making in all areas of businesses.
The Less Formal Description
Cognitive/AI systems take a number of existing technologies like data mining, natural language processing, and machine learning, and combine them with a lot of math (algorithms) to try and imitate natural human thought processes to solve questions without the direct involvement of humans. Think of a system that continues to “learn” as it processes ever greater volumes of data. One measure of human intelligence is the ability to spot patterns. The same is true of artificial intelligence.
The Fidget Spinner Analogy
Sometimes too much stimulus (data) is noise, and it is distracting, and it can lead to anxiety. A person suffering from ADHD may seek an outlet for their pent-up energy to allow them to concentrate on specific tasks and goals. This is where the fidget spinner champions claim their device is a positive tool for those suffering with such disorders. The argument holds that the object is not to give the user something idle and distracting to do — rather the opposite: to reduce the surrounding noise to allow the users to apply their skills and faculties in a more effective way. The same is true of cognitive/AI. The goal is not to replace the human but to reduce the “heavy lifting” of raw data sifting and analysis to let the living and breathing analyst hone in on existing learnings and knowns, extrapolating insights through them that only a human can provide.
Not surprisingly, and like the fidget spinner, many are still wary of the promises of cognitive technologies. On the tech side, there are concerns that it is not mature enough to justify even exploratory use. On the user side, some are worried how the implementation of Cognitive/AI capabilities will be perceived by skeptical knowledge workers. In even the most IT-mature corners of the world, uptake is still working up momentum. In the Central and Eastern European (CEE) region, adoption is limited and focused on a handful of use cases. Widespread implementation is not going to happen soon.
That said, a comparison of which use cases are being adopted in CEE versus Western Europe and the U.S. provides some interesting insights. As a percentage of total cognitive/AI spending worldwide, the U.S. leads the way in areas such as quality management investigation and recommendation systems, diagnosis and treatment systems, and automated customer service agents. Whereas, in CEE, these use cases account for relatively small investment levels. Conversely, the region is well ahead of the world (in terms of share of total spending) in automated threat intelligence and prevention systems, fraud analysis and investigation, IT automation. Western Europe has distinguished itself with strong demand for sales process recommendation and automation, supply and logistics, and regulatory intelligence facilities. CEE users are motivated by security concerns, while Western Europeans are focused on sales and processes, and Americans are driven by quality of services.
As with any other new breakthrough to arrive on the IT scene, CEE firms and organizations often take a wait-and-see approach. They want to examine case studies and success stories before they put any stock in “fads”. This is particularly true of innovation accelerators like Big Data, cloud and, in this case, cognitive/AI. This is not a bad thing: Jumping in with both feet is a dangerous gamble with any emerging technology. Healthy skepticism can serve companies well. In this region, “All the other kids are doing it” is rarely a compelling element of a sound business case.
Those success stories do exist, however. Increasingly, companies are seeing dramatic returns on investment in terms of money saved, efficiencies gained, and enhanced productivity. Each industry is unique and has its own applications for cognitive/AI capabilities. More and more vendors are looking to differentiate themselves from the pack when it comes to cognitively enabled functionalities. Startups are focusing on specific use cases for one or two industries. The offerings are proliferating and the choices for companies across all industries are multiplying. There must be a reason for this. If CEE companies want to continue waiting on the sidelines for proof of cognitive/AI’s value, they can. Soon, however, the evidence will be too overwhelming to ignore.
For those companies in the region that are more convinced of the value of these technologies, there are a few things to consider when defining strategy. Generally put: Be realistic.
More granular advice:
- If you are looking to replace humans with cognitive/AI capabilities, you are already on the wrong path.
- Identifying the right use cases takes time and thought.
- Collaboration between IT and LOBs will be critical to success.
- Understand that relevant data, not just more data, is needed.
- Cognitive systems don’t fix bad inputs and untrained users.
- Cognitive can only assist, not replace.
- Clearly communicate the reasons and goals for the initiative. Knowledge workers need to perceive this as a benefit, not a threat.
Chances are, in two years from now, the fidget spinner will be a nearly forgotten fad and “so 2017”. I am rather certain the same will not be said of cognitive and AI technologies.