Home » Artificial Intelligence » Digital Transformation Often Begins at the Dumb End of Cognitive Technologies

Digital Transformation Often Begins at the Dumb End of Cognitive Technologies

July 3, 2019


A lot has been written about the need for organizations to transform into digital enterprises in order to be successful in the digital age. Every day oceans of new data are generated by sensors, searches, and sales. Many companies, sag under the weight of this data and have realized it can only be analyzed using artificial intelligence (AI) techniques. The goal of a digital enterprise is to leverage data to create a competitive edge. Because AI is essential for extracting insights from AI, some people have labeled the digital enterprise the “cognitive company.” Few enterprises have reached that lofty state. Nicole Laskowski (@TT_Nicole) asserts, “The so-called cognitive company — powered by AI and machine learning tools that transform work as we know it — is still more myth than a matter of fact.”[1] When beginning the digital transformation journey, Laskowski reports Thomas H. Davenport (@tdav), a distinguished professor at Babson College, recommends “[thinking] simply and incrementally when implementing cognitive technology.” In other words, start at the dumb end of cognitive technologies and grow as you learn. At Enterra Solutions®, we call this a “crawl, walk, run” approach to implementation.


The impact of AI is already being felt in the business world


The staff at PYMNTS.com note, “The Information Age is often associated with the Digital Revolution, which is what the world is currently undergoing. Through the eyes of the Digital Revolution, there’s a shift in daily processes, infrastructure and company culture. Basically, what’s occurring in the advancements in the economy has a direct impact on businesses and the way they operate.”[2] Most of us, whether we’re aware of it or not, have interacted with some sort of cognitive technology. McKinsey Global Institute (MGI) partners Michael Chui (@mchui), James Manyika, and Mehdi Miremadi observe, “Artificial intelligence seems to be everywhere. We experience it at home and on our phones. Before we know it — if entrepreneurs and business innovators are to be believed — AI will be in just about every product and service we buy and use.”[3] That’s impact.


Despite the potential of cognitive technologies, they note there are currently some limitations holding AI back. The first limitation, they suggest, is data labeling. “Most current AI models are trained through ‘supervised learning.’ This means that humans must label and categorize the underlying data, which can be a sizable and error-prone chore.” The second limitation is obtaining massive training data sets. Despite the oceans of data being collected, they note, “The current wave of machine learning requires training data sets that are not only labeled but also sufficiently large and comprehensive.” The third limitation offered by the group is the explainability problem. They note, “Larger and more complex models make it hard to explain, in human terms, why a certain decision was reached (and even harder when it was reached in real time).” Enterra® addresses this problem using Massive DynamicsRepresentational Learning Machine™ (RLM). The RLM can help determine what type of analysis is best-suited for the data involved in a high-dimensional environment and it accomplishes this in a “glass box” rather than “black box” fashion.


A fourth limitation noted by the MGI partners is the generalizability of learning. They explain, “Unlike the way humans learn, AI models have difficulty carrying their experiences from one set of circumstances to another. In effect, whatever a model has achieved for a given use case remains applicable to that use case only. As a result, companies must repeatedly commit resources to train yet another model, even when the use cases are very similar. One promising response to this challenge is transfer learning. In this approach, an AI model is trained to accomplish a certain task and then quickly applies that learning to a similar but distinct activity.” A fifth limitation, and one that has received a lot of attention, is bias in data and algorithms. “Bias is a different kind of challenge,” the MGI partners explain. “Potentially devastating social repercussions can arise when human predilections (conscious or unaware) are brought to bear in choosing which data points to use and which to disregard.”


Despite these limitations, businesses are finding cognitive technologies are already providing benefits. Arpit Dhanjal notes, “Businesses these days are making use of this technology in business intelligence decision-making, pattern analysis, predictions etc.”[4] He lists five ways companies are currently using cognitive technologies. They are:


  1. Improving Inventory Management: “With AI technology, businesses can apply predictive analysis on their inventory data. … Making your inventory smart will make you to clearly understand the clients.”
  2. Enhancing Employee Engagement: “Artificial intelligence has emerged as a powerful tool which helps in building [employee] engagement.”
  3. Understanding Customer Behavior: “Consumer behavior is continuously changing. … Artificial intelligence in marketing help businesses to target appropriate audience for their advertising.”
  4. Fraud Prevention: “Businesses are using AI enabled tools to protect against the fraud schemes.”
  5. Smart Recruitment: This topic is more controversial than the other benefits mentioned by Dhanjal. Nevertheless, he reports, “According to the Forbes report, it has been estimated that the entire global recruitment market has reached $200 billion. AI has made this recruitment process easier for HRs. It has reduced the time and efforts they were putting on screening and researching candidates with the automated applications.”


For these and other tasks, companies are moving forward with cognitive technology efforts. The PYMNTS.com staff asserts, “Overall, it appears that AI like bots and robotics is likely going to see a positive impact on enterprises that choose to embrace it and lift up employees through retraining in the newly developed workforce skill sets. Those choosing not to partake in incorporating AI or who are slow to upgrade legacy systems risk being left behind.”


Concluding thoughts


The MGI partners note, “[Business executives] should have a general familiarity with the capabilities of today’s tools, a sense of where short-term advances are likely to occur, and a perspective on what’s further beyond the horizon.” Having a vision, creating a strategy, and starting slow is good advice. Davenport reports many companies have become comfortable starting at the dumb end of cognitive technologies. “By far the king of ROI is robotic process automation,” he states, “which is, perhaps, the dumbest cognitive technology out there. It doesn’t learn, there are some versions that are starting to learn a little bit, but huge and vast ROIs in the 700% range in some companies.” His point is that starting at the dumb end of cognitive technologies doesn’t mean you are not going to see a measurable return on investment. The MGI partners conclude, “The promise of AI is immense, and the technologies, tools, and processes needed to fulfill that promise haven’t fully arrived. If you think you can let the technology develop and then be a successful fast follower, think again. It’s very difficult to leapfrog from a standing start, particularly when the target is moving so rapidly and you don’t understand what AI tools can and can’t do now.”


[1] Nicole Laskowski, “Start at the ‘dumb’ end when implementing cognitive technologies,” TechTarget, 28 June 2017.
[2] Staff, “AI/BOT: Digital Evolution Of The Enterprise,” PYMNTS.com, 19 June 2017.
[3] Michael Chui, James Manyika, and Mehdi Miremadi, “What AI can and can’t do (yet) for your business,” McKinsey Quarterly, January 2018.
[4] Arpit Dhanjal, “5 Ways Big Data and AI are Changing the Business World,” Irish Tech News, 24 September 2018.

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