The Top Skills Needed for Workers in Cognitive Enterprise

Published: Jan 17, 2020
Modified: Feb 07, 2020

By Jamiel Sheikh

What Is Cognitive Enterprise?

Cognitive enterprise exists at the crossroads between big data and intelligent computing, designed to think and respond in a user-friendly way. While there’s still a long developmental road ahead, the general direction of cognitive enterprise is moving toward computing systems that learn and reason like a human.

The potential applications are significant. Corporations will find new ways to boost the efficiency of their business practices and save on expenses. Data will be analyzed to previously unrealized levels and the insights gained will be used to market products more efficiently. Likewise, it’s predicted that customer service interactions will be increasingly handled by cognitively enabled systems.

Key to the principles of cognitive enterprise is that it primarily deals with technology designed to assist humans in their decision making. These are systems meant to mimic the mind, hence the “cognitive” aspect of their name. While cognitive enterprise is an emerging technology, there are still plenty of applications that are already on the market. Let’s have a look at what’s happening in the field.

What Are the Applications of Cognitive Enterprise?

Cognitive enterprise has the power to disrupt across many business value chains. For example, shipping and logistics companies could streamline their routes with the help of cognitive enterprise in order to maximize efficiency and minimize costs. Those tech companies which currently rely on image classification and object recognition could gain a boost from cognitive enterprise as millions or billions of images are analyzed, and patterns determined. The fast and efficient classification of images will likewise improve the technology behind self-driving cars.

Banks and fintech companies could use cognitive enterprise to reduce fraud, potentially helping to process thousands of transactions a second to look for anomalies. As cognitive enterprise systems become more popular, they may change the way a person’s credit score is calculated. The systems could find correlations in data that no human would ever have recognized. These findings will be used to inform banking companies, which can then be able to more accurately offer credit.

What Skills Should Companies Teach Their Employees to Get Ready for Cognitive Enterprise?

We’ve discussed how cognitive enterprise will fit in to future businesses, but how should companies prepare their employees? Traditionally, artificial intelligence (AI) has acted on its own, and computer systems have reviewed data and made decisions without human intervention. While cognitive enterprise has a lot in common with AI, the crucial difference is that it doesn’t usually make decisions. Instead, it makes recommendations.

Therefore, there are two skills that employees will need to learn in order to maximize the value of a cognitive enterprise system. The first is how to make effective use of cognitive enterprise recommendations. The second is to trust cognitive enterprise’s judgement—which could be a sticking point for widespread adoption.

Good doctors, for instance, have a justifiable belief in their ability to treat patients. However, computers are becoming increasingly skilled at diagnosing diseases. As a result, it might be difficult for a doctor to trust AI’s judgement. However, if the computer is statistically more accurate than a human, then the right thing to do is to trust the computer. The doctor still treats the patient and answers their questions; however, he defers to a cognitive enterprise system for those tasks which it does better.

In a more traditional setting, cognitive enterprise systems will make it easier for employees to sort through big data sets and get answers that make sense. In this sense, employees will need to learn how to effectively interact with cognitive enterprise systems in order to ask the right questions.

How Will Companies Who Use Cognitive Enterprise Compete with Other Businesses?

One of the largest potential use cases of cognitive enterprise will most likely be in marketing. Cognitive enterprise systems will sort through unparalleled amounts of data in order to make marketing suggestions. For instance, the system may look at a user’s current mood based on social media posts, expenditures in the last week, user location, historical spending during this time of the month and so much more. Based on these data sets, cognitive enterprise systems will be able to detect patterns and suggest optimal advertisements. This will most likely lead to increased sales for companies using this technology.

Cyber security will also benefit. Cognitive enterprise systems will analyze data in order to find the most common ways that criminals compromise systems. They will be able to make security suggestions based on that data. Crucially, cognitive enterprise systems will operate in real time based on potential real-time streaming and long-run accumulated data. They will offer suggestions in seconds or minutes not days, weeks or months. This could save companies millions of dollars in security expenses and fraud costs associated with hacked accounts or corporate theft.

Cognitive enterprise systems will also be able to spot business opportunities that would be harder or impossible for humans to ferret out. This will allow for pivots and new products that could potentially be extremely lucrative. Again, the cognitive enterprise system does not implement the change, it makes the suggestion.

While it remains up to employees to take action in implementing cognitive enterprise solutions, the companies which have cognitive enterprise systems making suggestions and monitoring their systems may stand to outcompete those companies which fail to implement this new innovative technology.

About the Author

Jamiel Sheikh is CEO of Chainhaus, an advisory, software development, application studio and education company focused on blockchain, artificial intelligence and machine learning. Jamiel has more than 15 years of experience in technology, capital markets, real estate and management working for organizations like Lehman Brothers, JPMorgan, Bank of America, Sun Microsystems, SONY and Citigroup. Jamiel is an adjunct professor at Columbia Business School, NYU and CUNY, teaching graduate-level blockchain, AI and data science subjects. He runs one of the largest blockchain, AI and data science Meetups in NYC.