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Digital Transformation and Cognitive Computing

September 28, 2023


Business experts continue to insist that, in the current era, companies must transform into digital enterprises or risk going out of business. What does that really mean? Depending on the economic sector in which an enterprise operates, a digital enterprise can take many forms. The simplest definition of a digital enterprise is one that upgrades its business processes by leveraging data to optimize and automate them. That definition, however, focuses heavily on the technology side of digital transformation. Too many companies, when contemplating their digital transformation journey, think only about implementing new technologies. That’s a mistake. Any good consultant will advise business leaders to focus simultaneously on people, processes, and technology.


Tricia Wang, a self-described Tech Ethnographer & Sociologist, explains, “A lot of companies treat digital as if they are ‘doing digital’ — this is ‘digitization’ at its worst — as if it’s some checklist of things to do. It’s very transactional, and people are so busy doing digital they don’t even know WHY they are doing it in the first place! Whereas [some companies] embrace ‘being digital’ — this is ‘digital transformation’ at its best — it’s a total paradigm shift in the culture and operations — it’s not just about buying the latest digital tool, but about creating a new system, new cadence, new mindset.”[1] Before discussing how cognitive computing can help digital transformation efforts, I want to make it clear that a strict technology focus is not a path for success.


Why Cognitive Computing Can Help


Just as the definition of what constitutes a digital enterprise is a bit fuzzy, so is the definition of cognitive computing. The staff at CIO Applications Europe notes, “Cognitive computing is often classified as simply marketing jargon, so having a working concept is important. … There’s no consensus that industry experts have suggested, [however,] cognitive computing systems actually aim to replicate human thought processes.”[2] When IBM coined the term “cognitive computing,” they viewed it as an augmented intelligence system aimed at helping business leaders deal with information overload. Ginni Rometty, former CEO of IBM, explained, “The idea was to help you and I make better decisions amid cognitive overload. … It’s the idea that each of us are going to need help on all important decisions.”[3] This is especially true when business leaders have to make decisions amid conflicting or ambiguous information. The now defunct Cognitive Computing Consortium once noted:


The cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is ‘best’ rather than ‘right’. Cognitive computing systems make context computable. They identify and extract context features such as hour, location, task, history or profile to present an information set that is appropriate for an individual or for a dependent application engaged in a specific process at a specific time and place. They provide machine-aided serendipity by wading through massive collections of diverse information to find patterns and then apply those patterns to respond to the needs of the moment.”


Not only can a cognitive computing system deal with ambiguous situations, it can embed tribal knowledge so that the machine plays the role of a data scientist or subject matter expert to help optimize a business and help it run at the speed of the marketplace. McKinsey & Company analysts note, “Speed is a true superpower for any company and the only way to be prepared for a world of rapid change. … Speed needs to be applied not for its own sake but with some clear end-goals in mind. A key question to ask is, what value is currently being trapped — and where?”[4] Some value is being trapped or slowed because some decisions that could be made by artificial intelligence are still being made by humans. That’s why Enterra Solutions® is focused on advancing Autonomous Decision Science™ (ADS®).


Another benefit of cognitive computing systems is ease of use. The term “artificial intelligence” can generate fear in some employees who may feel a bit technically challenged; however, cognitive computing systems are designed with human interactions in mind. The staff at Tian Shan Net explains, “Cognitive computing systems are designed to be more intuitive and user-friendly than traditional computing systems, and they are able to interact with humans in a more natural way.”[5] This “natural way” of interacting with humans involves natural language processing (NLP) or natural language understanding (NLU). The Tian Shan Net staff explains:


Natural language understanding is a subset of artificial intelligence (AI) that focuses on the interaction between computers and human language. NLU systems are designed to understand the meaning behind human language and to respond in a way that is appropriate to the context of the conversation. These systems are used in a variety of applications, including chatbots, virtual assistants, and customer service systems. The relationship between cognitive computing and natural language understanding is a close one. Cognitive computing systems rely heavily on NLU to understand human language and to interact with humans in a natural way. NLU is a critical component of cognitive computing, as it allows these systems to process and analyze vast amounts of data in a way that is meaningful to humans.”


The Tian Shan Net staff suggests five ways cognitive computing systems can help businesses.[6] They are:


1) Advanced analytics. The Digital Era is all about the data — and there is lots of data to analyze. The Tian Shan Net staff notes, “One of the most significant benefits of cognitive computing is its ability to process large amounts of data quickly and accurately. … Cognitive computing can analyze this data and provide insights that can help businesses make informed decisions.”


2) Constant improvement. According to the Tian Shan Net staff, “Another benefit of cognitive computing is its ability to learn and adapt over time. As it processes more data, it becomes more accurate and efficient. This means that businesses can use cognitive computing to continuously improve their operations and stay ahead of the competition.” As I noted above, Autonomous Decision Science goes beyond traditional data science because the Enterra ADS® platform can Sense, Think, Act, and Learn®.


3) Cost savings and improved efficiency. The Tian Shan Net staff explains, “Cognitive computing can also help businesses reduce costs and increase efficiency. By automating repetitive tasks, such as data entry and analysis, cognitive computing can free up employees to focus on more strategic tasks. This can help businesses reduce labor costs and increase productivity.”


4) Better decision-making. As discussed above, cognitive computing was developed to improve decision-making. The Tian Shan Net staff insists, “By providing real-time insights and predictions, cognitive computing can help businesses make more informed decisions. This can help businesses avoid costly mistakes and capitalize on new opportunities.”


5) Improved customer service. “Finally,” the Tian Shan Net staff notes, “cognitive computing can help businesses improve their customer service. By analyzing customer data, cognitive computing can help businesses understand their customers’ needs and preferences. This can help businesses tailor their products and services to meet those needs and provide a better customer experience.”


Concluding Thoughts


The Tian Shan Net staff asserts, “Cognitive computing has the potential to revolutionize international business. … By embracing cognitive computing, businesses can stay ahead of the competition and capitalize on new opportunities.” Cognitive computing is the foundation upon which the Enterra System of Intelligence™ is built. These systems offer a cutting-edge approach that combines the power of a human-like reasoning, trusted generative AI, explainable machine learning, and real-world optimization to drive intelligent decision-making and fuel business growth. The Tian Shan Net staff predicts, “As we continue to develop and refine these technologies, we can expect to see even more exciting applications in the years to come.”


[1] Trevor Miles, “Let’s be clear: Digitization is not the same as Digital Transformation,” Kinaxis Blog, 8 December 2017.
[2] Staff, “Key Differences between AI and Cognitive Computing,” CIO Applications Europe, 1 February 2021.
[3] Megan Murphy, “Ginni Rometty on the End of Programming,” Bloomberg BusinessWeek, 20 September 2017.
[4] Homayoun Hatami, Dana Maor, and Patrick Simon, “All change: The new era of perpetual organizational upheaval,” McKinsey & Company, 15 June 2023.
[5] Staff, “The Basics of Cognitive Computing and Natural Language Understanding,” Tian Shan Net, 24 June 2023.
[6] Staff, “The Benefits of Cognitive Computing in International Business,” Tian Shan Net, 24 June 2023.

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