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Machine Learning and Decision-making

April 6, 2022


Every day we make myriad decisions without giving much thought to many of them. Businesses are no different. Bain analysts, Michael C. Mankins and Lori Sherer (), explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[1] They add, “People in organizations make thousands of decisions every day. The decisions range from big, one-off strategic choices (such as where to locate the next multibillion-dollar plant) to everyday frontline decisions that add up to a lot of value over time (such as whether to suggest another purchase to a customer). In between those extremes are all the decisions that marketers, finance people, operations specialists and so on must make as they carry out their jobs week in and week out.” Commonsense tells us that companies making the best decisions on a consistent basis perform better than their competitors. And Mankins and Sherer insist they have the data that backs that up. They report, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”


Improving Decision-making


Mankins and Sherer highlight three characteristics of good business decision-making. First, the decisions must be better than competitors’ decision-making. Second, decisions must be made at the speed of today’s business — which is computer speed. Finally, those decisions must be executed more effectively than rivals. Let’s dive a little deeper into those characteristics.


Better decision-making. Mankins and Sherer note, “The need for human knowledge and judgment hasn’t disappeared — you still require skilled, experienced employees. But you have changed the game, using machines to replicate best human practice.” Changing the game is exactly what Enterra Solutions® is doing with its Autonomous Decision Science™ (ADS®) technology. Enterra ADS® technology is the next wave of analytic innovation beyond data science. Our AI software leverages human-like reasoning to serve as your data scientist, subject matter expert, and trusted counselor. It analyzes your data, understands your business processes and logic, and makes decisions as if it were your best expert. Using cutting-edge decision science, Mankins and Sherer assert companies can achieve the following results:


• Generally better decisions: “The incorporation of expert knowledge makes for more accurate, higher-quality decisions.”


• More consistent decisions: “You have reduced the variability of decision outcomes.”


• More scalable decisions: “You have suddenly increased your organization’s test-and-learn capability.”


Machine learning (ML) models, methods, and algorithms help organizations make better decisions because they are backed by data, rather than by feelings or guesswork. Of course, human experts also use data. The challenge today is that businesses are drowning in data, whether it’s operational data, customer data, third party data, or supplier data. Only cognitive technologies are powerful enough to deal with so much data and so many variables.


Faster decision-making. Jon Taylor (@jontaylor91), Content Manager at Peak, writes, “AI decision-making is where companies utilize AI in their processes to help make faster, more accurate, more consistent decisions by leveraging datasets with AI. Unlike humans, AI can analyze large datasets in seconds without errors, freeing up your team to focus on other work. … McKinsey predicts that by 2030, around 70% of businesses will be using at least one type of AI technology, and roughly half of all large companies will have a full range of AI tech embedded in their processes. AI decision-making has the power to increase the world’s economic output, and the estimates are huge: a possible boost of around $13 trillion to the world economy by 2030, which is an extra 1.2% of global GDP.”[2]


Advanced decision-support systems, like the Enterra ADS platform, can achieve unmatched understanding and generate insights at a level of speed and accuracy previously unachievable. And, it can achieve those results with limited human intervention. As a result, your enterprise can perform end-to-end value-chain optimization and make decisions in minutes and hours — not days, weeks, or months.


Better decision execution. Generally speaking, advanced analytics systems generate insights that are provided to human decision-makers. It has been up to those human decision-makers to execute effectively the decisions they’ve made. The trend, however, is shifting towards machines executing decisions. Pengcheng Fu and Joseph Morris, Research Scientists Lawrence Livermore National Lab, explain, “The decision-maker used to be either a human being or a group of human beings; now it can be an artificial intelligence using different combinations of ML and traditional algorithmic programming.”[3] The fact that Enterra® is advancing the subject of Autonomous Decision Science indicates we believe that trend will continue. Machine decision-making, however, can be controversial. Fu and Morris explain, “As soon as people got a sneak peek into the potential of ML–based or ML–augmented decision-making, related ethical and legal implications became important topics of discussion.”


Fortunately, not all decisions have ethical or legal implications. Letting machines make simple business process decisions has been going on for a long time. Today, however, machines are being asked to make decisions that can affect peoples’ lives, such as job hiring and loan applications. According to Fu and Morris, “Acknowledging limitations is the first step to avoiding pitfalls. A better future lies in a robust understanding of how decisions are made, how errors are generated, ML’s limitations, and how to efficiently include humans in the decision process.” When machines are involved in decisions affecting peoples’ lives, journalist Martin Heller (@meheller) agrees with Fu and Morris that human-in-the-loop machine learning is the way to go. He writes, “In a nutshell, human-in-the-loop machine learning relies on human feedback to improve the quality of the data used to train machine learning models. … Human-in-the-loop processing can contribute to the machine learning process at two points: the initial creation of tagged datasets for supervised learning, and the review and correction of possibly problematic predictions when running the model. The first use case helps you bootstrap the model, and the second helps you tune the model.”[4]


Concluding Thoughts


The Nintex team writes, “Machine learning and AI decision-making will change the modern workplace as we know it. While managers may not be fully replaced by algorithms, machine learning technologies could provide valuable management guidance and support.”[5] In the future they see two possible (but not mutually exclusive) scenarios. In the first scenario, “machine learning capabilities evolve further along the same path — to support managers to make fast, informed, and accurate decisions without the intervention of C-Level executives (although the board will still be required for exceptional decision-making situations).” And, in the second scenario, “An AI agent makes decisions on behalf of the team, with the attributes and capabilities of a person running a department.” Mankins and Sherer conclude, “To get the most out of their company’s investment in analytics, leaders have to focus on the decisions that matter most. They have to use analytic techniques to ‘clone’ the organization’s best decision-makers, incorporate these new approaches into the company’s decision processes and (most important) overcome human resistance to the new approach. It’s a lot to do — but that’s what it will take to realize the enormous promise of these dazzling new tools.”


[1] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[2] Jon Taylor, “AI decision making: the future of business intelligence,” Peak Blog, 23 August 2021.
[3] Pengcheng Fu and Joseph Morris, “Robust Decision Making in the Era of Machine Learning,” Towards Data Science, 4 November 2021.
[4] Martin Heller, “What is human-in-the-loop machine learning? Better data, better models,” InfoWorld, 4 February 2022.
[5] Staff, “How AI and machine learning can improve business decision-making,” Nintex Blog, 14 January 2018.

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