Everything in the world revolves around decisions, no matter how big or little the business is. There are a variety of technologies that businesses employ to help or automate their decision-making processes. Let’s start by asking where choices are made to learn more about these technologies. Organizations are continuously making choices, as we’ll see. Some of the stakes are enormous, and as a result, the company’s customers, partners, and rivals can see them. Others are so regular that even the individuals who pick them up on a daily basis lose interest in them because they have gotten so used to seeing them.
A place and time for automating decision-making
There are three activities that occur simultaneously in any organisation. Initiatives to plan for the future, daily operations to keep the company running smoothly, and strategic choices to maximise daily operations and long-term projects. From the CEO in charge of the whole company to the technician who is in charge of a single machine, decisions are taken at every level of the business. We will break down the organization’s choices into strategic, tactical, and operational considerations in order to make exploration easier.
Decisions made in the context of strategy
Our definition of a strategic choice is a series of action plans or policies targeted at reaching big or global objectives. The essence of strategic choices is one of change. If you’re looking for an example of this, consider a merger, acquisition, or initial public offering (IPO).
As of now and in the near future, strategic choices can’t be made entirely by machines. All situations demand creative thinking that goes beyond artificial intelligence (AI). However, this does not imply that strategic choices are not influenced by the use of technologies.
Excel spreadsheets and business intelligence (BI) technologies have been used by industries and corporations for decades. Since big data and machine learning have emerged, businesses are increasingly incorporating data on the national economy as well as information on their target markets into their sales predictions.
There are, of course, instances in which organisations employ cutting-edge techniques to aid in strategic planning. To decide whether to invest in a new oil or gas field or to build a bridge, big majors in the energy, oil, and gas, and construction industries have used real options for decades, just as big investment banks do when they decide to buy options on complex financial products listed on the stock exchange.
A tactical choice is a collection of activities that are planned and implemented in order to attain a specified goal in this article. This kind of choice is similar to a strategic one, but it is limited in scope and time frame.
Before now, corporations routinely used analytical, optimization, and statistical methods to make strategic choices. Cloud computing and big data have led to an increase in the use of machine learning. The forecasting of sales of a new product in each region where it will be marketed for the first time, or the personalization of discount coupons to help customers save money, the brand raise sales, and the merchants get foot traffic, are examples from the sector of consumer goods products.
In most cases, machine learning is used to tackle decisioning difficulties. To decide, direct an action, or refine a system, one must be able to forecast the future. Briefly stated, prediction serves as a tool, not a goal.
As a result of their expertise
A professional’s background knowledge, established expertise, and custom-tailored heuristics are all used to arrive at an expert judgement.
The ’80s and ’90s were a golden period for automating expert judgements. Machine learning was formerly known as knowledge-based and expert systems, which are now the focus of several conferences, journals, and books. After interviewing subject matter experts, developers known as knowledge engineers were able to learn how they made judgments.
Decisions are stored as condition-decision rules in a knowledge-based or expert system. When all the requirements are met, an inference engine makes a conclusion based on that information. For the inference engine, the subject matter expert’s heuristics have been refined for years, if not decades.
In contrast to popular assumption, knowledge-based and expert systems, such as those used to correlate alerts in a telecommunications network, configure an electronic device, or diagnose broken equipment, addressed many more issues than previously thought.
This current method to decision-making, known as business rules, does not need expert interviews but provides experts with the tools they need to make their own conclusions, without the need for previous understanding of a rule programming language.
Consequences of actions
Thousands and maybe millions of operational choices are made every day by corporations. Operational choices are at the heart of many businesses, such as those in the financial services and insurance industries. In every product they sell, there are a plethora of legal limits, eligibility requirements, and risk factors to consider before making a decision.
Decisions made at the operational level may resemble those of experts, but they are in fact distinct. Oftentimes, operational choices are prescriptive in nature, enforcing industry laws, corporate policies, or company plans irrespective of the views, knowledge, or preferences of individuals involved in their implementation. Loan officers at banks and insurance agents use similar reasoning to make their decisions on whether or not to lend money or how much an applicant should pay in premiums depending on their health conditions and medical treatments.
Expert decisions, on the other hand, are frequently descriptive in that they describe how managers or experts make decisions based on available information and knowledge. An example of a healthcare professional utilising data and an analytical model to make treatment decisions is the doctor at the hospital, or the trader purchasing a highly volatile asset using market data and an analytical model.
There was a rush to use decision management systems to automate operational decision-making. There are a slew of different technologies hidden under the jargon of decision management systems. Trees and graphs are the simplest options. The most advanced models include both rules and predictions.
The human being is constantly up-to-date
It’s interesting to note that no matter what approach is used, some human effort is required. One or more humans must intervene, regardless of the amount of complexity of the instrument and the level of automation it permits. Both to specify the issue, adjust the settings, or verify that the solution is effective.
To solve a problem in mathematical optimization, practitioners must first identify the parameters, constants, and constraints; then select and run an algorithm on these data; in some cases, the algorithm may never converge, in which case, they have to relax some constraints and re-run the optimization process until they find an acceptable solution.
As part of machine learning, they must divide data into training and testing data; choose a model and fine-tune its parameters; run it and repeat until they are pleased with their findings. As soon as it goes into production and is being used, the model’s performance must be closely monitored since fresh data may differ from the historical data on which it was constructed.
Because the world is never simple to capture in a single step, knowledge-based systems must collect domain knowledge from experts and encode it into rules. Organizing rules into manageable knowledge sources becomes necessary when the quantity and variety of rules exceed a particular threshold of complexity.
They need to examine the influence of individual choices on company performance on a global scale via systems of decision management. Even a little blunder in a single choice may have significant ramifications for a company’s reputation, income, and even legal action. Dashboards with real-time decision analytics let users monitor and change their choices as the circumstance changes.
When is it OK to use software to make decisions?
Organizations have relied on decision support and automation technologies since the dawn of the personal computer era to help them make better strategic, tactical, expert, and operational choices. The press, media, and social networks focus much too much on big data and machine learning these days, but these aren’t the only factors that go into making decisions inside firms. Because there isn’t just one way to do things, businesses use an array of methods and technologies that work together in concert.
Organizational decision-making is a conundrum that no one decision process has been able to solve. Practitioners continue to depend on optimization and statistics for strategic and tactical choices, but the proliferation of data is forcing them to include more machine learning approaches. They employ decision trees, decision graphs, rules, and machine learning to make expert and operational judgments.
Strategic and tactical judgments are supported by decision support tools, while operational decisions are automated by decision automation tools. Decision support and decision automation tools are always in the hands of people who have to setup, monitor, and fine-tune them. Regardless of the category of the tool. In the near future, we won’t be able to see an algorithm that can handle it all.