Shockingly, decision-making in some companies is often as effective as looking into a crystal ball! Intuition and emotional influences can determine decisions even up to the highest levels. Studies show that some decision-makers use only 22% of available insights and recommendations and many managers fail to apply “best practices” 98% of the time. The perception that high-quality company decisions are made is reported by just 57% of respondents. In that case you may as well just flip a coin! A new era of Artificial Intelligence can at last take decision-making out of the mystical clouds and bring it down to earth. The key to achieving this is Decision Intelligence (DI), one of the most important technology trends of 2022 (according to market researcher, Gartner).
Why it is imperative that we approach decisions differently today
Imagine if we could finally begin to make smart decisions and never wake up in the morning to regret the night before again! (Just kidding, not even AI can prevent that!) But, to be honest, that just shows how human nature is not very efficient at making good decisions. Most of the time, whether we like it or not, our judgments are usually based on emotions and influenced by unconscious biases. Plus, during decision-making situations it is always usual to pick the quickest and easiest solution to a problem rather than the best and most effective – this is known as “satisficing”. People want to act rationally, but it’s easier to sacrifice some things to obtain satisfaction – there are natural limits to how much information can be absorbed and processed. The business world has become so complex that many existing decision-making processes in companies are no longer sufficient. We need to rethink decision-making.
What is Decision Intelligence?
Whilst it is common for us humans to make poor conclusions due to lack of capacity, Artificial Intelligence lacks emotion and social factors that could adversely effect a wise decision. The young academic discipline of Decision Intelligence combines the best of both worlds. DI allows traditional decision-making processes to be combined with advanced technologies such as AI, machine learning and everyday language (NLQ) data queries. These cognitive technologies analyze all available data, evaluate the information, identify decision needs and suggest every available solution. Consideration is given not only to raw data, but also to a multidimensional set of data that includes text, images, video and audio.
Automated decision making vs. augmented intelligence
There are various ways of applying DI in many areas of decision-making. For routine business tasks, production and customer operations, an end-to-end automation is the fastest and most profitable solution. Using programmed processes, repetitive tasks and actions can be executed flawlessly and without interruption. But beyond these predefined processes there are innumerable choices to be made which require intuition, flexibility and coordination between all personnel involved. Close co-operation between humans and machines (augmented intelligence) allows for informed and sound decisions with the help of AI. Just like us, machine-learning systems learn from experience and independently find solutions to new and unexpected problems as they prepare the entire process from data analysis to decision recommendation. Decision makers can then make the right decisions by choosing from all proposed alternatives. In other words, using Decision Intelligence never involves leaving critical decisions to machines, but rather combines human experience and intuition with automation to take decision-making to a whole new level.
What are the benefits of Decision Intelligence for businesses?
Those who have already decided to gain a competitive advantage from working with intelligent decision-making are in a good starting position but, surprisingly, the number of DI users is still relatively small. Analyst Dr. Pieter J. den Hamer predicts that 33% of large companies will begin implementing Decision Intelligence by 2023.
The reasons for this are self-evident: AI-supported decision-making can reduce unnecessary costs that arise from slow processes and high failure rates plus decisions are made transparently and measurably. These reasons combine to greatly increase a company’s knowledge management in the long run. Successful footballers perform by using an old saying that “success must be repeatable” – in this sense, the ability to consistently and logically reproduce perfect decisions again and again is the perfect platform for the company’s next steps forward.
Here is our list of the top five benefits of using Decision Intelligence to help move a company forward in the long run and provide competitive advantage:
- Making use of complex data sets
AI-driven decision-making processes analyse combinations of previous results and a rich set of information from multiple sources to deliver findings, results and pros and cons.
- Keeping processes flexible
Technology makes it possible to adjust chosen parameters at any time, depending on situations and requirements. As a result, the company always remains in a position to identify a number of alternatives for solving a problem and to decide on the best option.
- Correct and value-free judgment
External factors such as cognitive or behavioral influences as well as human error are eliminated from the entire analysis and evaluation process. The proposed options remain free of bias and error.
- Making decisions measurable
DI provides managers with a system that enables them to track how decisions are made. Key figures and feedback loops make it possible to learn more about processes and how to optimize them.
- Shortening decision-making processes
The analysis process of unorganized and scattered data volumes can be significantly reduced to enable your company to make the right decision in less time.
By the way, to illustrate the dimensions of how impactful slow decisions can be, McKinsey extrapolated its survey results to the top U.S. companies in terms of revenue: “Ineffective decision-making has significant implications for company productivity. On average, respondents spend just 37% of their time making decisions and, even worse, more than half of this time was thought to be spent ineffectively. For managers at an average Fortune 500 company, this could translate to more than 530,000 days of lost working time and roughly $250 million of wasted labor costs per year.”
What should my approach be to using DI in the company?
The use of AI technology in itself is by no means enough to suddenly outpace the competition. It usually involves a rethinking of a company’s culture and pulling away from focusing purely on IT. According to Gartner’s analysts, a company that wants to fully exploit the benefits of Decision Intelligence should make its decisions as follows:
Decisions have a mutual impact on individual personnel of an organization so the process must be much more connected at all levels. Sharing data and insights is the bread and butter of this process.
Any alternative decision being considered must be evaluated beyond the constraints of a single event or transaction.
Companies must respond to both opportunities and disruptions as quickly as possible. Decision-making is increasingly becoming a continuous process.
Companies set their starting point for using DI by analyzing the current state of their decision-making processes. At what point are the decision-making processes so complicated that they became unmanageable? At what point is there a huge amount of data but little insight? Where is the opportunity to merge multiple decision silos? Meetings where decisions are made should be monitored along with organizing interviews with decision-makers and asking them to explain some examples of the way decisions are being made. There are certainly some rules for making operative decisions within a company that can be documented. This allows decision-making principles to be defined and decision-making habits to be identified. Following on from this and selecting customized technologies and tools will then make it possible to review step by step important use cases before scaling the DI approach for the entire company.