How AI Is Used in Decision Making
Explore how AI decision making can make your team more efficient across sales, inventory, and so much more.
Figuring out when to schedule your next inventory order, planning your sales strategy for the next quarter, and taking steps to improve your meetings. These are just the start of the decisions you’re faced with every day. For both big and small choices, decisions take lots of time and effort—and the less of each you can expend while delivering quality, the better.
If you’re bogged down by decision fatigue, artificial intelligence decision making might be the solution. Read on for a guide to AI decision making, including its benefits, challenges, and some examples of how decision intelligence technology can help your team.
- What is AI decision making?
- Importance of AI in decision making
- AI challenges in decision making
- When to use AI in decision making
- AI decision-making examples
What is AI decision making?
AI decision making is an umbrella term for using machine learning and data analysis to inform business actions. From automating simple tasks to cutting back on hours of number crunching, AI decision making can save you and your team valuable time. It can also give you more energy for big-picture thinking while cutting down on basic human errors.
AI decision-making models generally fall into one of three categories:
Decision support
AI decision support systems can review mountains of data at lightning speed and deliver cold, hard facts. These systems take care of the analysis and data presentation for you while leaving the actual decision making in your hands. For example, AI decision support tools can help identify peaks and valleys in your sales cycle; with this data, you can make smarter inventory purchasing decisions.
Decision augmentation
Decision augmentation lets you take an active role in evaluating the quality of an AI algorithm’s decisions. When the algorithm produces a decision, you can choose to accept, reject, or edit the result. The predictive or prescriptive analytics that decision augmentation systems offer can be helpful for tasks such as choosing vendors that offer the best value.
Decision automation
For straightforward tasks that don’t require human touch, decision automation keeps business moving. Decision automation systems take actions based on rules that you set, as well as prescriptive and predictive analytics.
With decision automation, you can set boundaries to prevent anyone from taking action that would violate your organization’s rules. For instance, you can set a rule that no invoice be issued without a signed contract. On a larger scale, decision automation also has the potential to make energy systems more efficient.
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Importance of AI in decision making
If you want to get through projects faster and with greater accuracy, AI decision making can help you reach your destination. Below are some key reasons to incorporate AI decision making into your team’s processes.
- Enhanced precision in risk detection
- Accelerated decision making
- Efficient and accurate results
- More detailed data-driven insights
Enhanced precision in risk detection
When it comes to predicting and detecting risks, AI can spot what the human eye might miss before it becomes an issue. This is especially important in the world of cybersecurity as threat actors get more advanced in their attacks. With decision automation, you can set rules that prevent threats such as data breaches, giving you and your clients peace of mind.
Accelerated decision making
As humans, our energy is finite. Making decisions that require a ton of brain power takes precious time and resources away from other projects. Plus, if you get too worn out to finalize a choice, you can cause a domino effect of delays and missed deadlines. AI decision making helps you conserve your cognitive resources. When tedious data work is off your plate, you can wait for the final numbers and get back to focusing on your bottom line.
Efficient and accurate results
Speed is one part of improved decision making—the other is making a choice based on accurate information. AI decision making decreases the risk of human error quite drastically, leading to faster, smarter choices. For instance, let’s say you want to determine a customer’s lifetime value. Your AI can integrate with your customer relationship management (CRM) software and make the correct calculation.
More detailed data-driven insights
Looking at data for long periods makes you increasingly likely to miss little details as you keep reviewing your numbers. AI systems can spot patterns and nuances that are easy for the average person to miss.
For example, you might think you have a good grasp on customer behavior; AI’s predictive analytics, though, can help give you the whole picture. With so much data to draw from, AI decision-making systems can analyze customer buying patterns, from customers’ favorite products to when they make purchases. When it’s time to define the target audience for your next marketing campaign or product release, you’ll know how to keep customers happy.
AI challenges in decision making
AI decision making has many advantages, but it’s still far from perfect. Here are some common issues to watch out for to keep your decision-making process running smoothly.
- Data integrity and trustworthiness. AI offers incredible data analytics capabilities, but its results can only be as good as the data you provide. If you’re using outdated metrics and figures, your results won’t be accurate.
- Human insight deficiency and contextual gaps. AI analytical tools can offer insights you might have missed, but on the flip side, data itself doesn’t always tell the whole story. AI’s lack of nuance and human understanding can especially cause issues with text generation. If you’re using AI language models to develop marketing communications, thorough proofreading is essential to be sure the narrative makes sense.
- Ethical contemplations in data handling. One of the biggest concerns around AI use is ethical decision making. AI may cut down on numeric errors, but when trained by people, these systems run the risk of replicating harmful human biases. AI recruiting software, for instance, has been shown to demonstrate gender biases when trained on information mostly based on one gender group.
- Clarity and understandability in interpretation. If you fully understand the results, AI systems can offer incredibly helpful insights. The more complex your request for an AI, the more difficult it may be to understand the end result. For example, suppose you’re trying to do a deep dive into one particular variable for decision augmentation. However, you wind up not understanding how your AI system used that metric to propose a decision. This makes it pretty challenging to trace that thread back to the beginning.
When to use AI in decision making
AI may not be a perfect solution for every decision, but it can still be your best option in plenty of scenarios. AI decision making, for example, especially excels with simple and time-sensitive decisions. Choices that are straightforward and predictable are thus ideal for delegating to AI. If you have to make a quick choice based on consistent data, decision-making automation or augmentation can lend a hand.
If you’re trying to make more complex decisions, AI support tools will be your best friend. Think of using these tools as a form of collaborative decision making. If you’ve got a non-urgent issue with multiple factors at play, AI is best used as a supplement. Once the data has been analyzed, you and your team members can work to put the full story together in your next decision-making meeting. Be sure to log all the choices you make in your decision log as well.
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AI decision-making examples
AI decision-making models can be hard to understand in the abstract, so concrete examples are always helpful. Here are some real-world examples of when organizations like yours deploy AI in their decision-making processes.
- Setting salaries. Many organizations use AI to standardize their team members’ starting salaries. AI systems can quickly review pay rates for similar roles and responsibilities to determine a fair base pay.
- Identifying supply chain disruptions. Although supply chain decisions are often highly complex, AI tools can offer ample support in monitoring inventory status and transportation delays. Namely, they take in huge swaths of data from various sources and analyze it to give you a full understanding of your supply chain. With this bird’s-eye view, you can make much smarter inventory purchasing decisions.
- Better meetings. Creating an agenda for your weekly meeting is one more thing to add to your to-do list—and AI can take it off. Using AI to generate meeting agendas can save you time and keep you and your team on track. You can also use AI meeting transcription software to fully document your meetings without any of the usual tedious work.
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AI decision-making systems can keep you on the right path when used correctly. They can analyze vast amounts of data for complex decisions or automate your simplest day-to-day choices. In either case, they change your work for the better when used with a smart eye on their potential drawbacks. Fellow and its AI features can help you and your team maximize your time together, especially if more efficient meetings are a priority. In addition to Fellow’s AI summaries for post-meeting success, you can access over 500 meeting agenda templates to streamline your preparation. With Fellow, you get everything you need for genius decision making before, during, and after your meetings.
