Businesses are excited about AI, and for good reason. Studies show AI could automate up to 29.5% of work hours in the U.S. by 2030 and potentially raise global GDP by 7%. But the real value of AI isn't just in chatbots or AI-generated content. It's in using AI to make better decisions. This approach, called AI decision-making, uses new decision intelligence software platforms to improve how companies operate. It enhances their most fundamental process – decision-making.
AI decision-making is more than a tech trend. It's a comprehensive method that applies AI across an entire organization. It improves data analysis, streamlines decision implementation, and learns from results. By focusing on decision-making, companies can tap into AI's potential to boost productivity and drive growth.
But how exactly does AI play a role in decision-making? In this article, I will discuss:
In late 2022, OpenAI released a new type of AI called Generative AI (GenAI). Executives and investors saw that AI could help companies boost growth and cut costs. This kicked off a rush for companies to start using AI. The growing interest is evident in the numbers.
In 2023, Fortune 500 companies talked about AI more than twice as much in their investor calls compared to 2022. This rise in AI discussions also lined up with higher stock prices. On average, companies that mentioned AI saw their stock prices go up by 4.6%, while those that didn’t only saw a 2.4% increase.
But figuring out where and how to use AI isn't easy. Many companies try to use AI for small things, like chatbots for customer service or AI-powered knowledge management systems. While these can be helpful, they don't always fit into a bigger plan for using AI across the whole company. This can lead to a messy mix of AI tools that don't work well together and don't give companies the benefits they hoped for. Enter: AI decision-making.
Decisions are the perfect place to implement AI. Companies make around 10 million decisions per year. These moments represent the crossroads where data meets human insight and where processes turn into action. By weaving AI into this decision-making process, we enhance how businesses operate and strategize.
The excitement about AI decision-making is backed by real results. A study by Bain & Co. found a 95 percent correlation between how well a company makes decisions and how well it does financially. This shows why AI decision-making is so important. It's not just about using new technology; it's about improving a core part of how businesses work.
As companies realize the power of AI decision-making, they're starting to think differently about how they make choices. This new way of thinking focuses on making the decision process itself better. It's called decision-centricity, and it's changing how businesses operate.
This shift has led to a new category of technology called Decision Intelligence Platforms. Decision Intelligence Platforms (DIPs) include different tools and methods that use AI to help businesses make better decisions.
DIPs are special software that combine human knowledge and AI to help with decision-making. They use different types of AI to assist with every step of making a decision. This combination of different AI technologies working together to make recommendations is sometimes called Composite AI.
AI is at the core of Decision Intelligence Platforms, enhancing every stage of the decision-making process. From spotting the need for decisions to implementing them and learning from the results, different types of AI work together to support better choices. But how does AI in these platforms contribute to each step of the decision-making process?
Predictive AI constantly monitors data to identify significant trends or events. This AI can notice unusual patterns or predict future trends based on past information. It's great at figuring out when a company needs to make a decision.
Predictive AI uses techniques like anomaly detection and due-to decomposition to find the reasons behind unusual events. This means Predictive AI doesn't just identify problems; it also helps us understand why they are happening, giving businesses better insights to address the issues effectively.
For example, let's say a snack company is using a decision intelligence platform. Predictive AI might notice that the cost of some ingredients is going up because of inflation. This information would tell the company they need to think about whether to raise their prices. The AI doesn't decide to raise prices. Instead, it alerts company decision-makers that they need to make a decision about pricing and why.
Once a company knows it needs to make a decision, Generative AI steps in to help. This AI is like a smart assistant that asks all the right questions. It creates a set of questions to help people think about the decision clearly and comprehensively.
Generative AI suggests questions that are tailored to the organization's purpose, goals, and mission. It can also create decision frameworks that guide stakeholders towards high-quality decisions. This helps ensure that each decision is considered from all angles.
If our snack company is considering raising prices, Generative AI might ask questions like: "How much will sales drop if we raise prices?" or "What will our channel partners do if we change our prices?" These questions help the company think through all the important aspects of the decision.
Making good decisions requires a lot of information. But too much information can be overwhelming. That's where Synthesis AI comes in. This AI is like a super-smart analyst. It can look at loads of different information and find the important bits that are relevant to the decision in question.
Synthesis AI uses techniques like multi-variate trend analysis and forecasting to provide insights into potential outcomes. It can also use neural networks to discover underlying relationships between different sets of data. This can lead to novel insights that might not be obvious to human analysts.
For a snack company, Synthesis AI might look at past sales data, how much it costs to make the snacks, and what's happening in the snack market. It then puts all this information together in a way that's easy to understand. This helps the people making the decision see the big picture.
After analyzing and synthesizing data, Composite AI moves on to making recommendations. This step involves several AI types working together to suggest the best course of action. Let’s explore a decision where three types of AI work together: scenario optimization, neural networks and generative AI.
Scenario optimization AI takes the lead by imagining different possible situations. It runs through various "what if" scenarios to find the best outcome. At the same time, neural networks blend the insights from data analysis with human business knowledge. This combination creates recommendations that are both data-driven and practical.
Once the best options are identified, generative AI steps in. Its job is to explain these recommendations in clear, easy-to-understand language. This ensures that everyone, from executives to team members, can grasp the reasoning behind the suggestions.
Consider how this works for the snack company. The AI system might recommend a 5% price increase based on its analysis. But it doesn't stop there. It could also suggest launching a targeted marketing campaign to explain the value of the snacks at the new price point.
This Composite AI recommendation aims to balance two key goals: maintaining customer loyalty and covering the rising costs of production. The Composite AI doesn't simply crunch numbers; it considers the broader business context and potential customer reactions.
Once a decision is made, it needs to be put into action. That's where Automation AI comes in. This AI is like a super-efficient assistant that can do repetitive tasks quickly and without mistakes.
Automation AI often takes the form of Robotic Process Automation (RPA). It can handle mechanical, repetitive tasks associated with executing a decision. This ensures that decisions are implemented swiftly and uniformly across all relevant systems.
If our snack company decides to raise prices, Automation AI could update the prices in all the company's computer systems. It could also help personalize messages to channel partners explaining the price change. This ensures the decision is carried out quickly and consistently.
Learning AI is all about improving decision-making over time. After a decision is made and implemented, this AI analyzes the results to learn from them. It's like having an automated system for conducting thorough post-decision reviews, helping the company understand what worked, what didn't, and why.
Learning AI also examines the various factors that influenced a decision, determining which elements had the greatest impact on the outcome. These factors might include market conditions, customer behavior, operational efficiency, or competitive actions. The AI then feeds these insights back into the decision-making system for future use. This creates a continuous improvement cycle that enhances the decision-making process over time.
Let's say our snack company decided to raise prices. Learning AI would look at how this affected sales, whether customers stayed loyal, and how it changed profits. The AI could find that the impact on sales was less severe than anticipated, but customer loyalty was affected more than expected. Or it might find that the competitive response was stronger than predicted.
AI decision-making changes how companies operate in significant ways. These changes affect daily work practices, how different parts of a company interact, and how decisions are made across the organization.
First, employees can focus on thinking creatively instead of spending hours sorting through data. As AI takes care of tasks like gathering and processing information, people can spend more time interpreting results and developing creative solutions to problems. This shift allows companies to make better use of their human talent.
Furthermore, different parts of a company can work together more effectively because everyone can access the same AI-enabled shared knowledge base. Decision intelligence platforms help break down silos between different departments. They create a unified framework that allows teams across the company to see how their decisions affect other parts of the business. This leads to more coordinated action across the organization.
Lastly, local teams can make decisions that fit their specific needs while still supporting the company's overall goals. AI provides these teams with access to the company's shared knowledge, allowing them to make informed decisions that fit their local situation. This approach helps companies be more responsive to local market conditions while maintaining a clear direction for the entire organization.
For companies wanting to take advantage of AI decision-making, Cloverpop offers a complete, end-to-end solution. In fact, Gartner, the respected technology research company, recognized Cloverpop in their 2024 Market Guide for Decision Intelligence Platforms.
Cloverpop stands out by combining human and artificial intelligence in its Decision Intelligence platform. This approach helps companies make decisions 2-4 times faster while saving 30% on analytics costs. Even more impressive, it can lead to up to 5 times higher shareholder returns through better decision outcomes.
Cloverpop uses almost all the types of AI discussed earlier. It uses Predictive AI to help companies spot when they need to make decisions. Generative AI helps frame decisions by creating comprehensive decision-making guides. Synthesis AI helps make sense of all the information needed to make a decision. Composite AI turns all this data and analysis into clear recommendations. Finally, Learning AI helps companies get better at making decisions over time by tracking how well past decisions worked out.
By bringing all these AI types together, Cloverpop helps companies make smarter choices faster. This way, companies can grow and adapt.
The impact of AI on business is huge. For the biggest companies, using AI strategically will add billions in enterprise value. But the real power of AI isn't just in making things more efficient. It's in helping companies make better choices, adapt quickly to changes, and spot new opportunities before others do.
As we've seen, AI can help at every step of making a decision. It can spot when decisions need to be made, help frame the questions, analyze data, make recommendations, put choices into action, and learn from the results. Companies that use AI in this way are setting themselves up for growth and competitive success.
Ready to see how AI can improve your company's decision-making? Cloverpop offers a complete solution that brings together all these types of AI. To learn more about how Cloverpop can help your business make smarter decisions faster, schedule a demo today. Take the first step towards improving your decision-making process with AI.