For decades, data has been the lifeblood of business that guides decision making. 

But let's face it, sometimes there just aren't enough hours in the day to sift through the ever-growing mountain of information and make the right call. 

While our brains can only handle so much, AI isn't quite ready to take the reins entirely. It lacks the human touch, the ability to apply wisdom and gut feeling that can make all the difference.

You can’t replace human judgment with AI. But when the two work together, they analyze data at lightning speed and streamline processes, all to make your business faster and smarter.

In this article, I’ll go through what is enterprise AI and how you can use it for data-driven decision-making.

What is AI-driven decision making?

AI-driven decision-making refers to the use of artificial intelligence to make informed choices or predictions. In other words, it is an informed analysis driven by AI. 

AI helps in processing large amounts of information quickly and accurately to support decision-making processes.

The process relies on technologies like machine learning, natural language processing, and predictive analytics to learn from data and improve over time. 

The AI system then generates insights or predictions based on the analysis of this data. 

Nike’s New York flagship store, for example, uses generative AI to create personalized shoe designs tailored to meet each person's specific needs by integrating customer preferences.

They consider color choices and design influences with data on foot shape and performance metrics. 

Similarly, Bank of America uses virtual reality (VR) to train employees on tasks ranging from account opening to handling complex service calls. 

What is Enterprise AI?

Enterprise AI is the use of artificial intelligence in large companies to help businesses make smarter decisions and operate more efficiently. 

In other words, enterprise AI is AI software made for large organizations. Now enterprise AI software could vary from organization to organization according to their business needs.

For instance, Amazon uses AI to manage its vast inventory and recommend products to customers, which improves efficiency and personalization. 

By integrating AI, organizations can automate processes, analyze large datasets quickly, and get insights that drive smarter, faster decisions.

This synergy between AI and business strategies helps companies stay competitive and responsive to market changes.

Limitations of traditional decision making

Before I jump all in on enterprise AI use cases, let's talk about the traditional way we make decisions. 

Sure, we humans are pretty smart, but we also have some built-in limitations. Human decision-making is prone to biases and subjectivity. 

People may make decisions based on personal experiences, emotions, or incomplete information that leads to errors. 

In 2023, an international study by Oracle and author Seth Stephens-Davidowitz found that around 85% of business executives experience "decision distress," which involves regretting, feeling guilty about, or questioning a decision made within the past year.

Unlike humans, enterprise artificial intelligence isn't easily swayed by biases (although it’s not bias-free altogether) or overwhelmed by information. It analyzes vast amounts of data in a flick and identifies patterns and trends that humans might overlook. 

So, businesses using AI for enterprise make informed decisions that are based on data rather than intuition alone. 

Enterprise AI solutions: How can enterprises use AI for better decision making?

Now, let’s look at some specific enterprise AI solutions and AI applications that help businesses around the world make smarter decisions.  

1. AI data analytics

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Companies these days collect all sorts of information—sales figures, customer reviews, social media mentions, and a lot more. Making sense of all of it is difficult, and often, it becomes a messy jumble. 

AI algorithms are super good at spotting patterns and relationships within data. AI-driven CRMs convert unorganized data into organized data structures.

You can identify things like which marketing campaigns are working best, what products are about to become popular, etc, to make business decisions based on accurate, up-to-date information. 

Enterprise AI data analytics can help with a wide range of industry-specific tasks, including 

• Real-time credit card fraud detection
• Aiding in disease diagnosis
• Forecasting demand in retail
• Modeling user behavior in gaming apps

2. AI-integrated expert systems

Expert systems are digital repositories of knowledge crammed with information and best practices from various fields. Law, finance, medicine—you name it, there's probably an AI expert system for it.

All that wisdom wouldn't be much of use if it was buried in a dusty textbook. AI makes this knowledge searchable and easy to understand. 

You can ask the system specific questions and get clear, concise answers.

AI expert systems aren't meant to replace human decision-making. Instead, they empower employees with the knowledge and insights they need to make informed choices.

Many companies are already reaping the benefits of AI-integrated expert systems. 

For instance, insurance companies use them to streamline fraud detection, while manufacturers use them to optimize production processes.

‍MYCIN is an early healthcare AI expert system made to treat blood infections. It diagnoses patients by analyzing symptoms and test results. 

If needed, it asks for more patient details and recommends additional tests. After assessing the data, MYCIN suggests treatment plans.

3. AI modeling techniques

An AI model uses algorithms to analyze data, identify patterns, predict outcomes, or make decisions automatically, without human involvement.

There are different AI modeling techniques for different tasks. 

• Supervised learning, where you show the AI model examples of data with clear labels (like spam emails), and it learns to identify those patterns in new data. 

• Unsupervised learning, where an AI model finds hidden structures and groupings within the data on its own. It is helpful for tasks like product recommendation or market segmentation.

Within these categories, there are many other specific enterprise machine-learning techniques, like decision trees. All of them allow AI to learn from data and make increasingly accurate predictions. 

This is what empowers businesses to make smarter decisions in all sorts of areas, from optimizing marketing campaigns to predicting equipment failures.

4. Virtual advisors

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Virtual advisors are AI technologies trained on massive amounts of company data. They can crunch numbers, pull up reports, and identify trends you might have missed.

For instance, in diagnosis and treatment planning, virtual advisors can analyze patient symptoms, medical history, and research data to suggest possible diagnoses and treatment options. 

In administrative tasks, virtual advisors can assist with scheduling appointments and managing patient records

Epic, an Electronic Health Records (EHR) provider, is using Microsoft’s generative AI to help hospitals save time on documentation, coding, and billing. 

Health systems like UC San Diego Health, UW Health, Stanford Health Care, and Sutter Health are already testing this tool to reduce the documentation workload for healthcare providers. 

Epic has now expanded its partnership with Microsoft to include more enterprise AI technologies that directly influence decision-making. 

These new tools include:

• An AI-assisted In Basket for faster documentation.

• Nuance’s Dragon Ambient eXperience Express AI for better documentation in Epic’s platforms and mobile app.

• An AI tool to help with medical coding by suggesting codes based on clinical documentation.

• Generative AI features for users via SlicerDicer.

5. Real-time tracking of on-the-ground businesses

Enterprises can use AI to track their on-the-ground activities in real-time. Tiny trackers and sensors are placed in vehicles and sometimes even carried by employees for a constant stream of data on location and activity. 

This technology helps businesses monitor and manage their operations more efficiently. 

Having a constant view of your field force allows you to make better decisions about staffing, scheduling, and resource allocation. 

An excellent example of AI in the enterprise using real-time tracking is the ILR Industries in Ontario needing to keep workers safe during COVID-19 while maintaining operations. 

With over 40 workers in a 100,000-square-foot facility, close proximity caused stress and fear of illness. They implemented TRACE Sensors, using Quuppa’s indoor positioning and ThinkIN’s location intelligence software, managed by i-Virtualize. 

This system used tags to monitor worker locations and ensured proper distancing while tracking asset locations and productivity.

The results were improved safety, increased productivity, and enhanced efficiency. Employees felt safer and more engaged while working on the ground. 

6. Virtual role-play

You can use an enterprise AI platform to create realistic simulations of real-world scenarios. Employees then use the virtual environment to practice their responses and decision-making skills in a safe space. 

The beauty of virtual role-playing is that it happens in a simulated environment. So, trainees can experiment with different approaches without any real-world consequences.

Nailing a virtual role-play can give trainees a real confidence boost, which translates into success in the real world.

For example, in customer service training, AI creates scenarios where employees interact with virtual customers. Employees learn to practice handling various situations, like complaints or inquiries, in a safe environment.

Verizon, a U.S. telecom company, used VR technology from Strivr to train customer service agents. The VR simulations allowed trainees to step into the customer's shoes and see problems from their perspective. 

As a result, agents became better at handling interactions and making decisions that improved customer satisfaction.

Make better decisions with Formaloo’s AI tools!

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We've explored just a few of the many ways enterprise AI is being used to make smarter decisions, from real-time tracking of field teams to intelligent data analysis.

What if you could build your own AI decision-making tools? Formaloo AI, built on advanced machine learning engines and data analytics, empowers businesses to create custom AI solutions without coding. 

You can train your AI system with your data and build applications powered by your unique AI model.

What’s more? The platform lets you conduct AI-powered complaint surveys to gather reliable customer data, analyze data with AI-powered business intelligence dashboards, and utilize AI focus groups – all without compromising data privacy.

Sign up for free today and explore how Formaloo AI can help your business thrive in the age of intelligent decision-making.