Artificial intelligence and machine learning are terms businesses hear constantly, and they’re often used as though they mean the same thing.
They don’t.
Machine learning is part of artificial intelligence, but AI is a much broader category. Understanding the difference can help business leaders make better decisions about new technology without getting caught up in technical terminology or the latest AI trend.
More importantly, businesses don’t necessarily need to become experts in either one. They need to understand what these technologies can do, where they may already be using them, and when they can solve a real business problem.
What Is Artificial Intelligence?
Artificial intelligence (AI) is the broad term for technology designed to perform tasks that typically require some form of human intelligence.
That can include understanding language, generating content, recognizing images, identifying patterns, analyzing information, making recommendations, or helping solve problems.
Generative AI tools have made artificial intelligence much more visible to everyday users. Microsoft Copilot and other AI assistants can help employees summarize information, draft content, work with documents, analyze data, and find information more efficiently.
But generative AI is only one type of artificial intelligence.
AI has been working behind the scenes in business technology for years.
What Is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence.
Instead of programming a system with a specific rule for every possible situation, machine learning uses data to identify patterns and make predictions or decisions based on what it has learned.
A simple way to think about it is:
AI is the larger concept. Machine learning is one of the ways we make AI work.
Businesses may encounter machine learning in systems that:
- identify unusual network behavior;
- detect potential fraud;
- recognize spam or phishing attempts;
- forecast demand;
- analyze customer behavior;
- identify patterns in large amounts of data; or
- make recommendations based on previous activity.
Employees may benefit from machine learning every day without ever realizing that’s the technology operating behind the scenes.
AI vs. Machine Learning: What’s the Difference?
The easiest way to understand the relationship is that machine learning falls under the larger AI umbrella.
AI describes the broader goal of creating technology capable of performing tasks associated with intelligence.
Machine learning describes a particular approach in which systems learn patterns from data rather than relying entirely on explicitly programmed instructions.
Today’s business technology may combine machine learning with other forms of AI, automation, data analytics, and generative AI.
That’s one reason the terminology can become confusing.
For most business leaders, however, the technical distinction isn’t the most important part.
The important question is:
What problem are we trying to solve?
Where Businesses Are Using AI
AI is increasingly built into applications businesses already use.
Some common applications include:
Microsoft Copilot and productivity tools: AI can help employees summarize meetings and documents, draft communications, find information, analyze data, and work more efficiently within familiar applications.
Customer service: AI assistants and chatbots can answer common questions, route requests, summarize conversations, or help employees respond more quickly.
Document and information management: AI can help summarize, classify, search, and extract information from large collections of documents.
Workflow improvement: AI can assist with repetitive administrative work and can be combined with automation to reduce manual steps.
Cybersecurity: AI can help security platforms analyze large amounts of activity and identify behavior that may require attention.
The best use cases aren’t necessarily the most impressive ones. They’re the ones that save meaningful time, reduce repetitive work, improve access to information, or help employees make better decisions.
Where Machine Learning Shows Up
Machine learning tends to be less visible to employees because it often works behind the scenes.
For example, a cybersecurity platform may learn what normal activity looks like and flag behavior that appears unusual.
A forecasting system may analyze historical information to identify patterns and estimate future demand.
A financial system might identify transactions that don’t match typical behavior.
Marketing and sales platforms may use machine learning to identify trends or make recommendations based on previous activity.
In these situations, the employee isn’t necessarily interacting with “an AI.”
The technology is analyzing data in the background and helping another system make a better prediction, recommendation, or decision.
Do Businesses Need to Choose Between AI and Machine Learning?
Usually, no.
A business doesn’t typically sit down and decide, “We need machine learning instead of AI.”
Instead, the organization identifies a problem and then determines which technology can solve it.
For example:
If employees spend too much time summarizing meetings, an AI assistant may help.
If a company wants to automate repetitive steps between applications, traditional automation may be enough.
If a business needs to identify patterns in large amounts of historical data, machine learning may be involved.
If employees need help finding information across approved company data, generative AI may be useful.
Start with the business problem. Then choose the technology.
AI Readiness Is Also an IT Question
Before introducing AI broadly, businesses should consider the technology environment surrounding it.
One of the biggest considerations is data.
AI can potentially make existing information easier to find and use. That’s extremely powerful when permissions and data are well managed.
It can also expose existing problems when they’re not.
Businesses should understand:
- where important information is stored;
- who has access to it;
- whether permissions are appropriate;
- which AI applications employees are using;
- what information employees can enter into those applications;
- how sensitive information is protected; and
- whether the organization has guidelines for appropriate AI use.
This is especially important when businesses begin evaluating tools such as Microsoft Copilot that can work with information employees already have permission to access.
AI readiness isn’t just about purchasing licenses.
It’s also about making sure the underlying environment is ready.
Don’t Overlook Security and Governance
AI adoption can move faster than company policies.
Employees may begin using public AI applications on their own because they’re useful and easily accessible. Without guidance, sensitive business, customer, employee, or proprietary information could end up being entered into tools the organization hasn’t evaluated.
Businesses don’t need to prohibit AI to address this.
They need practical rules.
Employees should understand which tools are approved, what types of information can be used with them, and when they should stop and ask before sharing something sensitive.
Organizations should also consider access controls, data permissions, licensing, security, and how new AI applications fit into the existing technology environment.
Start Small and Measure the Results
Businesses don’t need an enormous AI initiative to begin.
Start with one or two clearly defined use cases.
Identify the problem.
Choose an appropriate technology.
Make sure the data and security environment are ready.
Then measure what happens.
Did employees save time?
Were manual steps eliminated?
Did people find information faster?
Did the tool improve a business process?
Did the benefit justify the cost?
If the answer is yes, expand thoughtfully.
If not, you’ve learned something without committing the entire organization to another technology platform.
AI and Machine Learning Should Serve the Business
Artificial intelligence and machine learning will continue to become more deeply integrated into everyday business technology.
But adopting them isn’t the goal.
Improving the business is the goal.
Understanding the difference between AI and machine learning helps business leaders evaluate these technologies more clearly, but the decision should always come back to the same question:
What are we trying to accomplish, and is this the right technology to help us do it?
Business Network Consulting helps organizations evaluate AI and automation opportunities within their existing technology environments, including Microsoft 365 and Microsoft Copilot, while considering data readiness, security, permissions, licensing, and business processes.
The right place to start isn’t with the latest AI tool. It’s with understanding where technology can make a meaningful difference in the way your business works.
Want to Go Deeper?
Our AI Essentials and Tools for SMBs webinar explores practical ways small and midsized businesses can use AI, including examples of tools and opportunities within the Microsoft environment.
Watch the webinar below to learn more about where AI may fit into your business.
Missed Our AI Webinar? Here’s What You Can Still Learn
If you missed our recent AI Essentials and Tools for SMBs webinar, don’t worry—you can still catch up on the valuable insights we shared! In the webinar, we covered the basics of AI and how small and medium-sized businesses can leverage it to improve efficiency, reduce costs, and stay competitive. We also demonstrated real-world examples of AI tools in action and provided tips on integrating AI into your current systems, especially within the Microsoft environment.
Get In Touch With BNC To Get Started
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