Machine Learning vs. AI: What’s the Difference?

The era of artificial intelligence (AI) and machine learning (ML) has arrived. In some cases, this is framed as competition: machine learning vs. AI. More often, the two terms are treated interchangeably, which is not the case. In truth, ML and AI are interdependent, but not identical.

ML is how products like AI agents can improve over time — learning from data, refining its processes, and producing more relevant outputs. Businesses are starting to see real benefits by creating AI agents that use AI powered by machine learning.

AI agents already can resolve more than 80% of customer issues without human intervention, while 92% of service teams say that AI reduces their costs. The results are improved ROI with AI agents and enhanced customer service.

Here's what you need to know about AI, ML, and how these technologies can help your business.

What we'll cover:

• What is artificial intelligence?
• What is machine learning?
• Difference between machine learning vs. AI
• How companies use AI and machine learning
• Better together: AI and ML

Artificial intelligence (AI) is a technology that helps machines mimic human thinking processes such as learning, reasoning, and problem-solving. That might seem like a simple definition of AI, but there’s much more to it.

It's an ever-evolving field, once boxed in as a tool that helps train AI chatbots and AI assistants to answer simple questions. However, emerging solutions like generative AI involve processing and parsing complex natural-language queries and finding connections, patterns, and solutions among vast amounts of data.

The field of AI also has several emerging areas within it. Some of its multiple subsets are neural networks, natural language processing (NLP), robotics, and machine learning.

Machine learning (ML) is a subset of AI that uses algorithms, or complex formulas, to identify patterns and learn from experience to refine its processes and results.

Traditional programming uses explicit instructions to generate specific results, but ML algorithms are set up with guidelines and then given access to large datasets. The result is a machine-learning model that parses the information. As it ingests and analyzes more data and has its results confirmed as correct or not, outputs become more accurate.

While ML algorithms learn over time, it's worth noting that output accuracy is tied to initial model constraints and parameters set by humans. For example, if an algorithm for identifying images of dogs is given an incorrect definition — that dogs have two legs, wings, and beaks — the resulting outputs will not be accurate.

The idea of machine learning vs. AI is a false dichotomy because machine learning is a subset of AI.

Along with other AI subsets, ML enables intelligent solutions to mimic human problem-solving and cognition. Without machine learning, the efficacy of AI tools is limited. Consider a traditional chatbot capable of understanding human language that uses a neural network to map conversational context. If no ML component is present, the chatbot can't evolve. While it can reply to pre-programmed questions, it doesn't "learn" from repeated interactions.

Machine learning makes it possible for the AI solution to evolve.

ML algorithms can operate on their own. For example, an internet service provider could build a machine learning algorithm to analyze when customers tend to use the internet the most, and forecast future patterns to help improve performance. These algorithms can also be paired with other AI subsets such as NLP using large language models (LLMs) or small language models to produce intelligent user interfaces.

AI agents are a good example. Users can ask these autonomous agents questions in plain language and receive answers that provide the stated information in a conversational context. Businesses can use these agents to resolve customer inquiries, engage with prospects, create bespoke marketing campaigns, and more.

For ML algorithms to effectively learn, they require training. The two common training models are supervised and unsupervised.

Supervised models start with a limited set of training data and a basic set of instructions. Using the internet example above, teams might provide ML algorithms with internet usage data for the previous year and ask the AI to predict the next six months.

Outputs are then compared with actual data to see if they align. If so, model parameters are expanded. If not, models are adjusted and training begins again.

Unsupervised models are provided with a basic set of instructions and access to large data sets. The tools are allowed to analyze data and evolve model parameters independently. Outputs are periodically analyzed for accuracy, and adjustments are made if necessary, but human interaction is minimal.

Unsupervised models have benefits and drawbacks. While they may initially return inaccurate outputs, they're often better equipped than unsupervised models to make unexpected connections that lead to better predictions.

It's also worth noting that machine learning has its own subset process known as deep learning. Deep learning uses multilayered neural networks to simulate human decision-making. Where traditional machine learning may use neural networks with one or two layers, deep learning is different than ML in that it leverages hundreds or thousands of layers to produce more accurate outputs.

In addition, deep learning models are capable of evaluating and refining their decision-making processes to improve outputs over time.

The advantages of AI and the self-improvement benefits of machine learning make them ideal for multiple business use cases. As you’ll see, it’s less about machine learning vs. AI and more about how machine learning improves AI.

The integration possibilities depend more on what companies want to accomplish with enterprise AI, rather than any AI constraints.

Common use cases include:

Sales teams can use ML-equipped AI applications to help pinpoint ideal product price points, forecast future demand, and improve customer segmentation.

Consider a company releasing a new version of its most popular product. Using AI-based analysis, the product team can compare the retail prices of competitors' offerings that have similar features and functions and identify the preferred price range.

Depending on the sophistication of AI models and the data sets they access, companies can create granular pricing models that include consumer preferences, historical purchase patterns, and evolving market forces.

For example, with Agentforce — the agentic layer of the Salesforce platform — sales departments can use machine learning-fueled AI to generate sales pitches and make the most of meetings.

Service is an area where AI tools excel, whether it's customer-facing or internal software that helps streamline operations.

From a customer standpoint, AI agents can field common questions and resolve simple service issues without human intervention. The benefit is two-fold: Customer wait times are reduced, and service representatives can spend more time handling high-priority concerns.

Internal AI service tools, meanwhile, can use machine learning to monitor performance and pinpoint solutions. This can be particularly useful for predictive maintenance. For example, a manufacturing company might have AI apps with internet of things (IoT) sensors that regularly scan production machines. When it detects values that exceed specified limits, the AI tool can alert a team to take action.

Marketing teams use AI to help better understand customers and their preferences. ML algorithms can perform sentiment analysis, which uses NLP to analyze the underlying meaning and intent of customer statements. This ties into churn analysis, which can help companies better understand why customers choose not to return after a purchase or service interaction.

Companies can also use a marketing AI solution like Agentforce to manage campaigns and generate content that resonates with customers, since it draws from trusted customer data.

Using AI, commerce companies can recommend products based on customers' purchase history, feedback, and service interactions. These personalized recommendations are commonplace in services such as video and music streaming and are often leveraged on retail sites.

There's a growing set of use cases for AI in platform development and automation. Businesses can use a complete AI solution like Agentforce to perform tasks like code generation and support, automated code testing, or automated process creation. When applied at scale, these processes can improve code quality and reduce development time.

AI and ML may also be used for industry-specific use cases.

• In healthcare, AI can help create patient-specific treatment plans and streamline data entry.
• In hospitality, tools use conversational AI to provide personalized recommendations for hotel rooms and vacation packages. AI can even help hoteliers predict seasonal demand.
• In government, AI can improve demographic and geographic data analysis to better understand socioeconomic forces.
• In manufacturing, ML and AI can optimize production line processes to balance speed, quality, and the need for proactive maintenance.

When it comes to machine learning vs. AI, there is no competition — only cooperation.

ML acts as a subset of AI and is a required component for artificial intelligence solutions to discover new connections, evaluate decision-making performance, and improve output accuracy. Together, these solutions pave the way for a multilayered, multifaceted AI strategy that helps improve business operations.

Ready to explore the advantages of AI and ML for your business? Try Agentforce today.