The difference between ML & AI and what it means for business leaders

Artificial intelligence (AI) and machine learning (ML) are buzzwords that have taken the tech world by storm. You’ve probably heard them used interchangeably by tech giants, businesses, and media outlets alike.

But what exactly do these terms mean? And in their application to tools in your team’s tech stack — what’s the difference between them?

Understanding the difference can help business leaders better equip teams with tools at each stage of their revenue generation process. And assist in decision-making as to whether you should be using tools with generic AI/ML models or custom-built models, based on the output they will give and the results you will achieve.

At a high level, AI refers to the broad concept of building intelligent machines that can simulate human cognitive abilities. ML is a specific subset of AI that trains algorithms to learn from data and make predictions — without being explicitly programmed.

To break it down further, let’s expand on AI first.

What is artificial intelligence?

Artificial intelligence is a broad concept that encompasses any technology that enables machines to mimic human-like intelligence and behaviors like reasoning, problem-solving, and learning. This includes everything from rule-based expert systems to neural networks. The long-term goal of AI research is to develop artificial general intelligence (AGI) that can match or exceed human cognition across all tasks.

Within business applications, AI, including ML as a subset, seeks to increase efficiencies and automate outputs based on both ruled-based programming and learnings from data sets.

The reality is that today’s “AI” technologies are actually Narrow AI — highly specialized systems trained for specific tasks like playing chess, recognizing speech, or translating languages. Even highly hyped systems like ChatGPT are still Narrow AI focused on natural language processing.

That said, these technologies have already accelerated what is possible in many areas of our personal and work lives. By applying Narrow AI tools to specific use cases, we increase efficiency and in some cases, the accuracy of what we are trying to achieve.

What is machine learning?

Machine learning algorithms use statistical techniques to identify patterns in data. The algorithms “learn” from these patterns to make highly accurate predictions or decisions without relying on rules-based programming.

A simple example of ML is linear regression models that find the best-fit line to predict an output value (like housing prices) from input data (like square footage). More complex ML techniques like deep learning use artificial neural networks with multiple layers to enable more sophisticated pattern recognition across unstructured data like images, audio, and natural language.

We’ve touched on two other areas now — deep learning and generative AI — so I’ll explain these briefly, so you have the full picture.

What is deep learning?

Deep learning is an advanced type of machine learning that uses artificial neural networks with multiple layers to extract high-level features from a data set. Deep learning models can automatically learn hierarchies of features directly from data, enabling them to handle highly complex tasks like image and speech recognition.

An example of deep learning would be facial recognition systems used for user authentication. The deep learning model is trained on a large dataset of faces to learn how to identify and classify facial features. Once trained, it can reliably identify individuals in new images or video.

In the B2B tech world, deep learning is used extensively for areas like:

• Computer vision for image and video analysis (e.g. defect detection)
• Natural language processing for semantic understanding and generation
• Predictive analytics to forecast demand, churn and other business metrics
• Anomaly detection to identify fraudulent activities or technical issues

What is generative AI?

Generative AI refers to artificial intelligence models that can generate new content like text, images, audio, code and synthetic data.

We’ve all had a go on ChatGPT by now, and this is a prime example of generative AI like large language models that can engage in open-ended dialogue and produce human-like writing on almost any topic from scratch.

In the business world, generative AI is being applied for a variety of use cases:

• Automating content creation (reports, emails, social media posts, marketing copy)
• Generating training data for AI model development and testing
• Creating personalized experiences at scale (customize emails, chatbots, recommendations)
• Aiding human creativity and ideation for product design, R&D and more

The relationship between AI and ML

Hopefully now the different forms of AI are becoming more distinct and you can visualize the relationship, with AI as the broad umbrella term and machine learning and deep learning as powerful tools within it.

Nearly all current AI applications leverage deep learning (part of machine learning) to automatically derive insights from data.

The more data these deep learning ML models can train on, the better their accuracy and performance. But they still have limitations around factual consistency, generalization, and true reasoning.

To summarize this area, many AI or AI-driven tools marketed today are in fact a powerful combination of both AI and ML. Taking our product, Jiminny, as an example, we harness both in various capacities to achieve features such as:

• Automatically generated call summaries and action items (from a subset of generative AI called large language models or LLMs)
• Revenue forecasting (from both AI + ML)
• Instant sales coaching feedback (from AI LLMs)

Bringing it all together for business applications

So what does this mean for business and revenue leaders?

AI and ML technologies are rapidly transforming business operations across the board. In revenue operations, conversation intelligence platforms (our area of the playing field) leverage LLMs and natural language processing (NLP) to automatically analyze sales call recordings and surface insights that were previously impossible with manual processes alone.

Rather than having to listen to each call recording, AI can instantly identify things like:

• Key discussion topics and deal risks
• Positive/negative customer sentiment
• Areas of strength or coaching opportunities for each rep

This helps managers make smarter strategic decisions around messaging, pipeline forecasting, and performance optimization and provides reps with insights and resources for coaching and more productive workflows.

While it’s still early days, AI and ML will only become more deeply embedded across the sales tech stack. Understanding the core differences and relationship between these transformative technologies is critical for taking full advantage of their capabilities.

If you’re employing AI or ML in your tech stack, knowing the differences can enhance your conversations with platform providers and assist in making better buying decisions. Understanding what is happening to the data as it is processed through the tools you use, gives you a better picture of the output and results you can expect, making for better business planning.