Let’s set the scene: You’re the go-to person on your team for “figuring things out.” So when leadership says, “We need an AI-powered CRM — can you find one?” you nod, position yourself in your favorite scrolling stance, loosen up your scrolling hand and dive into the internet’s bottomless pit of product pages, pitch decks and artificial-intelligence promises.

A few weeks later, your company signs on to a fancy platform with “predictive lead scoring,” “generative customer journeys” and something called “AI intent detection.” Victory, right?

Not exactly.

Fast forward two months: The sales team can’t figure out why the CRM keeps surfacing unqualified leads. Marketing is stuck in a sea of confusing dashboards. And you’re realizing that “natural language search” does not, in fact, mean the CRM understands sarcasm. Oops.

The problem? Jargon does not equal clarity

Regarding AI-supported CRMs and platforms, the buzzwords fly fast — and sometimes a little loose. It’s easy to assume you know what something means. (“Sure, predictive must mean it predicts … something useful?”) Still, the reality is that many of these terms are misunderstood or misapplied, leading teams to buy tools that don’t quite solve the problem they hoped to fix.

Business research found that 75% of businesses fail to leverage their CRM systems, which results in workflow inefficiencies and lost revenue.

To avoid being “that guy,” let’s break down a few of the most commonly misunderstood terms so you can shop smarter — and prevent your own “Wait, what did we buy?” moment.

1. Predictive analytics vs. prescriptive analytics

Predictive analytics tells you what might happen — like when Salesforce forecasts which leads are likely to convert based on past data. Prescriptive analytics takes it a step further by suggesting what you should do next — like recommending which lead to call first or what discount might close the deal.

Think of it as the difference between a weather app saying it might rain versus one telling you to bring an umbrella and leave 10 minutes early to beat traffic.

What people think it means: The system will tell me exactly what to do.

What it actually means: Predictive = forecasting outcomes based on patterns (e.g., this lead might convert). Prescriptive = recommending specific actions to improve outcomes (e.g., send this email now to improve conversion).

What to ask the vendor: Can your system make recommendations, or does it just surface patterns?

2. Generative AI

Generative AI sounds super high-tech — and it is — but many people get tripped up thinking it’s just about chatbots or writing tools. In reality, it’s all about creating something new from patterns in existing data, whether text, images, code or even product recommendations.

The confusion usually comes from the name “generative,” which sounds creative, but it’s still grounded in the data it’s trained on. So, if your CRM promises “generative AI,” make sure you know what it’s generating and how it’s helping your team work smarter.

What people think it means: It generates everything — emails, journeys and strategy!

What it actually means: It can produce content (like text or summaries) but still relies on prompts and context. It’s not a strategic mind in a box.

What to ask: Can we customize the prompts or outputs? How is content quality reviewed or improved over time?

3. Intent data

Intent data is one of those buzzwords that sounds super powerful — and it can be — but it’s often misunderstood. At its core, intent data tracks signals that show what a person or company might be interested in buying, like the content they’re reading or the keywords they’re searching.

The tricky part? Not all intent data is created equal. Some of it is super vague or delayed, and without the right context, it’s easy to chase the wrong leads. So, before you build a whole campaign around “high intent,” make sure you actually know where that data came from and what it really means.

“High-intent data can be overwhelming if it isn’t mined and segmented properly,” said Megan Ross, Fullcast’s director of RevOps. “You first need to know how to read it, sort it and categorize it before you can actively use it. If done correctly, that high-intent data can become a very lucrative target list of qualified leads.”

What people think it means: The platform knows exactly when someone is ready to buy.

What it actually means: It analyzes signals (like website visits or content downloads) to guess where a buyer might be in their journey — but it’s not mind-reading.

What to ask: What sources are used to determine intent? How is accuracy validated?

4. Natural language processing (NLP)

To put it in simple terms, Natural Language Processing (NLP) is how computers learn to understand and respond to human language — like when your CRM suggests email responses or pulls insights from messy meeting notes. It sounds like magic, but here’s the catch: NLP isn’t perfect, and it’s often misunderstood as being way more “human” than it really is. Sure, it can pick up on patterns and keywords, but tone, sarcasm or messy context? That’s still a work in progress. So, while NLP can be a huge help, know that it’s not reading your mind — it’s reading your grammar.

What people think it means: You can talk to the CRM like it’s ChatGPT.

What it actually means: It might understand basic human language patterns, but not all platforms are created equal. Some are glorified keyword matches.

What to ask: How does the platform handle complex queries or ambiguous phrasing?

5. AI integration

AI integration sounds fancy, but it really just means integrating AI into the tools your team already uses — like your CRM, email or customer service platform — to make them smarter and more helpful.

Think automated lead scoring or chatbots that get what people are asking. The tricky part? While 90% of us have at least heard of AI, only about 30% can name real-life examples.

That gap is where confusion lives. People expect AI to be some robot overlord when, in reality, it’s often working quietly in the background, making your tech stack faster, sharper and more efficient (if it’s set up right).

What people think it means: Everything is seamlessly automated with AI magic.

What it actually means: The platform has AI capabilities — but that doesn’t mean they’re automatically turned on, configured properly or aligned with your workflows.

What to ask: Which AI features are available out of the box, and what requires a custom setup?

Here’s a hot tip: You don’t have to be the AI expert

If you’ve been tasked with evaluating CRM or RevOps tech and you’re feeling lost in a forest of acronyms and AI hype, don’t panic. Bring in someone from your data team, ask the vendor for real-world use cases (not just feature lists) and get clear on your internal needs before jumping into demos. The truth is, AI can be a game-changer, but only if you know what game you’re playing.

J’Nel Wright is senior content manager at Fullcast, a Silicon Slopes-based, end-to-end RevOps platform that allows companies to design, manage and track the performance of their revenue-generating teams.