UNBOUND 2026, HubSpot’s newly renamed flagship event, brought GTM leaders to Boston to talk about building an AI-powered, unified go-to-market. There was no shortage of ambition, with teams keen to put AI to work across how they target, engage, and convert buyers. 

But our conversations kept coming back to a more basic question: can teams actually trust the marketing data they rely on? Plenty had invested in intent data, but far fewer could point to what it was delivering. It showed which accounts looked active without connecting that activity to the people behind it, and it rarely explained what was driving that activity in the first place. 

That question gets harder to ignore as GTM becomes more automated. A unified GTM strategy depends on a data foundation teams can trust, especially as more decisions get handed to AI systems that act on the data exactly as it’s given. 

Here are our five takeaways from Boston, and what they mean for GTM teams right now. 

1. Intent data shows accounts, not people 

Intent data works at the account level. It can show that a company has been researching a topic, but not which people inside it are behind that activity or part of the buying decision. 

It also can’t tell the difference between one person and a whole team. A single employee reading two articles and five people from different departments researching the same problem over several weeks can produce the same result: an account flagged as active. 

When an account surfaces, teams are left to work out who to reach out to. That usually means pulling a contact list and making a best guess, and the people driving the decision often aren’t on it. 

2. Contact data shows people, not problems 

A contact list fills in the names and job titles, but nothing about what those people are working on, what’s prompting them to look, or whether they’re engaging at all. 

A job title tells you someone’s role, not the business problem they’re trying to solve. Two people with the same title at similar companies can be researching the same thing for completely different reasons, and they need very different conversations. 

Neither intent data nor contact data shows who’s involved in the buying decision or what they need. Outreach ends up going to whoever is easiest to find, with a message built around a topic rather than a real business problem, and it’s easy to ignore. 

3. AI is only as good as the data feeding it 

Teams are planning to use AI agents for prospecting, scoring, and outreach, and most of the conversation is about which agents to use and how to get started. Much less of it is about the data those agents will be working from. 

AI doesn’t question the data it’s given. If that data can’t show who’s engaging or why, an agent will act on it anyway, prioritizing the wrong accounts and reaching the wrong people. Where a person might make that mistake on a handful of accounts, an agent makes it across hundreds at once. 

If a new CRM or AI agent is set up on data like that, every workflow built on it inherits the same blind spots. Fixing that once automation is running is much harder than getting it right at the start. 

4. Most of the buying group never shows up in the data 

The one person who shows up in your CRM is rarely the only one involved in a decision. That’s because most marketing data only capture the person who filled out a form or opened an email, not the colleagues researching alongside them. 

B2B buying decisions have become more complex, with more people involved in the buying group. Each person has their own priorities and their own hesitations that need addressing before anything moves forward. 

When outreach targets one person with one message, it rarely lands with the rest of the group. Without their confidence, the decision stalls or goes elsewhere. 

5. A unified GTM strategy relies on real buyer engagement 

A unified GTM strategy needs data that connects what the previous points pull apart: the people engaging, the business problem behind their activity, and the rest of the buying group around them. That only comes from linking buyer interactions to the real people behind them. 

That’s what shows teams where a buying decision is genuinely taking shape. It’s also what makes the data explainable, so teams can see why an account matters and who to reach, and trust it enough to move. 

Prioritization, targeting, and outreach can then all start from the same view of what buyers are actually doing, whether it’s a person or an AI system doing the work. 

What this means for GTM teams 

Before adding another tool or agent, it’s worth asking a few simple questions of the data you already have. Can you see who inside an account is engaging? Do you know why? Can you see the rest of the buying group? And could your team, or any system you plug in, act on it without someone filling in the gaps? 

If the answer to any of those is no, your data is where to start. That doesn’t mean adding another platform to your stack. It means making sure the data you act on shows who’s engaging, what they’re trying to solve, and who else is involved in the decision. 

pharosIQ: Intelligence Beyond Intent 

pharosIQ is a buyer intelligence and demand generation company that helps B2B GTM teams move beyond the limitations of intent data. Our proprietary intelligence engine, atlasIQ, captures observed buyer engagement across our owned B2B content ecosystem, showing who’s engaging, what they’re trying to solve, and how the buying group is forming. 

That intelligence can run in your own systems, or power the demand generation programs we run for you.  

Convert observed buyer engagement into pipeline: Let’s talk.