Real AI competition is not about models or data. It’s about the context.

Every company I talk to right now is convinced they have an AI problem.

Your AI writes emails that no one responds to. It researches accounts and uncovers leads that the sales team closed six months ago. Boring sessions of copying and pasting between tools produce content that sounds exactly like what every competitor is publishing. Executives invest in tool after tool, conduct training after training, and still face the same question: Why isn’t AI actually moving the needle?

Here’s what they’re not telling you: The problem isn’t your model. The problem is not your data. The problem is the context: the specific knowledge about your company, your customers and what they need right now, and how your team actually works. It is also the hardest problem to solve and the one the industry has been slowest to address.

The context is the infrastructure, not the feature

Here’s the distinction that I think is lost. Data is what happened. Context conveys meaning about real events, what they mean, why they are important, and what can be done about them. Context is not a function; It is a necessary infrastructure.

Your CRM records that a deal closed eighteen months ago. This is data. The context is knowing that the deal closed because your champion changed companies, the pricing had to be adjusted three times before closing, and the customer is now recommending multiple new deals per year and hates being contacted through automation. A person who handled this account knows all this. Almost no AI does this because almost no platform is built to capture it.

That’s the gap. No model gap. No data gap. A context gap. And this is exactly the problem HubSpot solves with the Agentic Customer Platform. When Yamini introduced our Agentic Customer Platform earlier this year, she described the foundation: a place where all your customer data and business context lives, available to your team and AI agents the moment they need it.

The best infrastructure is invisible. It runs in the background, staying up to date with changes in your company and ensuring your team doesn’t repeat themselves. This is the standard that AI should adhere to and is almost never achieved.

The hidden costs of context gaps

There are costs your team pays every day that don’t show up in your AI budget. We call it the briefing tax: the time and repetition required to give the AI ​​enough background knowledge to produce something useful.

You explain your brand voice before asking them to write. You paste account history before asking them to research. Before any meaningful task, describe your pricing structure, your competitive landscape, your customer profile. And the next day you do it again. It doesn’t learn your business. The real cost is not the hours your team loses re-briefing the AI, but the opportunity cost: the insights the AI ​​could have gained if it had actually known your company.

The briefing tax is just daily friction. The harder problem is the one you don’t see: what happens to the context over time? Your competitive positioning changes. Your ideal customer profile is changing. Your playbook will be updated. Your AI doesn’t know anything about it. It’s not that it’s forgotten. It has a memory of the conversation. It simply has no connection to the business behind it.

To GTM teams, this looks like an AI that is certainly wrong. A project changes, your team adapts, but the AI ​​continues to rely on outdated context. The exits start ringing. Recommendations no longer fit your goals.

If your AI isn’t connected to the bigger picture, it can never develop the complete, dynamic knowledge it needs to create real value. It remains A Tool. It never becomes a trustworthy teammate.

Growth teams need their own context

Not every context is the same. Personal AI tools like ChatGPT create personal context: your preferences, your conversation history, your communication style. Enterprise tools like Glean create the organizational context: your documents, wikis, and institutional knowledge. At HubSpot, we build a context for growth: the deep, high-quality, and precise understanding AI needs to drive results in marketing, sales, and customer success.

This is not a concept. We’re building a real infrastructure that means we capture and maintain that context for customers while giving them the ability to self-manage. We view the growth context as five dimensions:

• Business context is all about what you do, how you compete and what makes you worth buying. Your product positioning, your differentiation, your pricing justification, your brand voice. In this context, AI sounds like your company and not like any other company. Your category. Capturing it requires more than uploading a trademark document. It requires a system that structures this knowledge and automatically applies it to every interaction.
• Team context is the way your people actually work. Your sales methodology, your qualification criteria, your escalation paths. Not the version included in your onboarding documents, but the version your best employees actually use. This is what distinguishes an AI that follows a script from an AI that exercises real judgment. Such a context does not exist in any CRM area. It lives in call recordings, business memos, and the patterns only visible through thousands of interactions.
• Process context This is what your workflows look like in practice. What triggers a handover? What makes a deal high priority? How your campaigns are structured and what the success of each one looks like. This allows AI to take action, not just provide information. To integrate this with AI, you need to understand your actual workflows, not just describe them, so that the system can respond to them rather than refer to them.
• Customer context is the collected history of your relationships. What each account bought, why they bought it, what their goals are, where conflicts arose, and what the next logical conversation should be. This makes outreach feel like a conversation rather than a cold call. This category is the most difficult to maintain because it is constantly changing. Keeping this automatically updated at every touchpoint is the infrastructure problem that most platforms have not solved.
• Network context is the one dimension of the growth context that no single company can build alone. HubSpot works with more than 280,000 companies. This means we’re seeing broad trends in how teams go to market, how campaigns work, and how customers buy, on a scale that no single company could replicate on their own. This collective intelligence becomes a layer of growth context available to every business on the platform, shaping your AI’s recommendations before you’ve ever run a single campaign.

What the right questions look like

When you’re evaluating AI for your team, the real important questions aren’t about the model. Models are increasingly becoming commodities. The right questions are about context.

• It can capture and act in the big picture? Not just the structured and unstructured data in your CRM, but also the reasoning, judgment, and institutional knowledge that typically lives in people’s minds.
• Is the context automatically preserved? Or does your team have to manually keep the platform up to date, making an investment in the platform a maintenance expense?
• Is it specifically designed for growth? Or is it a general knowledge layer that happens to contain some customer data?
• Does it get worse over time? Or is constant reinvestment required to stay relevant?

Answer “no” to any of these questions and your AI will not work with your business, it will work on a version of your company no longer exists.

This is the real AI race. The companies that use the growth context correctly not only use AI better. They get further every time they use it.