How many browser tabs do you have open right now? 20? 30?
I see you. We've all been there.
And we’ve all got a version of this. A browser that looks like a cry for help. A new “AI-powered” app for every task, because surely the answer is one more tab away. (Right??!!!)
But something keeps coming up on calls with marketing and association leaders: teams are searching for capabilities they may already have. The software is there. The subscription is active. The AI features are available. Yet the actual process still runs on exports, copy-and-paste, and someone remembering to follow up.
Before buying another AI tool, it’s worth asking: What could we do with the stack we already have?
The “we never turned it on” gap
Sometimes the adoption problem really is a setting. More often, “we never turned it on” means nobody has made the feature part of how the team works.
A person tried it once. The output was generic. Nobody knew which information it could access. The team went back to the familiar process, and the feature became one more thing they were technically paying for.
HubSpot is a useful example. Its AI settings control access to generative features and the data those features can use. Some AI settings are even on by default. So the right question is broader than “Did we flip the switch?” It’s “Can our people use this, with the right context, to finish a real task?” HubSpot’s AI settings guide explains those controls.
Availability still matters. Specific capabilities can depend on subscriptions, permissions, configuration, and HubSpot Credits. “Built in” doesn’t automatically mean everything is included in your plan. HubSpot’s adoption guide outlines those requirements.
That’s a reason to investigate your account before shopping for another one.
Start with the process that keeps eating your afternoon
“We should use more AI” is a difficult assignment. “We need to get useful follow-up to webinar attendees without spending an afternoon assembling it” gives a team somewhere to start.
Pick one recurring task with a clear beginning and end. For a marketing or membership team, that might be event follow-up, preparing for a sponsor conversation, drafting a renewal message, or pulling together a weekly engagement summary.
For a sales team, it could be finding people in your ICP not yet in your database, automatically drafting personalized outreach to those people, and then automatically sending follow ups after that initial outreach. (Plus adding them to your database, LinkedIn profile and all.)
For architecting additional time saving and transformational workflows: watch how the work actually happens. Where does someone export a spreadsheet? Where do they copy information between systems? Which questions require three tabs and a Slack message? Where does a finished draft sit because no one owns the next step?
Those details tell you much more about the opportunity than a list of AI features.
Audit your existing stack before adding to it
A useful review can start with five questions:
- What outcome are we trying to improve? Choose something you can observe, such as faster follow-up, fewer manual handoffs, or more relevant member conversations.
- What can our current tools already do? Check the actual subscription, available AI features, permissions, usage costs, and existing integrations.
- What information is missing? Identify the records, definitions, approved content, and organizational context needed to produce useful work.
- Who will own the process? Name the person who reviews output, handles exceptions, and makes sure the work gets finished.
- How will we know it helped? Compare the current process with a small pilot. Measure total effort, including review and corrections, alongside the quality of the result.
If drafting becomes faster but checking the output takes longer, you need to know that. If the team saves time but the follow-up becomes less relevant, that matters too.
A good pilot gives you evidence for the next decision. It may show that an existing feature is enough. It may reveal a specific gap that makes another tool worth buying. Either outcome is more useful than another subscription nobody quite owns.
The context work is part of the work
Your CRM may hold useful history, but it doesn’t automatically contain everything your team knows. Your priorities, member needs, brand voice, and rules for handling an exception may still live in someone’s head.
That’s why getting value from AI involves organizing knowledge and helping people apply it. I explored this in Context Is Queen: What One Prompt Built in HubSpot. A short prompt can sit on top of a much larger foundation of context.
At GrowthHubAI, our HubSpot optimization work and organizational AI work address both sides: making systems more useful and equipping people to use them in their everyday work.
If your team is looking at tool number 48, and has 134 tabs open, let’s first look at the work you need to accomplish and the capabilities you already have. Book a strategy call to talk through where to start.
Tab 135 can wait.