Practical AI
AI where it actually earns its place. We know which use cases are worth it - and which ones aren't yet.
Our Take
The pressure to do something with AI is real. So is the risk of doing the wrong things - wasting cycles on implementations that don't deliver, training teams on tools they'll stop using in a month, or building workflows that depend on AI capabilities that aren't mature enough for your use case yet.
Kelly has been in GTM since 2000. She's watched enough technology cycles to know the difference between a shift that changes how work gets done and noise that sounds like one. AI is the real thing. But not every application of it is worth pursuing right now - and the ones that are look different depending on your team, your systems, and where you actually have friction.
We approach AI the same way we approach everything else: diagnosis first. We look at where your team is spending time they shouldn't be, where your systems are creating manual work that could be eliminated, and where AI can genuinely reduce friction - not where it looks good in a demo. Then we build, train, and document it so it runs without us.
We use AI in our own work deliberately - faster diagnostics, cleaner documentation, sharper analysis across every engagement. We don't advise on tools we haven't tested ourselves.
What this covers:
AI readiness assessment - where you are, where the real opportunities are, and what would need to be true to pursue them
AI workflow design and implementation - automations that eliminate real manual work across marketing, sales, and revenue operations
Platform-native AI feature activation - most of your existing tools (HubSpot, Salesforce, Marketo, and others) already have AI capabilities worth using; we know which ones and how to configure them properly
Custom AI workflow builds - where platform-native features fall short, we design and build what fills the gap
Team training - hands-on, specific to your tools and workflows, designed so your team actually uses what we build
AI governance and evaluation frameworks - so your team can assess new AI capabilities as they emerge, without needing to call us every time
Flexible. Start small, scale up.
AI advisory and implementation work typically starts as a 10-20 hr/month retainer. Teams with active build work often move to 20-39 hrs/month. We structure it around your roadmap, not a preset package.
Other services
FAQs
How do you decide which AI use cases are actually worth pursuing?
We look at where your team is spending time they shouldn't be, where manual work is creating friction or error, and where the AI capability is mature enough to solve it reliably. We've watched enough technology cycles to know the difference between something that's ready for production and something that looks impressive in a demo but breaks in practice. We won't recommend something we haven't seen work.
Our team is skeptical about AI. Will this work if they're not bought in?
Skepticism is healthy - it's a better starting point than blind enthusiasm. We design AI implementations around the people who have to use them, starting with the problems they actually feel rather than the ones that make for a good AI story. Buy-in follows usefulness. If what we build genuinely makes their work easier, they'll use it.
Does this require investing in new AI tools?
Not necessarily. Most of the AI opportunities we see involve capabilities already built into tools you're paying for - HubSpot, Salesforce, Marketo, and others have significant AI features that most teams haven't activated or configured properly. We start there before recommending anything new.
