Over the last three-and-a-half years, we’ve witnessed one of the largest technology investment cycles in history. Trillions of dollars have been poured into semiconductors, hyperscalers, and foundation models.
That investment has laid the foundation for the next decade of innovation. But like every technology cycle that came before, value doesn’t come from infrastructure alone. It comes from what you build on top of it.
And now, from the hundreds of conversations I’ve had with go-to-market leaders, I see the focus shifting: from what AI can do to what it can actually deliver for scaling businesses.
It’s the start of a new era. An era where the true value of AI will be determined. The Outcomes Era.
Delivering features to delivering outcomes
Software used to work like this: vendors shipped features, customers adopted them, and value came from how people used it to grow. Software delivered capabilities. Humans delivered the outcomes.
AI fundamentally changes that relationship. Software can now help deliver outcomes, not just give people the capabilities to achieve them.
One thing that hasn’t changed is the outcomes go-to-market teams care about.
It’s still about building demand, winning deals, and delighting customers. Marketers want more leads. Sellers want higher close rates. And service teams want higher customer satisfaction scores.
Now, AI has the potential to deliver those outcomes, directly for businesses.
But AI’s intelligence alone isn’t enough. To turn it into real business outcomes, you need context.
Why context determines outcomes
That is our thesis: Intelligence is necessary. But it is not sufficient.
AI models learn by finding patterns across enormous collections of publicly available data. They learn relationships between words, ideas, and events. Then, they use those patterns to produce a useful response to a new input. Frontier models are getting smarter every month. And we have no doubt they will continue to improve. But no matter how intelligent a model becomes, if it only learns from public information, it will never know your business as well as you do. It will not know which campaigns are working, which deals need attention, which customers are at risk, what a customer has already been promised, or how work moves between teams.
It’s the context made available to these models that has the potential to turn intelligence into outcomes.
Here’s a simple definition: context is the dynamic knowledge about your business, your customers, and your team.
It tells AI what is true about your business now. And every time AI takes an action, it learns from the result. This creates a feedback loop, allowing an agent or AI tool to identify patterns that are unique to your business.
Consider what this looks like in practice. Any AI can help write a sales email. But an AI with context knows that this particular prospect visited the pricing page three days ago, that the deal has been stalled for two weeks, and that the last demo did not cover integrations. The email it writes for that account will be completely different and materially more likely to move the deal forward.
The model can be the same. But the outcomes are not.
What the data says
This isn’t just what we believe. We looked at data from across our customer base of 300,000+ companies. What we found was super clear:
Good context delivers great outcomes.
With good context:
- MQLs generated improved 264%
- Deals closed won improved 197%
- Customer meetings booked up 200%
This makes perfect sense. The more AI knows about your business, your customers, and your team, the better the outcomes.
But here’s the truly surprising finding:
When the same AI models were paired with bad context, not only did outcomes fail to improve, they were worse than using no AI at all:
With bad context:
- MQLs generated declined 28%
- Deals closed won declined 27%
- Customer meetings booked declined 49%
This might be surprising at first, but remember that humans can often correct for bad context. Your team intuitively understands your business and can control bad outputs before they have a downstream impact. AI doesn’t have the same intuition. It will act with confidence with whatever it has. It might recommend a product that is no longer in the catalog or route an approval to a manager who has changed roles.
The same model can produce dramatically different outcomes depending on the context it receives. That is why good context will become more valuable as the models grow more capable.
What good context actually looks like
So good context matters. A lot. What is it exactly? What ingredients does it have?
Good context has three ingredients:
First, you need business context: what you do. This includes things like your company goals, products and positioning, and brand voice. It’s the knowledge about you and your business and yours alone.
Second, you need customer context: who you serve. This includes the full history of every customer relationship: your conversations, contract and revenue details, your ideal customer profile, and their needs and sentiment. That specificity helps AI make every interaction more relevant.
Finally, you need team context: who you are. This includes your workflows and handoffs, roles and permissions, approval rules, and escalation triggers. It’s the foundation for how AI and humans work together inside your organization.
Across each of these areas, good context also needs to be accurate, current, and relevant to the task. Stale data, a missing record, or excessive information can send the output (and the outcome you seek) in the wrong direction. This is why HubSpot has reimagined the CRM to be self-updating, not only to reduce manual entry for teams, but to make sure humans and AI are working from the freshest context.
Helping scaling businesses grow in the Outcomes Era
There is a lot of debate about what AI means for SaaS and who will capture the value. Here’s what we think:
We are at the beginning of an exciting new chapter in how businesses use software. AI can do more than make existing work faster. It can help businesses build demand, win deals, delight customers, and grow.
But that potential depends on context. AI needs an accurate, current, and connected understanding of the business, its customers, and how work gets done. Without it, even the most capable model can take the wrong action. With it, AI can turn intelligence into outcomes.
At HubSpot, we are building the best context layer for scaling businesses, so they can put AI to work with confidence and drive the outcomes that matter most.
The Outcomes Era is here.
For scaling businesses, the opportunity to turn intelligence into outcomes is just beginning.
