"Over the past two years, I've had hundreds of conversations with business leaders about AI. The pattern is always the same: initial excitement about what AI can do, followed by frustration about what it actually delivers. "It writes great emails," they tell me. "But are they converting?" Not really. "It can research prospects in seconds." Great. But is it finding the right ones? Usually not."
"Think about your best team members. They know which prospects are worth prioritizing because they've seen similar patterns before. They write emails that convert because they understand your brand voice and your audience. They solve customer problems quickly because they remember what worked in the past. That's context: having the right information at the right time, combined with the judgment to know what to do with it."
AI often produces impressive outputs that fail to generate the business outcomes organizations need because those outputs lack context. Human team members apply contextual knowledge—pattern recognition, brand voice, prioritization, and memory of past solutions—to make decisions that drive results. Most contextual judgment and the reasons behind decisions are fragmented across people’s heads, email, Slack, and disparate systems. When AI operates without consolidated context, it delivers generic, one-size-fits-all answers that do not address specific customer needs or business priorities. Consolidating and supplying contextual information to AI is required to close the gap between output and outcomes.
Read at Hubspot
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