Rethinking lead scoring
Lester Lee
•
4 min read
Lead scoring has always been a necessary evil. The idea seems straightforward enough: prioritize leads based on how likely they are to convert, assigning points for behaviors or profiles to identify potential value. But as sensible as it sounds, there's a fundamental issue: lead scoring is inherently subjective and imprecise.
Why do we score leads at all? Mainly to decide where to invest our limited time and sales resources. Which leads deserve immediate attention from sales? Which can wait? Yet the very existence of lead scoring reveals a deeper problem—it presumes resources are scarce and expensive.
But what if that's no longer true?
Imagine a future where AI agents are abundant and directly aligned with outcomes. This transforms everything:
Built-in Prioritization: In an AI-driven sales process, agents naturally pursue high-value activities without manual scoring. Just like how the best Go-To-Market teams intuitively prioritize high-EV accounts, performance-based AI agents continuously optimize actions toward maximum returns.
Unlimited Scale: AI agents are exponentially cheaper and faster than humans. For the price and effort of sending one personalized human-written email, an agent can easily send a thousand. Suddenly, prioritizing a $50,000 opportunity over a $5,000 one stops making sense. Every lead, regardless of size, can be pursued with equal effort.
Precision Over Guesswork: Human-assigned scores—say, 20 points for visiting a website or 5 points for opening an email—are crude approximations at best. AI models, fed enough data, can accurately determine the expected value of leads down to the decimal, eliminating guesswork entirely.
This isn’t theoretical. When AI agents are judged strictly on results, prioritization is built-in. Sales and marketing teams can now treat every lead seriously, without arbitrary thresholds or biases.
In the era of AI abundance, every lead matters. Lead scoring, at least as we know it today, might soon become obsolete—not because it wasn’t useful, but because we’ve finally found a better way.