Artificial Intelligence (AI)
AI applied to GTM is not a feature to add to the product. It's a bet on which part of your commercial process will first stop requiring a person to be executed well.
“AI doesn't fix a disorganized commercial process. It executes it faster, which often means reaching failure sooner, not avoiding it.”
Otto GTM ObservatoryWhere AI generates real value in B2B GTM
AI applied to go-to-market generates the most immediate value in high-volume, low-discretionary-judgment activities: large-scale initial lead qualification, first-draft content generation, predictive analysis on churn patterns, multi-touch attribution across large data volumes. Strategic judgment, complex negotiation, and trust building remain areas where human intervention stays decisive.
The most common mistake is adopting AI tools as an additional layer on top of already inefficient processes, without first measuring and redesigning the underlying process: automation amplifies the efficiency of a good process and equally amplifies the flaws of a bad one.
Anti-patterns
Common mistake: adopting AI tools to speed up a commercial process never measured for effectiveness, only discovering afterward that automation simply made a process generating mediocre results faster.
Second mistake: implementing AI with the expectation it will completely replace human judgment in complex sales phases (enterprise negotiation, managing multiple stakeholders), where human added value remains irreplaceable in the short-to-medium term.
Practical Application
A company introduces an AI-based lead qualification tool without first defining clear, shared qualification criteria between marketing and sales: SQL volume doubles but the SQL-to-Opportunity conversion rate halves, because the AI simply automated the ambiguity already existing in the criteria. After defining explicit qualification criteria before reactivating the tool, the conversion rate recovers and exceeds the pre-automation level by 18%.