Revenue leaders are pressured to show AI ROI, but focusing on the shiniest new AI tool is a mistake. Real gains come from addressing foundational issues like internal data silos and poor data quality before deploying AI, as the technology is only as good as the data it's fed.
Since all competitors can access public data through common AI tools, it offers no sustainable advantage. To drive more pipeline and revenue, companies must seek out and integrate proprietary or non-public data sources aligned with their Ideal Customer Profile (ICP), creating a unique data asset for their AI to leverage.
Instead of just providing reps with AI tools for self-service research, RevOps teams should proactively use AI to automate time-consuming tasks like territory planning and account intelligence. This shifts the burden from the rep, drives adoption, and ensures consistent application of AI for efficiency gains.
The most tangible benefit of AI for sales teams right now is drastically reducing the time spent on pre-meeting research. Leaders should focus AI adoption on these efficiency gains, as core human functions like complex negotiation won't be automated by agent-to-agent AI for at least five years.
Don't assume AI output is inherently correct. An expert at Databricks shared that running the same prompt across the top five AI providers yields distinctly different answers. This proves human oversight is crucial to question, validate, and contextualize AI-generated responses before acting on them.
The successful approach to AI isn't applying the technology broadly and searching for value. Instead, leaders must first define a specific business outcome, such as improving pipeline conversion. From there, they can work backward to identify and procure the exact data needed to enable AI to solve that targeted problem.