To conceptualize what's possible with modern AI data tools, RevOps leaders should frame the problem at the micro level. Instead of thinking about macro data fields, they should imagine having unlimited time and resources to fix one account record. This mental model helps identify high-value, manual processes that AI can now automate at scale.
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Instead of replacing top performers, AI should be used to do work humans physically cannot. Salesforce targeted a backlog of 100 million 'orphan leads,' using an AI agent to work through 8,000 dormant leads in three weeks. This generated $500,000 in pipeline that would have otherwise been zero.
The best initial use for AI in marketing operations is automating high-volume, low-complexity "digital janitor" tasks. Focus AI agents on answering repetitive questions (e.g., "Why didn't this lead qualify?") and cleaning data (e.g., event lists) to free up specialist time for more strategic work.
To truly leverage AI, professionals must change their approach to tasks. Instead of automatically assuming personal responsibility, the first question should be whether an AI tool can perform it. This proactive mindset shift unlocks significant productivity gains by automating routine work.
A powerful, untapped use case for AI is reviving neglected leads directly within your CRM. An agent native to Salesforce can access all historical data to send highly personalized follow-ups to thousands of leads your team previously ghosted, effectively turning forgotten data into new opportunities.
A killer app for AI in IT is automating tedious but critical tasks. For example, investigating why daily cloud spend deviates by more than 5%. This simple-sounding query requires complex data analysis across multiple services—a perfect, high-value problem for an AI agent to solve.
Don't get distracted by flashy AI demonstrations. The highest immediate ROI from AI comes from automating mundane, repetitive, and essential business functions. Focus on tasks like custom report generation and handling common customer service inquiries, as these deliver consistent, measurable value.
To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.
Instead of guessing where AI can help, use AI itself as a consultant. Detail your daily workflows, tasks, and existing tools in a prompt, and ask it to generate an "opportunity map." This meta-approach lets AI identify the highest-impact areas for its own implementation.
Adopt a 'more intelligent, more human' framework. For every process made more intelligent through AI automation, strategically reinvest the freed-up human capacity into higher-touch, more personalized customer activities. This creates a balanced system that enhances both efficiency and relationships.
Instead of broadly implementing AI, use the Theory of Constraints to identify the one process limiting your entire company's throughput. Target this single bottleneck—whether in support, sales, or delivery—with focused AI automation to achieve the highest possible leverage and unlock system-wide growth.
