There's a fascinating irony playing out across revenue organizations right now. Companies are racing to adopt AI, investing millions in the latest tools, and proudly announcing their "AI-first" strategies. Yet most are solving the wrong problems entirely.
Teams are using AI where humans add the most unique value. AI is being used for creative tasks that require personalization, empathy, and strategic thinking, while ignoring AI where humans add the least value, repeatable and reliable tasks like data entry, data analysis, reporting, and coordination.
The Wrong Focus: Fixing People Instead of Processes
We’ve spoken with many revenue leaders over the past few months. When asked about their AI strategy, the majority are using siloed solutions that:
Generate personalization at scale
Automate outbound messages
Provide tips and replies during sales calls
Create "AI SDRs" to replace underperforming reps
We see a problem with this. AI is being sold as point solutions that narrowly solve problems, rather than fundamentally rethinking the GTM strategy, enhancing revenue predictability, and freeing people from redundant, demoralizing work.
Vendors have flooded the market with solutions focused on the first 10% of the pipeline. We see an issue with this. The top-of-funnel activities that get the most attention are often not where teams lose the most time and value. Instead, revenue leaders need to focus on the other 90% of the pipeline to enhance durability, especially for established enterprises.
Sales reps spend hours each week on data entry, marketers drown in manual reporting, and customer success managers cobble together QBR decks from six different systems. This is the work that actually drains productivity and morale. Why has it been left largely untouched?
The Current State of AI in GTM Teams
Most organizations are stuck in a pattern of:
Automating tasks in isolation without reimagining the workflow or having access to the necessary inputs
Enhancing decision-making with tools that require large amounts of data that are often disparate
Isolated point solutions deployed by role or use case with no integration strategy
This approach treats AI as just another tool in the stack rather than a revolutionary, foundational shift that should permeate the entire GTM motion. It's the equivalent of buying a smartphone to only make phone calls without using the rest of the phone’s functionality.
The Future State: RevAI Done Right
Effective AI outcomes start with a different foundation entirely. It requires four core principles:
Revenue is the key outcome - use AI to improve reliability, predictability, and growth
Data matters - make ALL sources accessible, usable, and queryable
Agents should work ahead of humans - predict what you need before you do, and allow you to focus on the important work that maintains the relationship with your prospect or customer
Offload the ops - Focus on automating operational tasks that meaningfully change revenue outcomes
Here’s what this actually looks like across your GTM team:
Sales: Eliminating the Admin Burden
Sales reps spend an estimated 65% of their time on non-selling activities. Most AI tools try to make them better at selling. The smarter move is to give them back that 65% of their time for strategic work.
Effective AI for sales eliminates administrative burden:
Auto-logging every call, email, and meeting, and the signals from those interactions to make changes to deals in real-time (e.g., close probability)
Predict deal close rate precisely rather than subjectively, by consuming all the signals internally and externally
Predictive forecasting and pipeline management, including improving sales operations (e.g., territory)
Executing high-value actions based on enriched data regarding the buyer persona, company information, and other research to pre-empt certain negotiations or compliance needs
Predictive coordination for items that move a deal forward: meetings with the executive, meetings with the technical teams, RFP, legal/compliance
This strategy works because reps want to escape operational work and focus on relationship-building and strategic thinking. This allows them to partner with an AI agent and also direct what items are most relevant by providing meaningful signals. If done well, interacting with transactional CRM will be a relic of the past.
Marketing: From Campaign Operator to Strategic Architect
Marketing teams face a similar challenge. They're drowning in operational tasks and managing tech stacks that keep them from strategically building markets and demand.
Effective AI for marketing automates the operational engine:
Constantly validating and adjusting the ICP and buyer personas
Generate actions based on campaign outcomes and provide real-time reporting
Attribution model predictions
Budget pacing alerts and allocation adjustments
Tag and UTM parameter adjustments
This isn't about AI writing better ad copy or designing better landing pages. It's about freeing marketers from operational quicksand so they can focus on strategy, creativity, and experimentation. And most importantly, create new and consistent revenue.
Customer Success: Aggregating Signals, Anticipating Needs
Customer Success teams are uniquely positioned to benefit from AI, yet they're often stuck manually coalescing data from many systems.
Effective AI for CS creates proactive intelligence:
Predictive account health and churn analysis
Real-time account metrics and usage trends (aligned to health and churn)
Identifying key hurdles in the onboarding or support processes that are limiting upsell and cross-sell
AI should handle data aggregation, pattern recognition, and documentation so CSMs can focus on relationship-building and strategic advising.
The Compounding Benefits of the right Rev AI Adoption
Revenue impact. Growing revenue is 90% science. Listening to signals and then acting on them in real-time allows your sales, marketing, and CS teams to grow and retain revenue, predictably.
Compounding benefits. Time saved on operations creates space for higher-value human activities. A rep who saves 10 hours per week on admin can spend that time building relationships, managing complex deals, or mentoring junior team members.
Immediate adoption. When you eliminate work people don’t like doing and meaningfully impact outcomes, teams don't resist the tool. There's no change management battle when the change makes someone's job is tangibly better.
Usable data. The strategic AI use cases everyone wants (predictive scoring, intelligent recommendations, automated insights) all require accessible data. When AI handles data entry, data accessibility, enrichment, and predictions, the data quality improves dramatically, which makes the "smart" AI use cases actually work later.
What's Holding Teams Back
If the path forward is this clear, why aren't more teams taking it?
Treating it as an IT project rather than a GTM transformation. AI initiatives get handed to RevOps or IT, isolated from the people who actually do the work. Without Revenue leadership owning the strategy, these projects never scale beyond pilots.
Point solutions instead of systemic thinking. Companies buy 10 different AI tools for 10 different use cases, creating an inconsistent stack that doesn't unify data. The result is more complexity, not less.
Lacking executive buy-in or a clear AI strategy. Without top-down commitment, you get scattered experiments that never consolidate into a coherent capability. Teams try tools, see mixed results, and abandon them.
The Bottom Line
The AI revolution in GTM is happening, and teams need to stop approaching it in one-off solutions.
Focus on the revenue impact first. Use the data available to you as your secret weapon that creates durable revenue. Let AI work for your teams, ahead of them, empowering them. Eliminate the operational friction between teams, systems, and processes.
Focusing on these use cases will unlock far more value than any AI-generated email sequence ever could. You will hit your revenue goals, your team will be more productive, your data will be cleaner, and you'll have built the foundation needed for those advanced AI capabilities to actually work.
Ready to rethink your GTM strategy? Let’s chat.