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Podcast
March 6, 2026
Episode 83: Why You Should Stop "Doing AI" and Start Solving Problems
Discover why 95% of AI initiatives fail and learn practical strategies to implement AI that actually solves problems along with other expert insights.
When 95% of AI projects fail before delivering meaningful business outcomes, it's not because the technology isn't ready—it's because teams are approaching AI backwards. Instead of identifying problems and building solutions, they're chasing shiny AI features without understanding what they're trying to fix.
In this episode of the RevOpsAF Podcast, host Matthew Volm sits down with Em Wingrove, Chief Marketing Officer at Aptitude 8, to cut through the AI hype and deliver practical strategies that actually work. Em shares why most AI initiatives stall, which teams are ready to adopt AI today, and the specific plays that are transforming go-to-market operations right now.
The AI landscape feels like drinking from a fire hose. New features launch daily, executives demand "AI transformation," and operators are left scrambling to make sense of it all. Em identifies this overwhelm as the root cause of AI disengagement across revenue teams.
"I think that disengagement is a product of overwhelm. ‘I can't keep up with all of these AI features. There's too much going on. The people around me can't keep up. Maybe I can, but the people around me can't. And I'm frustrated about it.’" — Em Wingrove
This frustration compounds when leadership pushes for AI adoption without understanding the implications. Teams feel pressure to "do AI" without clarity on what problems they're solving or how success will be measured. The result? AI initiatives that fail before they start, leaving operators more skeptical than before.
The solution isn't to avoid AI—it's to approach it differently. Em advocates for starting small with independent use cases that don't impact other team members or systems. This creates a safe space to experiment and build AI literacy without the risk of organizational disruption.
Most AI failures stem from adoption approach, not technology limitations. Em draws a clear distinction between top-down mandates and bottom-up experimentation, with dramatically different success rates.
Top-down AI initiatives—where executives demand "AI transformation" without specific outcomes—consistently fail because they lack connection to real operational pain points. These broad mandates create pressure without providing direction, leading to technology implementations that don't solve actual problems.
Bottom-up adoption, however, starts with individual operators solving their own challenges. These initiatives build consensus organically as team members see tangible results and become curious about replicating success.
"The bottom up, I think we have a much better shot. Because it's usually starting with independent operators solving for their own use cases. They start to build consensus among their team, and it slowly starts to build upward." — Em Wingrove
This approach aligns with proven RevOps process management principles—start small, prove value, then scale. It also addresses the education gap that plagues many AI initiatives.
Before implementing any AI solution, teams must address their data foundation. Em emphasizes that AI is predicated on clean, complete, and well-structured data—something many revenue operations teams lack.
"AI is predicated on data. If your data is messy, incomplete, your associations of objects, like the object schema is not making sense... if you don't have documented SOPs like your own business processes, guess what? AI needs to understand your business processes to be able to do agentic actions for you." — Em Wingrove
This connects directly to the people, process, and tools framework that underpins effective revenue operations. Without documented processes and clean data, AI tools will amplify existing problems rather than solve them.
The data readiness checklist includes:
Teams that skip this foundation work often find their AI implementations producing incorrect results or failing to deliver expected outcomes.
Not all revenue teams are equally positioned for AI success. Em identifies go-to-market teams, particularly marketing, as the most AI-ready functions due to their content-heavy workflows and lower-risk experimentation opportunities.
Marketing teams excel at AI adoption because:
Sales operations follows closely, especially for data entry automation and pipeline management. Customer success operations can leverage AI for health scoring and automated outreach.
Finance and accounting teams, however, tend to adopt AI more slowly due to higher risk tolerance and regulatory requirements. These teams need more mature AI solutions with proven accuracy before implementation.
Em shares specific AI implementations that deliver immediate value without requiring extensive technical expertise. These "recipes" focus on solving real business problems rather than showcasing AI capabilities.
One of Aptitude 8's most successful AI implementations addresses the perennial attribution challenge. Traditional digital tracking misses the moment of consciousness when prospects connect multiple touchpoints into a buying decision.
Their AI attribution engine combines:
This approach provides marketing teams with better data for budget allocation decisions while remaining completely internal to the marketing function.
The most comprehensive AI play Em describes tackles the entire marketing-to-sales transition with multiple integrated components:
"Think about all the marketing touches. Do you think a salesperson is really going go into a contact record and scroll and see, 'Okay, they went to this page on this day...' No. We need to pull all of that together into a complete story that is easy to understand and digest for the salesperson." — Em Wingrove
This comprehensive approach addresses the handoff problems that plague most revenue operations teams while leveraging native platform capabilities.
AI-powered onboarding creates personalized experiences without manual effort:
These implementations focus on customer experience improvement rather than internal efficiency, aligning AI capabilities with revenue outcomes.
Despite the focus on automation, Em emphasizes that successful AI implementation enhances rather than replaces human capabilities. The goal is to eliminate busywork so teams can focus on relationship building and strategic thinking.
"With AI entering our worlds, the thing that will win at the end of the day is humans being human. The things that AI can't do are the things that will win if we can do them well." — Em Wingrove
This philosophy aligns with the broader RevOps evolution toward strategic partnership rather than tactical execution. AI handles data processing and routine tasks, freeing operators to focus on analysis, strategy, and cross-functional collaboration.
The most successful AI implementations create opportunities for human connection rather than replacing it. Automated data entry enables more time for customer conversations. AI-generated insights support better strategic decisions. Predictive analytics inform more thoughtful planning processes.
For teams ready to begin their AI journey, Em recommends starting with a simple question: "What problem are we trying to solve here?" This problem-first approach prevents the common trap of implementing AI for its own sake.
The implementation framework follows these steps:
This approach mirrors successful change management practices in revenue operations, emphasizing gradual adoption and stakeholder alignment.
The AI revolution in revenue operations isn't about replacing human judgment with algorithms—it's about eliminating the tedious work that prevents teams from focusing on strategy, relationships, and growth. As Em demonstrates through Aptitude 8's implementations, the most successful AI initiatives solve real problems rather than showcasing technological capabilities.
The path forward requires patience, experimentation, and a commitment to building proper foundations before pursuing advanced capabilities. Teams that approach AI with this mindset will find themselves ahead of competitors still chasing the latest features without understanding their purpose.
"We have to arrive at a place together. And if we've got a bunch of ego in the room and people telling us, go, just go do it. Do the thing. I don't think we'll get to that truth and that answer fast enough." — Em Wingrove
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