In the last year, the role of Revenue Operations has shifted from enabling execution to designing transformation. AI isn’t just another tool in the stack, it’s reshaping how go-to-market teams operate, collaborate, make decisions, and scale.
The Agentic Edge is a four-part series exploring how forward-thinking Revenue Operations leaders are reimagining their teams, tools, and processes for an era of agentic AI, where intelligent systems don’t just assist human work, they participate in it. From mindset shifts to infrastructure rebuilds, each post features a deep dive with one operator who’s not just adopting AI, but operationalizing it at scale.
This post spotlights Mollie Bodensteiner, VP of Revenue Operations & Enablement, who is leading a company-wide transformation anchored in proactive decision-making, transparent adoption, and human-centered design. Her approach offers a blueprint for teams looking to embed AI not just in strategy, but in the day-to-day heartbeat of GTM operations.
From reactive to proactive: A three-part transformation
For Mollie, staying ahead in RevOps means no longer relying on backward-looking reporting. Her team is building for foresight and speed, with AI acting as the connective tissue.
“For us, staying ahead means shifting from reactive to proactive revenue operations. We're focusing on predictive analytics and real-time decision support rather than just historical reporting.”
That shift is already in motion across her org. As Mollie explains, their transformation has taken shape in three specific ways:
- Earlier signal detection: AI now analyzes cross-channel interactions to surface buying intent earlier in the customer journey.
- Pipeline automation: Repetitive tasks in pipeline management are offloaded to AI, allowing reps to stay focused on high-value conversations.
- Support transformation: AI-powered chat and phone agents instantly retrieve relevant case details and product information, dramatically reducing resolution time while boosting satisfaction.
“It's not just about efficiency—it's about creating service moments that turn support interactions into expansion opportunities.”
That mindset of turning every operational upgrade into a customer value multiplier is central to Mollie’s strategy.
Earning trust through transparency and shared ownership
But such a bold transformation doesn’t come without friction. Mollie’s team initially encountered skepticism from those wary of overhyped tools or past failed attempts.
“The resistance primarily centered around trust—trust in the technology's accuracy and trust that we weren't just implementing AI for its own sake.”
Rather than pushing harder, they went deeper, anchoring every rollout in clear intent and thoughtful education.
“We addressed this by being transparent about the ‘why’ behind each implementation… We also demystified the technology by walking teams through how the models were trained and what data they used… The breakthrough came when we positioned AI as augmentation rather than automation, with humans maintaining meaningful oversight.”
By making space for people to understand the tools and choose how they were used her team turned skeptics into champions. Their approach wasn’t top-down. It was partnership-driven.
Rebuilding systems for agentic collaboration
With AI adoption underway, Mollie’s team quickly hit a second challenge: their existing infrastructure wasn’t built for intelligent agents. The bottleneck wasn’t just process, it was architecture.
“Legacy systems operate on rigid workflows that were built to function under human operators and judgment… but our platforms don't always easily allow for this flexibility.”
To solve that, they’re systematically rethinking how workflows are structured.
“We're systematically rebuilding workflows to be ‘agent-friendly’ by restructuring data and reimagining processes from an AI-first perspective. It's not just about adding AI capabilities as an afterthought—it requires reconceptualizing how work happens when humans and agents collaborate.”
This isn’t just automation, it’s a ground-up redesign that aligns data, systems, and human input around a more dynamic, intelligent way of working.
What matters most: Interfaces, not algorithms
Looking back on the process, Mollie reflects that their biggest unlock didn’t come from modeling or engineering, it came from focusing on experience.
“The most important lesson has been that successful AI implementation isn't about the algorithm—it's about the interfaces.”
Her team originally invested energy into technical performance. But they learned that adoption and impact hinged on something else entirely: how seamlessly the AI fit into everyday workflows.
“AI needs to fit into existing workflows rather than creating parallel processes. The technology should feel invisible, with insights appearing exactly when and where people need them.”
This shift from building “cool features” to solving specific user problems has now become a design principle across the org.
Operator takeaway: Make it seamless, make it human
Mollie Bodensteiner’s work offers a clear blueprint for how RevOps leaders can drive meaningful AI transformation:
- Lead with intent, not hype
- Build systems around action, not dashboards
- Reimagine infrastructure with agents in mind
- Design for the interface, not the model
More than anything, Mollie reminds us that AI isn’t just about what’s possible, it’s about what’s usable. When trust, timing, and tooling align, even the most complex systems start to feel simple. That’s what it means to operate on the agentic edge.
