Let's face it: artificial intelligence (AI) is everywhere, especially in sales. Companies are jumping onto the AI bandwagon faster than ever, captivated by promises of increased efficiency, accelerated sales cycles, better customer experiences, and smarter business decisions. But here's the harsh reality that most don't anticipate: purchasing AI tools is only the beginning of the journey. Making these tools an integral part of your team's daily operations? That’s a much tougher challenge.
We've all witnessed the scenario: exciting new AI technology quickly turns into "shelfware," collecting dust while draining precious budgets. But why does this phenomenon happen so frequently?
To unravel this mystery and offer actionable solutions, we sat down with Amanda Whiteside, Global VP of Revenue Enablement at Freshworks. Amanda's unique perspective comes from her extensive experience on both sides of AI adoption, selling AI solutions and implementing them internally. To further enrich the conversation, Ashley Wilson, COO of Momentum, provided her insights on practical ways to ensure AI solutions truly stick and deliver long-term value.
Let’s get into it.
AI Ownership: Who’s Actually Responsible?
AI isn’t a one-and-done purchase. It needs ownership. But who should be responsible for it?
How Amanda’s Team Handles AI Ownership in the GTM Stack
One of the most interesting insights Amanda shared was how enablement is taking on the role of AI ownership at Freshworks. Traditionally, sales enablement teams focus on training, coaching, and ensuring reps are set up for success. But in a world where AI is becoming embedded in day-to-day sales motions, enablement is now responsible for ensuring AI tools are actually used and deliver value.
“We sit at the intersection of AI strategy and execution. We don’t just pick the tools—we make sure they’re integrated into workflows, adopted at scale, and optimized over time. That means enablement is playing a bigger role in AI success than ever before.”, stated Amanda.
So what does that actually look like?
Amanda’s team oversees a centralized GTM tooling strategy, which means:
- Selecting and Validating AI Tools – Before any AI investment is made, enablement works closely with RevOps and sales leadership to vet tools, ensuring they align with real business needs.
- Owning Adoption Strategy – The team doesn’t just hand AI off to reps and hope for the best. They create structured training programs, set usage benchmarks, and continuously measure engagement.
- Building an AI Playbook – AI success isn’t about handing out logins. Amanda’s team ensures sellers have clear use cases, examples, and business processes that make AI a natural part of their daily routine.
- Reinforcement & Iteration – AI tools evolve, and so do sales motions. Enablement at Freshworks monitors how AI is being used and makes adjustments as needed. If adoption drops, they step in to fix the friction.
- Driving Business Impact – An AI model has to drive revenue a prove its competitive advantage. Amanda’s team works with leadership to ensure AI tools tie back to core business KPIs like deal velocity, forecast accuracy, and rep productivity.
Essentially, AI enablement does not just cover the training portion, it is also about operationalizing AI across the entire sales motion. Without enablement owning AI strategy, reps are left to figure things out on their own, which leads to inconsistent usage and missed potential.
This shift in the adoption of AI is something more companies will need to embrace. AI isn’t just an IT or RevOps concern anymore, it has become a frontline sales enablement priority. The teams that recognize this early will be the ones that get real impact from AI, while others will struggle with underutilized tools and wasted budgets.
Evaluating Your AI Stack: What to Keep and What to Cut
Another frequent challenge is AI tool overload. Amanda describes it as a scenario where teams stack multiple AI applications without a clear strategy, often resulting in significant wasted budget and confusion among users.
To prevent tool overload, Freshworks follows a disciplined three-step evaluation process:
Step 1: Clearly Define the Problem
Amanda underscores clarity as essential: “If your team can't clearly articulate in one sentence exactly what problem an AI tool solves, that tool doesn't deserve a spot in your stack.”
AI projects at Freshworks must address one of these core needs:
- Improving seller efficiency by automating tedious, repetitive tasks.
- Enhancing decision-making capabilities through improved data insights.
- Personalizing and optimizing customer engagements.
- Accelerating revenue growth through smarter selling.
Step 2: Track Real Adoption (Beyond Logins)
Real adoption is about behavior change, not mere logins. Freshworks evaluates adoption by examining real-world impacts such as:
- Detailed feature utilization metrics.
- Feedback from sales managers and frontline reps.
- Performance differences via comprehensive win-loss analysis.
Step 3: Measure the Real Business Impact
Ultimately, Amanda insists on proving AI’s measurable impact through critical metrics:
- Revenue influence, including impact on pipeline growth and deal velocity.
- Efficiency improvements measured by time savings and productivity gains.
- Decision accuracy, ensuring AI insights significantly improve business strategy choices.
Why Most AI Implementations Fail
Adoption rarely happens naturally, contrary to popular expectations. Amanda describes it clearly: “AI isn't a ‘set it and forget it’ solution. It demands structure, intentionality, ongoing training, and constant reinforcement.”
Ashley Wilson, COO at Momentum echoed this, emphasizing three critical elements to ensure AI becomes part of your team's daily rhythm:
- Seamless Integration: AI must integrate smoothly into existing workflows, like Slack, CRM, and email, rather than creating new processes or additional complexity.
- Visible Leadership Usage: Leaders must actively use and champion AI-powered tools, demonstrating their value through real-world usage.
- Eliminating Friction: AI must simplify and enhance productivity, eliminating rather than introducing friction to sales reps' day-to-day activities.
Proving AI's ROI: The Hard Truth About Measurement
One widespread misconception about the potential of AI is expecting immediate, dramatic revenue boosts without a structured measurement framework. Amanda highlights the reality:
“You can't expect an AI tool to deliver without defining precisely what success looks like beforehand. AI requires clear benchmarks and consistent tracking to deliver a genuine ROI.”
Freshworks carefully tracks three types of metrics:
- Usage Metrics: Consistency and depth of usage.
- Operational Efficiency: Quantifiable reductions in time spent on manual tasks.
- Revenue Impact: Measurable improvements in deal cycles, close rates, and overall revenue influenced by AI.
Ashley reinforces the need for rigorous measurement: “Without clear, measurable outcomes, AI quickly becomes another cost center rather than a business accelerator.”
The Post-Sale Reality: AI Is a Journey, Not a One-Time Fix
Buying AI is just the beginning. The real challenge is sustaining impact over time.
Amanda made a key point: most AI implementations lose momentum because there’s no post-sale strategy. “AI success isn’t about the first 30 days. It’s about what happens in months six, twelve, and beyond. If you don’t have a plan for ongoing optimization, AI adoption will stall.”
Freshworks has a post-sale AI evaluation framework that keeps AI adoption on track:
- Continuous Feedback Loops – Regular check-ins with users to refine how AI is working in real workflows.
- Feature Optimization – AI systems improve constantly. If you’re not staying updated, you’re leaving value on the table.
- Performance Reviews – AI should be reassessed every quarter. Is it still driving results? Has adoption changed?
Ashley highlighted that AI is not a static tool. Companies that fail with AI treat it like a one-time deployment.
“AI isn’t a ‘set it and forget it’ tool. It evolves. Your team evolves. Your workflows evolve. If you’re not iterating, AI won’t keep delivering value.”
At Momentum, they encourage companies to take a test-and-learn approach for AI capabilities:
- Experiment with new use cases – AI’s role should expand as teams get more comfortable.
- Monitor adoption patterns – If usage drops, figure out why before AI loses credibility.
- Reinforce AI’s role in team goals – AI is a performance driver. It should streamline processes, not complicate them.
This ongoing approach separates AI winners from AI failures. Companies that embed AI into their long-term strategy see compounding returns. Those that don’t? They end up back at square one, wondering why their AI-driven tool isn’t driving results.
Final Thoughts: Intentionality is Key to AI Success
So, what’s the takeaway from this discussion? AI doesn’t work unless you make it work.
Amanda and Ashley’s show us that a successful AI adoption requires:
- Defined Ownership – Someone must be accountable for AI’s success.
- Integration into Workflows – AI should reduce friction, not create extra steps.
- Clear Success Metrics – AI needs measurable impact, not just anecdotal value.
- Ongoing Optimization – AI adoption isn’t static. It requires continuous refinement.
The companies winning with AI aren’t just buying tools. They’re embedding AI into their strategy, workflows, and performance culture.
That’s how you scale AI beyond the purchase and use it as your ally to achieve business objectives.
What's Next?
Interested in diving deeper into how businesses effectively leverage AI? Join our upcoming AI Advantage Webinar featuring Bryan Kurey, SVP at SBI Insights, exploring CEO-level strategies for AI initiatives success.
You can register for that on our webinars overview webpage
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Remember, the true impact of AI can be seen on how effectively your team leverages it. Ensure you're prepared to scale beyond the purchase and turn your AI investment into genuine, lasting success. 🚀