The Innovative Revenue Leader

In this episode of Innovative Revenue Leader, Seth Marrs sits down with Joseph Miller, Co-Founder and Chief AI Officer at Vivun, to explore how AI agents are fundamentally changing how revenue teams operate. Joseph shares how AI is moving beyond support tools and into real-time decision-making, joining conversations, surfacing insights, and accelerating outcomes.The conversation dives into the realities of building in an AI-first world, from making high-risk bets during uncertain market shifts to rethinking how deals are won. Joseph explains how AI is compressing sales cycles, reshaping team roles, and creating a new dynamic where human judgment and machine intelligence must work together.Takeaways:• AI is moving into real-time decision making. The most innovative shift is AI participating in live moments like sales calls and meetings. Instead of supporting work before or after, AI is now influencing decisions as they happen.• Companies must be willing to disrupt themselves. Joseph highlights the need to pivot early, even when it is uncomfortable. Waiting for certainty can leave companies out of position in fast-moving markets.• Building in uncertainty is required for innovation. Leaders must make bets without perfect information. Success comes from acting on conviction and adapting quickly as technology evolves.• AI is compressing sales cycles dramatically. Buyers increasingly want fast, direct answers rather than long relationship-driven processes. AI enables teams to deliver value quickly and close deals faster.• Human value becomes more important, not less. As AI takes over repetitive work, the remaining human contributions like judgment, creativity, and relationship-building become more critical.• There is an emerging “age of abundance” in work. AI is expanding what is possible by removing low-value tasks. This allows teams to spend more time on meaningful, high-impact work.• Exploration drives long-term innovation. Joseph emphasizes that broad exploration across disciplines builds better problem-solving ability and leads to more innovative thinking over time. Quote of the Show:“The most innovative things are happening in the moments where AI is actually joining the decisions.” - Joseph MillerLinks:LinkedIn: https://www.linkedin.com/in/likeascientist/Website: http://www.vivun.comWays to Tune In:Innovative Revenue Leader Website: InnovativeRevenueLeader.ai Spotify: https://open.spotify.com/show/4Hn0sJKCzneggQj3j4XKo3?si=02c4c44769dc41bb Apple Podcasts: https://podcasts.apple.com/us/podcast/the-innovative-revenue-leader/id1843401468 Amazon Music: https://music.amazon.com/podcasts/48477601-e33b-4c80-a4af-739863e58131 Podchaser: https://www.podchaser.com/podcasts/the-innovative-revenue-leader-6233089 

In this episode of Innovative Revenue Leader, Seth Marrs sits down with Nithya Lakshmanan, Chief Product Officer at Outreach, to explore what it really takes to adopt AI successfully inside modern revenue organizations.Nithya shares that while AI is often positioned as a technology transformation, the real challenge lies in aligning people, processes, and workflows. The conversation dives into how organizations can move beyond experimentation to meaningful adoption by embedding AI into daily operations, prioritizing real business problems, and ensuring teams are equipped to use it effectively. Takeaways:• AI adoption is a people and process challenge, not just technology: Organizations often underestimate the human side of AI. Success depends on aligning teams, workflows, and incentives so AI becomes part of how work actually gets done.• Start with real business problems, not tools: Many companies adopt AI without a clear use case. Nithya emphasizes that leaders must begin with specific problems tied to revenue, pipeline, or efficiency before introducing technology.• Workflow integration determines success: AI only creates value when embedded into existing systems and daily habits. Bolting on tools without integration leads to low adoption and minimal impact.• Enablement is critical for adoption: Teams need training, context, and confidence to use AI effectively. Without proper enablement, even the best tools will fail to deliver results.• Experimentation must lead to execution: Pilots and proofs of concept are only the first step. Organizations must scale what works and operationalize AI to drive measurable outcomes.• Leadership alignment accelerates transformation: When leadership is aligned on goals, expectations, and use cases, AI adoption becomes faster and more effective across the organization.Quote of the Show:“AI doesn’t fail because of the technology. It fails because it’s not embedded into how people actually work.” - Nithya LakshmananLinks:LinkedIn: https://www.linkedin.com/in/nithya-lakshmanan/Website: http://www.outreach.io/Ways to Tune In:Innovative Revenue Leader Website: InnovativeRevenueLeader.ai Spotify: https://open.spotify.com/show/4Hn0sJKCzneggQj3j4XKo3?si=02c4c44769dc41bb Apple Podcasts: https://podcasts.apple.com/us/podcast/the-innovative-revenue-leader/id1843401468 Amazon Music: https://music.amazon.com/podcasts/48477601-e33b-4c80-a4af-739863e58131 Podchaser: https://www.podchaser.com/podcasts/the-innovative-revenue-leader-6233089 

Take your sales performance to the next level with forward-looking insights from Trilliad’s 2026 Growth Imperatives. It’s time to move beyond simple efficiency and start architecting a sales system that actually sticks, and who better to guide you than IRL’s very own Seth Mars!In this episode, Seth dives deep into the final imperative: building progressive sales performance systems that enable sustainable seller effectiveness. You will learn how to replace outdated one-time workshops with "always-on" systems, leverage AI to personalize training for every individual, and finally bridge the gap between training activities and hard financial outcomes. Get motivated to transform your sales team into a high-performance engine destined to maximize revenue per seller and stay ahead of the competition.Takeaways:Pillar 1: Build Sustainable Performance Systems. Move beyond one-time workshops to create "always-on" systems that treat sales performance with the same analytical rigor as revenue forecasting.Pillar 2: Treat Skill Erosion as a Strategic Risk. Protect your investment by monitoring "training decay" in real-time and using targeted interventions to ensure sellers retain and execute what they’ve learned.Pillar 3: Scale Personalized Development with AI. Use AI-driven roleplay and conversation intelligence to move away from generic enablement and tailor training to the specific needs and personality of every individual seller.Pillar 4: Operationalize the Data Layer. Eliminate subjective opinions by integrating unstructured data from calls and emails into a single view to understand exactly what behaviors drive success.Pillar 5: Align Investments with Financial Outcomes. Shift the conversation with your CFO by proving how skill adoption directly correlates to key metrics like revenue per seller.Build an ecosystem, not isolated initiatives. The five pillars work together to transform traditional sales training into a comprehensive performance organization.Quote of the Show:“Last year was about AI enabling efficiency. This year is about taking that efficiency and turning it to effectiveness” - Seth MarrsLinks:Twitter: https://x.com/smarrs88 LinkedIn: linkedin.com/in/sethmarrsWebsite: https://sandler.comTrilliad’s Growth Imperatives: trilliad.com/2026-growth-imperativesWays to Tune In:Innovative Revenue Leader Website: InnovativeRevenueLeader.ai Spotify: https://open.spotify.com/show/4Hn0sJKCzneggQj3j4XKo3?si=02c4c44769dc41bb Apple Podcasts: https://podcasts.apple.com/us/podcast/the-innovative-revenue-leader/id1843401468 Amazon Music: https://music.amazon.com/podcasts/48477601-e33b-4c80-a4af-739863e58131 Podchaser: https://www.podchaser.com/podcasts/the-innovative-revenue-leader-6233089 

In this episode, host Seth Marrs sits down with Sahil Aggarwal, Co-Founder and CEO of Von & Rattle, to discuss how AI agents are beginning to transform revenue operations and the broader go-to-market landscape. Sahil shares his vision for AI-native systems that can analyze business context across CRM data, customer conversations, and internal workflows to generate insights and execute tasks that traditionally require multiple teams.The conversation explores why traditional dashboards may soon be replaced by intelligent agents, how AI can extract value from raw business data, and why productivity expectations for revenue teams are about to rise dramatically. Sahil also discusses how sellers and revenue leaders can adapt as AI becomes embedded into everyday workflows, empowering top performers to operate at an entirely new level of effectiveness. Takeaways:AI Agents Will Replace Dashboards: Traditional dashboards require humans to interpret data before action can be taken. AI agents will shift this model by analyzing performance and executing tasks automatically.AI Can Work Directly From Raw Business Data: Many organizations struggle with incomplete or inaccurate CRM data. Modern AI can extract insights directly from emails, calls, and other business signals to generate more reliable context.The “Super Agent” Model Is Emerging: Instead of relying on dozens of separate automation tools, companies will increasingly use powerful AI agents that coordinate workflows across multiple systems.AI Will Become Core Business Infrastructure: Just like the internet or electricity, AI will soon power nearly every application. The conversation will move from “using AI” to simply building products that run on it.Productivity Expectations Will Dramatically Increase: As AI removes manual work, leaders will expect significantly higher output from individuals and teams.Revenue Roles Will Become More Technical: Sellers, marketers, and revenue operators will need to understand how to work alongside AI systems to stay competitive.Quote of the Show:“AI is like electricity. It will power everything we do in business.” - Sahil AggarwalLinks:LinkedIn: https://www.linkedin.com/in/saggarwal2/Website: https://www.gorattle.com/Ways to Tune In:Innovative Revenue Leader Website: InnovativeRevenueLeader.ai Spotify: https://open.spotify.com/show/4Hn0sJKCzneggQj3j4XKo3?si=02c4c44769dc41bb Apple Podcasts: https://podcasts.apple.com/us/podcast/the-innovative-revenue-leader/id1843401468 Amazon Music: https://music.amazon.com/podcasts/48477601-e33b-4c80-a4af-739863e58131 Podchaser: https://www.podchaser.com/podcasts/the-innovative-revenue-leader-6233089 

In this episode of Innovative Revenue Leader, Seth Marrs sits down with Laura Valerio, Global Evangelist for GTM Performance at Highspot, to explore what it truly means to operationalize customer-centric growth inside modern revenue organizations.Laura shares a thoughtful and strategic perspective on aligning go-to-market teams around the customer journey, breaking down silos between sales, marketing, and customer success, and building scalable systems that drive consistent performance. The conversation dives into leadership discipline, operational clarity, and the structural shifts required to move from reactive selling to intentional revenue architecture.This episode is a masterclass in turning strategy into sustainable execution. Takeaways:Customer-Centricity Requires Structural Change: Aligning around the customer journey demands intentional redesign of processes, metrics, and incentives across revenue teams.Alignment Drives Predictable Performance: When sales, marketing, and customer success operate from shared definitions and goals, pipeline velocity and retention improve dramatically.Revenue Strategy Must Be Operationalized: Vision alone does not scale. Clear systems, defined handoffs, and accountability mechanisms turn strategy into measurable outcomes.Silos Destroy Momentum: Disconnected teams create friction for customers and inefficiency internally. Integration creates compounding growth.Leadership Sets the Standard for Alignment: Revenue transformation begins with leaders modeling cross-functional collaboration and disciplined execution.Metrics Should Reflect the Customer Journey: Organizations that measure the full lifecycle, not just isolated stages, make smarter decisions and reduce churn risk.Quote of the Show:“Customer centricity isn’t a slogan. It’s a structural decision you make about how your organization operates.” - Laura ValerioLinks:LinkedIn: https://www.linkedin.com/in/lauravalerio1/Website: https://www.highspot.comWays to Tune In:Innovative Revenue Leader Website: InnovativeRevenueLeader.ai Spotify: https://open.spotify.com/show/4Hn0sJKCzneggQj3j4XKo3?si=02c4c44769dc41bb Apple Podcasts: https://podcasts.apple.com/us/podcast/the-innovative-revenue-leader/id1843401468 Amazon Music: https://music.amazon.com/podcasts/48477601-e33b-4c80-a4af-739863e58131 Podchaser: https://www.podchaser.com/podcasts/the-innovative-revenue-leader-6233089 

Is AI really the end of SaaS as we know it, or just the beginning of a messy evolution? On this episode of Innovative Revenue Leader, we welcome back Anthony McPartlin, Principal Analyst at Forrester and one of the sharpest voices in revenue operations today. Anthony breaks down what’s actually happening beneath the headlines: $2 trillion wiped from SaaS valuations, the pressure on seat-based pricing, token-cost economics, AI governance challenges, and why RevOps leaders may be entering their most important era yet. We unpack the uneven impact of AI across sales motions, the rising tension between usage-based pricing and predictability, and why organizations must move beyond isolated AI use cases toward a long-term strategic transformation. If you’re a revenue leader trying to make sense of AI disruption, pricing chaos, and the evolving role of RevOps, this conversation is essential listening. Takeaways:AI role play is becoming a standard capability. Embedded AI-driven role play is transforming sales enablement by making practice scalable, personalized, and part of the daily workflow.The SaaS pricing model is under real pressure. Seat-based pricing alone is unlikely to survive. Hybrid models combining base fees with usage-based AI costs are emerging, but the transition will be rocky.RevOps accountability is increasing, not shrinking. As AI becomes embedded in forecasting, automation, and workflows, system precision and governance become mission-critical.AI shifts cost curves, it doesn’t flatten them. Productivity gains will likely raise expectations, driving higher quotas, tighter forecasting tolerances, fewer buffers, and increased economic accountability for RevOps.Use-case experimentation isn’t enough. Organizations must define a long-term AI strategy for Go-To-Market, including implications for compensation, territories, onboarding, benchmarking, and governance.New roles will emerge. Revenue architects and AI workflow managers may become essential to managing uneven deployment and cross-functional friction.Career advice for future leaders. Focus on assembling transferable skills, stay curious, embrace discomfort, and think in shorter skill-building arcs rather than fixed career ladders.Quote of the Show:“If you’re framing this as AI equals fewer sellers and fewer ops roles, you’re misleading yourself and others. The reality is far more complex than that.” - Anthony McPartlinLinks:LinkedIn: linkedin.com/in/anthonymcpartlinWebsite: https://www.forrester.com Ways to Tune In:Innovative Revenue Leader Website: InnovativeRevenueLeader.ai Spotify: https://open.spotify.com/show/4Hn0sJKCzneggQj3j4XKo3?si=02c4c44769dc41bb Apple Podcasts: https://podcasts.apple.com/us/podcast/the-innovative-revenue-leader/id1843401468 Amazon Music: https://music.amazon.com/podcasts/48477601-e33b-4c80-a4af-739863e58131 Podchaser: https://www.podchaser.com/podcasts/the-innovative-revenue-leader-6233089 

In this episode of Innovative Revenue Leader, host Seth Marrs sits down with Ryan McShane, Vice President of Product Marketing at Salesloft, to explore the growing generational divide inside revenue organizations.Drawing from research highlighting a $56 billion performance gap, Ryan explains how tension between experience-driven sellers and AI-native talent impacts results, and how AI can serve as a unifying force rather than a disruptive one. The conversation dives into codifying best practices, modernizing coaching, embedding AI into real workflows, and shifting focus from hours worked to measurable outcomes. The key insight: the future of revenue belongs to organizations that blend intuition with technology to build adaptive, high-performing teams. Takeaways:The $56B Generational Revenue Gap Is Real: Misalignment between experienced sellers and AI-native talent isn’t just cultural, it directly impacts quota attainment, productivity, and organizational performance.AI Codifies What Actually Works: Instead of relying on tribal knowledge or hierarchy, AI can identify patterns in winning behaviors, surface what drives higher ACV and conversion rates, and democratize those insights across the team.Experience + AI Is the Winning Formula: Veteran sellers bring intuition and strategic context that AI can’t replicate. Younger sellers bring comfort with automation and experimentation. The competitive advantage lies in blending both.Workflow Integration Determines AI Success: AI fails when it’s bolted on. It succeeds when it’s embedded into real jobs-to-be-done, pipeline generation, deal inspection, coaching, and forecasting.Coaching Is the Biggest AI Opportunity: Consistent, data-backed coaching eliminates recency bias, reduces inconsistency, and gives managers a structured way to develop sellers across generations.Results > Hours Logged: AI challenges the outdated “grind equals performance” mindset. Modern revenue teams must align around KPIs, outcomes, and efficiency rather than activity for activity’s sake.The Future of Revenue Is Adaptive, Not Rigid: Go-to-market is shifting from waterfall execution to a living, evolving system powered by experimentation, data, and cross-generational collaboration.Quote of the Show:“You have to start with the problem you want to solve, not the technology you want to throw at your team.”Links:LinkedIn: https://www.linkedin.com/in/ryan-mcshane-49a66011/Website: https://salesloft.comWays to Tune In:Innovative Revenue Leader Website: InnovativeRevenueLeader.ai Spotify: https://open.spotify.com/show/4Hn0sJKCzneggQj3j4XKo3?si=02c4c44769dc41bb Apple Podcasts: https://podcasts.apple.com/us/podcast/the-innovative-revenue-leader/id1843401468 Amazon Music: https://music.amazon.com/podcasts/48477601-e33b-4c80-a4af-739863e58131 Podchaser: https://www.podchaser.com/podcasts/the-innovative-revenue-leader-6233089 

The ground is shifting under every revenue team, and not because of another tool—because AI now demands a real operating model. We sit down with Julia Nimchinski, founder of Hard Skill Exchange, to unpack a 2026 predictions report that brings clarity to the chaos: AI becomes a managed role centered in RevOps, enablement turns into an agentic operating layer, and systems of action finally replace systems of record. If you’ve felt the tension between flashy pilots and durable performance, this conversation gives you the blueprint.We dig into the phases of AI in GTM—from human sellers with AI assist, to human sellers and AI buyers, toward more agentic organizations—and what it means for job design, governance, and measurement. Julia shares why the most important shift is methodological: unify theory and practice so AI augments the right parts of the workflow, under clear guardrails, with instrumentation that proves what actually works. We explore the uncomfortable truth behind adoption numbers: while surveys boast 70-plus percent adoption, real usage often sits near 7.6 percent. That gap isn’t a failure of tech; it’s a failure of method and measurement.From there, we get practical. How does enablement move beyond training to orchestrate agent-assisted workflows that show ROI within weeks? What telemetry proves that methodology use correlates with higher win rates and faster cycles? Why should sellers stop living in CRMs and shift into systems of action that do work on their behalf? We also tackle the convergence of B2B and B2C as buyer-side agents screen messages and shape journeys—and what sellers must change to reach real humans through that layer.If you’re building an AI-native revenue engine, this is your edge: treat AI as a managed role, elevate enablement to execution, and measure everything so your method evolves with the market. Enjoy the conversation, share it with a colleague who runs RevOps or Enablement, and subscribe for more deep dives on the future of go-to-market.

If your AI strategy feels stuck, it’s probably missing the fuel that matters most: conversations. We dig into how conversation intelligence turns buyer-seller dialogue into structured data that LLMs can analyze, answer questions about, and convert into real coaching and revenue impact. Instead of treating recording as the finish line, we map the full system that connects email, calendar, mobile, and in-person meetings, then associates each interaction to the right account and opportunity so insights actually land where work gets done.We break down the current tool landscape—from web conferencing and note takers to full revenue orchestration—and explain where each shines. Then we unpack the eight capabilities that separate helpful from transformational: accurate association, multi-channel capture, summaries that scale to account and opportunity, automated scorecards with snippet-linked coaching, natural-language questions across your dataset, smart triggers for objections and competitor mentions, AI-led discovery of emerging themes, and reporting that trends real change over time. You’ll hear why web calls represent only a slice of the truth and how to close the gaps that hide risk.Finally, we spotlight four workflows already being rewritten by AI: automatic CRM field updates that clean your pipeline without manual data entry, deal visibility that reflects what was said rather than what was remembered, on-demand account plans generated from the conversation graph, and one-click pre-call prep that levels up every meeting. The takeaway is simple and urgent: capture broadly, associate correctly, and push insights back to sellers where it counts. Subscribe, share with a teammate who owns your sales stack, and tell us which workflow you want to automate first.

The metrics look great, but the pipeline doesn’t. That tension sparked a frank conversation with Bill Hobbib, CMO of Demand Science, about the marketing data mirage—why so many programs appear to win on dashboards yet fail where it counts: qualified opportunities and predictable revenue. We dig into what really signals buying intent, how to stop chasing ghosts, and why AI-only content is quietly eroding brand trust.We start by breaking down the core problem: clicks and topic interest are not intent. Bill explains how provenance and context transform noisy activity into meaningful insight, and why multi-signal aggregation—combining behavioral data with executive hires, funding events, stack changes, and market dynamics—dramatically improves prioritization. If your team is still flooding sales with “hot accounts” based on anonymous clicks, this will reset your playbook.From there, we get practical. Bill shares examples of teams driving a 1:7 CAC-to-LTV ratio and slashing cost per qualified account by tightening the loop between signals, content, and activation. We talk about slimming bloated martech stacks, building transparent attribution that rewards real pipeline creation, and designing coordinated activation when thresholds trip. We also address the AI content backlash and outline a simple rule: let AI move faster, but let humans make it matter.If you’ve felt the confidence paradox—trusting your data while watching deals stall—this conversation offers a path out. Expect clear steps to upgrade your signals, sharpen your narrative, and focus your efforts on what buyers actually need. Subscribe, share with your team, and leave a review to tell us which metric you’d drop tomorrow and why.

Forget AI theater—this conversation gets into the real decisions leaders face when moving from copilots to autonomous agents. We unpack what the board actually cares about: where agents sit in the customer journey, how they reshape processes that humans or legacy software used to carry, and what that means for ROI, accountability, and experience design.John Arnold, Head of Product Marketing and Strategic Advisory at Creatio, brings hands-on insight from large enterprises and high-growth teams building with no code and agentic CRM. We break down the difference between assistants that draft and agents that act, and why that shift forces choices about redeploying people, rethinking service models, and defining your edge—human-led differentiation or agent-led speed. Expect concrete examples from banking and financial services, where back office volume meets customer expectations for instant outcomes, plus the math behind productivity gains that don’t automatically equal headcount cuts.We also confront the adoption gap in professional services. Tech leaders overwhelmingly see agents as critical, while many services firms hesitate. We explore why, and reveal the opportunity hiding in plain sight: data readiness, governance, agent design, and change management that clients will pay for when partners move beyond strategy decks to shipping safe, reliable systems. Finally, we show how enterprise-grade no code flips the delivery model—empowering technical business users, establishing fusion teams with IT, and putting guardrails in place so teams can build applications, workflows, and agents without waiting on quarterly release trains.If you care about turning AI into outcomes, this is your playbook for getting beyond pilots, aligning humans and agents where they’re strongest, and scaling responsibly. Subscribe, share with a colleague who owns an AI mandate, and leave a review with your biggest agent-related challenge—we may feature it next time.

If you’ve ever felt the boardroom’s optimism collide with the grind of the field, this conversation will sound familiar. We unpack fresh pulse data from roughly 175 people across sales, marketing, and customer success to reveal why executives say growth is up while sellers feel squeezed, and how AI is changing workflows in ways that actually stick.We start with the split: leaders reading future indicators versus sellers living inside over-assigned quotas. That gap shows up again when we compare small versus large organizations—lean teams use AI and streamlined processes to move faster, while big orgs wrestle with process debt. From there, we break down what each group truly values. Sellers overwhelmingly want sales effectiveness and coaching. Strategy, ops, and enablement prioritize planning. Executives talk about alignment, yet budget and focus often drift toward tech and planning instead of shared execution.AI’s real impact is clear and refreshingly practical. Preparation and planning top the list of wins, with sellers relying on AI for research, account intelligence, and meeting prep. Forecasting and planning tools are finally making inroads with leadership as embedded capabilities improve. What’s missing is as telling as what’s working: despite vendor hype, AI-led lead prioritization isn’t trusted or adopted at scale. We explore why that trust gap persists and outline a path to pilot prioritization with tight feedback loops, measurable outcomes, and seller input.We also map the tooling landscape and why “revenue orchestration” is becoming the seller’s workspace. Gong, Glean, and Clay surface repeatedly for their data-first approaches, focused agents, and top-of-funnel innovation. You’ll hear concrete use cases—contact enrichment, deep research, role play and coaching—that cut ramp time and lift conversions without adding bloat. By the end, you’ll have a playbook: align on one funnel and forecast, fund effectiveness at the frontline, measure AI by outcomes not demos, and build an operating rhythm that forces shared truth. If this resonates, follow the show, share it with your team, and leave a quick review to help others find it.

Growth doesn’t happen when a contract is signed. Growth happens when customers actually use what they bought, day after day. We wrap our usage-based sales series by connecting the dots between pricing strategy, operations, and compensation—showing a concrete path from “right to buy” to realized revenue you can bank on.We start by reframing the sales motion for consumption models. Winning access is only the opening move; the real work is guiding adoption and hitting a clear, data-backed ramp. Rather than forcing usage into traditional opportunities, we walk through why account-level management creates clarity across products, regions, and divisions. From there, we dig into forecasting: finance or a deal desk should own usage predictions with analytics and machine learning, not sellers guessing run rate. You’ll learn how to stack committed volumes with live run rate to set honest targets and expose realization gaps before they become surprises.Role design gets a reset too. AEs close the first purchase and stay accountable through the ramp window, then hand off to CSMs or AMs to maintain and deepen value while they open the next wedge—new divisions, higher tiers, or complementary products. We share practical ways to instrument telemetry, trigger alerts when adoption stalls, and align incentives to ramp and sustained usage. The result is a simple, repeatable operating model: forecast with data, manage at the account, pay for realization, and hunt for expansion where customers already show proof of value.Ready to turn promises into proof and surface the growth hiding in your consumption revenue? Follow the show, share this episode with a teammate who owns forecasting or CS, and leave a quick review telling us your ramp window and how you define success.

Most comp plans buy the wrong behavior in a usage-based world—and the results show up as stale pipelines, noisy dashboards, and hunters who drift into farming. We sat down with seasoned sales ops leader Chuck Lee to unpack how to pay for outcomes that actually matter: a clean start, a predictable ramp, and a scalable hand-off that sticks.  We trace a real transformation inside a large inbound motion where reps were incentivized to chase the oldest leads and obsess over consumption they didn’t own. By stripping the plan to a simple, action-focused design and shutting off post-implementation pay for AEs, the team saw a 40%+ lift in conversion. Chuck explains why high-velocity sales demands fewer choices, not more; how to align quotas to volatile demand without eroding trust; and the telltale signs your plan is buying noise instead of revenue.  Then we go deep on usage-based mechanics. The true “deal won” is when the customer starts using the product, and the second milestone is the ramp to forecast. Chuck shares how to set the AE’s window in the deal using historical ramp curves, why FP&A should co-own the model, and how SLAs between sales and CS prevent credit confusion and dropped hand-offs. We also confront the perpetual commission trap that turns hunters into farmers, and outline a cleaner split: hunters own start and ramp-to-target, farmers own adoption, expansion, and problem-solving.  If you’re wrestling with comp design for usage-based sales, this conversation gives you practical guardrails, from monthly quota tuning and points-based payouts to role clarity that protects new logo growth. Subscribe, share with your revenue team, and tell us: what behavior is your comp plan really buying?

The loudest AI often isn’t the smartest. We dig into fresh research with Curtis Schroeder, Head of Research and Insight at Varicent, to unpack a striking pattern among 150 revenue leaders: most expect the biggest ROI from system-level AI—forecasting, territory design, quota setting, incentive modeling—while investments still chase seller-facing tools that look great in a demo but struggle to compound impact.Curtis explains why AI for revenue is really two markets. Seller AI promises instant productivity stories yet demands training, process change, and continuous behavior shifts. System AI upgrades decision quality at the core, creating compounding gains across the org—better coverage, cleaner attainment, faster re-planning, and more credible forecasts. We explore how to separate hype from value, why human skepticism remains the top blocker, and why adoption improves when AI becomes invisible inside workflows rather than another tool reps must learn.You’ll hear how leaders pair quick visible wins with deeper system investments, how to make forecasting an always-on signal rather than a month-end ritual, and how to link territory potential to quota for fairer, higher-yield plans. We also get real about ROI proof: attribution is messy, but speed, decision quality, and resilience to market shocks are measurable and persuasive. If you’re navigating mandates to “do AI” while chasing durable growth efficiency, this conversation offers a practical blueprint to build trust, compress planning cycles, and invest where results compound.If the episode sparks ideas, follow the show, share it with a teammate, and leave a quick review—what’s one system-level decision you’d upgrade with AI next?Download the report mentioned in this podcast: https://www.varicent.com/info/ai-roi-sales-revops

Headlines scream about AI every day, but the real story is quieter: the teams winning with AI aren’t chasing shiny tools, they’re rebuilding how revenue work gets done. We sat down with Dan Morgese, Director of Content Strategy and Research at Gong, to unpack the new State of AI report and reveal what separates impact from noise. The report pairs a survey of 3,000 director-plus leaders with Gong Labs analysis of 7.1 million closed opportunities, giving us both market sentiment and inside-the-workflow evidence.What stood out first is a mindset shift: productivity just jumped to the number one growth lever, reframed from time saved to revenue per rep. That changes everything. Instead of using AI to draft more emails, top teams use it to guide seller actions, expose deal risk, and align coaching with what actually moves win rates, cycle time, and ASP. Depth of adoption beats breadth—leaders who treat AI as a core driver of strategy, not a sidecar, see stronger commercial outcomes across the board.We also dig into the underappreciated frontier: forecasting, strategic planning, and initiative tracking. Adoption for these systemic use cases surged as teams realized forecasting improves when you combine call intelligence, pipeline dynamics, and engagement signals. Planning gets smarter when AI informs territory design and compensation scenarios. And tracking initiatives in the wild lets leaders see whether new messaging lands with customers and whether it moves revenue, closing the loop from strategy to impact.Trust inevitably comes up. Sixty-seven percent of leaders say they trust AI, but the smarter framing is trust in data. Domain-specific systems that capture reality—conversations, signals, and activity—beat manual CRM fields when accuracy and explainability matter. With AI quickly becoming table stakes, the advantage shifts from “Are you using AI?” to “Are you using it well?” If you’re ready to move beyond pilots, this conversation offers a blueprint: pick systemic use cases, build depth, measure what matters, and let revenue per rep be your scoreboard.If this resonated, follow the show, share it with a colleague who owns forecast or RevOps, and leave a quick review so more revenue leaders can find it.

Unlock the secrets of usage-based sales models and revolutionize your growth strategy with insights from Daragh King, Vice President of Sales Operations at XBO. This episode is your ticket to understanding how transforming from traditional CRM-based opportunity management to innovative usage-based approaches can give you a competitive edge. Through Daragh’s expertise, we explore the intricacies of customer promise and realization, and how a simple pricing agreement can lay the groundwork for accurately predicting and realizing revenue opportunities. Get ready to expand your horizons as we dissect the nuances of this transformative model, particularly in industries like financial services, distribution, and transportation.Get ahead in the fast-paced world of sales with strategic Revenue Operations (RevOps) insights that optimize processes and deepen customer relationships. Discover how the integration of opportunities with existing revenue and shipments can evolve into an early warning system, shifting the focus from mere data reporting to actionable insights through AI and machine learning. As the year draws to a close, learn how prioritizing daily tasks using CRM signals can enhance sales efficiency, streamline priorities, and reduce administrative burdens for a more productive workday. Join us on this insightful journey and seize the opportunity to harness these strategies for tangible improvements in revenue and organizational benefits.00:03) Driving Growth Through Usage-Based Sales(13:03) Optimizing Sales With RevOps Insights(16:27) Increasing Sales Efficiency Through Prioritization (00:03) Driving Growth Through Usage-Based SalesNature's usage-based sales models drive growth in financial services, distribution, and transportation industries, with accurate prediction of customer promises being crucial. (13:03) Optimizing Sales With RevOps InsightsRevOps integrates opportunities and data to enhance sales processes and customer relationships through AI and machine learning. (16:27) Increasing Sales Efficiency Through PrioritizationCRM signals can enhance sales team productivity by prioritizing tasks and reducing administrative work, leading to tangible improvements in revenue.

What if you could revolutionize your sales process and boost your team's productivity without adding a single new hire? Join us as we uncover the transformative power of AI in sales with insights drawn from a compelling Salesforce study and engaging conversations with three seasoned sales leaders. Discover how AI is reshaping sales workflows, enabling teams to reclaim valuable time for direct customer interactions and driving substantial growth and value creation. We emphasize the critical role of structured initiatives in harnessing these capacity gains and how well-trained sellers can outperform their peers, ultimately generating significant revenue.In another exciting segment, we explore how Chief Revenue Officers can leverage AI and coaching to maximize sales effectiveness. By reallocating time saved through AI into deeper, high-quality engagements, sales teams can seize opportunities even in limited-deal scenarios. Learn about the use of conversation intelligence as an early warning system to prevent lost deals, and get a sneak peek into the future of sales with a discussion on the shift from traditional SaaS contract pricing to usage-based models. Tune in for valuable insights from our guests and get ready to explore the evolving landscape of sales with industry innovators.(00:05) Using AI to Drive Sales Efficiency(07:33) Leveraging AI for Sales Improvement(00:05) Using AI to Drive Sales EfficiencyThis chapter explores how AI can drive value and efficiency within sales organizations, emphasizing that the future of sales is about collaboration between humans and technology. By reimagining workflows, sales teams can use AI to reduce non-selling time and increase direct customer interaction, ultimately boosting productivity. We examine a Salesforce study suggesting that by enhancing efficiency in five key areas, sales teams can reclaim 5% of their time for direct selling, potentially generating significant revenue without additional hires. The discussion highlights the importance of structured initiatives to maximize capacity gains and improve key performance indicators. Additionally, we touch on the impact of effective training, noting that well-trained sellers can significantly outperform their peers. Overall, the focus is on leveraging AI for both efficiency and effectiveness, aiming for substantial growth and value creation in sales organizations.(07:33) Leveraging AI for Sales ImprovementThis chapter focuses on the innovative ways CROs can harness AI and coaching to enhance sales effectiveness. I discuss how reallocating time saved through AI into deeper, higher-quality engagements can maximize opportunities in limited-deal scenarios. Additionally, I highlight the importance of using conversation intelligence to set up tripwires, allowing sales teams to detect and address issues in individual engagements early, thus preventing lost deals. Looking ahead, I introduce the upcoming topic for November, which examines the transition from traditional SaaS contract pricing to usage-based models. This shift involves moving revenue generation from upfront contracts to ongoing usage, posing new challenges and opportunities for sales organizations. I look forward to exploring this with revenue innovators across various industries.Download the report mentioned in this podcast:https://enterprise.sandler.com/whitepaper-why-cros-who-adapt-with-ai-will-lead-the-next-era-of-growth?utm_content=350656110&utm_medium=social&utm_source=linkedin&hss_channel=lcp-14155

What if aligning your sales, marketing, and customer success teams could unlock the hidden potential for revenue growth? Join us as Matt Naeger, Trilliad's Chief Solutions Officer, shares his insights on breaking down organizational silos to foster collaboration and data integration, as revealed in Trilliad's 2025 Sustainable Growth Survey. With Matt's expertise, we uncover the strategic advantage of a unified approach, emphasizing the significance of setting KPIs with a customer-centric focus and the transformative power of shared data across departments.Explore the remarkable role of AI in boosting organizational alignment and the often-overlooked potential of customer success as a strategic pillar. We tackle the challenges businesses face with AI adoption, highlighting the need for continuous reinforcement and balanced leadership to enhance sales, marketing, and customer success functions. Discover how AI can predict customer behavior, improve retention, and give your organization a competitive edge. This episode is a must-listen for those eager to harness the power of data and technology for sustainable growth, offering a fresh perspective on the evolving dynamics between sales, marketing, and customer success teams.(00:05) 2025 Sustainable Growth Survey Insights(14:18) Maximizing AI for Organizational Alignment(19:19) Organizational Alignment and Data ManagementThis chapter explores the findings of Trilliad's 2025 Sustainable Growth Survey, highlighting the importance of data integration across B2B sales, marketing, and customer success functions. We discuss how aligning these typically siloed departments can drive substantial revenue growth, with companies that utilize data throughout the entire customer journey being over 50% more likely to anticipate increased revenues. I talk with Matt Naeger, Trilliad's Chief Solutions Officer, who emphasizes the need for data sharing and establishing KPIs with a customer-first approach rather than a departmental focus. We also touch on the challenges posed by organizational egos and silos, which often lead to fragmented data views, and how high-functioning organizations overcome these obstacles to achieve better-than-average growth rates. (14:18) Maximizing AI for Organizational AlignmentThis chapter explores the challenges and opportunities for B2B organizations in leveraging technology and AI to enhance sales, marketing, and customer success. We discuss the importance of continuous reinforcement in using new tools, emphasizing how consultants often advise others without applying the same principles internally. A significant focus is on the underutilization of customer success as a strategic advantage and the widespread dissatisfaction with current AI initiatives, often due to misaligned focus on efficiency rather than insightful data utilization. The conversation highlights the potential for AI to improve organizational alignment across different functions, stressing the importance of having a Chief Revenue Officer who provides balanced guidance across sales, marketing, and customer service. We also address the potential of AI in predicting customer behavior, offering proactive insights to client delivery teams to enhance customer retention and competitive positioning. (19:19) Organizational Alignment and Data ManagementThis chapter explores the intricate dynamics between sales, marketing, and customer success teams, focusing on the importance of alignment among these departments. Despite sales often being perceived as independent and solely numbers-driven, we uncover that a significant 54% of sellers value alignment, particularly with marketing. This suggests that sales teams seek better integration of data and communication to enhance lead quality and customer onboarding. We discuss how alignment should prioritize the customer experience rather than internal biases.

Join us as we explore the transformative shift towards usage-based sales models in enterprise organizations, especially within the SaaS industry. In this episode, we're thrilled to have Anthony McPartlin, a seasoned expert in sales operations and enablement, share his invaluable insights. Together, we unravel the compelling benefits of usage-based pricing, from aligning better with perceived customer value to enhancing net revenue retention. With examples from industry leaders like Snowflake and Datadog, we highlight how this model can drive growth and foster lasting customer relationships. However, this transition also brings challenges, particularly for sales leaders who must rethink compensation plans and sales strategies. This episode aims to equip Chief Revenue Officers with the knowledge they need to navigate this significant shift.Listen in as we also examine the evolving roles in revenue operations and the implications of usage-based pricing on sales compensation and forecasting. The discussion underscores the importance of advanced data analytics in making informed decisions and highlights the shifting responsibilities of Customer Success Managers towards revenue generation. Anthony and I discuss the potential merging of Customer Success Manager and Account Manager roles, emphasizing the need for collaboration to drive strategic growth and maintain strong customer relationships. With Anthony's expertise, this episode promises to be an enlightening journey into the future of revenue operations and sales models.(00:05) Usage-Based Sales in Enterprise Organizations(10:56) Evolving Roles in Revenue Operations(00:05) Usage-Based Sales in Enterprise OrganizationsThis chapter focuses on the transition to usage-based sales models in enterprise organizations, particularly within the SaaS industry, driven by AI data and customer preferences. We explore the benefits of usage-based pricing, including better alignment with perceived customer value, lower barriers to entry, and higher net revenue retention, exemplified by companies like Snowflake and Datadog. While acknowledging that this model can enhance growth and create durable customer relationships, I also address the challenges it poses for sales leaders, such as adjusting compensation plans and sales strategies. By offering insights into how this model can drive stickiness and provide accurate signals of product-market fit, we aim to equip CROs with the knowledge needed to navigate this significant shift. (10:56) Evolving Roles in Revenue OperationsThis chapter focuses on the complexities of transitioning to usage-based pricing models and the implications for sales compensation and forecasting. We explore how quota and compensation design must adapt to ensure fairness and motivation for sales representatives, given the variability in usage patterns. The discussion highlights the importance of advanced data analytics and telemetry in making informed, data-backed decisions, reducing reliance on sellers for forecasting. We also address the evolving roles within organizations, particularly the shift in Customer Success Manager (CSM) responsibilities towards revenue generation, and the potential merging of CSM and Account Manager (AM) roles. Finally, the chapter emphasizes the need for collaboration between these roles to strategically drive growth and maintain strong customer relationships.