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.