Episode Summary

You can buy the best conversation intelligence platform on the market and still get zero behavior change. That’s the Coaching Chasm: data exists, but the field doesn’t improve because managers don’t have time, feedback arrives too late, and reps experience AI as surveillance instead of development.

This episode lays out a new coaching philosophy: move from manual inspection to automated orchestration using AI-driven skill scorecards, best-in-class “golden moments,” and focused training sprints. The outcome is a measurable feedback loop that improves sales processes, accelerates ramp time, and connects skill improvement directly to revenue generation.

Major Highlights

• The Coaching Chasm: why “insights” die in dashboards and never translate into sales success or behavior change.
• The cultural failure mode: when AI feels like a “gotcha,” reps get defensive and sales management loses trust and momentum.
• The shift from manager-as-detective to manager-as-performance-architect: automated orchestration beats manual inspection every time.
• How AI changes coaching dynamics: objective data reduces opinion battles, faster feedback increases relevance, and trend analysis supports development conversations.
• Case example (Andela): using AI scorecards to drive agenda-setting adoption from 17% to 49% in two weeks and compress cycle time through better process adherence.
• Case example (Appen): using curated call snippets to cut onboarding time in half and transfer technical and renewal Messaging quickly.

Action Items for This Month

• Pick 3–5 skills that correlate with wins and define them as measurable scorecard metrics (not vague competency labels).

• Establish a baseline for each skill and set automated weekly reporting to track trends, not one-off call critiques.

• Build your first Best-in-Class Library: curate 10–15 “golden moments” as short clips organized by skill (discovery, objection handling, Value selling, pricing pushback, competitor mention).

• Run one training sprint (2–4 weeks) focused on a single skill, supported by daily micro-learning and scorecard-based monitoring for adoption.

• Rewrite your coaching framing: replace “you’re below average” with “your metric improved X% month-over-month” to reduce defensiveness and increase ownership.

• Create an ROI narrative: connect skill lift to conversion rate improvement, cycle time compression, and ramp time reduction to justify ongoing investment in AI-enabled sales processes.

• This week, pilot the Golden Moment: pull one 60-second clip from a top rep that demonstrates elite Messaging or execution, share it with the team, and explain why it worked.

Join the B2B Sales Lab

If you’re trying to turn conversation intelligence into real performance improvement, you don’t need more dashboards. You need a repeatable coaching system and peers who’ve already pressure-tested it. B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It’s a space to ask real questions, share proven practices, and connect with others who are serious about improving revenue performance. Designed and led by veteran sales leaders, the Lab is where strategy meets execution. Join us at b2b-sales-lab.com

Episode Summary

You can’t coach what you don’t see, and most sales managers only hear a tiny fraction of their team’s calls. This episode introduces Augmented Coaching: using artificial intelligence and conversation intelligence to analyze every customer conversation and surface specific, teachable moments without adding hours to your week. You’ll learn how AI-driven insights like talk-to-listen ratio, question quality, and sentiment shifts turn coaching from gut feel into repeatable sales management. The outcome is simple: tighter sales processes, faster ramp, stronger messaging consistency, and more reliable revenue generation.

Major Highlights

• The “coaching gap” problem: when managers review only a small percentage of calls, most rep behavior lives in a black box, and bad habits compound.
• Why this is a capacity issue, not a willpower issue: managers get buried in forecasts, deal support, admin work, and internal meetings.
• The shift from intuition-era coaching to Augmented Coaching: AI monitors and analyzes; the manager coaches the moments that matter.
• What conversation intelligence platforms do (examples include Gong and Chorus.ai): record, transcribe, and analyze calls to produce objective coaching data.

• Creating a “collective sales brain”: capture what top performers do (phrasing, objection handling, discovery patterns) and scale it across the team.

• The “Game Tape” approach: use short clips (often 2 minutes or less) to coach discovery, agenda-setting, objection handling, and value selling.
• Business impact examples discussed: improved win rates, reduced ramp time, and reclaiming manager hours through automation and targeted coaching.
• Tool selection and architecture: conversation intelligence belongs in the Optimization and Learning layer, supported by a solid data foundation and intelligence layer.
• Budget-friendly options: lighter-weight tools like Fireflies.ai, Otter.ai, or Fathom can still provide transcription, recaps, and action-item capture.
• Call Libraries as a force multiplier: curated playlists of best-in-class calls accelerate onboarding and standardize sales strategies across the team.
• Change management guidance: position AI as coaching support, share team trends before individual call-outs, and celebrate improvement publicly to build trust.
• The leadership upgrade: stop being a pipeline inspector and become a performance coach focused on the skills that drive sales success and revenue management.

Action Items for This Month

• Audit your coaching coverage: calculate total team calls last month and the percentage you actually reviewed. Treat that percentage as a leading indicator for performance risk.
• Pick one skill to improve: agenda setting, discovery quality, objection handling, or messaging consistency. Avoid trying to “fix everything” at once.
• Run a small proof-of-concept: choose one rep and use a free trial tool to record five calls. Review talk-to-listen ratio and question quality before you listen to any recordings.
• Start a Call Library: save 3 examples of “great discovery,” 3 examples of “clean agenda-setting,” and 3 examples of “strong value selling.” Use them in onboarding and team huddles.
• Adopt the Game Tape Review cadence: schedule two five-minute reviews per rep per week using clips and metrics, not full-call listening sessions.
• Set expectations with the team: frame AI as a coaching accelerator, not surveillance. Share team-level trends weekly and recognize measurable improvement.

Join the B2B Sales Lab

B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It’s a space to ask real questions, share proven practices, and connect with others who are serious about improving revenue performance. Designed and led by veteran sales leaders, the Lab is where strategy meets execution. Join us at b2b-sales-lab.com

Episode Summary

Buying AI alone does not increase revenue. The real constraint in most B2B organizations is salesperson productivity, not tool availability, because reps spend too little time on revenue-producing work and too much time on administrative drag.

This episode introduces the “Tollbooth Effect,” the buildup of small approvals, handoffs, and system tasks that quietly tax every deal and slow revenue generation. You’ll learn how to treat artificial intelligence as an architectural teammate, automate the input work, humanize the output, and prove impact through cycle time, win rate, and pipeline quality improvements.

Major Highlights

• Why executives are done funding “transformation” and are now asking the only question that matters: where is the revenue impact from AI?

• The real productivity problem: most salespeople spend roughly a third of their week on revenue-producing work, while administrative drag consumes the rest.

• The Tollbooth Effect explained: small, reasonable steps in isolation that become a system-wide tax on execution, deal momentum, and messaging quality.

• Why adding headcount breaks in 2026: rising cost, fragile retention, and top performers resenting being turned into well-paid administrators.

• The core operating principle: automate the input and humanize the output. Use AI to remove research, data entry, record hygiene, routing, and documentation burdens so humans can focus on judgment.

• A strategy-first approach to artificial intelligence: treat AI as an operating layer that keeps your revenue engine consistent, not a content factory that produces more noise.

• The “sales nervous system” model: an autonomic layer handles repetitive functions reliably, while reps stay focused on decisions, stakeholder navigation, value selling, and next-step commitments.

• The deal-decay moment most teams ignore: the gap after a call. Speed and structure in follow-up protect urgency, improves conversion, and strengthens revenue management.

• The discipline prerequisite: AI amplifies your system. If your sales processes are fuzzy, your discovery is weak, and your stage criteria are unclear, AI will accelerate inconsistency.

• Data hygiene as a revenue lever: always-on hygiene builds trust in the CRM, reduces double-checking, improves forecasting integrity, and restores selling speed.

Action Items for This Month

• Run an admin audit: identify the three repetitive weekly tasks that require zero creativity, zero empathy, and zero strategic thinking. Pick one to eliminate first.

• Define standards before automation: tighten stage exit criteria, discovery requirements, and follow-up rules so sales management and coaching are consistent.

• Fix the post-call gap: create a structured workflow that captures commitments, unresolved issues, stakeholders mentioned, and next steps immediately after meetings.

• Simplify CRM requirements: capture only what drives revenue generation and decision-making, then automate the capture and routing of those fields.

• Commit to always-on data hygiene: implement rules and tools that flag duplicates, enforce formatting, and detect record conflicts so the system stays trustworthy without “data days.”

• Prove impact with outcomes: track selling time recovered, follow-up speed, cycle time changes, win rate movement, and pipeline quality rather than tool adoption metrics.

Join the B2B Sales Lab

If you are working through AI adoption and want practical help that improves sales productivity, you do not need more theory. You need peers, standards, and real operating examples you can put to work. B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It’s a space to ask real questions, share proven practices, and connect with others who are serious about improving revenue performance. Designed and led by veteran sales leaders, the Lab is where strategy meets execution.

Join us at b2b-sales-lab.com

Episode Summary

Most sales teams underestimate the hidden “sales tax” that hits after every good meeting: recaps, CRM updates, follow-up emails, and task creation that quietly kill momentum. In this episode of AI Tools for Sales Pros, we break down how artificial intelligence and AI meeting assistants can eliminate that post-call drag while improving accuracy, consistency, and professionalism. You’ll learn how to move from manual note-taking to an orchestrated workflow that produces a structured recap, action plan, and CRM updates in minutes. The result is better sales processes, faster follow-up, and a practical path to sales success without adding headcount.

Major Highlights

• The real cost of post-meeting admin work: why most teams lose deal velocity after a “great call” and how that impacts revenue generation.
• The “sales tax” concept: how small frictions compound into hours of lost selling time and weaken revenue management.
• The shift in operating philosophy: stop treating reps like court reporters and move them into a Producer / Editor role focused on value selling and human connection.
• AI meeting assistants (examples: Fireflies.ai, Otter.ai, Fathom): transcription is the baseline, but structured extraction is where the leverage appears.
• Orchestration beats transcription: connecting transcripts to an automation platform (Make.com or Zapier) to produce structured outputs aligned to your sales strategies and sales management system.
• Prompting as a sales process tool: how to instruct an LLM to extract pain points, budget signals, stakeholders, competitive mentions, objections, and next steps with owners and dates.
• Human-in-the-loop protocol: why the system should draft the follow-up email but never auto-send, protecting trust and improving messaging quality.
• Self-healing CRM behavior: how structured AI outputs reduce missing data, improve forecast hygiene, and strengthen revenue management discipline.
• Ethics and consent: a practical, value-forward disclosure script that protects the relationship while using artificial intelligence responsibly.
• The “Post-Call Lag Check” audit: a simple way to measure your current performance baseline before investing in any tooling.

Action Items for This Month

• Run a Post-Call Lag Check: time how long it takes (end of call to done) to send the follow-up email and fully update the CRM for three meetings.
• Record five calls using a meeting assistant trial: review transcript quality, speaker identification, and how well the tool captures action items.
• Use a methodology-based prompt: paste one transcript into ChatGPT or Gemini and extract pain points, budget details, stakeholders, objections, competitors, and next steps into a structured format.
• Adopt the Editor workflow: generate the follow-up email as a draft, spend 60 seconds editing for accuracy and tone, add one personalization detail, then send.
• Standardize your recap format: define a single executive-summary structure your team uses so customers receive consistent messaging and your sales processes become repeatable.
• Create CRM task automation rules: ensure every next step gets a due date, owner, and description so commitments don’t drift and sales success becomes predictable.

Join the B2B Sales Lab

If you want to implement these workflows without starting from scratch, join the B2B Sales Lab. B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It’s a space to ask real questions, share proven practices, and connect with others who are serious about improving revenue performance. Designed and led by veteran sales leaders, the Lab is where strategy meets execution. Join us at b2b-sales-lab.com

Episode Summary

In this episode of AI Tools for Sales Pros, we break down why delayed awareness of buyer intent is quietly killing revenue. Many sales teams believe they have strong artificial intelligence and AI-enabled systems in place, yet still lose deals because critical signals arrive hours or days too late. This episode explores how real-time prospect alerts close the speed-to-lead gap and transform sales processes from reactive to proactive. The result is faster deal cycles, stronger value selling, and measurable improvements in revenue generation.

Major Highlights

• The hidden cost of information lag and why knowing about intent too late is functionally useless.
• How polling-based integrations create delays that undermine sales success and revenue management.
• Why webhooks enable real-time visibility compared to scheduled data pulls.
• Using automation middleware like Make.com, Zapier, and n8n to deliver AI-powered alerts without custom development.
• The difference between noisy alerts and context-rich alerts that guide action.
• Four categories of high-value signals: website activity, email engagement, CRM changes, and external intent signals.
• How artificial intelligence can summarize external signals and turn them into relevant outreach opportunities.
• Real-world examples of teams improving sales strategies, sales management effectiveness, and conversion rates.

Action Items for This Month

• Identify one high-intent action, such as a pricing page visit, that should trigger an immediate alert.
• Enable a webhook in your marketing platform instead of relying on scheduled syncs.
• Route real-time alerts directly into Slack or Microsoft Teams where sales reps already work.
• Apply simple filtering rules to prevent notification fatigue and protect rep focus.

Join the B2B Sales Lab

B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It is a place to ask real questions, share proven practices, and collaborate with peers focused on real sales success. Designed and led by veteran sales leaders, the Lab is where business acumen, AI, and execution come together to improve revenue generation. Learn more and join at b2b-sales-lab.com.

Episode Summary

This episode addresses one of the most common frustrations in modern sales management: the pressure to adopt AI and artificial intelligence tools without the budget to justify experimentation. We explore how sales leaders can build a highly effective, zero-cost “Minimum Viable Pilot” using free platforms to validate value before making any investment. You’ll learn how AI-driven sales processes can dramatically improve productivity, reduce administrative burden, and enhance messaging without requiring upfront spend. The discussion provides a practical roadmap to prove ROI, mitigate risk, and accelerate revenue generation using accessible tools already available today.

Major Highlights

• The financial paradox facing sales leaders: expectations to innovate with AI while budgets remain frozen.
• How shadow IT emerges when reps adopt unapproved free tools—creating risk for data privacy and revenue management.
• The concept of “Validation Before Investment”: using the freemium economy to test, measure, and prove value before requesting budget.
• A full breakdown of the Zero-Cost AI Stack: ChatGPT Free for content, Make.com Free for automation, and HubSpot Free for CRM operations.
• Understanding breakpoints—when free tiers stop enabling sales success and start limiting scale, collaboration, or compliance.
• Why business acumen matters when evaluating AI upgrades: identifying reasoning complexity, privacy requirements, and automation volume.
• A phased roadmap from individual experimentation to enterprise deployment, aligning AI adoption with measurable revenue generation outcomes.
• How AI-enhanced sales strategies deliver significant time savings, productivity boosts, and more precise value selling opportunities.

Action Items for This Month

• Audit your current sales tech stack—identify what you pay for, what is unused, and what could be replaced temporarily by free tools during validation.
• Select one workflow that slows your team down and build a Zero-Cost Pilot using AI and automation tools to test improvement.
• Assign one rep to document “before and after” time savings on a specific sales process, generating real data for a future budget request.
• Determine your breakpoints: when will free tiers limit automation throughput, data privacy, or team collaboration?
• Use AI to improve your messaging by drafting emails, proposals, or call prep through free-tier platforms to measure quality improvements.

Join the B2B Sales Lab

B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It is a space to ask real questions, share proven practices, and connect with others who are serious about improving revenue performance. Designed and led by veteran sales leaders, the Lab is where strategy meets execution. Join us at b2b-sales-lab.com.

Episode Summary

In this episode, we examine how dirty data quietly destroys sales productivity and what it takes to build an always-on, self-healing CRM using artificial intelligence. You will hear how data decay, duplicate records, and inconsistent company naming conventions lead to wasted time, inaccurate scoring models, and broken sales processes. We unpack the real financial impact of poor data hygiene and walk through the modern tools and AI-driven methods that keep your system clean 24/7. This episode offers a roadmap for transforming your CRM from a liability into a revenue-generating asset.

Major Highlights

• Why duplicate records and inconsistent company names sabotage sales management, sales success, and revenue generation.
• The true financial cost of data chaos, including how sales reps lose nearly a full day per week on administrative cleanup.
• How data decay destabilizes sales strategies, value selling, messaging, and revenue management.
• Why AI-driven fuzzy matching outperforms traditional CRM duplicate detection.
• How tools like Cloudingo and Dedupely use AI to continuously scan, merge, and maintain clean prospect and account records.
• How to build a hierarchy of data trustworthiness and design strategic Smart Merge Rules.
• The connection between clean data and accurate lead scoring, contact enrichment, and automated personalization.
• Why Always-On Hygiene is superior to the “Spring Cleaning Panic” approach.
• A step-by-step playbook for conducting a manual data quality audit to quantify the problem inside your CRM.

Action Items for This Month

• Run a duplicate analysis inside your CRM using its native tools to create a baseline count.
• Have one SDR or sales rep track all data-related cleanup activities for a week to quantify lost selling time.
• Survey your entire sales team to capture weekly hours spent on manual data cleanup and verify the true cost.
• Map your data ecosystem and begin designing your hierarchy of data trustworthiness in preparation for AI-driven deduplication.

Join the B2B Sales Lab

B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It is a space to ask real questions, share proven practices, and connect with others who are serious about improving revenue performance. Designed and led by veteran sales leaders, the Lab is where strategy meets execution. Join us at b2b-sales-lab.com.

Episode Summary

In this episode of AI Tools for Sales Pros, Sean O’Shaughnessey explores how artificial intelligence is transforming contact enrichment and why this capability is essential for modern sales management. You’ll learn how sales teams can replace manual prospect research with automated workflows that provide real-time business acumen, firmographic data, and buying signals. Sean introduces key tools, including Clay, Clearbit, Apollo, and ZoomInfo, demonstrating how they support effective sales processes and value-driven selling strategies. By implementing these AI-driven systems, sales leaders can significantly enhance messaging, reduce research cycles, and increase overall sales success.

Major Highlights

• Understanding the difference between collecting more data and collecting the correct data for strategic outreach.
• Four major AI platforms: Clay, Clearbit, Apollo, and ZoomInfo, and their unique value to sales management and revenue generation.
• How automated contact enrichment transforms generic outreach into value-based sales conversations with deep business acumen.
• Practical workflow integration tips for CRM systems like Salesforce, HubSpot, and Pipedrive to automate data flow and eliminate redundant steps.
• Real-world ROI examples demonstrate a reduction in research time from hours to minutes and a 40% increase in response rates.
• Proven strategies for tracking performance metrics: response rate improvement, meeting conversion rates, and research time savings.
• Three immediate steps for testing enrichment include manual validation, building intelligence checklists, and comparing enriched versus standard outreach results.

Action Items for This Month

• Identify one key prospect and manually gather enriched data using a tool like Clay or Apollo to see how it changes your outreach strategy.
• Create a standard checklist of key intelligence elements, such as funding events, leadership changes, and technology stacks, that enhance your sales processes.
• Test enriched outreach messaging against your regular communication and measure response quality and meeting conversions.
• Evaluate which AI-based enrichment platform integrates best with your CRM for long-term automation and revenue management improvement.

Join the B2B Sales Lab

B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It’s a space to ask fundamental questions, share proven practices, and connect with others who are serious about improving revenue performance. Designed and led by veteran sales leaders, the Lab is where strategy meets execution. Join us today at ⁠b2b-sales-lab.com⁠.

Episode Summary

In this episode, host Sean O’Shaughnessey explores how artificial intelligence (AI) can be embedded into your sales processes to eliminate the qualification bottleneck and dramatically improve revenue generation. He shares a real-world case where two-thirds of web leads were being lost due to slow response times and unstructured follow-up, and then walks through how using chatbots and intelligent forms tied to the MEDDPICCC qualification framework automates strategic discovery. Listeners will gain clarity on how to deploy an AI-powered “zero lead-decay funnel” that works 24/7, aligns with their sales management methodology, and frees human sellers to focus on closing high-value deals.

Major Highlights

• The foundational problem: a company generating 400+ qualified web leads per month lost 67% of them because the average response time was 18 hours, while research shows lead qualification drops by 900% if not responded to within five minutes.
• Explanation of the “qualification bottleneck”: when marketing generates more leads than sales can respond to quickly and strategically, resulting in wasted resources, frustrated prospects, and lost revenue.
• Introduction of the “zero lead-decay funnel”: a system that uses AI-powered chatbots and intelligent forms to engage every prospect immediately, qualify them using a rigorous framework, and deliver strategic insights to the sales team.
• Deep dive into the MEDDPICCC framework (Metrics, Economic Buyer, Decision Criteria, Decision Process, Paperwork Process, Identify Goal, Coach, Champion, Competition) and how AI can systematically ask each of these questions in a conversational way, capturing the strategic context necessary for value selling and complex B2B sales.
• Examples of tools and platforms: native CRM chatbots (HubSpot, Salesforce Einstein Bots), and advanced platforms like Conversica, Drift, and Qualified — all of which can be leveraged to embed AI into your lead-qualification process.
• Implementation roadmap:
  1. Map current qualification process and identify which MEDDPICCC elements matter most for your business.
  2. Design conversation flows for different visitor segments (first-time vs. returning; small business vs. enterprise).
  3. Create escalation triggers for high-intent prospects (to alert live sales reps immediately).
  4. Establish handoff procedures from AI to humans with full context.
  5. Configure data capture and CRM routing of the structured MEDDPICCC data.
• Success metrics to track: qualification completion rate, time to qualify, qualified-to-opportunity conversion, conversation completion rates, escalation accuracy, along with ROI calculation (cost per qualified lead before vs. after, time savings for sales, improved close rate).
• Common pitfalls to avoid: over-automation (replacing humans vs. augmenting), generic questioning for every visitor, weak handoff procedures, ignoring mobile experience, insufficient testing of edge cases and conversation paths.
• The hybrid approach: AI handles initial screening and strategic qualification; live sales reps handle high-value interactions; the system preserves context throughout; leads are routed appropriately based on score/timeline; nurturing sequences vary based on qualification status.
• The payoff: faster, smarter qualification; more time for your sales team to focus on value-selling; shorter sales cycles; higher conversion rates; marketing leads actually worked and revenue performance improved.

Action Items for This Month

1. Identify one high-volume inbound lead you can manually qualify using three MEDDPICCC questions this week.
2. Map your current lead-qualification process end-to-end this month, including all steps from web-form submission to first human contact. Annotate where delays exist, where leads may drop, and which MEDDPICCC elements are not being captured.

Episode Summary

In this episode of AI Tools for Sales Pros, we examine why traditional points-based lead scoring fails and how artificial intelligence can transform sales management, sales processes, and revenue generation. You’ll learn how predictive models convert scattered activity signals into a clear probability of conversion, aligning marketing and sales around objective truth. We connect AI-driven insights to value selling and messaging so your team focuses on the highest-impact opportunities. The result: measurable sales success through better prioritization, faster cycles, and stronger business acumen across the revenue organization.

Major Highlights

• Why rules-based lead scoring breaks down: assumptions about activities, one-size-fits-all logic, and zero adaptability to changing buyer behavior.
• The AI alternative: predictive lead scoring that blends behavioral signals, firmographics, engagement data, and historical outcomes to produce a conversion probability.
• From friction to alignment: an objective score becomes the shared language for marketing and sales, improving forecast accuracy and revenue management.
• Implementation roadmap: clean your data, define an evidence-based ICP, identify key behaviors, activate your CRM’s predictive features, and build workflows by score tier.
• Common pitfalls: too little historical data, ignoring negative signals, “set-and-forget” models, and replacing (instead of augmenting) human judgment.

Action Items for This Month

1. Audit your data quality: pull 20 closed-won and 20 closed-lost deals and compare firmographics, behaviors, and lead sources side by side.
2. Define (or refine) your ICP using real outcomes: document the traits your best customers actually share to guide value selling and messaging.
3. Enable predictive lead scoring in your current stack (e.g., Salesforce, HubSpot) and let it run for 30 days to establish a baseline.
4. Operationalize score tiers: top 20% get immediate calls, middle 60% enter tailored nurtures, bottom 20% move to long-term nurture or disqualification.
5. Schedule quarterly reviews to retrain models, recalibrate thresholds, and keep pace with evolving buyer behavior and revenue generation targets.

Join the B2B Sales Lab

B2B Sales Lab is a private, member-led community for sales professionals who want actionable insights, not theory. It’s a space to ask real questions, share proven practices, and connect with peers focused on sales strategies, sales success, and revenue management. Designed and led by veteran sales leaders, the Lab is where strategy meets execution—spanning artificial intelligence, value selling, messaging, and modern sales processes. Join us at b2b-sales-lab.com.