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Uncategorized

Why Your Salesforce Data Isn’t Ready for AI Agents

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Why Your Salesforce Data Isn’t Ready for AI Agents AI 8 min You’ve started evaluating Agentforce, or Copilot, or one of the dozen AI tools now plugged into your GTM stack. The demo looked great. The pilot got greenlit. And somewhere in week three, things started going sideways. Wrong recommendations, missed context, an agent confidently citing a contact who left the company eight months ago. Before you conclude the AI isn’t ready, it’s worth asking a different question: Is your data ready for AI? Get our latest insights into your inbox The Problem isn’t the Model Across the Salesforce ecosystem right now, a consistent pattern is emerging in post-mortems on stalled AI deployments. It is rarely the algorithm. A widely cited industry estimate puts the figure starkly: 88% of enterprise AI agent pilots fail to reach production, not because the agents are weak, but because the CRM data underneath produces confidently wrong outputs at scale. That’s a different failure mode than what most teams plan for.  Bad data has always been a CRM annoyance. Duplicate records, an outdated phone number, a stale job title. Humans navigate around these problems instinctively. A sales rep glancing at an incomplete contact record fills in the blanks from memory. A sales manager catches an obviously wrong forecast before it reaches the board deck. AI agents don’t do that. As one analysis of Salesforce data quality puts it, garbage in, garbage out was the old principle. The 2026 version is sharper: garbage in, confidently wrong out. Agents do not pause to verify a stale record the way a human would. They act on it, then propagate the action across thousands of records before anyone notices. Salesforce’s own product marketing has converged on the same message. As the company’s Tableau product marketing director put it, an AI strategy without a data framework is just a wish list. Attempting to deploy AI agents without one leads to inconsistent results, security risks, and a lack of user trust. What “data readiness” actually means It’s tempting to treat data readiness as a vague hygiene goal. “Clean up the CRM” without a concrete definition. Salesforce’s own guidance on the topic is more precise, and worth using as a working checklist before evaluating any agent deployment: Is your data unified and harmonized? If your data is fragmented across Sales Cloud, Service Cloud, spreadsheets, and a dozen point tools, the agent will deliver fragmented and inconsistent experiences. Unification isn’t optional. It’s the precondition. Have you resolved identities and is the information current? The same contact often exists as three different records: full name, abbreviated name, email-only. And each one tells the agent something slightly different. Old, incorrect data leads to frustrating experiences for customers and unreliable outcomes, including outright hallucination. Do you have governance and security in place? An agent should only access the data it needs to do its job, and that access needs to be auditable. Can you activate the data in real time? Data sitting in a warehouse, updated weekly, doesn’t power an agent that needs to act now. Is there a feedback loop? Agents need humans in the loop checking whether they’re acting on the right information, not a “set and forget” deployment. Separately, a widely referenced breakdown of what “good” CRM data looks like for AI purposes narrows it to three properties: data needs to be complete (the full picture, not partial context), structured, and effective for the specific task the agent is meant to perform. Without completeness, AI models miss vital context: what stage a contact is at, what previous interactions occurred, who else is involved in the decision. The numbers behind the problem are larger than most teams expect This isn’t an edge-case concern. Recent industry data paints a fairly stark picture of how unprepared most enterprise data actually is for agentic AI. Fewer than one in five companies has a high level of data readiness, and only 9% are fully prepared for the data integration and interoperability that AI requires, according to a 2025 Capgemini report on AI agents.  A separate analysis found that 81% of companies say fragmented data is preventing them from unlocking AI’s potential. Service agents miss complete customer histories, sales agents miss signals because marketing interactions aren’t visible, and analytics agents produce unreliable insights that undermine decision-making. The trust problem compounds this. Industry surveys cited by Salesforce found that nearly six in ten AI users say it’s difficult to get what they want out of AI right now, with over half saying they don’t trust the data used to train the systems they’re working with. Separately, a survey found that 90% of high-level data professionals believe company leadership isn’t paying enough attention to bad or inadequate data, even as AI initiatives accelerate. Only 9% of organizations report fully trusting their data which directly affects their confidence in CRM reporting. The forecasting impact is direct and measurable. Inaccurate forecasting tied to poor data quality affects a meaningful share of sales organizations, and several industry analyses tie data quality directly to financial loss. Duplicate or incomplete customer records cause missed opportunities, double-booked engagements, and wasted marketing spend when AI-driven outreach unknowingly targets the wrong contacts or duplicates effort. Why this is structurally different from past CRM problems Traditional CRM issues included duplicate records, missing fields, outdated contact info. A salesperson could work around a few mistakes in a report. A direct mail piece sent to an old address was a minor, contained error. When an AI agent built on top of that same data starts making autonomous decisions, the stakes change entirely. The agent doesn’t know it’s working from a flawed record. It acts with full confidence on whatever it’s given. The moment AI starts acting on it, a small inaccuracy in CRM data gets magnified, not corrected. This is also why simply buying a better AI agent product doesn’t solve the underlying issue. As one technical breakdown of Salesforce AI failures put it plainly: it’s not the algorithms, it’s not

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RevOps

The Enterprise RevOps Playbook

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Playbook The Enterprise RevOps Playbook​ How to Build, Scale and Optimize Revenue Operations for Sustained Growth RevOps isn’t back-office support. It’s the command center of modern revenue. The old GTM funnel (lead to opportunity to closed-won) doesn’t hold up against a business built on renewals, expansion, and retention. This playbook draws insights from enterprise leaders on what it actually takes to build RevOps that scales with the business instead of scrambling behind it. What you’ll take away: The four categories of metrics that power the GTM engine. How to hire for RevOps at every stage. A framework for building your RevOps data stack. Why win-loss analysis is more than a checkbox, and how to turn it into pricing, messaging, and product input. Where AI actually earns its place in RevOps today. The operating rhythms that keep teams aligned without adding meetings for the sake of meetings. Turn Buying Group Theory Into Revenue Impact​ Explore Buying Group Usecase See how leading GTM teams build complete buying groups without relying on manual CRM updates with Nektar Download The Playbook From The Revenue Lounge Podcast​ Related Webinars Ready to Turn your Data into Revenue Outcomes? Book a Demo

Buying Group

The Buying Group Playbook

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Playbook The Buying Group Playbook​ A practical guide to adopting opportunity-based marketing.​ Stop chasing MQLs. Start tracking the committee that actually buys. Only 1% of MQLs convert to revenue. Meanwhile, the average B2B deal runs through 6-10 stakeholders who never show up as a lead score. This playbook pulls together conversations from The Revenue Lounge with GTM leaders at Palo Alto Networks, Reltio, and G2 on why they walked away from lead-centric marketing and rebuilt their funnel around buying groups instead. What you’ll take away: Why coverage, campaign-to-opportunity influence, and buying group completeness are replacing MQL volume, lead scores, and email opens as the metrics that matter. The four buying signals to check for any account. A stakeholder role map and buying signal tracker template you can put to use immediately. Real numbers: 2.4x larger deal sizes, 22-23% faster sales cycles, and 60% less pipeline fallout when buying groups are fully mapped. How to handle the objections you’ll get internally with pilot-first responses that don’t require new budget. Get the full breakdown, templates, and results included. Turn Buying Group Theory Into Revenue Impact​ Explore Buying Group Usecase See how leading GTM teams build complete buying groups without relying on manual CRM updates with Nektar Download The Playbook From The Revenue Lounge Podcast​ Related Webinars Ready to Turn your Data into Revenue Outcomes? Book a Demo

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How Nektar Powered Mimecast’s Agentic GTM AI

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How Nektar Powered Mimecast’s Agentic GTM AI with Complete Customer Signals INDUSTRY Cybersecurity HEADQUARTERS London, England WEBSITE https://www.mimecast.com/ Hear directly from Tim about the challenges, decisions, and lessons behind Mimecast’s AI and GTM transformation. Watch Webinar $10M in total expansion revenue $150M+ in additional pipeline identified 2K+ new activities logged through historical data backfill As enterprise AI evolves, one thing is becoming clear: models are getting easier to access, and agent frameworks are becoming easier to build. The real advantage comes from something harder to replicate — proprietary data. Mimecast recognized this early in its AI journey. The company was investing in internal generative AI capabilities to surface better customers and prospect insights across the go-to-market lifecycle. The vision was ambitious: build AI applications that could support teams across acquisition, expansion, retention, renewal, and prospecting. But like many enterprises, Mimecast ran into a familiar challenge. The data needed to power those applications was spread across disconnected systems, inconsistent workflows, and siloed teams. Valuable engagement signals existed, but they were difficult to access, difficult to standardize, and hard to use at the level of granularity required for meaningful AI outcomes. That challenge made one thing clear: if Mimecast wanted AI to create real business value, it first needed a stronger data foundation. The Challenge: AI is only as strong as the data behind it Mimecast had already built its own internal generative AI engine to identify customer and prospect insights. But success depended on capturing and organizing the right GTM data inside its own data model. That was easier said than done. Critical information about customer engagement, buying committee members, and deal influence was spread across multiple processes and systems. Some GTM tools did not provide access to the data Mimecast needed. In other cases, the data was available, but not in a form detailed enough to support the outcomes the team was after. As Tim Seamans, VP of AI Acceleration at Mimecast, explained: Tim SeamansVP, Al Acceleration & Transformation We build our own generative AI engine internally to identify customer and prospect insights. What’s paramount for success is capturing available data and aligning it in operational systems. He added: “It’s really difficult to access data across disparate processes and systems so that we can get the right data, in the right place, at the right time.” Mimecast’s challenge was not a lack of AI ambition. It was the difficulty of bringing together the underlying data required to make AI applications accurate, useful, and scalable. Why proprietary data became central to Mimecast’s AI strategy Mimecast’s approach was rooted in a clear belief: while models and agents continue to improve, proprietary data is what ultimately creates a durable advantage. That thinking shaped the company’s AI roadmap. Mimecast began building 8–10 specialized AI applications and agents across the customer lifecycle, including applications for: Acquisition Expansion Retention Renewal management Prospecting These applications were designed to help teams act on customer and prospect signals more intelligently. But for them to work well, Mimecast needed better access to engagement data and customer context across the business. The priority was not simply generating more output. It was making sure AI systems had the right inputs to produce accurate, trustworthy, and business-relevant outcomes. The Solution: Unlocking the GTM data layer with Nektar Nektar helped Mimecast access the data and metadata it needed to strengthen the foundation behind its AI strategy. By capturing GTM engagement data that had previously been fragmented or unavailable, Nektar helped Mimecast unify important customer signals and make them available downstream. Just as importantly, that data could be delivered into the systems where Mimecast needed it most — including its CRM and data lake. That meant Mimecast could use Nektar not as another destination system, but as a data layer that supported its existing architecture and internal AI applications. Nektar helped us get the data (and metadata) we needed that was previously locked up or not available. And they can pipe it to our CRM or our data lake. Tim SeamansVP, Al Acceleration & Transformation This was a meaningful shift. Instead of relying on incomplete signals or inaccessible information, Mimecast could work with a richer and more structured view of customer engagement. Nektar helped Mimecast turn fragmented GTM data into an AI-ready signal layer Mimecast’s AI strategy depended on one thing: having complete, usable customer and prospect data inside its own systems. Nektar helped make that possible by unlocking and structuring engagement data that had previously been siloed, incomplete, or inaccessible. With Nektar, Mimecast was able to add meaningful scale and depth to the data powering its internal AI applications, including: 24K+ net-new contacts added 200K+ historical and ongoing emails captured and enriched 1K+ hours of manual rep work saved annually That data foundation gave Mimecast a much richer signal layer for customer insights, prospect intelligence, feature engineering, and AI-driven workflows. Instead of working from partial records and missing context, the team could feed its AI applications with complete interaction history across the customer lifecycle. Building for accuracy, not just automation For Mimecast, the goal was never to deploy AI for its own sake. The goal was to make AI outputs reliable enough to drive action. That required more than models. It required structured data, stronger context, and the ability to capture the signals that shape real customer outcomes. With Nektar helping fill those gaps, Mimecast was able to improve the quality of inputs behind its AI applications. That, in turn, supported more accurate insights across critical GTM workflows and spending more time acting on actionable signals and less time finding and structuring the data. This was especially important for a company building specialized applications across the customer lifecycle. Better data meant better context, better context meant better output, and better output made it easier to tie AI efforts to tangible business value. The business results came quickly. In the first 80 days after launching just one of Mimecast’s Proprietary AI tools, “Expansion AI”, the company achieved: The impact: $2M in expansion revenue and

AI, GTM, RevOps

Unlocking Revenue Intelligence: Bridging Data Gaps with AI & GTM Strategies

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In this episode of the Revenue Lounge Podcast, host Randy Likas and guest Uday Sharma discuss the critical importance of data trust and hygiene in modern revenue operations. They explore how fragmented data can lead to poor decision-making and the necessity of building a centralized data system to enhance revenue intelligence. Uday emphasizes the role of analytics in shaping strategy rather than merely reporting metrics, and the conversation also delves into the implications of AI on data quality and governance. Uday shares insights on how to effectively advocate for funding data initiatives and the importance of changing organizational behavior to improve data practices.

GTM

PLG to Enterprise GTM: Playbook for Experimentation, Signal Discipline and AI

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PLG to Enterprise GTM: RevOps Playbook for Experimentation, Signal Discipline, and AI That Actually Works A conversation with Stephanie Couzin. Executive Summary Lucid’s GTM evolution is not a “PLG vs Sales” story. It’s an operating model story. In this episode, Stephanie Couzin (VP, GTM Strategy & Ops at Lucid) breaks down how modern revenue teams can scale experimentation, build enterprise sales muscle, and adopt AI without turning their tech stack into a Frankenmonster. Readers will learn: PLG → Enterprise is a signal shift: you move from optimizing for users to optimizing for accounts, buying committees, and expansion readiness. Experimentation only works if comp risk is managed: GTM tests touch variable pay, so pilots must be designed as true win-wins. Psychological safety is a growth lever: teams ship better ideas faster when people can speak up, fail fast, and share learnings without fear. Data is the cost of entry for GTM testing: if upper-funnel metrics and activity data are messy, your “experiments” turn into opinions. Standardize AI or suffer whack-a-mole: pick a core AI platform to reduce tool sprawl and enable repeatable adoption. Agentic vs Copilot AI are different games: agentic is replacement (parity + cost savings), copilot is augmentation (productivity + more customer time). Start with the use case, not the tool: the fastest path to value is defining the workflow problem first, then deciding buy/build. One priority beats twelve “priorities”: focus drives execution, and execution is the only feature that matters. Facebook Twitter Youtube The Hidden Shift: From “Users” to “Accounts” Early Lucid was deeply product-led, built on a mature self-serve engine. Then came the layering: Sales motion Segmentation Enterprise complexity Multi-threaded post-sales workflows Stephanie describes it as moving from user-centric growth to account-centric expansion. PLG gives you usage.Enterprise GTM demands orchestration. The real challenge isn’t adding sales reps.It’s building the infrastructure to know when, where, and why sales engagement should happen. Experimentation Built Lucid’s Sales Muscle Stephanie points to Lucid’s early experimentation with a Product Qualified Motion. Most PLG companies start with lead scoring at the user level. Lucid evolved it further: Not just which user is engaged But what is the account telling us? And who should we reach out to now? That shift is everything. “Where that really evolved over time was identifying at the account level, what are the signals we need to outreach at the right time with the right messaging.” — Stephanie Couzin That’s the marriage of: First-party product signals Third-party intent and context Segment-aware targeting Persona-aware outreach Stephanie calls it the “special sauce” of modern lead scoring. The Account Expansion Checklist (Lucid’s Internal Recipe) Stephanie hints at what many GTM teams lack: an internal definition of “ready.” Lucid has an internal checklist: “A set of account signals where we say: this account is locked and ready to expand.” If the checklist isn’t met, Lucid doesn’t stop. They reverse-engineer value: You use Slack? You use documentation tools? You have workflows Lucid integrates into? Then Lucid doesn’t sell harder. They sell smarter: “This is how you get more value.” Cross-Functional Alignment: Experimentation Without Chaos Testing in GTM is not like testing in product. Because in revenue… someone’s commission is always involved. Stephanie puts it bluntly: “Testing something new will impact someone’s variable compensation.” — Stephanie Couzin So experimentation requires designing for buy-in. Lucid ran a coverage pilot by: Using lower-risk accounts Making rep trades fair Ensuring nobody felt punished for participating A GTM experiment only works if it’s a win-win. Otherwise reps sandbag, ignore, or resist. And your “pilot” becomes theater. Psychological Safety Is the Real GTM Scaling Lever Stephanie goes deep here, referencing Google’s Project Aristotle – Google studied what makes teams high-performing. The #1 factor wasn’t IQ.It wasn’t experience.It wasn’t process. It was: psychological safety. “They could speak up without fear of consequence. They felt freedom to fail fast.” — Stephanie Couzin Stephanie teaches this internally at Lucid. And she’s clear: This isn’t about politeness.It’s about intentional leadership. Practical mechanisms Lucid uses: Weekly project show-and-tells “Smart Fridays” where reps share plays Normalizing learnings over perfection The GTM Psychological Safety Loop Safe to speak → More ideas → Faster experiments → Better learning → Higher trust → Stronger execution Data Discipline: The Unsexy Requirement for GTM Experiments Stephanie delivers one of the hardest truths in RevOps: Lower funnel data is solid. Upper funnel data is… suspicious “As you move further up funnel… fewer eyes are on those metrics.” — Stephanie Couzin Testing requires: Full-funnel accuracy Integrated activity capture Shared KPI definitions Governance (tagging, segmentation discipline) AI in GTM: Standardize or Suffer Stephanie’s AI governance advice is refreshingly simple: “Standardize on something. Otherwise you’re in whack-a-mole.” Lucid standardized on Google Gemini. Not because it solves every revenue use case. But because: It reduces fragmentation It sparks shared experimentation It creates repeatable workflows It prevents reps from duct-taping random tools together The goal is not AI everywhere. The goal is AI that fits into systems. The Most Important GTM AI Rule: Start With Use Cases, Not Tools Stephanie nails this: “What use case are you trying to solve, not what tool do you want?” Because shiny object syndrome is real. Most orgs buy AI like toddlers choosing cereal: “Ooh, the box is shiny.” Lucid forces the opposite: Define workflow pain first.Then evaluate tooling. AI Use Case Intake Form What GTM workflow breaks today? What manual effort exists? What does “better” look like? Is this assistive or agentic? Can current stack solve 80% already? What data sensitivity is involved? What adoption friction will occur? Agentic AI vs Copilot AI: Stop Confusing the Two Agentic AI Replaces human work Measured in cost savings Goal is parity (“do no harm”) Copilot / Assistive AI Enhances workflows Measured in productivity and customer time Harder to quantify, but more transformative “Most AI we adopt for revenue teams is assistive. It makes teams more productive.” That’s where the real gains are: Better prep Faster follow-up Less manual CRM work More customer-facing time The GTM Takeaway Stephanie Couzin’s playbook isn’t about

How Nektar helps AI Hypergrowth companies move even faster
AI

How Nektar helps AI Hypergrowth companies move even faster

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How Nektar Helps AI Hypergrowth Companies Move Even Faster Artificial Intelligence 10 min Fast-moving AI companies are having a moment. Every week a new AI-native startup crosses $100M ARR in what feels like record time. Accel’s 2025 Globalscape report shows a “new breed of AI-native applications” hitting scale much faster than previous generations of SaaS, with some reaching $100M ARR in just a few years. That velocity is backed by unprecedented capital. Prominent AI companies like Cursor, Writer, Groq and Fireworks are raising huge rounds, hiring at triple-digit growth rates, and building products that spread virally from individual builders into the world’s largest enterprises. AI application categories like developer tools, finance, cybersecurity and vertical AI each attracted multiple billions of dollars in 2025 funding alone. Nektar sits right in the middle of this wave. Over the past year, we’ve partnered with some of the fastest-growing AI companies in the US – including Writer, Cursor, Groq, Chainguard and Fireworks  to help them turn raw go-to-market activity into clean, structured, AI-ready data they can actually execute on. This blog looks at why AI companies grow differently, what that does to their GTM data, and how Nektar helps them grow even faster. The new AI growth curve: Speed, Efficiency and Youth Funding and company maturity Accel’s data makes one thing clear: AI is no longer a niche category. It’s the new centre of gravity for software investing. Total EU/US/IL cloud & AI funding (excluding models) has climbed into the ~$180B+ range annually, with 2025 setting fresh records.   AI model funding is heavily concentrated in the US, but on the application side, EU/IL funding now represents roughly two-thirds of US levels, showing how global this wave has become. The winners look very different from the last SaaS cycle: over 65% of the Accel US & Europe AI 100 are 0–3 years old, and US winners skew especially young at 2.4 years on average. Put simply: AI companies are raising big, hiring fast, and still figuring out their GTM motion on the fly. Bottom-up adoption and insane efficiency AI-native tools are spreading from the bottom up: Developers using AI coding assistants jumped from 36% in 2023 to 90% in 2025 – in just two years. Tools like AI IDEs, agents and copilots are hitting milestones such as “$100M ARR in 8 months” and “10x YoY growth,” according to Accel’s case studies of leading AI-native apps. This isn’t just fast growth – it’s efficient growth. Accel estimates that leading AI applications now generate 3–10x more ARR per employee than prior generations of SaaS companies. But that speed and efficiency create a GTM paradox: You can scale product adoption and revenue incredibly fast. But your GTM data, process and tooling often lag badly behind. The hidden tax of hypergrowth: messy GTM data Most fast-growing AI companies share a few traits: They sell into large, multi-person buying committees (Fortune 500, Global 2000, high-growth tech). They run hybrid motions – PLG bottoms-up adoption plus enterprise sales, often with heavy founder-led or executive-led outbound. Their GTM stack is complex and evolving: Salesforce + Gong + Snowflake + ABM + sequencing tools, changing every few quarters. They are young – which means processes, definitions and data hygiene were rarely “designed,” they just happened. That shows up in four chronic problems: Invisible buying groups Activity sits at the account or activity object level, not tied to which humans are actually influencing a deal. Contact roles are incomplete, incorrect, or simply not used. Multi-threading that’s impossible to measure Leadership wants reps and CSMs to multi-thread. But nobody can answer basic questions like: “How many net new stakeholders did this SDR actually bring in?” “Which deals progressed because we pulled in the economic buyer early?” Broken marketing attribution for enterprise deals First-touch and last-touch models collapse when there are 10–20 stakeholders, dozens of events and campaigns, and long sales cycles. “Marketing sourced” covers only a small fraction of reality. No shared view of the customer journey Pre-pipeline engagement, in-pipeline meetings, onboarding, success reviews, expansion conversations – they live in different systems owned by different teams. This is exactly the gap Nektar is built to fill. Nektar as the data backbone for AI GTM At its core, Nektar is a revenue data platform that: Harvests metadata from communication tools (email, calendar, meetings, sequences). Cleans and transforms that data. Writes it into Salesforce against the right opportunities, accounts, contacts and leads. Automatically creates and updates Opportunity Contact Roles (OCRs) with accurate personas (economic buyer, champion, influencer, etc.). Generates revenue signals that help teams act – from “missing exec sponsor” to “multi-threading risk” to “QBR overdue.” Writer is a great illustration of how fast-moving AI companies use this foundation Writer: building an AI-ready GTM engine on top of Nektar Writer is an enterprise AI platform selling into Fortune 500 and Global 2000 organizations. Their GTM complexity is huge: multi-persona deals, long cycles, and a mix of PLG, partner, and enterprise motions. One activity capture layer for Sales, CS and Marketing Writer started with Nektar in sales, then expanded to sales engineering, customer success and now marketing. Nektar: Captures emails, meetings and other activities from tools like Gmail and calendar. Associates them correctly with accounts, opportunities and contacts in Salesforce. Backfills historical data by “travelling back in time” across past emails and calendars, so data isn’t limited to post-implementation activity. Creates missing contacts and writes them into Salesforce as OCRs with mapped personas. Compared with their previous setup (Gong plus internal workarounds), Writer’s RevOps leaders called out that Nektar simply does a better job of capturing and correctly associating activities, especially in complex account structures with multiple open opportunities. This gives Writer a single, reliable activity dataset they can push into their warehouse (GCP) and model in Omni for analytics – a critical enabler for AI-driven GTM. Making multi-threading measurable (and compensable) Writer wants SDRs and AEs to multi-thread aggressively – and they want to pay them for doing it. The problem: Nektar was so good

Nektar.ai vs People.ai: A Buyer's Guide
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Nektar.ai vs People.ai: A Buyer’s Guide

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2026 Guide for Enterprise GTM Teams Seeking People.ai Alternatives Buyer’s Guide 10 min Jan 27, 2026 Introduction: Two Different Approaches to the Same Problem Both People.ai and Nektar.ai operate in the revenue data capture category, helping enterprises automatically capture GTM activity and enrich their CRM using AI. However, they solve fundamentally different problems for different buyers. People.ai is an established revenue intelligence platform with strong analytics capabilities, recent recognition as a Visionary in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration, and a mature suite of tools including ClosePlan, account planning, and leadership dashboards. Nektar.ai is an advanced data-first GTM telemetry solution focused on delivering clean, accurate, AI-ready CRM data directly into standard Salesforce objects, designed specifically for enterprises that want to power their existing BI stacks rather than adopt another analytics platform. This guide is intended for GTM leaders, RevOps leaders, Sales Operations teams, and Data teams evaluating both solutions. It draws on direct enterprise evaluation feedback, product analysis, and independent research to help you determine which solution fits your specific needs. Get our latest insights into your inbox Who This Guide Is For This comparison is most relevant if your organization: Already operates a mature BI stack (Databricks, Snowflake, Looker, Tableau) Has dedicated RevOps or SalesOps teams building custom analytics Prioritizes CRM data accuracy over out-of-the-box dashboards Needs granular control over what data syncs to Salesforce Requires specific details around internal and external participation or meeting attendance intelligence (not just invitee data) If your priority is comprehensive analytics UI, pre-built dashboards, and account planning tools, People.ai may be the stronger fit for your organization. But if you are looking at solving the data problem at its core without putting the additional enablement effort on a new training, Nektar is a better bet. This guide focuses on scenarios where data infrastructure is the primary buying criterion. The Core Difference: Analytics-First vs Data-First The fundamental difference between these platforms comes down to philosophy: People.ai is built around the premise that revenue teams need better analytics and insights delivered through their platform. Data capture exists to power their dashboards, scorecards, and AI-driven recommendations. Nektar.ai is built around the premise that enterprises already have analytics tools they trust. What they lack is clean, accurate, complete, unified rep activity data in CRM to feed those tools. Nektar focuses on being the best possible data layer. Neither approach is inherently superior; they serve different organizational needs. The question is which approach matches your GTM infrastructure strategy Considering an Alternative to People.ai? See how Nektar delivers 95%+ attribution accuracy directly into your Salesforce, without the People.ai price tag. Check the Comparison Why Enterprises Evaluate People.ai Alternatives Based on conversations with enterprise buyers evaluating both platforms, several consistent themes emerge: Existing Analytics InvestmentMany large enterprises have already invested significantly in Databricks, Snowflake, Looker, or Tableau. Their internal ops teams build custom dashboards tailored to their specific sales motions. For these organizations, adopting another analytics platform creates redundancy rather than value. They want the underlying data, not another UI. Salesforce Integration Model People.ai uses a managed package approach that creates custom objects in Salesforce. While this provides rich functionality within People.ai’s ecosystem, some enterprises report challenges including: Additional automation required to map data into standard Salesforce fields Complexity when using captured data in existing workflows or forecasting Duplicate participant records requiring cleanup Nektar writes directly to standard Salesforce objects (Events, Tasks, Contacts), which can simplify integration with existing processes but may offer less specialized functionality. Meeting Attendance Requirements A significant differentiator for some buyers is meeting attendance intelligence. People.ai’s meeting data typically relies on calendar invites and recorded calls via CI platform integrations. Nektar captures both invitees and actual attendees, along with meeting status (completed, cancelled, no-show, under 10 minutes), without requiring recording. For organizations focused on coaching, churn analysis, or executive involvement tracking, this distinction can be decisive. Data Volume Control Some enterprises express concern about data volume and Salesforce storage costs. Nektar offers granular sync controls that let administrators define which activities to capture, which contacts to create, and what thresholds to apply. People AI’s capture approach may generate higher data volumes, which can be beneficial for analytics but challenging for storage-conscious organizations. Key Differentiators: A Deeper Look Opportunity Matching Accurately attributing activities to the correct opportunity is critical for pipeline analytics and forecasting. The two platforms take different approaches: People.ai uses configurable, rule-based matching logic that can be customized per deployment. This approach offers predictability but may require ongoing maintenance as your sales process evolves. Nektar employs graph-based machine learning that analyzes email content, domain patterns, calendar metadata, and historical matching to attribute activities. Nektar reports accuracy rates above 90% in multi-opportunity environments, with the model improving over time through self-learning. Meeting Intelligence This is one of the most significant differentiators between the platforms: People.ai captures meeting data primarily through calendar integration and conversation intelligence partners (Gong, Zoom IQ, Webex). Insights from recorded calls are available in their analytics but may not be written as structured fields in Salesforce. Nektar captures both invitees and actual attendees directly from Zoom and Teams (without requiring recording), writes meeting status to Salesforce, and distinguishes between internal and external participants. This enables use cases like: Tracking which executives actually joined renewal calls Identifying no-show patterns that predict churn Measuring SE and CSM involvement in deals Coaching based on actual participation, not calendar entries Engagement Scoring People.ai provides engagement scoring as part of its analytics suite, displayed through their dashboards and scorecards. The scoring methodology is largely pre-defined and optimized for their UI. Nektar offers customizable engagement scoring that writes directly to Salesforce fields. Organizations can define their own scoring formulas based on email, meetings, touches, attendance, roles, and recency, making it easier to integrate into existing workflows and BI tools. Multi-User Attribution Modern enterprise sales involve multiple internal stakeholders: AEs, SEs, CSMs, AMs, and leadership. Accurate attribution matters for: Understanding true time allocation Measuring team effectiveness Forecasting with complete engagement data Pod-based and team selling models People.ai primarily attributes activities to

AI, Customer Success

Transforming Customer Success in the Age of AI

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Rebuilding Customer Success for the AI Era: Lessons from a VP of Customer Success A conversation with Chad Gorman. Executive Summary This article examines how customer success leaders should rethink AI adoption, using insights from an in-depth conversation with Chad Gorman, VP of Customer Solutions and Success (North America) at LivePerson, on the Revenue Lounge Podcast hosted by Randy Likas. Rather than focusing on automation or AI features, Gorman argues that AI-ready customer success is fundamentally about visibility, data discipline, and relationship intelligence. Readers will learn: Why most AI initiatives in customer success fail before deployment  How unifying fragmented CS data is a prerequisite for AI or automation What an effective early warning system looks like  Why engagement and relationship depth are stronger leading indicators than product usage alone How AI can expose relationship “white space” across complex buying committees Where buy vs build decisions actually differ across enterprise and mid-market segments How to embed AI into CSM workflows over-relying on automation Which metrics matter when measuring AI’s impact on retention, risk, and productivity Why the real promise of AI in customer success is reclaimed time for strategic customer work Facebook Twitter Youtube From Call Centers to Customer Outcomes Gorman’s perspective is shaped by an unusual career arc. He started in contact center operations, moved into IT at DirecTV, and then crossed over to the vendor side after a colleague recruited him to Splunk. “I didn’t even know what a CSM was,” he admits. “But once I saw how customer success could be built as a scalable engine, I was hooked.” — Chad Gorman From Splunk, he went on to lead global cloud customer success at VMware, before joining LivePerson, where he now oversees customer success and professional services across North America. That mix of operator, builder, and enterprise leader shows up in how he thinks about AI. Practical. Outcome-driven. Skeptical of hype. ​​AI Adoption Fails Before Deployment Most AI initiatives stumble long before a model is ever deployed. According to Gorman, the real friction points show up earlier in the buying and approval cycle. The Hidden Gates to AI Adoption Governance reviews and AI councils Legal, compliance, and security documentation Industry-specific scrutiny, especially in financial services Undefined success metrics “You can sell software all day long. But if you are not there to shepherd customers through governance, compliance, and approval gates, adoption will stall.” — Chad Gorman Customer Success Has Become Revenue Insurance In volatile markets, customer success is no longer a post-sale support function. It is a revenue protection layer. That shift forces CS leaders to answer harder questions: Where is risk building right now? Which accounts look healthy but are quietly disengaging? Where is expansion hiding in plain sight? The answer, Gorman says, is an early warning system built on stitched data. https://youtu.be/sDdV747jBJA?si=fVE8O2bqTcf2yTeN The Anatomy of an Early Warning System Gorman is blunt about the prerequisite. “Data is non-negotiable. Full stop.” — Chad Gorman Before AI enters the picture, organizations must understand what their book of business actually looks like. Engagement Is the Most Underrated Risk Signal Product usage is table stakes. Engagement is the differentiator. Gorman emphasizes that many churn events are preceded not by usage decline, but by relationship decay. “If engagement drops and you do not notice, you end up ghosted and surprised later.” — Chad Gorman What Engagement Actually Means Engagement is not email volume or meeting counts alone. It is relationship depth across the buying group. Who shows up to meetings? Who stopped showing up? Which roles are missing entirely? Who influences decisions but never engages directly? This is where Gorman believes AI has its most immediate impact. Relationship Intelligence: Where Art Meets Science Gorman describes relationship intelligence as the intersection of human judgment and system-derived insight. “We think we know our accounts. AI shows us the white space we missed.” — Chad Gorman AI-Assisted Relationship Mapping AI can analyze: Calendar data and meeting attendance Email and collaboration patterns Role changes and stakeholder turnover Sentiment from meeting notes and transcripts At LivePerson, Gorman’s team increasingly relies on workspace-level intelligence using Google Gemini to surface patterns across meetings, documents, and communications. You can literally ask, ‘Who used to attend and no longer does?’ and get an answer.” — Chad Gorman Buy vs Build Is No Longer Binary Enterprise customers increasingly want flexibility. Some bring their own LLMs. Others rely on vendor-provided AI. Most land somewhere in between. Enterprise: Build and bring your own models Upper mid-market: Hybrid Down-market: Out-of-the-box AI The common denominator remains the same: clean, structured, accessible data. Embedding AI Into CSM Workflows Even the best insights fail if CSMs do not trust them. Gorman stresses three adoption levers: Data transparency Always link insights back to source systems. Prescriptive guidance Do not just flag risk. Recommend next steps. Respect experienceAI should augment gut instinct, not override it. Measuring AI Impact in Customer Success AI success is not measured by novelty. It is measured by outcomes. What’s Next: Agentic AI and Time Reclaimed The next wave, according to Gorman, is not better summaries. It is execution. The thing CSMs hate most is admin. AI agents that actually do the work change everything.” — Chad Gorman Examples include: Auto-generated QBRs with live data Scheduled reporting without manual pulls Automated follow-ups and task execution The payoff is not speed. It is reclaimed time for strategic customer work. Leadership Lessons From the Field When asked what advice he would give his younger self, Gorman’s answer is simple. “Know your book. Be curious. Admit what you do not know.”” — Chad Gorman Growth mindset Curiosity within and beyond the “box” Meticulous organization Executive presence Tight partnership with the AE  Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

Andy Mowat
GTM, RevOps

Future of RevOps: GTM Systems, Hiring Tactics and Career Strategies

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A RevOps Playbook on the GTM Power, Careers and Hiring Strategies A conversation with Andy Mowat Executive Summary Andy Mowat has navigated the go-to-market journey from every angle—entrepreneur, operator at four tech unicorns (Box, Culture Amp, Carta), and now founder of Whispered. In this conversation, Andy shares hard-earned lessons on what separates strategic RevOps leaders from tactical executors, why the GTM tech stack is dying, how to take control of executive interviews, and why most people are dangerously cheap about investing in their careers. This isn’t theory. It’s a playbook built from someone who’s been the “wrong person for the job” four times and figured out how to win anyway. Readers will learn: RevOps is evolving from an execution function into a strategic GTM decision engine. The best RevOps leaders earn trust by forcing trade-offs, not by saying yes to everything. Legacy GTM stacks are breaking. Data fluency and AI-ready systems are becoming mandatory. GTM engineers are emerging under RevOps to automate execution and scale insight. Most executive roles are never posted. Senior hiring happens through networks and backchannels. Hiring favors builders who can get into the weeds, not just managers of managers. Strong candidates take control of interviews and show how they think, not just what they’ve done. Career leverage now comes from networks, reputation, and visible thinking, not applications. Facebook Twitter Youtube From Accidental RevOps to GTM Architect: Andy’s Career Arc Andy did not plan to end up in revenue operations. He stumbled into it the way many of the best RevOps leaders do. Early in his tech career, he joined Upwork. There was no CRM. So he built one. There was no outbound engine. So he figured out how to send a million emails. There was no formal RevOps function. So he became it. This pattern repeated. At Box, post-IPO, he took over post-sales operations, then marketing ops. At Culture Amp, he helped scale revenue from roughly $5M to $150M. At Carta, he entered during another inflection point, surrounded by leaders who understood that GTM decisions compound quickly, for better or worse. Across these roles, Andy learned something that most operators learn too late. RevOps is not a service desk. It is the economic engine room. The Real Difference between Tactical & Strategic RevOps Most RevOps leaders think their job is to execute requests efficiently. Andy believes that is how RevOps loses credibility. The inflection point in his thinking came at Box, when the company’s CCO told him “he’s not getting headcount unless the business gives it to him”. Instead of asking for budget, Andy began forcing trade-offs. He showed Sales, Support, and Customer Success how RevOps leverage could outperform incremental hiring. When leaders realized that one RevOps hire could unlock more growth than two frontline hires, budget appeared quickly. Andy’s rule is simple. If RevOps says yes to everything, it is not strategic.If RevOps forces prioritization conversations with executives, it is. “The wrong answer is ‘We got it.’The right answer is ‘Here’s the priority order I see. If we disagree, let’s take it to the CRO.” — Andy Mowat Where RevOps is Actually Headed Andy does not believe today’s GTM stack survives the next five years. He is tracking more than a dozen “CRM 2.0” challengers. His core criticism of legacy tools is structural, not cosmetic. Current GTM systems have: Clunky user experiences Data models not designed for AI Endless bolt-ons that fragment signal quality The Non-Negotiable Skills a RevOps pro Must Have Data literacy. Know what ETL and DBT actually do. Tight partnership with product and data teams. Embedded analytics and BI inside RevOps. Automation ownership, not tool babysitting. He also pushes back on the myth of the “GTM Engineer” as a shortcut. There is no shortcut. But there is a new function emerging. “GTM Engineers should live under RevOps.Their job is to automate the business and innovate for the reps.” — Andy Mowat https://youtu.be/H3CesaCmKWA What is Whispered? Whispered did not start as a company. It started as a survival mechanism. After a failed startup, Andy found himself asking a question many senior leaders never admit out loud.“Will anyone Hire me again?” At senior levels: Roles are rarely posted. Recruiters control access. Company quality is opaque. Networks go stale quietly. “Your next role won’t be posted. It’ll be whispered.” — Andy Mowat Whispered is designed for VP+ GTM leaders who are curious but cautious. It combines: Career playbooks Company backstory intelligence Unposted role discovery Network swarming across 300,000+ first-degree connections A private community that trades signal, not hype People join for roles. They stay for the network. Strategies for Hiring Senior Executives Through Whispered Hiring, Andy has interviewed dozens of CEOs, CROs, and CMOs about how they evaluate senior talent. Several patterns repeat. 1. Back-Channels Are the Highest Signal Everyone uses them. Everyone admits it. The best advice he heard recently: “Back-channel before you fall in love with a candidate.” 2. Builders Are in Demand Even at senior levels, companies want leaders who can still get into the weeds.AI has increased this expectation, not reduced it. 3. Rigid Thinkers Lose Andy calls it anti-rigidism, not ageism. Leaders who cannot adapt get filtered out quickly. 4. Slope Matters, but Only with Pattern Recognition High-growth companies love people who can outgrow their role. But leadership teams need both: Builders with slope Operators with scars Out perform your next Interview call Andy comments, treat your interview as a Sales call. Andy’s favorite interview opener is disarming. “I’m excited to meet you. What questions do you have?” Then he watches. Great candidates take control. Weak candidates wait to be prompted. Lazy questions kill momentum. Deep questions reveal how someone thinks. Interview Question Upgrade Instead of:“What’s your strategy?” Ask:“Here are three GTM constraints I see. Which one worries you most right now?” The goal is not to impress. It is to create signal. Personal Brand Without Becoming an Influencer Andy draws a line between thought leadership and performance. He writes because he cares and because writing clarifies thinking. It

RevOps

Building Revenue Engines that Scale: Lessons on Forecasting, Alignment and Multi-Product Complexity

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Building Revenue Engines that Scale: Lessons on Forecasting, Alignment and Multi-Product Complexity A conversation with Jeff Perry Executive Summary This article unpacks the operational blueprint behind scaling a revenue org from $15M to $500M+ ARR, while steering three parallel businesses at once. It’s distilled from an in-depth conversation with Jeff Perry, Chief Revenue Officer at Carta, who brings more than two decades of GTM leadership experience across Oracle, DocuSign, and Carta. Readers will learn: How Jeff’s career evolved through three distinct phases: growth, scale, and building What the “What’s Your Gut?” forecasting method reveals about improving accuracy Why cross-functional alignment is the real competitive advantage in revenue operations How to manage multi-product complexity with different ICPs and buying motions The characteristics that separate great sellers and managers from good ones Where AI fits (and doesn’t fit) in modern revenue operations   Facebook Twitter Youtube The Journey: Three Career Arcs That Shaped a Revenue Leader 1. Oracle: The Growth Phase Jeff started his career at Merrill Lynch depositing physical stock certificates—ironic given he now leads Carta, the company eliminating physical certificates. After a brief stint, he spent many years at Oracle where he learned: Sales fundamentals through structured training programs Leadership principles by observing great (and not-so-great) mentors Scale operations as Oracle grew from 30,000 to 120,000 employees Resources were abundant. Revenue operations, sales strategy, and training programs just happened. You operated inside a well-oiled machine. 2. DocuSign: The Scale Phase Jeff made what many considered a lateral or downward move: from leading 250+ AEs at Oracle to managing a 20-person SMB team at DocuSign. Why he did it: He needed to prove he could operate in a smaller, scrappier environment where: You’re hands-on with planning and execution Resources aren’t automatic You build the machine, not just run it Over four years, Jeff doubled his team size and took on additional verticals. This experience opened the door to Carta. 3. Carta: The Building Phase Jeff joined Carta in late 2018 when the company was at ~$15M ARR with 275 employees. Today: $500M+ ARR 1,800 employees Three distinct businesses operating under one roof “Sometimes you have to get one door opened up to lead to the next door. Oracle opened the DocuSign door. DocuSign opened the Carta door.” — Jeff Perry The Multi-Product Challenge: Managing Three Companies Inside One When Jeff joined Carta, it was a single-product cap table business. Today, it’s three distinct revenue engines. Venture-backed companies Venture Capital firms Private Equity The Growth Strategy: Classic Spreadsheet-to-Software Carta’s playbook: Identify a spreadsheet problem (cap tables, back office GL, ownership tracking) Build software to solve it Add adjacent products that create cross-sell and upsell opportunities “I can’t have one AE that sells cap tables to venture-backed C-Corps and fund administration to private equity firms. We’ve built teams within that align to the ICPs” — Jeff Perry Building Multi-Segment GTM Systems Without Chaos Each market Carta serves has its own logic: Startups care about cap tables, compensation benchmarks Venture firms care about GL automation, fund admin Private equity teams want scenario modeling and ownership accuracy The Challenge Different products have different: ICPs (leading to data segmentation issues) Sales cycles (transactional vs. enterprise) Buyer journeys Conversion benchmarks Example issue:A prospect reaches out to Jeff about their product. Jeff searches the CRM. The company isn’t there. Why? They were doing business under a different name. The data doesn’t connect. The RevOps Implication: As you add products with different ICPs, you can’t force unified systems. You need: Separate sales teams aligned to buyer personas Different quota structures Distinct sales motions (transactional vs. enterprise) The “Don’t Lose Alone” Philosophy: Why Lone Rangers Fail Jeff flips the classic advice: people say “Don’t try to be the hero.” He says don’t lose alone. Early-career reps want to be the savior who lands the big deal at the end of the quarter, makes a bunch of money, and gets recognized as the hero. Why this backfires: You limit your access to support and expertise Leadership lacks visibility to help you close You give yourself a lower probability of winning You don’t build career capital beyond the one deal The better approach: Involve your leadership team, product experts, and delivery teams early. “It does no good to be at the end of a quarter and say, I delivered this X hundred K deal and be the hero. You give yourself a better chance to win by involving the right people along the way.” — Jeff Perry Your future relationship builders are the SDRs learning your business today. If you automate away those roles, you lose the talent pipeline that becomes your future AEs and managers. Cross-Functional Alignment: The Secret Competitive Advantage Carta’s gone from 275 employees in 2018 to ~1800 today. Alignment usually decays with scale. But Jeff argues the opposite is possible, if leaders treat every metric like a shared asset. Marketing isn’t feeding leads to sales.Marketing and sales are feeding the same revenue engine. “Nicole, Carta’s CMO, doesn’t look at it as Jeff’s revenue number. She sees it as our revenue number.” — Jeff Perry Sales + Marketing: One Team, One Number The old model: Marketing owns lead generation Sales owns revenue Finger-pointing when numbers miss New model: Shared pipeline ownership Joint accountability for closed revenue Integrated planning across demand gen and sales capacity No classic sales-marketing friction Product isn’t building in a vacuum.Product is reacting to customers at lightning speed. He describes a moment at a Napa event: Prospects gave feedback at 11am.By evening the CPO confirmed engineers were already building on it. That’s alignment at operational speed. RevOps as the Connective Tissue Jeff’s perspective on where RevOps fits: It depends on company culture and structure, but: RevOps often has the clearest view of: The buyer journey Data flow across systems Change management needs Narrative resonance through metrics Many CEOs include RevOps in the small leadership group for exactly these reasons. The “What’s Your Gut?” Forecasting Method The problem with traditional forecasting is that most forecast calls

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