Log in
See a Demo

AI

AI

An Artificial Intelligence Layer is Only as Good as the Data Underneath It

/

An Artificial Intelligence Layer is Only as Good as the Data Underneath It CRM 12 min Updated: August 7, 2026 The sales tech market has consolidated hard over the past two years, and the AI layer sitting on top of it has moved from a differentiator to table stakes. What customers actually want hasn’t changed underneath that shift: technology that helps close more revenue and closes the gaps that exist across the customer lifecycle, from lead to opportunity to renewal. Tools that give unified visibility across the full funnel, built on clean, complete data and aligned teams, are the ones that win. An intelligence layer is what gets a company closer to its revenue targets, but only if the data underneath it can actually support it. Get our latest insights into your inbox What Is an Intelligence Layer? An intelligence layer is what unlocks the patterns that have long been trapped inside disconnected databases and applications. It makes use of data streaming continuously into your systems and surfaces insight at the moment it’s actually needed, adapting and evolving rather than staying static. Done well, it marries historical context with the constant flow of new data about accounts, opportunities, and prospects, offering predictive signals about what will actually move the buying journey forward at any given point. Those signals make real-time, proactive engagement possible instead of a reactive scramble after the fact. This is the system of action that determines who wins in sales tech: which solution delivers the best insight in a single interface that serves as a rep’s actual point of decision and point of action. Whether it’s deciding who to reach out to, which deal to prioritize this week, or which stalled deal to revive, an intelligence layer surfaces a predictive list of next actions that pushes a team toward close, keeping customer-facing teams focused on the highest-value, most-likely-to-convert accounts. The Real Story: It’s Not Just About the Algorithms The intelligence layer will keep winning in this category. But its usefulness depends entirely on the data underneath it, because AI needs meaningful, accurate input to recommend anything that actually improves revenue outcomes. Building a real intelligence layer requires a solid data layer underneath it first. Without that, even the best model in the world can’t undo what bad data does to revenue. Most businesses are still struggling with exactly this. Modern Sales Pros’ State of Your CRM Data report, produced with BuzzBoard, found only 6% of respondents highly confident in their own CRM data, 58% citing data accuracy as the top barrier to collecting quality data, and 37% saying poor-quality data directly leads to poor conversion rates. The questions revenue leaders actually need to ask: What will my data source be when I deploy an AI agent against it? Do I trust the quality of that data? Is it clean and accurate enough to drive reliable insight for the business? For a company unsure of the answer, adding even the most advanced AI solution to the stack doesn’t improve revenue outcomes. It adds tech debt on top of an already shaky foundation, and prevents the intelligence layer from delivering anything close to what it promises. Read Case Study See How Mimecast generated $150M+ in pipeline and attributed $10M in revenue by fixing their GTM AI foundation Bad Quality Data Leads to Poor Conversions Incomplete and inaccurate contact data has a direct, measurable impact on conversion rates, and therefore on revenue. Without the right data and insight, demand generation and sales teams can’t reliably get the right leads into the funnel, nurture them down it, identify the most viable prospects to engage, or run genuinely personalized outreach. This is exactly where the intelligence layer has to converge with real data to close an organization’s actual revenue gaps. Accurate, rich, complete account data is the foundation an AI-driven sales organization is built on, and companies that skip investing in that foundation won’t hold up against the pace of change in today’s sales environment. Characteristics of Good Quality Data Data quality rests on a specific set of characteristics. Good data is: Accurate, reflecting what’s actually true, not what was assumed or guessed. Automated, captured without depending on a rep remembering to log it. Complete, covering the full picture of an account, not just the fragments a rep happened to enter. Timely, current enough to reflect what’s actually happening right now, not what was true weeks ago. Together, these characteristics are the basis for good decision-making. A quality dataset is what supports AI that actually works, since any model is only as good as what you put into it. Checklist to Assess Your Data Quality If you think your data quality is already good, it’s worth checking again with this specific set of questions, organized by funnel stage. If the answer is no to any of these, it’s worth checking the actual state of your data hygiene directly rather than assuming it’s fine. With the right data strategy, these gaps are fixable. Download Checklist Dive deeper with our AI readiness checklist to see where you stand when it comes to your data foundation. Why This Matters More Now Than It Did a Few Years Ago The core argument here hasn’t changed. What’s changed is what’s riding on it. Salesforce’s April 2026 Headless 360 initiative made every core Salesforce capability available as an API or MCP tool specifically so AI agents can read, write, and execute workflows without a person opening a browser first. That’s a real shift in what bad data actually costs. A gap in the checklist above used to just produce a slightly-off report a manager could catch. Fed into an agent acting on that same gap directly, updating a field, prioritizing an account, flagging risk, the same gap becomes a wrong automated decision, at a speed nobody catches in time. Data quality isn’t just about having a lot of data to feed the system. It’s about trustworthy, complete data underneath it, because that’s the only thing that actually

Maintaining SF Data Hygiene
AI, CRM

Top 10 CRM AI Use Cases for 2026

/

10 CRM AI Use Cases for 2026 CRM 12 min Updated: August 7, 2026 91% of companies with more than 11 employees use a CRM. The gap between adopting a CRM and actually running AI on top of it well is still real: only 24% of B2B suppliers currently run true agentic AI, the autonomous, workflow-driving kind that actually replaces manual processes, even though 45% say they use some form of AI in their sales function. Most of that gap is point-tool automation dressed up as transformation. What’s changed since this category was first written about is the shape of the ambition. The conversation used to be “can a CRM chatbot answer a support question.” It’s now “can an AI agent update a record, prioritize an account, and trigger a workflow inside Salesforce without a person reviewing it first.”  Gartner projects 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from under 5% just two years ago. This guide covers what AI in CRM actually means today, and 10 real, current use cases, including where Nektar fits into several of them directly. Get our latest insights into your inbox What Is AI in CRM? A CRM manages relationships with customers, prospects, and other business contacts. AI in CRM means integrating AI technologies into that system to analyze customer data, predict behavior, automate tasks, and personalize interactions, moving a CRM from a passive record-keeping system toward one that actively surfaces insight and, increasingly, takes action on its own. The distinction that matters most in 2026 is between AI that assists a person (drafting an email, summarizing a call) and AI that acts autonomously (updating a field, triggering a workflow, prioritizing an account without a human approving each step).  PwC’s survey of 308 senior executives found 79% say AI agents are already being adopted at their companies, and 66% of those report measurable productivity gains, a genuinely strong result, but one concentrated specifically among companies running the second kind of AI, not the first. 10 CRM AI Use Cases for 2026 1. Automated Contact and Activity Capture AI can build comprehensive contact lists for every account by extracting them directly from a rep’s email inbox, calendar, and meetings, rather than relying on manual entry. Contacts and Opportunity Contact Roles get categorized by actual engagement and relevance to a live opportunity, and enriched automatically with current job titles and phone numbers as they change. Mimecast used exactly this kind of automated telemetry to identify $80M in pipeline and $2M in incremental expansion revenue within 80 days, signals that were sitting in email and meeting data the whole time but had never been structured or surfaced before. Why it matters: Every use case below (scoring, forecasting, personalization) is only as good as the underlying activity data. A predictive model reasoning over incomplete contact data produces a confident, plausible-sounding answer that may have nothing to do with what’s actually happening in the account. 2. Agentic SDR and Outreach 41% of marketing organizations now run at least one SDR agent, and companies running agentic outreach report roughly 19% of net-new pipeline sourced through it, with 2 to 3x improvements in pipeline velocity compared to manual prospecting alone.  SaaStr’s own published experiment running an inbound AI agent generated $1M in closed revenue within 90 days, with 71% of that quarter’s closed deals sourced from AI-qualified inbound leads, though SaaStr’s founder Jason Lemkin has also been candid that fully autonomous outbound agents perform “better than a mid-pack rep, but not better than a top performer,” a useful caution against over-claiming what this category actually replaces. Why it matters: This only works well when the agent has real, current account and contact data to personalize from. An agent working from a stale or incomplete CRM record produces generic outreach that undermines the exact personalization it’s supposed to deliver. 3. Qualified Pipeline Expansion AI can detect the absence of pre-engaged contacts or leads within the CRM and run targeted, compliant outreach campaigns to expand the pipeline and shorten sales cycles, identifying contact roles automatically to sharpen targeted outreach rather than a generic blast.  Qualified’s published case study on Demandbase’s deployment of its AI SDR reports 2x pipeline sourced and 2x more meetings from target accounts, while saving roughly 100 SDR hours and $80,000 in costs per month, a concrete illustration of expansion built on data the team already had rather than a new list purchased from outside. Why it matters: Expansion built on incomplete contact data just recycles the same blind spots at greater volume. The gains above depend on the underlying account and role data being accurate before the campaign logic runs on top of it. 4. AI-Powered Account-Based Marketing Recover inactive and lost deals, and influence active opportunities, by running ABM campaigns against current, first-party buyer contacts sourced directly from sellers’ inboxes and calendars rather than purchased third-party lists. Precisely targeting buyers based on real engagement within high-priority accounts, their actual buying role, and current sales stage measurably increases funnel conversion versus a generic account list. Palo Alto Networks saw a 15x pipeline impact after moving from MQL-centric marketing to a genuine buying-group model built on this kind of first-party engagement data. Why it matters: ABM targeting built on firmographic fit alone misses the signal that actually predicts conversion, which stakeholders are engaging right now, and how deeply. That signal only exists if the underlying activity data is captured in the first place. 5. Predictive Analytics and Churn Prediction AI algorithms can predict customer behavior, flagging potential churn risk or purchase intent before it becomes obvious, so teams can act proactively rather than reactively. Zendesk describes predictive prioritization, ranking accounts by usage intensity, ticket volume, sentiment, and communication frequency, as the single highest-impact CS use case, since it’s what actually changes a CSM’s day-to-day motion: which accounts need attention now, which can run on automation, and which are healthy. Why it matters: This depends entirely on having enough real behavioral and

AI, Customer Success

6 AI for Customer Success Use Cases

/

6 AI for Customer Success Use Cases CSOps 12 min Updated: August 3, 2026 Efficiency has been the dominant theme in tech stack decisions for several years running: if a tool doesn’t clearly improve ROI, it gets cut. That pressure lands hardest on customer success, where the difference between a renewed account and a churned one increasingly comes down to one thing: a genuinely meaningful relationship with the customer. The numbers back up why that relationship matters so much. Acquiring a new customer costs five to ten times more than retaining an existing one, and existing customers are consistently easier to sell to than new prospects, since they’ve already validated your product and built trust with your team. With that much upside on the table, AI has become the obvious lever for customer success teams to reach for, the question is where it actually earns its keep. AI can meaningfully improve net retention rate (NRR) and the customer success processes underneath it. Yet adoption has historically lagged the opportunity: the Customer Success Collective’s State of Customer Success 2023 report found 66% of customer success professionals weren’t using AI in their role at all, a real gap at the time, and one worth checking against more current research given how much AI tooling has advanced since. This guide covers why AI matters for customer success specifically, and six concrete use cases to build around. Get our latest insights into your inbox How Has Customer Success Changed? Customer behavior keeps shifting. Preferences, pain points, and priorities evolve continuously, which means CS has to be agile and quick to adapt rather than running a fixed playbook indefinitely. Remote and hybrid CS is the norm. More customer success roles operate remotely than ever, and the tools and processes supporting them have to work as well outside an office as inside one. Digital-led operations are standard. Onboarding is increasingly automated, and generative AI in chatbots resolves customer questions using real account context rather than a generic script. The growth outlook has shifted from acquisition to resilience. Businesses have moved from “growth at all costs” toward driving more value from existing customers and existing tools, which raises retention’s importance relative to new-logo growth. Churn prediction has become a genuine discipline. Monitoring customer health to catch potential churn early, so a CSM can intervene before sentiment turns, is now a core CS function rather than a reactive afterthought. Why You Need AI for Customer Success Now Retention matters more than ever, and continuously proving value to customers, efficiently, is the harder half of that job. The challenge is scale: the sheer volume of customer behavioral data generated daily makes it genuinely difficult for a CSM to find the signal that actually matters, and even when the data is found, translating it into an unforgettable customer experience takes more than access, it takes the right tooling to act on it. Left unaddressed, that gap shows up as customers who feel forgotten, unclear on how to get value from your product, and quietly disengaging. The upside of closing it is real: even a 5% increase in customer retention can increase revenue by 25-95%. A few more reasons AI earns its place in a CS stack specifically: It automates workflows without replacing the human relationship, cutting reliance on manual data entry so CSMs spend more time actually building relationships. It surfaces more accurate, complete data from a pile most humans can’t realistically comb through manually, cleaner data means sharper insight. It flags sentiment shifts, positive to neutral to negative, and suggests next-best steps to course-correct before the relationship actually sours. It highlights new ways for customers to get value from your product, driving additional value continuously rather than only at renewal time. It shifts CS from reactive to proactive, since a health-score change is visible before a support ticket or a cancellation notice ever arrives. 6 Use Cases of AI for Customer Success 1. Track and Evaluate CSM Activities Most CS leaders can tell you how many accounts a CSM owns. Far fewer can tell you, with any real precision, what that CSM actually did on those accounts last month, or whether it worked. Activity tracking in CS has historically meant a task log or a calendar count: how many calls, how many emails, how many meetings. That tells you volume. It doesn’t tell you whether the activity was any good. The distinction matters because CSM effectiveness varies enormously even at the same activity volume. Two CSMs can each log fifteen customer touchpoints in a month, one spending that time in surface-level check-ins with a low-influence contact, the other running structured business reviews with the actual economic buyer. A dashboard counting fifteen touchpoints treats both CSMs identically. A CS leader trying to coach, staff, or forecast off that dashboard is working from a genuinely misleading picture. This is where Nektar’s CSOps capability is built to do more than log activity, it evaluates it. Every customer interaction is treated as a chance to understand its actual impact on revenue growth or churn mitigation, not just as a box checked on an activity report. Specifically, Nektar: Monitors whether activities are actually taking place, closing the gap between what a CSM reports doing and what the captured email, meeting, and call data actually shows happened, without requiring the CSM to self-report. Assesses effectiveness with detailed insight, not just a raw count. The same underlying data that shows an interaction happened also shows which persona was involved, when it happened relative to the renewal timeline, and what kind of meeting it actually was, a QBR, a support escalation, a casual check-in, each of which carries a different signal about deal or account health. Surfaces patterns a manager can actually coach against. Once activity is tied to persona, timing, and meeting type, a CS leader can see, account by account and CSM by CSM, whether engagement is concentrated in low-influence check-ins or genuinely reaching the stakeholders who matter, and use that as the basis

AI, Sales

10 Killer Tips to Use ChatGPT for Sales in 2026

/

10 Killer Tips to Use ChatGPT for Sales Sales 10 min Updated: July 30, 2026 ChatGPT has moved well past the novelty phase in most sales orgs. Reps use it for drafting, research, and prep the same way they use a search engine, reflexively, without much thought.  The current generation of models (the GPT-5 family, as of mid-2026) supports persistent memory across conversations, live web browsing, and multi-tool agentic actions as standard capabilities, a meaningfully different tool than the ChatGPT most sales teams first tried in 2023. The real question in 2026 isn’t whether ChatGPT can help with sales. It’s where general-purpose prompting genuinely helps, and where it quietly runs out of road because it doesn’t know anything specific about your actual deals, accounts, or CRM data. This guide covers both: ten practical ways to use ChatGPT for sales work today, and where that approach hits a wall that only grounded, CRM-connected AI can get past. Sales enablement software exists to fix that ratio: giving reps the tools, content, and resources they need to spend more time selling and less time on everything around it. Get our latest insights into your inbox What Is ChatGPT, and What’s Actually Changed ChatGPT is OpenAI’s conversational AI assistant, built on its GPT model family. The capability gap between the original 2022 version and today’s is substantial: current models hold context and memory across sessions rather than starting fresh every conversation, can browse the live web instead of working from a frozen training cutoff, and can take multi-step agentic actions (running a workflow across several tools in sequence) rather than just answering a single prompt. What hasn’t changed: ChatGPT still only knows what you tell it in a given conversation, plus whatever it can find via web search, plus whatever’s in its memory of your prior chats. It has no native access to your CRM, your actual pipeline, your specific accounts, or your team’s real interaction history unless you explicitly connect it or paste that information in yourself. That single limitation is the throughline of this entire guide. Prompt Engineering Still Matters, But Less Than It Did Prompt engineering got treated as a standalone hot skill in 2023; current-generation models need far less prompt gymnastics to get a useful answer, since they’re better at inferring intent from a straightforward request. What still matters: being specific about the actual scenario, the industry, the objection, the buyer context, since a vague prompt still gets a generic answer regardless of how capable the underlying model is. 10 Ways to Use ChatGPT for Sales 1. Sales outreach drafting ChatGPT can draft personalized email templates and social outreach messages, and adapt tone and language across markets and cultures reasonably well. The catch: it can only personalize based on what you give it. A prompt that includes real, specific detail about the actual prospect (their stated priority, a recent company event, their role) produces a genuinely tailored draft. A prompt with a generic persona description produces a generic-sounding email with a name swapped in. 2. Upselling and cross-selling ideas Given a customer’s stated situation, ChatGPT can suggest complementary products or upgrades and help frame the value case for them. It can’t see actual purchase history or usage data on its own, that context has to come from you, pasted in or connected via an integration, or the suggestions default to generic “customers who buy X often want Y” reasoning rather than anything specific to your actual account. 3. Objection handling practice ChatGPT can generate objection-and-rebuttal scenarios for practice, and can role-play as a skeptical buyer raising realistic pushback. This is genuinely useful for rep training. It’s weaker as a real-time objection-handling tool mid-deal, since it doesn’t know what this specific buyer has actually said in prior conversations unless you feed that context in directly. 4. Lead qualification question design ChatGPT is good at generating a structured set of qualifying questions (budget, authority, need, timeline) tailored to an industry or persona. What it can’t do on its own is apply those questions to your actual leads and produce a real score, that requires connecting it to your actual lead data, which is a different (and more valuable) capability than prompting alone provides. 5. Market and competitor research Current models with live browsing can pull recent public information, reviews, and reporting on competitors reasonably well, a real improvement over the original ChatGPT’s frozen training cutoff. It’s still working from public information only. It has no visibility into how your specific deals are actually going up against a specific competitor in your own pipeline, which is a private, first-party data problem no general-purpose AI assistant can solve without being connected to your CRM directly. 6. FAQ and first-line support drafting ChatGPT can draft consistent answers to common customer questions, useful for building out a knowledge base or a first-pass chatbot script. For anything account-specific (a customer’s actual contract terms, their specific configuration, their support history) it needs that information fed in, or it will answer confidently and generically, which is worse than not answering at all in a support context. 7. Sales strategy brainstorming As a brainstorming partner, ChatGPT can synthesize market trends and general best practice into a reasonable starting point for positioning, segment strategy, or channel prioritization. Treat the output as a first draft to react to, not a strategy grounded in your actual pipeline data, performance history, or competitive reality, since it has none of that unless you supply it. 8. Sales training and role-play This is one of the strongest, least caveated use cases on this list. ChatGPT can realistically simulate a skeptical prospect, adapt the objections it raises based on how a rep responds, and give a rep low-stakes practice reps can’t easily get elsewhere. Current models are noticeably better at this than the original 2023 version, holding a more consistent persona across a longer role-play. 9. Deal scoring criteria design ChatGPT can help you design a scoring rubric, which factors matter, how to weight them, what

5 Reasons for Low AI Sales Tool Adoption (And How to Fix It)
AI, RevOps, Sales

5 Reasons for Low AI Sales Tools Adoption (And How to Fix It)

/

5 Reasons for Low AI Sales Tool Adoption (And How to Fix It) RevOps 11 min Updated: July 16, 2026 AI sales tools are everywhere in the stack now. AI SDRs for outbound, conversational assistants that summarize calls, AI-powered forecasting layers, AI note-takers, AI enrichment tools bolted onto the CRM. Adoption of the category has grown fast: 43% of sales reps now actively use AI tools in their daily work, up from 24% in 2023, a real jump in two years. It still hasn’t grown as deep as the buying pattern suggests. 42% of sales and marketing professionals report real dissatisfaction with the AI tools they’ve used, mostly citing data quality and hallucination issues. Gartner projects more than 40% of current AI sales pilots will be cancelled outright due to unclear value or runaway costs. Teams are buying AI sales tools faster than they’re getting reliable value out of them. That gap, bought fast, adopted slowly, is the story of this post. It maps onto five specific, well-documented reasons, each with a fix that doesn’t require waiting for a better model. Get our latest insights into your inbox The AI Sales Tool Adoption Gap, in Numbers 70% of sales organizations say data quality is the single biggest obstacle to getting real value from AI sales tools, ahead of cost, integration difficulty, or which vendor they picked. 42% of sales and marketing professionals report dissatisfaction with the AI tools they’ve used, citing data quality, security, and generative AI “hallucinations” as the main drivers, per ZoomInfo’s State of AI in Sales & Marketing 2025 report.  56% of sales professionals use AI daily, and those who do are roughly twice as likely to exceed their targets than reps who don’t, so the upside is real for the teams that get past the adoption barrier. 24% of sales organizations report low user adoption specifically, with 41% of reps actively resisting the AI tools they’ve been given, a rep-level resistance rate well above what most other sales tech categories see. None of these are model-quality problems. They’re data, trust, and rollout problems that happen to be wearing an AI label. 5 Reasons for Low AI Sales Tool Adoption (and How to Fix Them) 1. The Problem: The AI Tool Is Only as Good as the CRM Data Feeding It This is the most consistently cited barrier specifically for AI sales tools, and it’s the least visible until something visibly breaks. An AI forecasting tool, AI deal-risk flag, or AI-generated account summary built on stale contacts, missing stakeholders, and unlogged activity doesn’t produce a cautious, hedged answer. It produces a confident, wrong one, since the AI tool amplifies whatever data it’s given rather than correcting for what’s missing from it. This is also where an old, familiar problem gets new stakes. Dirty CRM data used to just slow a rep down doing a manual lookup. Fed into an AI sales tool that surfaces a recommendation or, increasingly, acts on the data directly, the same dirty record can now produce a wrong output at machine speed, before anyone reviews it. The Fix: Fix the Data Foundation Before You Add an AI Layer on Top Don’t bolt an AI sales tool onto a stack you already know has gaps in contact and activity data. Fix your data foundation as a first step.  Nektar’s Data Foundation automatically captures every email, meeting, call, and calendar event across a team and writes it natively into Salesforce, HubSpot, or Dynamics, with zero rep effort required. Whatever AI sales tool sits on top of that data, Nektar’s or anyone else’s, only gets more reliable once the foundation underneath it is complete. 2. The Problem: Multiple AI Sales Tools Lead to Mixed Priorities Selling doesn’t get easier just because more of the stack is now labeled “AI.” MuleSoft’s 2026 Connectivity Benchmark found the average organization now runs 957 applications, and only 27% of them are actually integrated. And organizations already using AI agents run even more on average, 1,103 apps versus 957.  Adding an AI SDR, an AI note-taker, and an AI forecasting layer on top of a stack that already doesn’t talk to itself just gives a rep three more disconnected tools to check, each with its own partial view of the deal. The same research found this is now a governance problem specifically, not just a sprawl one: 50% of AI agents currently operate in isolated silos, disconnected from any cohesive system, and 86% of IT leaders agree that without proper integration, AI agents introduce more complexity than value rather than less.  If the head of sales asks which AI tool actually flagged a deal as at-risk, a rep might have to check three separate AI features across three separate tools to find out, which defeats most of the point of automating it in the first place.   The Fix: A Unified Data Layer the AI Tools Actually Share An AI sales tool is only as useful as the data it’s working from, and that data has to be the same data every other tool in the stack sees, not a fourth silo with a chatbot interface on top. A unified data layer automatically captures contact, activity, and intent data, the same underlying record every AI tool in the stack should be reasoning over, instead of each one working from its own fragment. Platforms like HubSpot’s Dashboard and Reporting Software show what this looks like when it’s done well: sales, marketing, service, and revenue data centralized under one dashboard, so an AI-generated forecast or attribution report is drawing from the same complete picture a rep sees, not a narrower slice of it. That consistency is what determines whether an AI tool layered on top of the stack actually reduces the number of places a rep has to check, or just adds one more. 3. The Problem: Reps Who Get Burned Once Stop Trusting the Tool at All Trust, not raw capability, is the actual bottleneck for most AI sales tools, and

AI readiness checklist thumbnail
AI

The AI Data Readiness Checklist

/

Checklist The AI Data Readiness Checklist Built for teams evaluating or expanding Agentforce, Microsoft Copilot, or any AI agent built on top of Salesforce Your AI agent is only as reliable as the data it runs on. Most enterprise Salesforce instances aren’t ready. 88% of enterprise AI agent pilots fail to reach production. The models are capable. The agent frameworks work. The bottleneck is almost always the same: the CRM data underneath is incomplete, fragmented, and only half-trustworthy. This checklist gives revenue and RevOps teams a practical, honest assessment of where their Salesforce data stands before deploying Agentforce, Copilot, or any AI agent built on top of CRM. 33 checkpoints across 5 categories to give you a clear picture of what’s in place and what isn’t. What you’ll walk away with: A category-by-category audit of your data foundation across completeness, identity resolution, unification, governance, and monitoring. Clarity on which gaps will cause your AI agents to fail first. And in which order to fix them. Specific checkpoints on the data problems most teams don’t know to look for: partner-attributed activities, deduplicated activity records, meeting intelligence capture, bounced contact suppression, and junk filtering. A reference your team can return to as you scale AI use cases across the GTM lifecycle. See how Nektar closes the gaps this checklist surfaces automatically. Learn More About Nektar’s Revenue Telemetry Nektar captures every customer interaction and writes it into Salesforce without any rep input. Download The Checklist From The Revenue Lounge Podcast​ Related Resources Ready to Turn your Data into Revenue Outcomes? Book a Demo

Why Your Salesforce Data Isn't Ready for AI Agents thumbnail
AI

Why Your Salesforce Data Isn’t Ready for AI Agents

/

Why Your Salesforce Data Isn’t Ready for AI Agents AI 8 min July 3, 2026 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

AI, GTM, RevOps

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

/

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.

How Nektar helps AI Hypergrowth companies move even faster
AI

How Nektar helps AI Hypergrowth companies move even faster

/

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

AI, Customer Success

Transforming Customer Success in the Age of AI

/

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

ai transformation
AI

Aligning AI Initiatives With Business Goals

/

Aligning AI Initiatives with Business Goals A conversation with Tim Seamans, VP of Business Transformation, AI Acceleration at Mimecast. Executive summary Mimecast’s AI transformation program is not a pilot. It is a company-wide operating system shift run by a small central team reporting to the Chief Digital Officer, with board-level sponsorship and clear commercial targets. In the first 80 days of a major go-to-market initiative, the team directly attributed 2 million dollars in expansion revenue and 30 million dollars plus in pipeline by consolidating signals, standardizing processes, and pairing predictive models with generative tools at the point of action. Today, every department uses generative AI and more than 60 percent of employees hold a gen-AI certification, supported by a structured AI fluency program embedded into new-hire induction. The program measures outcomes across acquisition, expansion, retention, and productivity, with security and governance built in from the first pilot. Below is the complete playbook from Tim Seamans, VP of AI Transformation at Mimecast, on how to design the charter, win stakeholder alignment, fix data, implement governance, measure results, and step toward agentic AI. Facebook Twitter Youtube The Mandate and Where the Function Sits Mimecast placed AI Transformation under the Chief Digital Officer who also oversees IT. That created proximity to platforms and data, without burying the team as a pure infrastructure group. The model works because the mandate is explicit and backed by the CEO and the board. Charter in one line Embed AI in how the company works to improve productivity and efficiency. Govern AI across product, operations, and customer interactions. Build proprietary AI capabilities for durable advantage, not just tool parity. “We went from something we might do if we had the right expertise to something we have to do. We are driving our business using AI.” — Tim Seamans The Team: Small, Specialized & Outcome Focused We asked Tim about how his team is currently structured. Here’s his breakdown: Core capabilities: Engineering and Architecture. Owns build vs buy, data and model architecture for scale. Data Science. Four specialists across predictive modeling and generative techniques. Program Management. Orchestrates cross-functional delivery and partner ecosystem. AI Fluency. Strategy owned by the transformation team, executed with Enablement and L&D. https://www.youtube.com/watch?v=uhUXsWAbuBQ AI Fluency as a Business Capability Mimecast made fluency non-optional. A three-level program powers adoption and safe use. Level 1: Foundations for everyone. What AI is, how to use it safely, and where it fits in your job. Delivered in new-hire induction with a short certification. Level 2: Builders. Power users who design task assistants and simple workflows. Level 3: Data scientists and advanced builders. Rolled out after Levels 1 and 2 saturate. “People using AI will replace your relevance. The bus is already moving. Get on it or get left behind.” — Tim Seamans Adoption funnel: Applicants to Level 1 → Certified users → Level 2 builders → Team-embedded champions → Program mentors How GTM Value Was Created and Measured The GTM program combined machine learning signals with generative tools at the moment of action. The team unified disparate workflows around outcomes, not around a single mega-platform migration. Case snapshot: Expansion motion What changed: Signals from CRM, product usage, and recent acquisitions were unified into a single expansion workflow that suggested what to sell, to whom, and why. How it worked: Predictive propensity + recommended offers + gen-AI for messaging and objection handling. Outcomes in 80 days: $2M in directly attributed expansion, $30M plus in pipeline. “We brought everything together based on outcomes. Signals, the right opportunities, and gen-AI assistance for the conversations.” — Tim Seamans What is actually measured: Top line: New logo acquisition, expansion rate and mix, retention and churn avoidance. Productivity: Hours saved translated to dollars only when tied to a business outcome. Adoption: Assistant usage, recommendation acceptance, win-rate deltas, time-to-first-action. Leading vs lagging: Recommendation acceptance and assistant usage are leading indicators. Retention is lagging and requires patience.   Stakeholder Alignment: Start with Goals, Not Tools The team begins every engagement with a simple sequence: Goal → Pain → Option. Ask business leaders to state their goals in commercial terms. Map pain points that block those goals. Decide build vs buy and define a thin slice to prove value. “If you start with technology, you will likely have a longer road. Take a thin slice, prove value, then scale with champions.” — Tim Seamans Checklist: Thin-slice pilot readiness Specific goal with a numeric success threshold Data access path documented Process owners signed up to change work patterns Governance controls defined before any user touches the tool Instrumentation for adoption and outcome attribution Data Strategy: Fix Availability, Standardize, Then Expose The biggest friction is not algorithms. It is data availability, fragmentation, and security constraints, especially after acquisitions and product evolution. What Mimecast did: Standardized core entities and created data that did not exist where needed Built secure pipelines into the CRM for contact and buying committee context Used a governed data store as the truth source for customer and prospect insights Accepted that some product feature telemetry still needs work and built a plan to fill gaps Governance & Security: Parallel to Innovation, Not After It Compliance, legal, security, and procurement are in the room from day one. The goal is to move fast with minimum viable governance, then scale safely. Governance controls in practice Approved tool list with monitoring for shadow AI Instructional guardrails for assistants and agents Red-teaming and hallucination checks before scale RACI for policy updates when assistants cannot answer Vendor review criteria built for gen-AI risks AI Agents: From Task Assistants to True Agentic Coworkers Internally, assistants handle repetitive tasks with a human in the loop. The next horizon is fully agentic systems that complete actions across tools with verified outcomes. “Think of true AI coworkers that collaborate across every function. The integration and security layers are the hard part, not the models.” — Tim Seamans Agent taxonomy: FAQ assistants. High-confidence answers for policies, routed to humans when unknown. Workbench copilots. Research, summarization, draft generation

Scroll to Top

Just one more step