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 Duplicate records, an outdated phone number, a stale job title.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

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