Tim Johnson

10 dashboards we’ve built for GTM teams to fix deal pipelines, missed targets, and slow leads. Over the past few years, we’ve built many custom dashboards for B2B SaaS companies in the $2M–$10M ARR range. Below are 10 dashboards we have built for marketing & sales teams to make better decisions: 1. Deals Pipeline 2. Lead Lifecycle 3. Sales Rep Overview 4. Sales Funnel 5. Campaign Performance 6. Sales Overview 7. Target Account Overview 8. Advertisement Performance 9. Website Insights 10. Lead Journey Scroll to see what each dashboard looks like ↓

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Hybrid GTM is now the default operating model. The strongest companies combine enterprise sales with bottom-up entry points and treat product-led growth as a core revenue motion. High-growth companies expect self-serve to drive about 20% of revenue in 2026, roughly double what peers project. This indicates a structural change. Bottom-up is no longer just a source of leads. It is becoming a meaningful and durable share of revenue. 👉 Download the full report: https://bit.ly/3PvXp3g *For educational purposes for company founders and executives. This is not investment advice or an offer to invest. Refer to Outreach Disclaimer link in profile.

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Here’s a practical test for any enterprise AE. Pick the biggest opportunity in your pipeline right now. Now take your product out of the conversation. Can you explain, in plain business language, what changes financially if the customer invests? How much value is realistically at stake? What has to happen operationally for that value to materialize? And why is this important enough to compete for capital now? If you can’t answer those questions, there’s a good chance your customer can’t either. That matters because eventually somebody responsible for the P&L is going to ask them. And they’re probably not going to ask about your demo, how many meetings you’ve had, or how well the technical evaluation went. They’re going to look at the investment. What are we spending? What do we get back? How confident are we that we can actually realize it? Why this instead of something else? That’s where a lot of “great deals” quietly become no decision. Not because the product lost or because procurement killed it…. The deal reached a level of the organization where somebody had to defend the economics and nobody had built a case strong enough to survive the conversation. That work shouldn’t begin at the end of the sales cycle. It should start early. As early as the intro call. When we’re sitting across from a P&L owner, we need our business hat on. Where’s the gap in the business? What does it mean financially? Or where could our asset, once operationalized, change cash flow, margin, profitability, growth, or another outcome they’re accountable for producing? We have to bring something to that conversation. (POV) Something economically relevant to say. Executive access doesn’t mean much if the conversation doesn’t change when we get there. Then we can close the intro meeting with a simple question. Is there a business reason to move forward? If there is, earn the next step. Work with the functional leaders to determine feasibility. If the economics continue to hold, co-build the business case and find out whether the outcomes can clear the company’s investment thresholds. If we don’t know whether there’s a business reason to continue, we don’t have a qualified investment opportunity yet. We have activity. And activity is expensive.

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Every sales leader I know uploads their transcript into ChatGPT, and every RevOps / AI GTM Architect is wiring Gong data into a custom LLM or Snowflake. Yet all of them say the same thing … These systems genuinely help in the first 6 months. But eventually, field usage starts to slip, and teams wonder if it’s worth throwing more engineering at the problem. Maybe adding more context will fix it. Maybe preprocessing more fields. Maybe forcing more calls to get recorded. Maybe tuning the prompt. Maybe tuning it again. I know this because we tried all of this ourselves - over and over - and context, RAG, indexing, prompts, UX, or all of them (or none of them) were always the blockers. These setups are useful. They move things forward. But they are not the “Age of Intelligence” we were promised. They work for simple queries, but only if I specify exactly which account or opportunity I’m referring to. And they claim they can handle the harder questions - until you see the output. So we decided to take a swing at solving this! It took rethinking a lot of things under the hood, but the result has become my day-to-day companion. My own analyst. My own data scientist. If you’re doing this manually or trying to improve what you’ve already built, you should come take a look. We’re calling it Von, and I’m doing a public demo on Dec 4th where I’ll show exactly how we made it work. Link is in the comments. Hope to see you there.

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GTM stacks changed from 2020 → 2025. Pipeline performance didn’t. Tools evolved. Budgets grew. But pipeline accuracy stayed flat. You don’t need another analyst report to see it. You just need to compare the stacks. The 2020 GTM Stack (Execution Era) The “modern GTM stack” was a checklist: → Salesforce for CRM → Outreach / SalesLoft for SEP → ZoomInfo for data → Clearbit / Apollo for enrichment → Gong / Chorus for conversations → LeanData for routing → HubSpot Marketing Hub → Clari for forecasting → Demandbase for ABM The model was simple: More reps → more tools → more activity. A CRO told me last month: “We scaled our stack. Pipeline accuracy never moved.” The 2025 GTM Stack (Intelligence Era) The stack flipped. Execution became automated. Decision-making became the constraint. The new landscape: → Revenoid → Salesforce Agentforce → Intercom Fin → ZoomInfo Copilot → HubSpot AI Agents → Outreach Grid + Smart Actions → Apollo AI Sequencing → Seamless AI Assistants → Multi-agent orchestrators (Revenoid, Crew, n8n AI routing) → Vertical agent stacks (Automox, PointClickCare, Rippling Unity, Personio Copilot) These aren’t just “tools.” They’re systems that decide and execute. Systems detect motion. Systems choose work. Systems handle the task. Humans now are validating direction. A RevOps director told me: “Half our workflows existed only because the system had no decision making capabilities.” Outcome pricing reflects the shift: *** $2 per conversation (Salesforce). *** $0.99 per resolution (Intercom). Access is no longer the product. Finished work is. ---- Meanwhile: → Automox reached 96% autonomous patch handling. → PointClickCare saw 168% more qualified leads by eliminating human response lag. → Rippling cut onboarding time through fully-agentic HR motions. The economics inverted overnight. As one CIO admitted: “Seat-based pricing died the moment tasks stopped needing humans.” --- So, where does Enterprise Sales AI sit? Not after sequences. Not during outreach. Not inside dashboards. Enterprise Sales AI sits before all of it — in the decision layer. It decides: → which accounts deserve attention → when internal motion is favorable → why this moment matters → what narrative aligns with readiness Pipeline quality is the output of that layer. Everything else is downstream execution.

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The evolving B2B tech landscape makes competitive displacement the default path for GTM teams. Simultaneously, it adds new layers of organizational risk, internal scrutiny, and emotional complexity for buyers. We created The Definitive Guide for Competitive Displacement to give GTM teams a smarter way forward—rooted in precise intelligence, not spray-and-pray ABM. Inside, you’ll discover: ▪️ Why traditional competitive intelligence leaves Sales, Marketing, CS, and Product flying blind ▪️ The exact data signals that show which accounts are truly ready to switch — and which never will ▪️ A step-by-step campaign framework for turning competitor intelligence into pipeline ▪️ GTM best practices from strategy to execution ▪️ How to fuse data, content, and activation into a single displacement play The switching window opens for buyers when the cost of staying exceeds the risk of moving. This guide helps you locate that window and act before it closes:

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Your GTM stack is an expensive mess. AI-native companies figured out why. Quick one. How many tools does the average B2B sales rep use to close a deal? Eight. Eight platforms stitched together with Zapier and prayer. 28% of reps hit quota last year. Down from 44% the year before. 72% of their time goes on non-selling activities. Cold email response rates have fallen 41% since 2019. We spent a decade buying tools to make selling easier. We made it harder. The average B2B org runs 305 applications. 53% of licences sit unused. It's $2 of sales and marketing spend for every $1 of new ARR. That's not a tooling problem. That's architectural debt. And the typical response - swap vendors, retrain the team, hire another SDR - is treating symptoms whilst the disease spreads. AI-native companies figured this out. Their response isn't to buy better suites. It's to build composable systems from scratch. Cursor hits $1B ARR with 300 people - $3.3M per employee. Traditional SaaS median? About $112K. That's not a rounding error. That's a different operating model entirely. I tracked 210 GTM roles across OpenAI, Clay, ElevenLabs, Notion, and Anthropic this month. 70% explicitly require AI building capabilities. Not "familiarity with AI tools." Actual building skills. Verkada automated 80% of their SDR workflows - 120 meetings a month versus the typical 20. Rippling doubled YoY cold email performance. Not by working harder. By building better systems. Most companies are assigning juniors to "experiment with AI tools." Nice UX, lightweight workflow tweaks. But when you're trying to 5x total turnover, getting a junior to explore ChatGPT plugins isn't going to move the needle (harsh but true). The companies making this work are hiring senior folk with commercial acumen and giving them product mandates. Sprint cycles. Release notes. Measured outcomes. I wrote up the full breakdown - the economics, the company implementations, and why the gap between intent and execution is a seniority problem, not a technology problem. Read the full post here →

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