# Matt Dornfeld

Matt Dornfeld works on AI enablement and go-to-market performance, helping teams identify process bottlenecks and put AI into practical workflows. Dornfeld's posts discuss sales automation, AI adoption, and training business teams to use AI.

- Profile: https://b2binfluencer.marketing/creators/matt-dornfeld/
- LinkedIn: https://www.linkedin.com/in/matt-gpt
- LinkedIn followers: 10,646
- Topics: artificial-intelligence, sales, revenue-operations, ai-adoption, go-to-market-strategy, sales-enablement, workflow-automation
- Based on 17 dated posts, latest 2026-10-07

## Summary

Matt Dornfeld is useful for process-first AI enablement and GTM performance advisory, not sponsored LinkedIn posts.

- Best for: B2B SaaS revenue org of a few hundred wanting AI adoption and GTM execution, via paid advisory.
- Not for: A mid-market CRM vendor buying sponsored LinkedIn posts with guaranteed engagement and named enterprise sales leader audience.
- Check first: Confirm whether they offer paid workshops or advisory and can provide audience analytics.

### Takeaways

- AI initiatives stall when tools go unused, sit disconnected, or are chosen before problem is clear.
- In a USC session, participants built a reusable AI skill without code.
- A revenue-team workflow turns scattered customer signals into suggested seller actions.

## Main ideas

- **Demos are easy; adoption is everything**: Dornfeld's central argument is that AI initiatives stall in the gap between a convincing demo and ordinary sales execution. They name three recurring failure points: teams launch tools nobody uses, new tools are never integrated into existing processes, and someone buys the new thing before working out what problem it solves. Leadership watches a demo, gets excited, spends money, then asks six months later where the return went—while basic sales enablement is still not dialled in. Their prescription is to map the process first, find the actual friction, then choose a solution that may be AI or simply better process design. The same logic explains why they favour tools that drop into an existing browser, CRM, or messaging app over tools that require a migration and a new habit.
- **Speed on a revenue team is a relevance and execution problem, not a headcount problem**: Speaking on a podcast hosted by Dennis Yao Yu, Dornfeld argues that slow revenue teams usually already hold the signals they need: product usage, intent, support tickets, pipeline movement, feature launches, website behaviour, and softer cues buried in calls and emails. The failure is that these signals are scattered and rarely converted into action quickly enough. Their proposed loop: capture the signals; align them to what matters, such as target accounts, ideal-customer profiles, messaging, new features or SKUs, and region; proactively recommend a next best action to the seller; and let the seller execute it in one click inside Slack. They are explicit that the goal is not “AI writing emails” but turning real-time customer context into timely, relevant outreach without adding process.
- **Teach AI as a thought partner, and start from the problem**: Dornfeld's teaching position is that the distance between being AI-curious and AI-capable is smaller than people assume, and mostly a framing problem. They tell business audiences three things: stop using AI like a search engine and treat it as a thought partner you iterate with; start with the problem rather than the tool, because reaching for AI without knowing where the leverage is gets you nowhere; and you do not need to be technical. They are more interested in the moment a non-technical person sees the work as accessible than in showing off capability.
- **Look at the system around the team before blaming the team**: A recurring argument is that stalled performance is usually a process problem rather than a people problem. Dornfeld points to outdated processes, administrative friction, and systems that buckle under pressure instead of getting stronger. One post lays out four operating principles: eliminate waste relentlessly, focus on healthy inputs over vanity metrics, design systems that perform better under pressure, and measure outcomes rather than activity. The pressure-performance post leans on broad corporate exemplars and abstract principles, while their AI-adoption writing insists on mapping specific friction before prescribing anything. Treat the principles as Dornfeld's stated operating philosophy, not as demonstrated case evidence.
- **The cost of waiting is real but invisible in the accounts**: Dornfeld argues that companies avoiding AI adoption carry a large opportunity cost that never appears as a line item. The money is in payroll hours spent on repetitive work competitors have automated; because it is not on the P&L, the cost surfaces indirectly as slowly declining margins, weaker pipeline growth, and stretching sales cycles while competitors do more with fewer resources. They contrast companies that call “careful consideration” their AI strategy with others building eighteen-month advantages. The financial figure they cite for mid-market companies is their own assertion. Ask for the calculation, sample, or case study behind the estimate.
- **Measure utilisation, not procurement**: The sharpest and most repeatable version of Dornfeld's position is that AI return in large organisations lives in day-to-day usage, not seats purchased. They say adoption patterns predict business outcomes better than procurement statistics, and propose measuring and rewarding utilisation instead of celebrating tool acquisition. The test they apply to their own proposals as well as to buyers: a tool only counts once it is woven into the process.
- **The AI work sits inside go-to-market operations**: Dornfeld's public framing of their role explains why the priorities above are operational rather than product-oriented. They describe moving from ecommerce partnerships and global programmes into a role helping go-to-market teams learn faster, execute better, and scale with AI built into their day-to-day work. The posts are written from that seat: the focus is making an existing revenue team faster, not building AI products or conducting research.

## Work to read

- [Announcing the ClickUp role: Director of GTM Performance and Productivity](https://www.linkedin.com/posts/matt-gpt_well-folks-after-career-defining-chapters-activity-7444390137532342272-zoJs)
- [What actually happened in an AI enablement session at USC](https://www.linkedin.com/posts/matt-gpt_heres-what-i-noticed-running-an-ai-enablement-activity-7454559700634927104-exqh)
- [The signal-to-action loop described on the AI for Business Leaders podcast](https://www.linkedin.com/posts/matt-gpt_ai-gtm-revenue-activity-7437905373329752064-u8Y1)
- [Delete the unused AI licenses](https://www.linkedin.com/posts/matt-gpt_ok-heres-a-bold-move-delete-all-your-teams-activity-7380957551766589441-YBuu)
- [The seven-figure invisible leak in companies avoiding AI adoption](https://www.linkedin.com/posts/matt-gpt_theres-a-7-figure-invisible-leak-at-companies-activity-7379543360681725953-pxP-)
- [Why AI initiatives stall between the demo and GTM execution](https://www.linkedin.com/posts/matt-gpt_ai-initiatives-live-and-die-in-the-gap-between-activity-7379182853445586946-_Tw2)

## Brand relationships

- **ClickUp** (employment)
- **BigCommerce** (employment)
- **Feedonomics** (employment)
- **Composite** (disclosed_partnership)

## Partnerships

There is no documented partnership or sponsorship offer from Matt Dornfeld: no media kit, rate card, audience-deliverable menu, stated terms for sponsored posts, or verified partnership inquiry route. Their posts show them promoting tools and products in the course of their own work, rather than as disclosed paid placements. In a September 30, 2025 post, they describe testing several browser automation tools and single out Composite, saying it "plugs into your existing browser" with "no migration, and no learning curve," then close with a signup link for the product. In a March 12, 2026 post, they describe "what we've been building": a signal-capture and next-best-action loop for revenue teams that executes in Slack, tagged with a product hashtag. Both posts read as first-person product advocacy tied to their own enablement and GTM work, not sponsored content with a stated commercial relationship. For a buyer planning a workshop or advisory engagement on AI enablement, the distinction matters: the posts provide nothing to price or scope for a sponsorship. Ask whether they offer sponsored placements and, if so, request terms and rates; otherwise, pursue the advisory or workshop route for a campaign aimed at AI enablement.

## Alternatives

- Rosalyn Santa Elena: Consider Rosalyn Santa Elena when the engagement is about forecast predictability and revenue-operations discipline rather than AI adoption. Santa Elena's posts show how to turn forecasting from art into a defined process: written forecast categories, shared stage definitions with simple yes-or-no exit criteria, required fields that gate stage movement, a fixed weekly cadence, accuracy tracked by rep, manager and segment, historical conversion rates used to pressure-test numbers, and an early-warning list for deals that stall. The license-allocation example makes the same point about RevOps maturity: the tactical admin reports that licenses are running out, while the strategic version shows allocation versus plan, recommends profile changes and asks whether to contact the account executive. If the buyer's first problem is unreliable numbers and unclear ownership, this is the closer match.
- Greg Spence: Consider Greg Spence when the live question is whether to add sales headcount or fix the sales engine first. Spence's argument follows a sequence: clarify the go-to-market strategy, define strategic account management standards, build sales enablement before ramp pressure, establish forecasting and KPI governance, and make CRM architecture usable before scaling the team. The C-Suite Network example describes going from zero to 11 BDRs with playbooks, proprietary tools, compensation tied to the strategy, AI-based data analysis and stable leadership alignment. The cross-industry post credits the same operating model with a 26% result beyond annual quota in commercial signage. For a company whose AI ambition is tangled up with a headcount plan, this framing puts the foundation first.
- Gianni Newman: Consider Gianni Newman when the buyer wants an operator's view of AI tool selection and a hands-on workflow example. Newman's posts offer a scoring approach to AI vendors — fit with the existing workflow, integration into the CRM, clear data boundaries, improvement over time, and a measurable ROI metric chosen before signing — and cite Gartner and MIT figures on agent adoption and GenAI returns. The pipeline-briefing example is concrete: connect Slack to the CRM, prompt for a seven-day summary of decisions, deals and follow-ups, turn last quarter's conversations into a briefing canvas, have Slackbot add enablers and blockers with channel citations, then save the prompt as a reusable skill scheduled for the last Friday of each quarter. If the buyer needs a practical filter for tools plus one working automation, this is closer to the immediate task than a broad programme.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/