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.
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.
Key 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.
Check first: Confirm whether they offer paid workshops or advisory and can provide audience analytics.
Main ideas
Demos are easy; adoption is everything · Speed on a revenue team is a relevance and execution problem, not a headcount problem · Teach AI as a thought partner, and start from the problem
Matt Dornfeld writes and teaches about putting AI to work in go-to-market teams, especially sales and revenue operations.
They focus on the gap between buying or demonstrating AI tools and making them useful in everyday work.
In a career update, they described moving from global programs and partnerships at BigCommerce and Feedonomics to joining ClickUp as Director of GTM Performance and Productivity.
Dornfeld’s distinctive contribution is to start with the work, not the tool: find the bottleneck, improve the process, then use AI where it helps.
In a post about AI initiatives and GTM execution, they argue that adoption stalls when tools go unused, sit disconnected from existing workflows, or are chosen before the problem is clear.
They make that principle tangible in a USC enablement session, where participants built a reusable AI skill without code, and in a revenue-team workflow that turns scattered customer signals into suggested seller actions.
They also urge teams to measure actual use rather than licenses bought (AI adoption) and to automate repetitive administrative work (sales process automation).
Readers evaluating AI for business can follow their work for practical questions about where automation fits, what makes it stick, and when process design—not AI—is the better fix.
Dornfeld argues that most AI programmes fail not on technology but on adoption: teams buy tools before identifying a problem, never integrate them into existing work, and then measure purchases instead of use.
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Their stated priorities are process mapping before tool selection, putting AI inside the tools and channels people already use, and judging success by day-to-day utilisation rather than licence counts.
Another thread is that revenue speed is an execution and relevance problem rather than a headcount problem, illustrated by a signal-to-action loop that turns customer data into a suggested seller action.
They teach AI as everyday business practice rather than an engineering discipline, and frame delayed adoption as an invisible operating cost.
The posts present a consistent operator's view of go-to-market teams.
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.
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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.
In one post, Dornfeld contrasts browser automation that plugs into the browser people already use, with no migration or learning curve, against tools that force people to change how they work.
Their conclusion: the friction-adding ones die on the vine.
In another, they call for deleting unused AI licences and tracking adoption patterns in a visualisation tool instead of procurement stats.
The posts move from proposing a practical audit exercise—a dedicated “process audit” day to map repetitive tasks and flag anything done three or more times as an automation candidate—to a harder measurement position: stop celebrating tool acquisition and start rewarding utilisation. The priority stays constant while the demand on the buyer gets stricter.
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.
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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.
An accepted recommendation triggers an automatic cadence that matches how the seller naturally writes, maps to the appropriate product for each prospect and persona, adapts by region and tone because US, UK, and Australian styles differ, and repeats as signals change. Dornfeld sums it up with an image of sellers starting to feel like they have a rocket strapped on rather than pushing a boulder uphill.
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.
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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.
At an AI enablement session they co-hosted with Dennis Yao Yu for the Randall R.
Kendrick Global Supply Chain Institute at the University of Southern California, executives, VPs, supply chain leaders, and private-equity partners from companies including Disney, Blue Origin, AMD, and Edwards Lifesciences submitted questions in advance.
They ranged from “how do I actually start using AI at work?” to “will AI replace my team?”
Dornfeld reads both as expressions of the same uncertainty about where people fit.
They walked the group through building a reusable AI skill with no code and no engineering background, and describe watching that click for people who assumed AI was not for them as the most powerful part of the session.
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.
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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.
Dornfeld proposes a “process audit” day and notes how many hours a sales rep once reported spending re-entering deal data into multiple systems.
The fix they describe is an AI-driven process that updates the CRM automatically when deal-related emails and calls happen.
The pressure-performance post points to Ford's lean manufacturing, Amazon's fulfilment network, and Netflix's recommendation engine as systems that improved as volume grew.
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.
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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.
Their closing question to readers—what expensive, repetitive work does their team still do by hand that AI could handle today?—serves as an opening diagnostic, not a claim about any particular company's losses.
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.
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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.
Dornfeld describes companies dropping six figures on generative-AI platforms while a large share of licences sit dormant, citing rates above 80 percent they have heard of in some cases.
They note competitors getting more from fewer tools at higher engagement.
Their suggested move is blunt: delete the unused licences.
Dornfeld's public framing of their role explains why the priorities above are operational rather than product-oriented.
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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.
Announcing the move, Dornfeld describes closing out earlier work at BigCommerce and Feedonomics covering global programmes and omnichannel partnerships, relocating from New York to Austin during the pandemic, and joining ClickUp as Director of GTM Performance and Productivity.
The clearest shift in the material is in the role: earlier posts deal with ecommerce partnerships, programme building, and team processes, while later posts concentrate on AI adoption inside revenue teams. The change of emphasis lines up with the change of job, rather than a reversal of view.
This post answers “what does Matt Dornfeld do now?”
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It names their current title and the scope of the job, making every other post easier to place.
Dornfeld describes wrapping up earlier chapters at BigCommerce and Feedonomics, where they worked on global programs and omnichannel partnerships, and moving from New York to Austin during the pandemic.
They frame the new role at ClickUp as helping go-to-market teams learn faster, execute better, and scale with AI built into their day-to-day work.
The post focuses on positioning rather than tooling: the AI work sits inside GTM operations, not a research or engineering function.
For brands planning a campaign
For a company shopping for an AI-enablement workshop or advisory, this is the problem Dornfeld sells against: making an existing revenue team faster, not building new AI products. It offers useful context for choosing between an operator-turned-enablement lead and a pure training vendor.
This is the clearest example of their teaching method: a real session, with a named co-host, audience, and three points they chose to emphasize.
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It answers “how would this person run a workshop for my team?”
Dornfeld co-hosted a session with Dennis Yao Yu and the Randall R.
Kendrick Global Supply Chain Institute, with executives, VPs, supply chain leaders, and private-equity partners from companies including Disney, Blue Origin, AMD, and Edwards Lifesciences.
Questions submitted beforehand ranged from "how do I start using AI at work" to "will AI replace my team," and Dornfeld reads both as coming from the same uncertainty about where people fit.
The three points Dornfeld highlights are: stop using AI like a search box and treat it as a thought partner you iterate with; start from the problem rather than the tool; and you do not need to be technical.
Dornfeld notes the group built a reusable AI skill without code, and calls watching that click for non-technical people the most powerful part of the session.
For brands planning a campaign
For a B2B SaaS team weighing a workshop, this shows the format and emphasis: hands-on, aimed at people who assume AI is not for them, and not a tooling demo. The recurring message that the gap between “AI curious” and “AI capable” is mostly framing suggests an engagement would focus on problem selection and workflow design before any product choice.
This is the fullest statement of their operating model for revenue teams: which signals matter, how the loop works, and where execution happens.
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It answers “what would this person actually install?”
Speaking with Dennis Yao Yu, Dornfeld argues that speed is a relevance and execution problem, not a headcount problem.
They list the signals revenue teams already hold, including product usage, intent, support tickets, pipeline movement, feature launches, website behavior, and softer cues in calls and emails.
The problem, they say, is that these signals are scattered and rarely turned into action quickly enough.
Their four-step loop: capture signals; align them to target accounts, ideal-customer profiles, messaging, new features or SKUs, and region; proactively recommend a next best action to the seller; and execute in one click inside Slack.
They add that an accepted recommendation should trigger a cadence that matches how the seller naturally writes, maps to the right product for each persona, and adjusts by region and tone.
The cycle then repeats as signals change.
They reject “AI writing emails” as the goal, framing it instead as turning real-time customer context into relevant, timely outreach without adding process.
For brands planning a campaign
For a SaaS company trying to improve AI adoption inside GTM, this is the most directly transferable piece: it names the exact inputs, intervention point, and constraint that the tool must not add process. A workshop or advisory engagement based on it would start by auditing which signals exist and where the handoff to selling breaks, rather than by buying another tool.
This is the sharpest version of their adoption-over-procurement argument, with a concrete question newcomers can ask about their own AI spend.
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Dornfeld argues that AI returns in large companies depend on day-to-day usage, not seats purchased, and that adoption patterns in a visualization tool predict outcomes better than procurement statistics.
They say some organisations spend six figures on generative-AI platforms while a large share of licenses sit dormant, citing reports of rates above 80 percent in some cases.
Competitors, they argue, get more out of fewer tools through higher engagement.
Their proposed move: stop celebrating tool acquisition and start measuring and rewarding utilization.
For brands planning a campaign
Use this as a diagnostic question before commissioning enablement work: how many purchased licenses are actually used, and by whom. It also signals that the engagement treats measurement and habit change—not rolling out a new platform—as the deliverable.
Here, Dornfeld explains why the cost of slow AI adoption does not appear as a line item, answering “why is this worth budget?”
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Dornfeld describes a seven-figure cost at mid-market companies with 500 or more people, driven by payroll hours spent on repetitive work that competitors have automated.
They say the pattern became clear after time with vendors, revenue-operations teams, and enablement teams: some companies call careful consideration their AI strategy while others build eighteen-month advantages and move on.
Because the gap does not appear as a single P&L line, they say, it shows up instead as slowly declining margins, weaker pipeline growth, and stretching sales cycles while competitors do more with fewer resources.
For brands planning a campaign
Use this framing for an internal business case: the argument is about opportunity cost within existing headcount, not new spend. It also signals their typical audience—mid-market revenue and enablement leaders—which matters when judging fit for an enterprise-scale programme.
This gives the diagnosis in its shortest form: three failure points and the argument that the boring fundamentals come first.
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It answers “what does this person think usually goes wrong?”
Dornfeld lists three failure points from their conversations: teams launch tools nobody uses; new AI tools are not integrated into existing workflows; and someone buys the new thing before identifying the problem.
The familiar sequence they describe: leadership watches a demo, gets excited, spends money, then asks six months later where the return is, while core sales enablement is still not dialled in.
Their prescription is to map the process first, find the actual friction, and then apply the right solution, which may be AI or better process design.
They close with the line: demos are easy, implementation is hard, and adoption is everything.
For brands planning a campaign
This makes the case for process mapping before tool selection: the first paid work should map processes, not select tools. It also sets expectations for a proposal focused on adoption and integration over a product recommendation.
Make AI adoption a workflow problem, not a software-buying problem3
Automate repetitive work to give revenue teams time back3
Improve the system around the team, not just individual performance2
Teach business teams to use AI through iteration and concrete problems1
Frame delayed AI adoption as a cost of missed execution1
Make AI adoption a workflow problem, not a software-buying problem
Dornfeld argues that AI initiatives stall when teams buy tools before identifying a real problem, fail to integrate them into existing work, or measure purchases instead of use.
Read more
They recommend mapping friction first and judging success by adoption and outcomes. Examples include unused AI licenses and browser automation that works inside familiar tools.
Uses a problem-first checklist and contrasts tool acquisition with integration, usage, and business results.
For brands planning a campaign
A SaaS brand focused on enterprise AI adoption could assess whether Dornfeld’s practical, workflow-first framing suits an adoption audit, enablement workshop, or campaign about turning AI investment into everyday use.
Automate repetitive work to give revenue teams time back
Dornfeld presents process automation as a way to remove administrative work that limits selling and execution.
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Examples include updating CRM records from deal-related emails and calls, and identifying repetitive tasks as automation candidates. They also describe a revenue workflow that connects customer signals to a suggested seller action and can trigger outreach with one click.
Starts with a process or signal, identifies a bottleneck, then describes how automation could fit into the existing workflow.
For brands planning a campaign
A CRM, sales-automation, or revenue-operations brand could explore a demonstration or case-based campaign showing a specific task moving from manual work to an integrated workflow.
Teach business teams to use AI through iteration and concrete problems
In a University of Southern California enablement session, Dornfeld and Dennis Yao Yu addressed questions about getting started with AI and its effect on teams.
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Dornfeld’s guidance was to treat AI as a thought partner rather than a search engine, begin with a clear problem, and show that people can build a reusable AI skill without coding. The post emphasizes the moment when participants saw the work as accessible to them.
Uses audience questions, practical instruction, and a concrete learning moment to make AI feel approachable.
For brands planning a campaign
An AI platform or business-training provider could consider a hands-on workshop or educational series centered on solving a work problem and building a reusable skill, rather than a general product demo.
Improve the system around the team, not just individual performance
Dornfeld’s posts challenge explanations that place stalled results solely on employees.
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They point instead to outdated processes, administrative friction, and systems that fail under pressure.
One post recommends removing waste, focusing on useful inputs, designing for pressure, and measuring outcomes rather than activity.
Ask for detailed case evidence before using the examples in a campaign.
Reframes a people-performance issue as a systems question and offers a short set of process principles.
For brands planning a campaign
A sales-enablement or operations brand could use this perspective for a process-review campaign, pairing the broad principles with a specific, verifiable customer example.
Frame delayed AI adoption as a cost of missed execution
Dornfeld argues that repetitive manual work and slow adoption can create opportunity costs that are not obvious in a company’s accounts.
Read more
They contrast extended evaluation with building practical advantages through implementation. The post makes a broad financial claim about mid-market companies; ask for the calculation or a case study before citing a specific savings figure.
For brands planning a campaign
An enterprise AI or consulting brand could use the underlying question—what repetitive work is still being done manually?—to open a discovery conversation. Treat the post’s financial estimate as a claim, not a proven outcome.
Dornfeld’s posts describe a role at ClickUp and earlier career chapters at BigCommerce and Feedonomics.
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They also promoted Composite; ask about any commercial relationship with the company.
ClickUp
Employment
Dornfeld announced they were joining ClickUp as Director of GTM Performance and Productivity, focusing on learning, execution, and AI use across GTM teams.
Matt Dornfeld writes on LinkedIn about making AI useful inside go-to-market teams rather than treating it as a software purchase.
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Start with the posts on adoption failure, then move through the workflow-automation examples and finish with the enablement session showing how they teach the material.
Across the posts, they return to three questions: Does a tool fit an existing process?
Do people use it after purchase?
Does the work get faster or just more complicated?
For readers exploring the subject rather than hiring, the most portable ideas are the process-first checklist, the signal-to-action revenue loop and the utilisation test for AI investment.
Both write about wiring AI into the CRM, Slack and sales workflows teams already use, and judge tools by fit, adoption and measurable return rather than demo quality.
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Newman's sales-pipeline-in-Slack workflow and five-criteria AI tool scorecard align with Dornfeld's signal-to-action loop and utilisation test.
Newman tends to offer step-by-step prompt and workflow recipes, backed by outside research statistics and tool-selection criteria. Dornfeld focuses on adoption diagnosis and process design, with fewer operational walkthroughs.
Both treat revenue operations as a discipline built on defined processes, consistent governance and clear stage or forecast definitions—not on buying more software.
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Santa Elena's forecast-methodology post and Salesforce licence example echo Dornfeld's process-before-tool position.
Santa Elena's examples focus on forecasting discipline, stage definitions and Salesforce administration; Dornfeld focuses on AI adoption, enablement sessions and automation inside seller workflows.
Both argue that revenue problems are operating-system problems before they are headcount or tool problems.
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They write from a go-to-market leadership viewpoint about building the infrastructure that lets teams scale.
Spence draws on cross-industry sales operating models, compensation design and CRM architecture across several sectors; Dornfeld focuses on AI adoption and workflow integration inside an existing revenue team.
For a campaign aimed at AI adoption and GTM performance, Dornfeld’s LinkedIn posts offer a clear topic fit: they address sales, RevOps, enablement, and GTM leaders on process bottlenecks, AI adoption, and workflow automation.
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A workshop or advisory engagement on AI enablement is plausible because the content speaks directly to the buyer’s challenge: moving teams from scattered signals and manual processes to practical AI workflows.
Examples include the USC supply-chain AI enablement session with executives from Disney, Blue Origin, AMD, and Edwards Lifesciences, and the AI for Business Leaders podcast discussion of capturing signals, aligning them to target accounts, and prompting next-best-action outreach.
These posts show the subject matter and type of room Dornfeld can address, but not who follows the account.
The LinkedIn account had 10,646 followers as of 8 October 2026.
The posts provide no information about followers’ age, gender, geography, seniority, industry, employer, or engagement, so buyers should not assume the audience consists of B2B SaaS decision-makers.
No YouTube or other platform audience data is published here.
For a campaign targeting AI adoption and GTM performance, ask whether Dornfeld can provide audience survey results or LinkedIn analytics showing job titles, industries, and regions, along with recent impressions, reactions, comments, and reposts for comparable AI-enablement and GTM-performance posts.
LinkedIn followers
10,646
LinkedIn profile metadata with the account record · 2026-10-08
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.
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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.
For a B2B SaaS company with a revenue organisation of a few hundred people that wants to lift AI adoption and tighten go-to-market execution, and is weighing a paid workshop or advisory engagement rather than a software purchase, this is a strong fit.
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Dornfeld's published work diagnoses this exact problem.
In a post on why AI initiatives stall, they name three failure points: tools nobody uses, AI that never gets integrated into existing workflows, and buying before defining the problem.
Their prescription is to map the process first, find the friction, and only then choose a solution—which may be AI or simply better process design.
That points to an advisory engagement whose first paid work is a process audit, not a product recommendation.
There is also a concrete operating model to hand over, not just opinions.
The signal-to-action loop they outline in the AI for Business Leaders podcast conversation is specific enough to run as a workshop: list the signals already held (product usage, intent, support tickets, pipeline movement, feature launches, website behaviour, cues in calls and emails); align them to target accounts, messaging and region; recommend a next best action to the seller; and execute in one click inside the tool the team already uses, with the cadence matching how the seller writes.
A buyer can compare that model against their own stack line by line.
The teaching format has been tested in front of a comparable room.
The University of Southern California session with Dennis Yao Yu and the Randall R.
Kendrick Global Supply Chain Institute put Dornfeld in front of executives, VPs, supply chain leaders and private-equity partners from companies including Disney, Blue Origin, AMD and Edwards Lifesciences.
The session covered getting started, whether AI replaces teams, treating AI as a thought partner rather than a search box, starting from the problem, and building a reusable AI skill without code.
An enterprise audience asking similar questions can see how the session was structured and what it emphasised.
The adoption-over-procurement argument gives the engagement a measurable spine.
The unused-licences post argues that returns come from daily usage rather than seats purchased, that adoption patterns predict outcomes better than procurement statistics, and that measurement and incentives should shift toward utilisation.
A workshop can therefore end with an adoption dashboard and habit change rather than a rollout plan.
The posts include no independent client case studies, measured before-and-after results or named customers of an advisory engagement.
The financial figures in the pitches are Dornfeld's own estimates, without published calculations.
Scope the engagement around diagnosis, workflow design and skill building; ask for two references from comparable revenue teams before signing anything tied to a pipeline outcome.
Dornfeld also holds a full-time go-to-market role at ClickUp, so settle availability and conflicts in writing early.
When to choose a specialist
A mid-market CRM vendor wants to buy a slate of sponsored LinkedIn posts with a guaranteed engagement benchmark and a named demographic target list of enterprise sales leaders.
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This is a poor fit for four reasons.
There is no documented partnership or sponsorship offer.
Dornfeld's posts contain no media kit, rate card, deliverable menu, stated terms or verified inquiry route, and do not establish that they sell sponsored placements.
A media buy cannot be priced or scoped from posts alone.
There is no audience information to underwrite a demographic guarantee.
The only measured audience figure available is a follower count of 10,646 as of 8 October 2026.
The posts give no job titles, industries, regions, seniority or engagement rates, so nobody can promise that the audience is enterprise sales leaders or that a post will hit a given reaction or impression benchmark.
Follower count does not show audience composition.
Paid-placement separation would be difficult to read.
Two posts look like product advocacy rather than observational commentary: one endorses a browser automation tool as the fastest return Dornfeld has seen from a browser tool and links to signup; another describes what they are building, including a loop that executes in Slack, under a product hashtag.
Both could reflect someone writing about tools used in their own work; that is not the same as a disclosed commercial relationship, and readers could have trouble distinguishing the two in a bought campaign.
Some of the strongest hooks cannot be repeated as proven claims.
The claim of a seven-figure invisible leak at mid-market companies of 500 or more people has no published calculation or case study behind it, so it cannot be used in paid copy as a substantiated figure.
Across the posts, the pattern is broad operating principles illustrated with famous-company examples, rather than named customer results.
For pure reach, an operator with a full-time job is a different kind of partner from a publisher with a rate card, audience profile and delivery guarantee.
A brand with a sponsorship budget and demographic target should look at creators who publish those terms, and approach Dornfeld through the workshop or advisory route instead.
A worked campaign example
A worked angle for a two-week AI adoption sprint for one revenue team, sold as diagnosis and capability rather than a platform rollout.
Week one.
Day one is a signal inventory: list every input the team already holds, including product usage, intent data, support tickets, pipeline movement, feature launches, website behaviour and the softer cues buried in calls and emails.
Then note where each one currently lands and who acts on it.
Day two is a process audit based on the model in the CRM automation post: map repetitive tasks that eat selling time, flag anything done three or more times as an automation candidate, and rank candidates by impact.
Days three to five focus on the single highest-friction workflow, rebuilt as a loop rather than a tool purchase: capture the signal, align it to target accounts and messaging, recommend a next best action to the seller, and let an accepted recommendation trigger a cadence that matches how the seller writes and adapts by region and persona.
Week two.
The team builds one reusable, code-free AI skill around that workflow, following the format used in the USC session, where the point was that non-technical people could produce something they would actually reuse.
Then comes measurement: instrument utilisation rather than licences, set the adoption view in whatever visualisation tool the team already uses, and agree which usage patterns count as success.
The closing session is a demo-to-execution review: take the three failure points from the stall post and score the project against each one, so the team can say what was launched, what got integrated and what problem was solved before any tool was chosen.
Deliverables a buyer can hold: a signal inventory, a friction and automation-candidate map, one working workflow with a named owner, the reusable skill, an adoption dashboard, and a 90-day plan that names the next two workflows to convert. The campaign line that follows from the posts is about execution rather than software: the gap between an AI-curious team and an AI-capable one is mostly framing and process, and the deliverable is a team that uses what it already pays for.
Two cautions for the proposal.
Price the engagement on diagnosis and skill transfer, not promised pipeline lift, because no measured client outcomes are published.
Keep every financial figure out of the sales deck unless the vendor provides its own calculation: the seven-figure estimates in the underlying posts are Dornfeld's own and are not substantiated.
Consider Rosalyn Santa Elena when the engagement is about forecast predictability and revenue-operations discipline rather than AI adoption.
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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.
The tradeoff is focus.
Matt Dornfeld's posts cover process and measurement design inside revenue operations, not AI enablement workshops, model selection or adoption programmes.
A buyer with solid forecasting hygiene who wants help bringing AI into seller workflows may be paying for groundwork they have already done.
With only two posts to compare and no published engagement formats or client outcomes, ask for examples of the work and its results.
Consider Greg Spence when the live question is whether to add sales headcount or fix the sales engine first.
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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.
The tradeoff is that this is sales-org building first and AI enablement second.
The posts focus on structure, sequence, KPIs and compensation, with AI as one ingredient rather than the subject.
They draw on Spence's own account of roles and results, not independently published client outcomes.
A buyer seeking a measured AI-adoption programme should ask for evidence of those results before treating this as an equivalent offering.
Consider Gianni Newman when the buyer wants an operator's view of AI tool selection and a hands-on workflow example.
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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.
The tradeoff is scope.
The posts cover tool-selection criteria and one workflow, not an end-to-end enablement programme or a leadership-team facilitation format.
They cite third-party research rather than Newman's own measured deployments.
A buyer who needs a workshop structure, skill-building session and adoption measurement plan should ask for those specifics, along with engagement terms and client references.
Matt Dornfeld's posts here include nine full LinkedIn posts, alongside LinkedIn account metadata observed 8 October 2026 and short comparator posts by three other revenue-operations writers.
The post text carries no publication dates, so the posts present a consistent body of work rather than a dated sequence, and they are not a full-lifetime archive.
No video, audio, transcripts, media kit, sponsorship terms, audience demographics, or client case studies are published here.
Financial figures quoted in the posts are the author's own estimates without published calculations and are claims, not verified outcomes.
