Ed Marsh works on go-to-market and sales improvement for B2B industrial companies, writing about sales leadership, marketing, and practical uses of AI in revenue operations.
Ed Marsh is most useful for assessing B2B industrial go-to-market and sales leadership systems.
Best for
A B2B industrial manufacturer with complex long-cycle sales seeking go-to-market and sales leadership improvement.
Not for
A fast-moving B2B SaaS or consumer brand seeking broad creator reach and paid placement formats.
Key takeaways
Trust and phone selling still matter as discovery moves across social, video, audio, communities, AI answers.
Most AI speeds up stale tactics; the opening is redesigning the work.
A CRM-connected dashboard is not reliable if underlying data is incomplete, inconsistent or poorly defined.
Check first: Confirm audience job titles, seniority, industry, company size, and comparable campaign metrics.
Main ideas
The buying journey moved; the inbound playbook did not · Most AI speeds up stale tactics; the opening is redesigning the work · Judge a sales leader by the system, not the record
Ed Marsh writes about go-to-market strategy and sales improvement for B2B industrial companies, connecting shifts in buying and marketing with the practical demands of sales leadership and revenue operations.
In their post on changing B2B buying and selling, they argue that trust and phone selling still matter even as discovery moves across social, video, audio, communities and AI answers.
They also make the case for using AI to rethink how teams work, rather than simply speeding up outdated tactics.
Readers evaluating industrial sales or marketing changes will find openness to new approaches alongside attention to fundamentals.
Marsh tests broad claims against operational questions.
For sales leadership, they argue that a strong track record is not enough: leaders must build a sales system that can withstand market shifts and stronger competition (Marsh's sales-leadership post).
On AI, they distinguish workflow redesign from automating stale tactics, stressing that tools cannot replace sound buyer understanding and process.
They apply the same scrutiny to analytics: a CRM-connected dashboard is not a reliable basis for decisions if its underlying data is incomplete, inconsistent or poorly defined (Marsh's dashboard post).
Their writing helps leaders weigh changes to sales, marketing and revenue operations by considering both what new tools make possible and what teams must get right for them to matter.
Ed Marsh writes about how B2B industrial companies buy and sell, and where AI genuinely helps.
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Three positions recur across the posts: early buying attention has moved away from inbound lead capture toward AI answers, passively consumed audio and video, social feeds and communities, raising the skill bar for salespeople; most AI in sales only accelerates stale tactics, while the real opening is redesigning the work; and hiring and technology decisions should be judged by whether the underlying system holds up under pressure, not by a track record or a dashboard. AI does not replace buyer understanding, and connected data is not the same as defined, usable data.
The buying journey moved; the inbound playbook did not
Ed Marsh starts from the view that the fundamentals of B2B selling are unchanged: buyers buy from people they trust, and the phone still works with senior executives.
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What changed is where attention happens before a buyer talks to sales.
They argue the conventional inbound marketing playbook is largely ineffective, websites now serve the bottom of the funnel, and early attention has shifted to AI answers, passively consumed audio and video, scrollable social feeds, and communities.
The same shift raises the bar on salespeople: average is not enough, and talent has to be strong in tactical sales skills, business acumen, business finance, and work ethic.
Ed Marsh names the channels where top-of-funnel marketing now happens — AI answers, passively consumed audio and video, social scrolling, and communities — in contrast to lead-capture inbound and websites.
Ed Marsh says the argument dates to early 2025, when AI was just getting started.
Most AI speeds up stale tactics; the opening is redesigning the work
In Ed Marsh’s account, most AI tools and promises for marketing and sales teams do not move the needle because they make existing playbooks and tactics faster.
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Creative teams use the same technology differently: to redesign workflows and build capabilities that were essentially unavailable a year earlier.
They cite a partnership with Day AI to build go-to-market models across board governance, strategy, product, demand generation, brand and field marketing, revenue operations, complex long-cycle sales, sales management, customer success and after-market selling, and to merge counterproductive silos.
The limits are clear: this is not an easy button, it does not replace rigour in understanding buyers, process and methodology, and mistakes are expected.
Ed Marsh contrasts handing prospecting to “mindless agents” that will also be mediocre with redesigning workflows, and lists the business areas — from board governance through customer success — where the Day AI work is meant to apply.
Judge a sales leader by the system, not the record
Ed Marsh argues that a strong track record is not proof of a great sales leader.
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The test is whether the leader builds a sales machine that holds up when the market turns or competitors get aggressive. That is why they favour validated assessment over résumé review when hiring for sales leadership, in a post tagged to sales leadership, private equity, industrial sales and go-to-market.
The concrete test is whether the selling system keeps working through a market turn or an aggressive competitor, not what the candidate achieved in a previous role.
A connected AI dashboard is not business understanding
Ed Marsh pushes back on the claim that wiring an AI assistant such as Claude into CRM and marketing automation means a company now understands how to grow.
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The reasons are guardrail and context questions, incomplete and non-normalized data, deterministic data conflated with probabilistic output, and missing definitions.
They give the approach an “A” for effort while saying business decisions need something better.
This marks the boundary: the difference is between rebuilding how work is done and bolting AI onto unready data and undefined terms.
They point readers to a LinkedIn Live conversation with Nikos Ntirlis of Databox.
Ed Marsh quotes the pitch directly — “Check out this cool dashboard we built!! We connected Claude to our CRM and marketing automation, and now we understand how to grow our business” — then lists four objections: guardrails and context, incomplete and non-normalized data, deterministic versus probabilistic data, and undefined definitions.
This post explains how Ed Marsh frames the current go-to-market shift: what still works, what has broken, and where AI fits.
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Start here for the central argument connecting sales, marketing, and revenue operations.
Ed Marsh writes that the core of B2B selling is unchanged: people buy from people they trust, and the phone remains effective with senior executives.
But the surrounding playbook has changed.
Inbound marketing is largely ineffective, websites have become bottom-of-funnel tools, and top-of-funnel attention now moves through AI answers, passively consumed audio and video, social scrolling, and communities.
Marsh argues that sales talent must be strong in tactical sales skills, business acumen, finance, and work ethic.
AI tools often fail because they only speed up stale tactics; creative teams can instead redesign workflows and build new capability.
Marsh announces a partnership with Day AI to build go-to-market models for family-owned industrial companies and middle-market sponsors and portfolio company operators, covering areas such as board governance, strategy, product, demand generation, brand and field marketing, revenue operations, complex sales, sales management, customer success, and merging counterproductive silos.
For brands planning a campaign
For an industrial manufacturer planning go-to-market and sales leadership improvements, this post argues against simply adding AI to existing lead generation. A campaign aimed at industrial manufacturers should include top-of-funnel discovery through audio, video, social, communities, and AI answers; sales capability building in finance and business acumen; and workflow redesign across marketing, sales, and customer success—not isolated tool adoption.
This post answers a practical hiring question: how should a company judge a sales leadership candidate beyond a résumé?
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It offers a short, direct entry point into Ed Marsh's view of sales leadership assessment.
Ed Marsh argues that a strong track record is not proof of a great sales leader.
The real test is whether a leader builds a sales machine that holds up when the market turns or competitors get aggressive.
Marsh says validated assessment beats résumé review when hiring for sales leadership, using hashtags that tie the point to sales leadership, private equity, industrial sales, and go-to-market.
For brands planning a campaign
A sales leadership improvement plan for an industrial manufacturer should not rely only on previous revenue results. Validated assessment can test whether leaders can build durable sales systems, then define which market-turn and competitive-pressure scenarios the current team must handle.
This post answers a common AI and revenue operations question: why connecting AI to CRM and marketing automation does not automatically produce reliable business understanding.
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It shows Ed Marsh's practical skepticism about data and decision-making.
Ed Marsh cautions that connecting Claude to a CRM and marketing automation system does not automatically mean a company understands how to grow.
Ed Marsh points to guardrail and context questions, incomplete data, data that is not normalized, deterministic data conflated with probabilistic data, and missing definitions.
Ed Marsh gives an “A” for effort but says business decisions need something better.
The post promotes a LinkedIn Live deep-dive conversation with Nikos Ntirlis of Databox on the topic.
For brands planning a campaign
For an AI-enabled revenue operations or analytics improvement plan, this post makes data readiness a campaign requirement: define terms, normalize data, set guardrails, and separate deterministic reporting from probabilistic AI output. Treat a dashboard as a decision system only after addressing those issues.
Ed Marsh argues that buying mechanics have changed, but the underlying principle has not: people still buy from people they trust, and the phone still works with senior executives.
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What has changed is where early-stage attention happens.
In their account, the conventional inbound marketing playbook is largely ineffective, websites have become bottom-of-funnel tools, and top-of-funnel marketing now runs through channels that match how buyers consume content: AI answers, passively consumed audio and video, scrollable social feeds, and communities.
They link this shift to a need for sales talent with strong tactical sales skills, business acumen, business finance knowledge, and work ethic—not merely average skills.
For brands planning a campaign
A manufacturer planning a go-to-market overhaul can use Ed Marsh to make the case for shifting budget and effort from lead-capture inbound to channels that reach buyers earlier, and for raising sales skills rather than simply adding tools.
In a post aimed at private equity and industrial sales audiences, Ed Marsh argues that a strong track record does not prove someone is a great sales leader.
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Their test is whether the person builds a sales machine that keeps working when the market turns or competitors get aggressive. That is why they favour validated assessment over résumé review when hiring for sales leadership.
For brands planning a campaign
For a manufacturer or investor hiring or upgrading sales leadership, Ed Marsh offers a specific evaluation argument: assess candidates’ ability to build durable selling systems under pressure, not just their past quota results. This is relevant when the goal is to improve sales leadership, not just marketing content.
Ed Marsh challenges the idea that connecting an AI assistant to a CRM and marketing automation system produces reliable business insight.
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They point to guardrails and context, incomplete and non-normalized data, deterministic data being conflated with probabilistic output, and undefined definitions as reasons the simple version does not hold. They give the approach an "A" for effort while arguing that business decisions need something better, and point readers to a deeper conversation with Databox on the topic.
For brands planning a campaign
This offers a counterweight when a manufacturer is being sold an AI reporting layer.
Ed Marsh can challenge data and definition readiness before revenue operations decisions are made.
The Databox discussion also suggests a format of expert interviews or live sessions, rather than written posts alone.
AI as workflow redesign, not efficiency for stale playbooks
Ed Marsh argues that most AI tools and promises fail to move the needle because they exist only to make old playbooks and tactics faster.
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They contrast that approach with creative teams using AI to redesign workflows and build capabilities that were not available before.
Their work with Day AI focuses on building go-to-market models on that platform across board governance, strategy, product and demand generation, brand and field marketing, revenue operations, complex long-cycle sales, sales management, customer success and after-market selling, and breaking down corporate silos.
They say this work does not replace rigour in understanding buyers, process, and methodology, and expect mistakes along the way.
Ed Marsh pairs a strong claim with a stated limitation or caveat in the same piece, making the argument useful in a planning discussion rather than purely promotional.
Ed Marsh says AI does not remove the need for buyer-understanding fundamentals; treating AI adoption as a shortcut would misread their position.
For brands planning a campaign
A manufacturer weighing AI investment can use Ed Marsh’s framing to distinguish tool adoption from workflow redesign, and to make the case for starting with buyer understanding and process before platform selection. Their stated focus on family-owned industrial companies and middle-market sponsors and portfolio operators gives campaigns a specific angle for both audiences.
Ed Marsh's posts document two brand relationships: a partnership with Day AI to build go-to-market models on its platform, and a LinkedIn Live conversation partnership with Databox about connecting CRM and marketing data.
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A third post names no brand relationship; ask for clarification.
Day AI
Disclosed partnership
Marsh says they are partnering with Day AI and its colleagues to build go-to-market models on the Day.ai platform for family-owned industrial companies, middle-market sponsors and portfolio operators.
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Original disclosure
I'm excited to partner with Day AI, Brian, Christopher, Daphne, Sam, Grace, and their colleagues to build GTM models on the platform.
Marsh promoted a LinkedIn Live conversation with Nikos Ntirlis of Databox about the complexity of connecting CRM and marketing automation data to AI tools.
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Original disclosure
Looking forward to my LinkedIn Live conversation with Nikos Ntirlis of Databox about this deceptively complex topic.
Follow Ed Marsh's argument across three posts: where B2B buying attention has moved, how to judge sales leadership, and where AI claims break down.
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Start with the piece on B2B buying and selling, which lays out the core case: trust and the phone still matter, but the inbound lead-capture playbook has gone flat.
Early attention now sits in AI answers, passively consumed audio and video, social feeds and communities, raising the bar on sales skills.
Next, read the sales-leadership post: a strong track record is not proof of a great leader; the test is whether someone builds a selling system that holds when the market turns.
Finish with the AI-dashboard post, which qualifies the first two: connected CRM data is not the same as defined, normalized, decision-ready data.
Across all three, the tooling and buying channels have changed, not the fundamentals of understanding buyers.
Treat the AI material as conditional on data and process being ready first.
Works the same ground from the operations side, writing about revenue operations as a discipline rather than order-taking.
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The forecasting post and the licence-request post both take an ordinary internal task and show, with before-and-after wording, what a more strategic version looks like. That concrete-example approach also runs through Ed Marsh's contrasts between stale playbooks and redesigned workflows.
Rosalyn Santa Elena stays inside the revenue operations function, focusing on forecast categories, stage definitions, governance and cadence. Ed Marsh's posts are outward-facing: the shift in where B2B buying attention happens, sales leadership assessment, and AI claims for industrial and investor-owned companies.
Makes a closely related argument about sales conversations following the buyer rather than the seller's internal steps.
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The buying-cycle-versus-sales-cycle post lands in the same place as Ed Marsh's on why the old process fails. Both also treat sales leadership and the capability of the selling team as the lever.
Kate Redfern's posts focus mostly on enablement and coaching offers — fractional leadership, training, onboarding, manager development — while Ed Marsh ranges across go-to-market channels, AI workflow redesign and hiring assessment for industrial and private-equity audiences.
Treats AI as a workflow design problem with guardrails rather than a tool to bolt on, and insists a human keeps the final judgment — the same distinction Ed Marsh draws between speeding up stale tactics and rebuilding the work.
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The signal-interpret-action-gate loop and guardrail sheet make operational what they argue in the abstract.
Grace KY Man writes for product growth and marketing teams and develops the method through a cohort course with live demos, whereas Ed Marsh applies the argument to industrial sales, sales management and go-to-market models, pairing it with explicit limits about buyer understanding and process rigour.
The placement question is whether Ed Marsh’s LinkedIn following is worth testing for a campaign aimed at a B2B industrial manufacturer seeking go-to-market and sales leadership support.
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The only audience fact stated is a LinkedIn follower count of 8,491, observed on October 8, 2026.
That indicates a real professional presence, but does not show the audience’s industries, job functions, seniority, or purchasing roles.
Their posts cover industrial sales, go-to-market models, sales leadership assessment, and AI/data readiness, making the content relevant to the buyer’s subject.
Ask for recent LinkedIn campaign metrics or an audience survey reporting job titles, seniority, industry, and company size, along with results from comparable posts about industrial go-to-market, sales leadership hiring, and AI in revenue operations.
Without that evidence, the posts support a test, not a confident audience match.
Ed Marsh's posts do not describe sponsorship or paid partnership formats available to brands.
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They show working collaborations: Marsh describes partnering with the AI go-to-market platform Day AI to build go-to-market models on it for family-owned industrial companies and middle-market sponsors, and promotes a LinkedIn Live conversation with Nikos Ntirlis of Databox.
A third post, about assessing sales leadership candidates, mentions no partner or offer.
There is no published partnership product to schedule against, so a brand would need to contact Marsh directly and ask about deliverables, pricing, media-kit specifications, exclusivity terms, application routes, and the format and scope of a potential collaboration.
Treat the Day AI and Databox items as examples of the kind of collaboration they engage in, not as an indication of what a sponsorship would cost or include.
Good fit — a B2B industrial manufacturer with a long, complex sales cycle and a mandate to improve go-to-market and sales leadership.
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A family-owned or middle-market manufacturer wants an outside voice to assess its current go-to-market model and sales leadership, then propose a practical improvement plan.
Ed Marsh’s posts directly address this buyer.
They name family-owned industrial companies, middle-market sponsors and portfolio company operators as audiences for go-to-market models across board governance, strategy, demand generation, revenue operations, complex long-cycle sales, sales management and customer success.
They argue that top-of-funnel attention has moved away from conventional inbound marketing toward AI answers, audio and video, social and communities, and that sales talent needs strength in tactical selling, business acumen, finance and work ethic.
They also make a specific case for assessing sales leadership: a strong track record does not prove someone is a great sales leader; the test is whether they build a sales machine that holds up when the market turns or competitors get aggressive.
For any AI or dashboard component, Marsh highlights data readiness, including guardrails, context, incomplete and non-normalized data, deterministic versus probabilistic output, and missing definitions.
This is a strong fit for a diagnostic, planning or leadership-improvement engagement.
The posts record 8,491 LinkedIn followers but no job titles, industries, seniority or campaign results.
Ask for audience data and campaign evidence before committing.
The posts document no sponsorship formats or official inquiry route, so scope and terms would need to be agreed directly.
When to choose a specialist
Poor fit — a fast-moving B2B SaaS or consumer brand looking for broad creator reach, a menu of paid placement formats and content that treats AI as a shortcut to growth.
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The need is to launch a low-cost sponsorship package across social and video, maximize impressions among general business buyers and present an AI-enabled dashboard as an immediate growth answer.
Marsh’s posts point the other way.
They express skepticism that connecting an AI assistant to CRM and marketing automation produces reliable business insight, citing questions about guardrails and context, incomplete and non-normalized data, deterministic data conflated with probabilistic output, and missing definitions.
They argue that most AI tools and promises fail because they exist only to make stale playbooks and tactics faster.
Their sales leadership argument targets private equity, industrial sales and go-to-market audiences, not broad consumer or generic SaaS reach.
The posts supply a follower count but no demographics, engagement rates or campaign benchmarks.
They document no sponsorship formats, pricing, deliverables, exclusivity terms or inquiry route.
The requested message would require Marsh to contradict published positions, and the commercial format would have to be built from scratch rather than selected from an existing offer.
Ask for audience data and available commercial terms before proceeding.
A worked campaign example
Worked campaign angle — “The trust-led sales machine” for a family-owned industrial manufacturer.
Objective: assess the current go-to-market and sales leadership model, then give the buyer a practical improvement plan.
Start with Marsh’s argument that people still buy from people they trust, while the path to early attention has changed.
Structure the engagement as three working pieces.
First, a diagnostic session or recorded expert conversation for the leadership team on where top-of-funnel attention has moved — AI answers, audio and video, social and communities — and what that means for a long-cycle industrial sale.
Second, a sales leadership assessment workshop: test candidates or incumbents against scenarios where the market turns or competitors get aggressive, rather than relying on résumé results alone.
Third, a data-readiness review for any AI or revenue-operations dashboard: define terms, normalize data, separate deterministic reporting from probabilistic output, and identify missing guardrails before the dashboard informs decisions.
Deliverable: a one-page improvement plan covering channel shift, sales leadership proof and data readiness, with owners and review dates.
This angle uses Marsh’s actual arguments, names the industrial and middle-market audiences, and avoids turning the campaign into an AI tool advertisement.
Because no sponsorship formats are documented, the brand should approach Marsh directly to agree on scope, format and terms, and test the audience against the manufacturer’s target titles before making a larger commitment.
Rosalyn Santa Elena is the stronger choice when the manufacturer's real problem sits inside revenue operations rather than at the level of go-to-market strategy: a forecast nobody trusts, stage definitions that differ by manager, commit categories that mean different things to different people, close dates that keep slipping, and deal inspection too thin to surface risk early.
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The posts lay out a concrete repair path — write the forecast categories down in plain terms, align stage definitions and yes/no exit criteria across the organisation at roughly five or six stages, gate stage movement with required fields, run the forecast on the same cadence each week with the same metrics, track accuracy by rep, manager and segment, pressure-test the numbers against historical stage conversion rates, and build an early warning system for deals that stop moving.
A second post makes the business-partner argument directly: the same licence request can be handled as an order taker or as a thought partner who brings allocation data, plan-versus-actual comparison, user-profile review and a recommendation with a next step.
If the deliverable the manufacturer needs is a forecast and revenue-operations improvement plan the sales leadership team can run week to week, this is the more specific match.
Choosing Rosalyn Santa Elena trades breadth of go-to-market and sales-leadership diagnosis for depth in forecasting and revenue-operations mechanics.
Ed Marsh's posts stay inside forecast process, stage discipline and the tactical-versus-strategic RevOps distinction; they do not address where top-of-funnel attention has moved, how to test whether a sales leader holds up when the market turns or competitors get aggressive, or the family-owned industrial and middle-market audience framing needed for a campaign targeting those audiences.
A manufacturer whose problem is demand generation, brand and field marketing, or judging an incumbent sales leader under pressure would still need that work from another source.
Two posts were reviewed, published 28 September 2026 and 7 October 2026.
Kate Redfern is the stronger choice when the manufacturer needs capability built inside the sales organisation rather than a strategic diagnosis of it.
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The posts describe fractional revenue enablement leadership, sales leadership and coaching, BDR/SDR strategy and roadmap work, manager development programmes, methodology rollouts such as MEDDPICC, Challenger and value selling, onboarding and ramp programmes, QBR design, and AI-enabled coaching systems, alongside reported outcomes including conversion, retention and net new revenue improvements.
The second post sharpens the buyer-side argument: executive buyers have already read reviews, spoken to peers and consumed content, so sellers who arrive as if the buying journey is starting from zero waste the meeting.
If the manufacturer's plan depends on training managers, rebuilding onboarding, standing up a prospecting function or installing a repeatable methodology, this is the closer match.
Choosing Kate Redfern means accepting a service-shaped engagement and less emphasis on strategy framing.
One of the two posts is largely a description of an offering and results the provider reports.
Ed Marsh's posts give less than the other material on how demand generation channels have shifted and on assessing whether a sales leader will hold up when the market turns; the emphasis is on building skills and process inside the commercial team.
Two posts were reviewed, published 1 June 2026 and 18 June 2026.
Ask the provider to substantiate the reported percentages and provide campaign benchmarks, audience data or third-party outcome confirmation.
Grace KY Man is the stronger choice when the improvement plan will actually be executed through AI agents and the manufacturer needs the operating model designed rather than described.
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The posts set out a growth loop — a real product signal with a threshold, interpretation that routes clear signals to a rules engine and messy ones to a larger model, an action written to the exact account and behaviour rather than a template blast, and a human gate on anything that cannot be undone — plus guardrails agreed before the agent runs: a per-action cost ceiling and model tier with human handoff past the cap, data scoped tier by tier, and an explicit list of which actions the agent may take alone.
The second post shows the same thinking applied to a taught programme, with a 90-day execution plan, live demos and governance, and student reports of turning agent work into an operating system rather than a set of tools.
If the plan's centre of gravity is a governed agentic workflow across research, content, outbound and CRM, this fits more closely.
Choosing Grace KY Man means the leadership-diagnosis half of a campaign aimed at assessing whether a sales leader can hold up under competitive pressure still needs another source.
The posts are written for product growth and marketing teams and delivered through cohorts and courses; they do not cover industrial long-cycle selling, large buying teams, or how to assess a sales leader under competitive pressure — the core of a campaign diagnosing sales leadership for industrial companies.
One post is a course reflection made up largely of participant testimonials, so the framework's fit for a heavy-industry sale is asserted rather than demonstrated.
Two posts were reviewed, published 30 July 2026 and 18 June 2026.
Ask for evidence of the framework's fit for industrial sales.
Based on three LinkedIn posts by Ed Marsh: one on how B2B buying and selling are changing, one on assessing sales leadership, and one on AI dashboards and data readiness.
The posts have no dates, so no order over time can be established.
Comparisons draw on posts from three other creators in adjacent areas of revenue operations, sales enablement and AI workflow design.
This is a small sample of the account, not a full archive: no transcripts, long-form articles, audience demographics, engagement measures or sponsorship terms are available, and the fit and campaign material rests on the posts here plus the topic, work, audience and relationship sections on this profile.
