Dale Shephard

The test in the posts is practical, not technical: name the task a tool should make faster or better, clarify the process underneath it, use what is already being paid for, and track return rather than cost.

• AI layered onto a vague process produces a faster version of the same mess.
• Rising spend on new tools is no proof that anyone is selling more, delivering faster or saving real time.

A founder Shephard spoke with had a stack of tools — CRM automation, an AI notetaker, a summary tool and more — and described the gap in her own words: "for someone who gets it, this is a five-minute job.

• For me it's a whole day, because I haven't got a clue what I'm doing."
• The conclusion: she did not need another tool, but someone to connect the ones she already had to her work.
• The post's line for buyers is blunt: "AI on a vague process just gives you faster mess."

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Anyone, or any software, can produce volume — emails sent, sequences live, coverage of the ideal customer profile — but it says nothing about whether the work changed the business.

• What matters is how many conversations started, how many deals closed and how much pipeline was influenced rather than merely touched.
• That requires clean, coherent data: pointing agents at a CRM with close dates that have moved four times and three versions of the same account fixes nothing; it just moves faster through the same disorder.

The post cites Gartner research that AI agents will outnumber sellers 10 to 1 by 2028, alongside the finding that fewer than 40% of sellers say those agents improved their productivity. Shephard dismisses numbers like "40,000 emails a month, 12 sequences live, full coverage of the ICP" and quotes Dan Gottlieb of Gartner on the underlying risk: if those systems are fragmented, agents will scale the fragmentation.

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Between a founder and a changed business sit procurement, compliance, enterprise sign-off, and the very human fear and doubt about doing things differently.

• The role they describe is a bridge: working inside the business, embedding the capability and ensuring the value lands.
• Big tech has renamed this forward deployed engineering; Shephard calls it professional services and treats it as the model, not a consolation prize for companies that could not build software.

The argument comes from a session with Chris Tottman, founder of Notion Capital, hosted by Sam Tidswell-Norrish and OPUS.

• The line that stuck was Tottman's: services is back.
• Shephard describes the same work at TrinityHawk — stepping into founder-led B2B businesses as fractional revenue leaders, building the commercial engine alongside the founder and staying until it runs on its own rails.
• The model is "easy to start, fast to value, hard to unpick once it is working."

Shephard frames the position as long held but hard to express — something believed "for years but struggled to articulate" — and credits the session with putting words to it. The idea is stable; what changes is how confidently it is stated and labelled.

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The described pattern is founders arriving with the solution already chosen — more leads, a salesperson, a new CRM — while the how is blank.

• The question used to expose this is what will actually be different in twelve months beyond the revenue line.
• Shephard also separates a lead problem from a "what-happens-next problem," and warns against buying a solution before naming the problem it is meant to solve.

The post lays out the questions to answer before spending: If we spoke in a year, what would be different beyond the revenue line?

• Why now, and what changed to make this the thing to solve this quarter?
• You want more leads — for what?
• Are you buying a solution before naming the problem?
• The closing formulation is that the number is the result of the plan, not the plan itself: "More money" isn't a goal — it is what happens when the goal is clear and the execution holds.

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Read alongside the Gartner post, it shows interest in agents changing how revenue operations runs, alongside a firm expectation that the value question stays the same — conversations started, deals closed, pipeline influenced rather than touched. Shephard is watching closely where agents are deployed in revenue operations, while warning that pointing them at fragmented systems scales the fragmentation.

Shephard registered for RevGenius Revolt on 14–15 October 2026, a free virtual event with 9,000+ GTM attendees, and singled out the revenue operations session as worth two days. Another post lists the metrics that do not count — emails per month, live sequences, ICP coverage — and asks instead how many conversations the work started.

Read original · Original 2

Dale Shephard pushes back on tool-stacking.

• A founder had CRM automation, an AI notetaker, a summary tool and more, but could not connect them to their work: 'for someone who gets it, this is a five-minute job.
• For me it's a whole day, because I haven't got a clue what I'm doing.' Their answer is not another purchase.
• The post asks buyers to name the task a tool should make faster or better, check that the underlying process is clear, use what is already paid for, and track return rather than cost.

For brands planning a campaign

For a stalled GTM engine, this supports a diagnostic that inventories tools, tasks, owners and measured outcomes before recommending new software.

Shephard cites Gartner research that AI agents will outnumber sellers 10 to 1 by 2028, while fewer than 40% of sellers say agents improved productivity.

• The post lists volume metrics — '40,000 emails a month, 12 sequences live, full coverage of the ICP' — and says they change nothing about the questions that matter: conversations started, deals closed, pipeline influenced rather than touched.
• Using Dan Gottlieb's phrase, they warn that if systems are fragmented, agents scale the fragmentation.

For brands planning a campaign

A 90-day plan should measure conversations, closed deals and influenced pipeline, not activity volume. Clean CRM data and processes before adding agents.

After a session with Chris Tottman of Notion Capital, hosted by Sam Tidswell-Norrish and OPUS, Shephard argues the hard part of AI is the organisation, not the technology.

• Procurement, compliance, enterprise sign-off and founder doubt stand between a business and changed operations.
• Big tech calls the bridge role 'Forward Deployed Engineers'; they call it professional services and describe TrinityHawk's model: step into founder-led B2B businesses as fractional revenue leaders, build the commercial engine alongside the founder, and stay until it runs on its own rails.

For brands planning a campaign

This is the clearest public description of the engagement model: an embedded operator who builds and hands over the revenue engine, rather than a playbook handoff.

Shephard treats 'we want to grow' with only more money behind it as a red flag, because there is usually no plan underneath.

• The common pattern: a founder has already picked a solution — more leads, a salesperson, a new CRM — and assumes growth will follow.
• They ask what will actually be different in twelve months beyond the revenue line, why now, and what the leads are for.
• In this framing, the number is the result of the plan, not the plan.

For brands planning a campaign

Before choosing tactics, a diagnostic should establish a non-revenue outcome and a why-now statement; otherwise, lead generation, sales hires and CRM changes are guesses.

Shephard registers for RevGenius Revolt on 14–15 October 2026 and flags a session asking what happens when AI starts doing the work in RevOps, with four VPs who run revenue operations at Gong, Couchbase and ZoomInfo. The post notes 9,000+ GTM attendees and that the event is free and virtual.

For brands planning a campaign

Useful for brands assessing where Dale Shephard spends attention in the GTM community.

One describes a founder paying for CRM automation, an AI notetaker and a summary tool, yet still unable to connect them to the work, with the line: "for someone who gets it, this is a five-minute job.

• For me it's a whole day."
• The other cites Gartner's projection that AI agents will outnumber sellers 10 to 1 by 2028, alongside the same research finding that fewer than 40% of sellers say those agents improved productivity, and quotes Dan Gottlieb of Gartner on fragmented systems causing agents to scale the fragmentation.
• Both posts apply the same test: name the task the tool makes faster or better, clarify the underlying process, use what is already paid for, and track return rather than cost.
• Shephard rejects send-volume numbers such as 40,000 emails a month or 12 live sequences in favour of conversations started, deals closed, and pipeline influenced.

Short diagnostic essays open with a founder anecdote, reduce the argument to a numbered checklist, then close with a reader question.

For brands planning a campaign

Suits an AI or RevOps vendor whose message is proof over novelty. Lead with a measurement question or process audit, not a feature tour, and expect a diagnostic checklist rather than a product demonstration.

Examples: Source 1 · Source 2

The pattern described: founders arrive with the solution already chosen — more leads, a salesperson, a new CRM — while the how is blank.

• The question Shephard puts to them is "We're talking again in twelve months.
• What's actually different?"
• If the answer is only a bigger number, the work starts before anything is bought.
• Shephard also separates a lead problem from a "what-happens-next problem," and warns against buying a solution before naming the problem it is meant to solve.

Objection-first framing states the red flag, explains why the ambition is not the problem, then gives founders questions to answer before spending.

For brands planning a campaign

Useful for demand generation, sales consultancy, or offers that need to disqualify fast-moving, tool-first buyers. A campaign built around the twelve-month question and a short pre-purchase checklist positions the offer as diagnosis rather than supply.

Examples: Source 1

They note that big tech now calls the bridge role Forward Deployed Engineer, and describe the same work at TrinityHawk: stepping into founder-led B2B businesses as fractional revenue leaders, building the commercial engine alongside the founder and staying until it runs on its own rails, summarised as "easy to start, fast to value, hard to unpick."

The framing comes through someone else's session as well as Shephard's own firm. Treat it as their positioning argument, not independent research.

For brands planning a campaign

This is the clearest statement of the service a brand would be associated with: embedded, hands-on revenue building for founder-led B2B companies. It suits campaigns that need a practitioner rather than a commentator, particularly B2B SaaS vendors selling to founders or revenue leaders.

Examples: Source 1

One registers for RevGenius Revolt on 14–15 October 2026, a free virtual event with 9,000+ GTM attendees, and singles out a session on what happens when AI starts doing the work in RevOps, featuring revenue operations leaders from Gong, Couchbase and ZoomInfo. The other credits a Notion Capital and OPUS session as "one of the best conversations I have been part of in a while," praising the quality of the room and the founders' questions.

Community participation and event recommendation posts, distinct in format and intent from the diagnostic essays.

For brands planning a campaign

Opens a community and event route alongside written placements: co-hosted sessions, panels, or sponsor-adjacent formats for GTM operators. The event-adjacent post recommends the event to peers, a different placement context from the diagnostic essays.

Examples: Source 1 · Source 2

Ask what their formal relationship with the business is.

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One post builds a Slack-and-CRM workflow that produces a weekly sales briefing from one prompt, then saves it as a reusable skill, judged by a single test: where is the money?

• Another lists what to score an AI vendor on before the demo: workflow fit, CRM integration and measurable ROI.
• Both writers cite the same Gartner figure—agents outnumbering sellers ten to one by 2028, with under 40% of sellers reporting a productivity gain—and argue that tools should fit how a team already works, not the flash of a demo.

Newman stays close to the build.

• The posts offer practical walkthroughs of prompt structure, scheduling and video workflows for sales and revenue operations practitioners doing the work themselves.
• Shephard writes a level up, explaining why the tooling question is easy while the organisational or process question decides the outcome.
• The engagement they describe is embedded and fractional, not a self-serve workflow.

Read original · Original 2

One post opens with broken account hierarchies—Google and Waymo sitting separately from Alphabet, Travis Kalanick still filed at Uber—and argues that the next era of go-to-market belongs to whoever gives an agent the best understanding of the account it operates in.

• The other sets out four layers behind every ABM play: a named account list, named buyers, conversion signals and account context.
• It warns that a wrong field at step one becomes the assumption every later step builds on.
• That echoes Shephard's point, citing Gartner, that fragmented systems leave agents with a mess to scale.

Tatulea focuses on data infrastructure: hierarchy resolution, context graphs and the order of orchestration.

• One post also serves as a product announcement, citing company-scale evidence such as ABM plays reaching $1.2bn.
• Shephard focuses on organisational hurdles—procurement, compliance, sign-off and the founder's own doubt—and offers an embedded operator who stays until the engine runs on its own.
• The diagnosis overlaps; the remedy differs.

Read original · Original 2

One post says fragmented systems get scaled rather than fixed, then lists the minimum that survives the translation down: one named owner of account and contact data; stage history and original source captured automatically and locked; a single definition of an account; and meeting outcomes as structured fields.

• The other runs HubSpot's free ICP Builder on a live site, credits what a website scan can do in four minutes, then tests it against closed-won, closed-lost and churn cohorts.
• When the two disagree, the data wins.
• Both writers assess AI tooling against evidence rather than enthusiasm, making measurement the deciding test.

Seyth writes the longer, sourced version, naming McKinsey and Gartner research and prescribing field-level data practices for revenue operations teams with analysts.

• Shephard writes shorter, founder-facing posts and gets to the decision: what will be different in twelve months, is this a lead problem or a what-happens-next problem, and who will sit inside the business to embed the capability?
• Seyth diagnoses the data model; Shephard diagnoses the organisation and offers a service to bridge the gap.

Read original · Original 2

Newman's posts describe a build-it approach.

• With Slack connected to the CRM, one prompt summarises the past seven days of sales activity; a second turns last quarter's conversations into a growth briefing canvas with enablers, blockers, and follow-ups; the prompt is then saved as a reusable skill and scheduled for the last Friday of each quarter.
• Newman also sets out a five-point filter for AI tools before a demo: it fits your existing workflow, it plugs into your CRM, data boundaries are clear, it improves over time, and you can measure ROI.
• That last point lines up with the need to measure whether AI tooling is driving revenue for a founder-led B2B company with a stalled go-to-market engine.
• For a buyer who needs a working workflow inside Slack and Salesforce in weeks, this is closer to the job than a strategy diagnostic.

The tradeoff is scope.

• Dale Shephard's posts cover tools and workflows, not work inside a founder-led business as a fractional revenue leader.
• If the problem is unclear positioning, no repeatable pipeline motion, or a growth plan that exists only as a bigger revenue number, automating reports treats the symptom.
• Newman's examples also assume a live Salesforce-and-Slack stack and a team that will keep acting on a saved prompt.
• Ask whether Newman can also address the underlying positioning and pipeline problems.

Examples: Source 1 · Source 2

Tatulea's posts describe how account records rot: Google and Waymo sitting as separate accounts with no link to Alphabet, a hierarchy that never changes after an acquisition, a person's current employer still listed as their old one.

• Tatulea describes an ABM play in four layers: a named list of target accounts, the named buyers at each, signals proven to predict conversion, and the context already known about each account, all brought into one place before any orchestration.
• That ordering is the argument: point an agent at fragmented records and it chains a wrong field into every later step faster than a person can catch it.
• For a company whose AI tools produce confident but unusable output, that diagnosis is often the missing step.

The tradeoff is that this is a data-and-programme view rather than an embedded operator view.

• Tatulea's posts sit close to a platform announcement about a GTM context layer, so a buyer should separate the general argument about record quality from any product interest.
• A founder who needs someone inside the business running the commercial engine quarter by quarter should ask how much hands-on operating work is on offer.

Examples: Source 1 · Source 2

Seyth's posts take published research and translate it for teams without a data department: one named person owns account and contact data; stage history and original source are captured automatically and locked; one definition of an account holds across CRM, billing, and the board pack; meeting outcomes are stored as structured fields.

• Seyth also ran HubSpot's free ICP Builder on a live site and then set out what a website scan cannot tell you: which segment renews, which deals cost triple the effort for the same contract value, and which apparently perfect-fit accounts were lost.
• The closing instruction is the useful part for a 90-day plan: run the generated version first, then test it against closed-won, closed-lost, and churn data, and when the two disagree, trust the data.

The tradeoff is delivery evidence.

• Seyth's posts show how they frame a decision through explanatory writing built on third-party research and one hands-on tool test.
• A buyer wanting a fractional leader to embed with a founder and build the commercial engine should ask for firsthand evidence of that work.

Examples: Source 1 · Source 2