Tom Cogswell is a B2B marketing leader focused on demand generation and marketing programs for complex, multi-stakeholder buying groups. Cogswell describes experience building global buying-group programs and using research and sales intelligence to shape enterprise marketing.
Tom Cogswell is most useful for buying-group demand generation and AI guidance with human review.
Best for
European enterprise B2B SaaS hiring a senior marketing leader for complex buying groups.
Not for
Paid creator content or reach-based sponsorship without a documented offer or rate card.
Key takeaways
AI makes imitation cheap, so authenticity becomes the signal buyers screen for.
In a crowded category, position comes from researched authority rather than a louder message.
AI-personalised content for buying-group members needs a human checkpoint to avoid scaling flawed inputs.
Check first: Confirm terms, audience data, and whether they seek a marketing role or partnership.
Main ideas
AI makes imitation cheap, so authenticity becomes the signal buyers screen for · Servant leadership should slow AI at the right point, not remove every obstacle · In a crowded category, position comes from researched authority rather than a louder message
Tom Cogswell is a B2B marketing leader whose posts focus on demand generation and marketing for complex buying groups.
A clear example of their work is a method for finding where enterprise messaging resonates and where a company can build authority competitors cannot easily displace.
They also write about using AI without losing buyer trust or removing human review.
Their posts describe nearly eight years at ServiceNow and a search for a senior marketing role; they also say they are still building deeper AI skills.
Cogswell connects buying-group strategy to practical choices about research, messaging, and review.
In one account of an award-winning project, they describe combining analyst input, customer conversations, synthetic research, and sales intelligence, then using first-party research to find opportunities for distinctive authority.
In another, they explain why AI-personalised content for different buying-group members needs a human checkpoint: flawed inputs can scale the same mistake across the group.
The mix of market analysis and operational caution helps readers weigh how to differentiate enterprise marketing without treating automation or content volume as a substitute for trust.
They also describe a structured, work-based AI programme and acknowledge that their fluency is not yet where they want it.
Tom Cogswell writes about B2B marketing in a world where AI has made speed and volume cheap, arguing that trust, human judgement and a researched position are now scarce.
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Across the posts, the through-line is clear: acceleration alone is not the win.
Buyers still commit on trust, AI-cloned voices make that trust harder to earn, and the marketing leader must decide where to keep a human in the loop and where to build authority a competitor cannot easily take.
Cogswell also treats AI skill as something earned through real work rather than certificates, and describes buying-group work as one integrated discipline spanning demand generation, buying-group strategy and campaign thinking.
They also bring a specific ability to translate US growth practices for European markets.
AI makes imitation cheap, so authenticity becomes the signal buyers screen for
Cogswell argues that AI has made it easy for real people to clone and scale their own voice convincingly, so buyers can no longer reliably distinguish a genuine professional voice from an amplified imitation.
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That matters commercially because trust remains the main reason buyers commit to a vendor, while B2B content output keeps climbing.
More volume and polish are arriving alongside growing buyer scepticism, making authenticity—already a competitive advantage—potentially the only one that counts.
Cogswell applies the same test to their own posting: AI helps with drafting, but choosing the topic, reading the research and checking it against their own thinking remain human tasks; they do not post the first draft.
The post opens with an observation from a connection about people using AI to clone their own voice.
Cogswell cites Dentsu B2B's Superpowers Index, a survey of more than 16,000 buyers across 21 markets, which found trust remains the number one reason buyers commit to a vendor.
They also cite a CMI B2B content and marketing trends report stating 91% of B2B marketers increased content output last year.
The post closes by asking readers what signals they now use to judge whether professional content is worth engaging with.
Servant leadership should slow AI at the right point, not remove every obstacle
Cogswell has long believed in servant leadership and its instinct to remove friction.
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They ask how the model should change when AI makes acceleration cheaper than any previous tool.
Their answer: some friction is worth keeping, specifically the human review point.
AI can now tailor content, emphasis and timing to individual members of a buying group at a speed that was impossible three years ago, a capability Cogswell takes seriously.
But the output is only as good as the inputs shaping it; when it goes wrong, it tends to go wrong consistently across the whole buying group.
The leaders rated highest here know precisely where to slow the process down.
Personalisation across three roles in a single deal: different content, emphasis and timing for the economic buyer, champion and technical reviewer, delivered when the deal cycle brings them together.
Cogswell notes that questions about servant leadership's applicability in an AI-paced world first started appearing a year earlier, prompting this reassessment.
In a crowded category, position comes from researched authority rather than a louder message
Cogswell describes an IT buying-group project built around a problem of convergence: enterprise software vendors were being pulled toward the same customer outcomes at the same time by shared market context, not by copying one another.
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Messaging alone could not hold a position, so the team developed a method rather than a slogan.
Analyst input, product marketing conversations with customers, synthetic research and sales intelligence shaped the messaging.
Anonymised first-party research of the TechTarget database, plus an Informa partnership, showed where the message genuinely resonated and where the team could build authority a competitor could not easily displace.
Omdia analyst reports supported authority content, and Informa helped plan against the remaining gaps.
The account is retrospective and tied to an award; ask for results data.
The sequence is the example: analyst input, customer conversations, synthetic research, sales intelligence, first-party resonance testing, authority content, then gap planning. Cogswell credits named colleagues across the team and notes that a colleague collected the award on their behalf after they had left ServiceNow.
AI fluency is a depth problem, closed on real work and in public
Cogswell draws a line between awareness and depth.
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The gap is not knowing that AI exists; it is understanding why a prompt did not work, why a workflow is not saving the time it should, and whether an agent will get a task wrong before it does.
Rather than pursue a certificate or online university course, they are running a self-designed twelve-week programme with Claude, two hours a day, calibrated to their marketing background and built around real work rather than generic exercises.
They will share findings along the way, framed around how they would want a team to work.
Cogswell describes the tension openly: they are further along with AI than most people at their level, but still not where they want to be.
The twelve-week Claude programme itself: two hours a day, built on real work, and designed to close the gap identified by the three questions above.
Buying-group work is one integrated job, and Europe needs its own translation
Cogswell prioritises complex, multi-stakeholder sales cycles where demand generation, buying-group strategy and integrated campaign thinking have to pull in the same direction—as one discipline, not separate functions.
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They describe two decades at the intersection of pipeline ambition and marketing rigour.
Geography is the other half of the position: Cogswell describes themself as a European who speaks Californian, meaning they understand how US-headquartered growth machines work and how to translate that for EMEA markets without losing what makes those markets different.
Their next step is Director to VP-level marketing roles at European-headquartered or remote-first enterprise B2B SaaS companies where AI is more than a slide in the deck.
The post closes nearly eight years and five roles at ServiceNow, during which Cogswell scaled with the company from around 5,000 to more than 30,000 people and built buying-group go-to-market programmes globally. It asks what actually made the difference for others who had run a senior job search in the same market.
This is the clearest account of Cogswell's buying-group work: the method, its inputs and the recognition it received.
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Start here for evidence beyond a job title.
Cogswell describes an award-winning project they led with the IT Buying Group One team.
The problem was clear: enterprise software vendors were being pulled toward the same customer outcomes by shared market context, not by copying one another.
Messaging alone could not hold a position.
The team developed a method.
Analyst input, product marketing conversations with customers, synthetic research and sales intelligence shaped the messaging.
Anonymised first-party research of the TechTarget database, along with an Informa partnership, showed where the messaging resonated and where there was room to build authority a competitor could not easily displace.
Omdia analyst reports helped create content that strengthened that authority; Informa helped plan against the remaining gaps.
Cogswell also names the colleagues who supported the work and credits the person who accepted the award on their behalf after they had left ServiceNow.
For brands planning a campaign
Useful when a brief needs defensible differentiation, not a louder message. The sequence outlined here—analyst input, customer conversations, first-party resonance data, then authority content where competitors are weak—offers a workable demand generation plan for a crowded category.
The piece answers where a human should remain in an AI-supported buying-group process, connecting Cogswell's stated management philosophy with their demand generation work.
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Cogswell starts with servant leadership—the instinct to remove friction—and asks how that model should change when AI makes process acceleration cheap.
One kind of friction is worth keeping: the human review point.
AI can tailor content, emphasis and timing for the economic buyer, champion and technical reviewer as they converge in a deal; Cogswell sees that as a real capability.
But output is only as good as its inputs, and errors can spread consistently across the whole group.
The conclusion: strong servant leaders in B2B marketing know where to slow AI down, not how to remove every obstacle.
For brands planning a campaign
Relevant to any plan using AI to tailor outreach to multiple stakeholders: the piece argues for naming a specific review checkpoint rather than assuming speed is the goal. A campaign can adopt that checkpoint as a stated control.
The piece lays out the trust problem Cogswell sees in B2B marketing, backs it with named research and shows how they use AI while keeping a human check.
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Cogswell opens with a contact's observation: real people are using AI to clone and scale their own voices so convincingly that the line between authentic and automated has nearly disappeared.
They use AI to help craft their own posts, but keep them authentically theirs by choosing the topic, reading the research and checking it against their own thinking rather than posting the first draft.
The question then widens: if buyers cannot reliably distinguish a genuine professional voice from an AI-amplified imitation, what happens when they evaluate vendor content and peer recommendations?
Two sources support the argument.
Dentsu B2B's Superpowers Index surveyed over 16,000 buyers across 21 markets and found that trust remains the number one reason buyers commit to a vendor.
A CMI B2B content and marketing trends report states that 91% of B2B marketers increased content output last year.
The conclusion: more volume and polish are arriving alongside growing buyer scepticism.
Authenticity was already a competitive advantage and may soon be the only one that counts.
For brands planning a campaign
Supports a strategy that prioritises demonstrable human expertise over content volume. A campaign brief can use the cited buyer-trust finding to justify fewer, more attributable pieces and named expert voices.
The piece shows how seriously Cogswell takes AI fluency and how they are developing it—useful if a campaign depends on AI-supported marketing rather than AI on a slide.
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Cogswell says they are further along with AI than most people at their level, but not yet where they want to be.
The gap, they say, is depth rather than awareness: understanding why a prompt failed, why a process is not saving as much time as it should, and whether an agent will get a task wrong before it does.
Instead of pursuing a certificate or online university course, they are using Claude to run a structured twelve-week programme, two hours a day, calibrated to their existing B2B marketing background and built around real work rather than generic exercises.
Cogswell calls the approach a little meta, says they will share findings as they go, and argues that a senior marketing leader's attention to their own development is no coincidence.
For brands planning a campaign
Useful when a brief calls for an AI-literate leader rather than an AI enthusiast. Cogswell evaluates tools against their own workflows and builds capability through real marketing tasks—a preference a hiring or partnership conversation can test directly.
It sums up what Cogswell's experience adds up to and what they are looking for now—the quickest way for a newcomer to place them before reading the marketing argument.
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Cogswell is closing a chapter of nearly eight years and five roles at ServiceNow, where they scaled with the company from around 5,000 to more than 30,000 people and built buying-group go-to-market programmes globally.
They are now looking for Director to VP-level marketing roles at European-headquartered or remote-first enterprise B2B SaaS companies where AI is more than a slide in the deck.
Their sweet spot is complex, multi-stakeholder sales cycles where demand generation, buying-group strategy and integrated campaign thinking must pull in the same direction.
They sum up two decades of work at the intersection of pipeline ambition and marketing rigour with a distinctive self-description: a European who speaks Californian.
They understand US-headquartered growth machines and how to translate them for EMEA markets without flattening local differences.
The post asks what made the difference for others who have conducted a senior search in this market.
For brands planning a campaign
Relevant to a European B2B SaaS brief with US parentage or US-style growth expectations. Cogswell's stated strength—running US growth playbooks in EMEA without losing local differences—is the specific claim to probe in a hiring or collaboration conversation.
How the writing works: long-form posts built on cited research4
AI is eroding the trust signals B2B marketing runs on1
Servant leadership, adapted for AI speed1
A documented method for building authority in a crowded category1
Building AI fluency on real work, in public1
Career direction and where the work goes next1
AI is eroding the trust signals B2B marketing runs on
Tom Cogswell argues that AI-generated voices are now convincing enough that buyers can no longer easily distinguish a genuine professional voice from an imitation.
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That matters commercially because trust is the leading reason buyers commit to a vendor.
The post cites Dentsu B2B's Superpowers Index, a survey of over 16,000 buyers across 21 markets, for the trust finding, and CMI's trends report for the statistic that 91% of B2B marketers increased content output last year — then points out that buyer scepticism has grown rather than shrunk.
They use AI to help draft these posts, but say the topic choice, research and sense-checking against their own thinking stay with them rather than relying on the first output.
Anchors the argument in published survey data, offers a personal practice as a counter-example and closes with a direct question to readers.
For brands planning a campaign
Useful for briefs built around credibility, expertise or standing out in an AI-saturated content market, including vendor content and peer recommendations. The argument draws on third-party data a brand can reuse, while the stated drafting routine offers a concrete example of a human-in-the-loop editorial process.
Tom Cogswell writes about servant leadership as a working philosophy and asks how it should change now that AI removes friction faster than any previous tool.
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Their answer is that some friction is worth keeping.
AI-driven personalisation can produce different content, emphasis and timing for the economic buyer, the champion and the technical reviewer, but removing the human review point means mistakes can spread consistently across the whole buying group.
The leaders they rate highest know precisely where to slow the process down.
Starts with a leadership principle, then turns it into one operational rule a marketing team can apply.
For brands planning a campaign
Fits briefs on buying-group or account-based personalisation where the buyer wants governance, review steps or stated limits on automation. The three-role example — economic buyer, champion, technical reviewer — gives a campaign a concrete structure.
A documented method for building authority in a crowded category
Tom Cogswell describes an IT buying-group project that won an award and credits named colleagues across the team.
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Enterprise software vendors were being pulled towards the same customer outcomes, so messaging alone could not hold a position.
The team combined analyst input, product marketing conversations with customers, synthetic research and sales intelligence, then used anonymised first-party research of the TechTarget database and an Informa partnership to find where the message genuinely resonated and where they could build authority that a competitor could not easily displace.
Analyst reports from Omdia supported authority content, with Informa planning against the remaining gaps.
Combines analyst input, customer conversations, synthetic research, sales intelligence and anonymised first-party database research, then tests where the message resonates before committing to a position.
The account is retrospective and tied to an award and a team. Ask for results data before using the approach as evidence of campaign performance.
For brands planning a campaign
The posts show how Tom Cogswell builds category position rather than one-off campaign assets. A vendor in a crowded enterprise category can adapt the sequence — analyst input, customer conversations, first-party resonance testing, authority content, gap planning — as a positioning strategy.
Tom Cogswell describes running a self-designed 12-week AI programme with Claude, two hours a day, calibrated to their background and built around real work rather than generic exercises, in place of a certificate course.
Read more
The goal is depth, not just awareness: understanding why a prompt failed, why a workflow is not saving the time it should, and whether an agent will get a task wrong before it does. They say they will share what they find along the way and frame that openness as how they would want a team to work.
For brands planning a campaign
Relevant to briefs about AI adoption, practitioner learning or working in the open. It also supports a hiring or partnership pitch where the buyer wants evidence that an AI message is grounded in practice rather than a slide.
Tom Cogswell has closed nearly eight years and five roles at ServiceNow, scaling with the company from around 5,000 to more than 30,000 people and building buying-group go-to-market programmes globally.
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Their stated next step is Director to VP-level marketing roles at European-headquartered or remote-first enterprise B2B SaaS companies, with a sweet spot in complex, multi-stakeholder sales cycles where demand generation, buying group strategy and integrated campaign thinking have to pull in one direction.
Two decades of work sit at the intersection of pipeline ambition and marketing rigour.
They describe themselves as a European who speaks Californian — able to translate US-headquartered growth practices for EMEA markets without losing what makes them different.
The posts make no sponsorship or paid partnership offer; ask whether Tom Cogswell is open to one.
For brands planning a campaign
This is the item to read alongside a search for a senior in-house hire: the stated target is a senior in-house role, and the post openly invites conversations.
For a paid engagement, ask about availability rather than assuming it.
The EMEA/US translation angle offers a specific campaign selling point.
How the writing works: long-form posts built on cited research
The recurring format is a substantial first-person LinkedIn post that opens with an observation or tension, brings in named third-party research, connects it to buying-group practice and closes with a question to readers.
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Posts about the award-winning buying-group project and the career change are shorter project and news updates in the same voice. Some posts use hashtags, including #B2BMarketing, #ServantLeadership and #BuyingGroups.
Observation, cited evidence, application to buying-group practice, reader question.
Ask for dated examples to assess how these themes and formats have developed over time.
For brands planning a campaign
Sets expectations for co-created content: collaborators can expect an argument with evidence and a reader question, not a product announcement. The question-led ending is a useful device for a campaign seeking comment activity rather than passive reach.
Cogswell's posts describe nearly eight years and five roles at ServiceNow, where they built global buying-group marketing programmes, and say they have since left and are looking for a senior in-house role.
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Their award-winning work drew on research and media partners Informa TechTarget and Omdia. The posts disclose no sponsorships, affiliate deals, named clients, or paid engagements; ask whether any apply.
ServiceNow
Employment
Cogswell says they spent nearly eight years in five roles at ServiceNow, scaling with the company from roughly 5,000 to more than 30,000 people and building buying-group go-to-market programmes globally.
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They say that chapter has closed and they are seeking Director to VP-level marketing roles at European or remote-first enterprise B2B SaaS companies.
Cogswell describes an Informa partnership that used anonymised first-party research from the TechTarget database to show where the team's messaging resonated and where it could build authority a competitor could not easily displace.
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They thank the Informa TechTarget team for recognising the buying-group project they led and say they collected the award after leaving ServiceNow.
Analyst reports from Omdia helped shape content for the buying-group project, which Cogswell describes as a way to build authority a competitor could not easily displace.
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The research came from a supplier used by their team's programme.
Read Tom Cogswell for a working view of how AI changes buying-group marketing.
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Start with the post on authenticity and buyer trust, which argues that AI-cloned voices have made genuine professional voice the signal buyers screen for, and the follow-up on servant leadership, which asks where human review should still sit when AI can personalise across a whole buying group.
The award-project post gives the fullest account of how they build category authority rather than one-off campaign assets, and the career post sets out the direction of their work, including the ability to translate US growth practices for European markets.
Then compare them with the creators listed below: Randy Likas and Delfin Vassallo write on adjacent B2B marketing and go-to-market questions, while Dale Gibbons takes servant leadership in a consultant-facing direction.
Writes long-form LinkedIn posts on B2B go-to-market: AI adoption, revenue operations and enterprise transformation.
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The teaching style closely matches Cogswell's: opening with a claim heard from practitioners, testing a common headline and closing with a question for readers. Both argue from conversations with named operators rather than a framework, keeping the focus on where AI belongs operationally.
Likas works closer to revenue operations and tooling, discussing implementation architecture, meeting data and vendor capabilities; posts sometimes end in a product pitch. Cogswell focuses on marketing leadership, buying-group personalisation, brand trust and how marketing teams should organise the work.
Names servant leadership as their approach to client work, the same principle Cogswell applies, and turns it into a numbered practice: listen first, advise second and measure success by client growth.
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Both write structured, first-person advice readers can act on, using a concrete analogy to carry the point.
Gibbons writes for independent consultants trying to win and keep high-ticket clients, with a quiz, newsletter and booking call as next steps. Cogswell applies the same leadership idea inside in-house B2B marketing teams and complex buying groups, without a consulting offer.
Comments on B2B marketing leadership from the same starting point of AI saturation, citing external research to argue that branding and authenticity still decide who makes a buyer's shortlist.
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Both argue that AI-generated volume has made trust and human connection more valuable, not less.
Vassallo posts shorter commentary and event round-ups, drawing on third-party reports and openly pitching to companies hiring a CMO. Cogswell writes fuller first-person arguments that develop into a described method, particularly on buying-group personalisation and building authority in a crowded category.
Cogswell’s LinkedIn posts target people working in B2B marketing, especially those dealing with enterprise buying groups, AI, and complex sales.
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They explore how to use AI without losing human judgment or buyer trust, and how research and sales intelligence can help a company find a distinct position.
In a post about leaving ServiceNow, they describe the senior marketing roles and European enterprise SaaS companies they are seeking.
They had 1,927 LinkedIn followers on 9 October 2026.
A buyer assessing reach among European marketing leaders should request platform audience data to verify followers’ roles, seniority, location, and purchasing influence.
Tom Cogswell's posts do not mention a sponsorship offer, media kit, rate card or partnership inquiry route.
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They offer their own commentary on B2B marketing, buying groups and the use of AI in campaigns; none carries a paid-partner disclosure or describes a commercial format a brand could book. A brand considering a partnership would need to ask directly about terms, as the posts publish no format or entry point.
European enterprise B2B SaaS company hiring a senior marketing leader for complex buying groups, with demand generation that builds trust and uses AI to engage buying groups.
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This closely matches what Tom Cogswell says they are looking for.
Their stated target is Director to VP-level marketing roles at European-headquartered or remote-first enterprise B2B SaaS companies where AI is more than a slide in the deck.
Their sweet spot is complex, multi-stakeholder sales cycles where demand generation, buying group strategy and integrated campaign thinking have to pull in one direction.
That is the need in the reader's own words.
The demand generation fit rests on a described method, not a job title.
The award-winning IT buying group project sets out a sequence: analyst input, product marketing conversations with customers, synthetic research, sales intelligence, then anonymised first-party research of the TechTarget database and an Informa partnership to identify where the message genuinely resonated and where authority could be built that a competitor could not easily displace.
Analyst reports supplied authority content, with gap planning for what remained.
A vendor in a crowded enterprise category can adapt that sequence to whatever first-party data it actually holds.
The AI fit matters for buying group work.
Cogswell argues that AI personalisation can now deliver different content, emphasis and timing to the economic buyer, the champion and the technical reviewer as they converge in a deal.
The risk, they say, is removing the human review point, allowing bad inputs to go wrong consistently across the whole group.
Their position is that the best leaders know precisely where to slow AI down.
It is a governance view a buyer can test, and it pairs with their self-run twelve-week AI programme built around real work.
The stated goals are understanding why a prompt failed, why a workflow is not saving the time it should, and whether an agent will get a task wrong before it does.
Trust is the strongest thematic match.
The post on AI and buyer trust cites Dentsu B2B's Superpowers Index, a survey of over 16,000 buyers across 21 markets, finding that trust remains the number one reason buyers commit to a vendor, and CMI data showing 91% of B2B marketers increased content output last year while buyer scepticism grew.
The post argues that authenticity may soon be the only competitive advantage that counts—the case for a trust-led demand generation plan.
The posts are five LinkedIn posts plus an account snapshot showing 1,927 followers on 9 October 2026.
The match is based on Cogswell's stated target and described method, not independently verified results or references.
There are no outcome figures for the buying group programmes or audience breakdowns by seniority, function or geography.
Before treating this as a live candidacy or engagement, ask for programme results and references, and clarify whether they are interested in an in-house role, a contract or advisory work; the post states they are seeking a senior in-house role.
When to choose a specialist
A marketing technology vendor looking to buy paid creator content: a sponsored LinkedIn series or paid placements on AI-in-B2B-marketing topics, purchased for reach and targeting among senior demand generation buyers in Europe.
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Based on the posts, this is a poor fit for standard paid placement, though a smaller, more expert-led engagement could work.
The commercial route is undocumented.
The posts include no media kit, rate card, sponsorship format or inquiry route; none carries a paid-partner disclosure or describes a bookable format.
There is no documented offer, though that does not mean sponsored work is never accepted.
A brand would have to negotiate terms directly, without a published structure to work from.
The posts do not justify a reach-based buy.
The account shows 1,927 followers on 9 October 2026, but no breakdown by job function, seniority, employer, geography, age or gender, engagement counts or performance data for previously promoted posts.
A campaign bought on impressions or audience size would be guessing who is reading—a poor basis for reaching a narrow senior buying audience.
The demonstrated format also cuts against a commissioned product announcement.
The posts are first-person arguments that open with an observation or tension, bring in named third-party research, apply it to buying group practice, and close with a question to readers.
One post is a project and award update in the same voice.
A vendor seeking a product launch post, feature announcement or promotional call to action would be asking for a format the posts do not demonstrate.
One further risk: a post states an active search for a senior in-house marketing role, so large paid creator work may not be what Cogswell is seeking.
Settle availability before designing a campaign around it.
If the buyer's goal shifts from reach to authority, a similar engagement could work: a co-authored or named-expert piece on buying group personalisation and where the human check stays, an advisory session, or a roundtable with a named marketing leader.
The value is the argument and the method, not impressions, and it should be priced and measured accordingly.
A worked campaign example
A worked angle for hiring a senior marketing leader: a European enterprise SaaS vendor selling into a complex, multi-stakeholder buying cycle, currently generating demand with volume content and fast AI personalisation, wants a plan that builds trust and uses AI on buying groups without losing human control.
The campaign angle: name the review checkpoint in the AI personalisation workflow, publish the reasoning behind it, and build authority content only where the market is crowded and competitors cannot easily displace a position.
First fortnight: find the ground.
Adapt the sequence described in the buying group project to the data the vendor actually has: analyst or category research, product marketing conversations with real customers, sales intelligence, and first-party data such as CRM win-loss reasons, review-site language and community threads.
The output is a short written statement of where the vendor's message genuinely resonates and where it can claim a defensible position rivals do not already hold.
Where first-party access is unavailable, say so and use the closest available substitute without treating it as equivalent.
Weeks two to six: build authority content only in the identified gaps.
Produce fewer pieces, each attributable to a named expert inside the company and making an argument rather than an announcement.
This applies the cited buyer-trust finding: output and polish are increasing as buyer scepticism grows, so the campaign trades volume for demonstrable human expertise.
If the vendor reuses a buyer-trust statistic in its own materials, credit the original survey and its scope.
Weeks three to twelve: map AI personalisation to the three roles named in the servant leadership post—the economic buyer, the champion and the technical reviewer.
Define what each role receives, with what emphasis, and when they converge in the deal cycle.
Then add the checkpoint: one senior marketer signs off on personalisation inputs before anything scales, on the stated logic that automated personalisation is only as good as its inputs and that failures repeat consistently across the whole group.
Document the checkpoint as a stop rule, including what must never be automated, so the control survives staff changes and campaign pressure.
Measure trust rather than reach.
Track reply quality and substance, meeting accepts from each named buying group role, pipeline attributed by role, and win-loss reasons over the campaign period.
Impressions and follower counts are not the test this angle is designed to pass.
Deliver three documents to the vendor: a buying group playbook with the grounded message position, a personalisation plan mapped to the three roles, and a stop rule covering what stays human. State the tradeoff plainly: this is a slower, narrower campaign than a volume programme, and it depends on the vendor having or obtaining first-party data.
Before commissioning, settle three things with Cogswell: whether they are interested in an in-house role, a contract or advisory work, given their stated search for a senior in-house position; what results exist for the buying group programmes described, since none are published in the posts; and whether the buyer is willing to fund the first-party research step, where the method begins.
Choose this comparison when the campaign need moves from buying-group messaging to the operating model behind AI adoption: getting past pilot mode, deciding what the business is actually willing to change, and measuring engagement by meeting quality rather than activity.
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The posts stay on that ground.
The August 2026 post argues the "Why AI pilots fail" headlines have become cliché, reports three conversations with executives leading AI and enterprise transformation, and builds on the idea that too many pilots "succeed" under favourable conditions and never face real ones.
It calls for understanding how work gets done before deciding where AI belongs, organising RevOps and data work around product-centred design, and treating AI transformation more like R&D.
The September 2024 post adds an efficiency angle for planning season: budget pressure, the observation that most leaders cannot say where their team's time goes, and a method for segmenting top accounts by churned, renewed and expanded and comparing the cumulative time influential stakeholders actually spent in meetings.
For a vendor whose problem is adoption and proof rather than positioning, this is the closer choice.
The tradeoff is depth in the wrong two places for a campaign that needs message positioning.
Likas' posts cover transformation adoption and engagement measurement, not the message-positioning sequence Cogswell describes: analyst input, customer conversations, synthetic research, first-party resonance testing, and authority content built only where a rival cannot easily displace a position.
A vendor choosing between these posts gets a stronger change-management and meeting-quality measurement case, but not the buying-group personalisation detail mapped to economic buyer, champion and technical reviewer, or the human-review checkpoint logic.
The September 2024 post also promotes a named meeting-analysis product and closes by inviting direct messages to learn how it can help this planning season.
Buyers should ask for programme results and separate the method described from the supplier pitch around it.
Choose this comparison when the need shifts from hiring a senior marketing leader to developing the person or firm doing the work: how to serve long-cycle clients, when to switch roles, and how to earn enough trust that clients follow the advice.
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These are coaching posts.
The October 2026 post sets out a "three hats" model — coach on the sideline spotting what players cannot see, consultant in the huddle with the systems and elbow grease to deliver, mentor a phone call away — and diagnoses solving problems in the wrong role as the reason consultants lose clients they should have kept.
The September 2026 post lays out eight trust practices under a servant leadership banner: listen before you lead, understand before you advise, teach plainly, tell the truth early, empower instead of rescue, lead by example, protect your standards, and measure success by client growth.
For a reader seeking an advisory or fractional partner to sharpen a marketing team's client-facing judgement, this is the closer match.
Gibbons' posts are about running a consulting business, not building demand generation for a complex enterprise buying group.
They contain none of the buying-group mechanics, AI personalisation mapping or buyer-trust survey evidence needed for a campaign targeting that group.
A vendor should ask for evidence of that capability before choosing on this basis.
The two posts also carry explicit commercial routing: a quiz for the reader's consultant archetype, a weekly resource letter reported as going to nearly 1,000 consultants, direct-message booking, and a stated goal of turning expertise into a "7-figure consulting business."
That makes the audience and offer clearer than in the other comparisons, but frames the reader as a consultant buying advice rather than a vendor buying marketing leadership.
Servant leadership appears in both Gibbons' and Cogswell's material; the choice is whether to apply it to a consulting practice or a corporate buying group, and price each accordingly.
Choose this comparison when the campaign's priority is brand and discoverability in European markets rather than buying-group mechanics: building a brand that decides who makes a shortlist, and making the company findable in AI search and language-model answers.
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The May 2026 post cites McKinsey's State of Marketing Europe 2026, reported as 500 senior marketing leaders across France, Germany, Italy, Spain and the UK, placing branding first and AI eighteenth for B2B marketing leaders.
It uses the Ehrenberg-Bass 95-5 rule to argue that the brand decides who makes the shortlist, and flags authenticity as the third-ranked priority in a market filling with AI-produced content.
The September 2026 post reports from a Helsinki B2B marketing event and covers marketing as a board-level business growth lever, collaborative approaches to marketing operations, context in the marketing operating system, AI search GEO, and winning share of voice in language models through LinkedIn.
Vassallo also states directly that anyone hiring a CMO should get in touch, speaking to the same hiring need with an explicit European multi-market frame.
The posts are strong on European market context, multi-market survey data, and brand and visibility strategy, but do not show the buying-group personalisation workflow — different content, emphasis and timing for the economic buyer, champion and technical reviewer — or the reasoning behind where to keep human review before AI personalisation scales.
A vendor choosing based on these posts gets a brand-demand and AI-search argument plus a direct signal of availability for a CMO conversation.
Ask for evidence of buying-group and AI-governance depth before choosing on that basis.
The event piece summarises several speakers' points, showing breadth across AI search and brand topics rather than depth on any single one.
Tom Cogswell's five LinkedIn posts address AI-cloned voices and buyer trust, servant leadership when AI can personalise across a buying group, an award-winning IT buying-group authority project, a self-designed twelve-week AI programme, and a career move after nearly eight years at ServiceNow.
The LinkedIn account shows 1,927 followers on 9 October 2026; the posts here carry no publication dates.
Comparisons with other creators draw on two posts each.
The posts cite external research, such as the Dentsu B2B Superpowers Index and CMI's content trends report; these citations do not establish a relationship or a verified result.
No long-form articles, talks, interviews or transcripts appear here, and no sponsorship terms, rate card or programme outcome figures are published in the posts.
The page describes the stated work and method rather than measured results.
