Christine A. Mackay is most useful for B2B thought leadership on AI, agency pricing and client-agency relationships.
Best for
A B2B marketing agency wanting a keynote or workshop on AI, pricing and client relationships.
Not for
A B2B software company seeking product-led demand generation, demos, case studies or pipeline metrics.
Key takeaways
Faster AI-assisted agency work should not automatically mean lower fees; clients pay for expertise and judgment.
AI raises marketing output volume, not the standard of it.
Good client-agency work comes from shared problem definition, not a big reveal.
Check first: Confirm whether they offer a keynote, workshop, sponsorship or paid collaboration and their audience details.
Main ideas
Faster agency work should not mean cheaper thinking · AI raises the volume of marketing output, not the standard of it · Complex B2B and technology work raises the bar for AI-assisted content
Mackay writes about B2B marketing and the changing work of agencies.
In three LinkedIn posts, they examine how AI affects creative quality and agency pricing, and what clients and agencies need from each other.
Their examples range from a village-fête marrow given a face to the four-second piece of advice that could save a client £100,000.
The posts offer practical perspectives on AI, creative judgment and agency relationships.
Mackay turns industry tensions into concrete questions.
They ask whether faster AI-assisted work should mean lower fees, then argue that clients also pay for experience and judgment.
In another post, a personified marrow illustrates their point: producing content is not the same as giving it personality or deciding whether it is accurate, on-brand and worth making.
Their account of working on both sides of the client-agency relationship adds that good work depends on clear problems, honest constraints and constructive challenge.
Follow them for perspectives on human judgment, creative work and collaboration in B2B marketing.
Mackay's recurring argument is that AI has changed how quickly marketing work gets produced, but not who decides whether it is any good — and that agencies sell judgement, not hours.
The posts focus on three linked priorities: keeping a human review layer over AI output, defending expertise-based pricing as production gets faster, and treating client-agency work as shared problem-solving rather than a product purchase.
Across output quality, commercial models and relationships, the claim is consistent: judgement is the scarce input, and efficiency should not price it at zero.
Faster agency work should not mean cheaper thinking
Mackay argues that under a time-based model, AI efficiency is self-defeating: the agency works faster, the job takes fewer hours and the agency earns less.
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But clients are not buying 37.5 hours of somebody's week; they are buying the experience to spot a problem quickly, the confidence to challenge a brief and the judgement to say something will not work.
They cite recent discussion involving ISBA, the IPA, CIPS and the WFA about moving marketing procurement beyond cost towards long-term value creation, including output-, outcome- and value-based pricing as alternatives to FTE and rate-card structures.
The post's formulation: better thinking plus greater efficiency equals greater value — not greater efficiency equals cheaper thinking.
Four seconds of advice — “I really wouldn't recommend that” — can save a client £100,000.
Mackay asks which the invoice is really valuing: the four seconds or the expertise behind them.
They put the question to clients, procurement people and agency owners.
AI raises the volume of marketing output, not the standard of it
Mackay's position is that the goal was never to generate material, but to produce the right material.
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AI can produce images, copy, presentations, concepts, video and campaigns, and gets faster by the week.
But someone still has to decide whether the output makes sense, is accurate, is on-brand, will be understood and remembered, has personality, and should have been made at all.
The post cites research among independent agency leaders: 89% expected AI to shift time from production towards strategic and creative work, around 27% reported that off-brand, off-brief or unreliable AI output created substantial human rework, and 64% named output quality and brand safety among their biggest barriers to adoption.
They also flag the legal risk of AI-created content.
The central question: is AI freeing up more time for thinking or simply creating more things that need checking?
A marrow entered in a Dorset village fête “guess the weight” competition was given eyes and a mouth and, in Mackay's telling, suddenly had a personality. That sets up the argument: giving something eyes and a mouth does not automatically give it personality, and producing more stuff was never the point.
Complex B2B and technology work raises the bar for AI-assisted content
Mackay argues that judgement matters more, not less, in complex B2B and technology marketing, where a single product may need to make sense to customers, investors, procurement, regulators, recruits and technical teams at the same time.
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That makes experience — spotting a problem quickly and knowing when something will not work — valuable. The argument links the output-quality post to the pricing one: the expertise that reviews AI work is what clients are paying for.
One product, six audiences: customers, investors, procurement, regulators, recruits and technical teams must all be able to make sense of the same product story.
Good client-agency work comes from shared problem definition, not a big reveal
Drawing on experience on both sides of the client-agency table, Mackay argues that the best work rarely comes from a client dictating the answer or an agency disappearing into a creative cave and emerging with a dramatic reveal.
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It comes from both sides clarifying the problem, being honest about constraints and challenging each other constructively.
The post points to what agencies often fail to see: clients managing stakeholders, budgets, politics, risk and procurement, as well as the colleague who joins in week six with “just one thought”.
Mackay sees a good relationship as less like buying a product and more like solving a problem together, a basis for longer-term business relationships.
A vague brief does not become a brilliant one because someone adds “be creative” at the bottom. The post asks each side what they wish the other understood better.
This is the clearest statement of the pricing question brands and agencies must answer before adopting AI at scale: if the work takes fewer hours, what exactly is the client paying for?
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Start here for the argument and industry context.
The post lays out a pricing paradox: under a time-based model, agency efficiency means fewer billed hours and lower fees, so AI makes an agency better at charging less.
It contrasts hours with expertise, using the example of advice that takes four seconds to give and saves a client £100,000 to ask what the invoice is really valuing.
Mackay anchors the argument in current industry discussion, naming ISBA, the IPA, CIPS and the WFA as bodies examining how marketing procurement can move beyond cost toward long-term value creation.
ISBA is specifically exploring output-, outcome- and value-based pricing as alternatives to FTE and rate-card models.
The memorable formulation: better thinking plus greater efficiency equals greater value—not greater efficiency equals cheaper thinking.
The post puts the question directly to clients, procurement people and agency owners.
For brands planning a campaign
A keynote or workshop on value-based pricing can use this post as its narrative spine.
The four-second example works as an on-stage illustration, while the named procurement bodies give the session a credible industry hook.
The closing question is ready-made for a live audience prompt.
This answers buyers’ first question about AI-assisted content: the output is fast, but is it any good—and who decides?
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It makes the argument memorable rather than abstract.
A marrow that won a village fête guessing competition gets eyes and a mouth and, in Mackay’s telling, suddenly has a personality.
That becomes the entry point to AI-generated images, copy, presentations, concepts, video and campaigns.
The post distinguishes production from judgement by listing what a person still has to assess in any output: does it make sense, is it accurate, is it on-brand, will anyone understand or remember it, does it have personality, and should it have been made at all?
It cites research among independent agency leaders finding that 89% expect AI to shift time from production toward strategic and creative work; around 27% reported off-brand, off-brief or unreliable AI output creating substantial human rework; and 64% named output quality and brand safety among their biggest barriers to adoption.
The piece then turns to complex B2B and technology work, where one product has to make sense to customers, investors, procurement, regulators, recruits and technical teams at the same time.
The marrow line returns at the end to land the conclusion.
For brands planning a campaign
This is a strong candidate for a keynote opening on human-AI collaboration: the marrow device is concrete and repeatable. The rework and brand-safety statistics give a workshop a data anchor for discussing review workflows and where human sign-off belongs in AI-assisted production.
This explains what makes a client-agency relationship work, from someone who has held both roles.
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It is the most direct starting point for the client-relationships half of a brief about AI and agency models.
Writing after time away, Mackay reflects on what each side of the client-agency table taught the other.
The client-side lesson: agencies are not mind readers, and a vague brief does not become a brilliant one by adding “be creative” at the bottom.
The agency-side lesson: clients manage internal complexity an agency rarely sees—stakeholders, budgets, politics, risk, procurement and the person who joins the project in week six with one more thought.
The post argues that the best work rarely comes from a client dictating the answer or an agency disappearing into a creative cave and emerging with a big reveal.
Instead, it comes from both sides being clear about the problem, honest about constraints and willing to challenge each other constructively.
Its summary frames a good relationship less as buying a product than solving a problem together, and connects that approach to long-term business relationships.
It closes by asking both audiences what they wish the other side understood better.
For brands planning a campaign
A workshop on briefing and client-agency collaboration can build around the two lessons here, using the week-six stakeholder as a recognisable scenario for group discussion. For a campaign on AI and agency models, this post supplies the relationship half of the argument, alongside the pricing and output posts.
Approach: short first-person narratives that end in a question3
AI output and the human judgement it still needs2
Agency pricing when AI makes work faster2
Client-agency relationships and better briefs1
Complex B2B and technology messaging1
AI output and the human judgement it still needs
The recurring argument across Mackay's posts is that AI has made it easy to produce a lot of material, but not to decide whether any of it is any good.
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They list the questions that still need a person: does it make sense, is it accurate, is it on-brand, will anyone understand or remember it, and should it have been made at all.
The argument comes through a village fête marrow that someone gave eyes and a mouth — it gained a personality, but that did not make it good company.
They draw on research among independent agency leaders: 89% expected AI to move time away from production and towards strategic and creative work, while 27% reported off-brand, off-brief or unreliable AI output creating substantial human rework and 64% named output quality and brand safety among the biggest barriers to adoption.
Opens with a concrete, lightly absurd personal image, pivots to the industry argument with named research figures, then closes by asking readers how AI is changing their own working week.
For brands planning a campaign
This is the clearest fit for a keynote or workshop on human-AI collaboration in B2B marketing. Ask Mackay to focus on the review and judgement layer — who checks AI output, against what standard, and where brand safety and legal risk sit — rather than on AI enthusiasm or tooling.
The most developed commercial argument in the corpus.
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If an agency becomes more efficient under a time-based model, the job takes fewer hours and the agency gets paid less — Mackay frames this as a hole in the boat.
The counter-argument is that clients are not buying hours but experience, judgement and the confidence to challenge a brief, illustrated by the point that four seconds of advice can save a client a hundred thousand pounds.
They point to recent ISBA, IPA, CIPS and WFA discussion about moving marketing procurement beyond cost towards long-term value creation, including output, outcome and value-based pricing models as alternatives to FTE and rate-card structures.
Poses the argument as a blunt question in the opening line, answers it through a worked contrast between time and expertise, then invites clients, procurement people and agency owners to answer a direct question about what agencies should charge for.
For brands planning a campaign
Strong material for a keynote or workshop on value-based pricing. Brief Mackay to bring concrete pricing models and procurement-facing language, not just the case against the billable hour, and to address the client and procurement audience directly.
Drawing on experience on both sides of the client-agency table, Mackay argues that good work rarely comes from a client dictating the answer or an agency disappearing into a creative cave and emerging with a big reveal.
Read more
It comes from both sides being clear about the problem, honest about constraints, and willing to challenge each other constructively. The post also names what agencies often fail to see: clients managing stakeholders, budgets, politics, risk, procurement, and the colleague who joins in week six with just one thought.
Frames the piece as a lesson from a holiday, sets up two symmetrical lists of what each side learned, then closes with a question asking each side what it wishes the other understood better.
For brands planning a campaign
Suits a workshop on briefing and client-agency alignment, or a thought-leadership piece about partnerships that last. A useful angle is the internal complexity on the client side and how agencies can plan for it.
One post makes the case that complexity is what raises the bar for AI-assisted work in B2B and technology: a single product may need to make sense to customers, investors, procurement, regulators, recruits and technical teams at the same time.
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The same argument values the experience to spot a problem quickly and the judgement to know when something will not work.
For brands planning a campaign
Useful framing for messaging projects and for a session on aligning one product story across stakeholder groups. Ask for worked examples of the same message tested against different audiences.
Approach: short first-person narratives that end in a question
The format is consistent across the three posts: a short first-person story or scene, a turn into a business argument, references to named research or industry bodies, and a closing question that invites replies.
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Recurring devices include nautical imagery, a lizard emoji sign-off, self-deprecating humour, and everyday objects used as analogies. The tone is conversational and accessible rather than formal or data-led.
Mackay's posts are commentary and opinion, with no client case studies, project outcomes, or named campaigns. Ask for case studies or campaign results to assess delivery and impact.
For brands planning a campaign
Plan for conversational, story-led formats — LinkedIn-length written pieces, keynote storytelling, moderated panels — and for copy that asks the audience something rather than asserting a position.
The three posts contain no sponsorship, affiliate, client, employment or investment disclosures.
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The LinkedIn account carries the business name Salamandra Design & Digital, but the posts do not state Mackay’s relationship with the business; ask whether they own, work for or contract with it.
The posts cite ISBA, the IPA, CIPS and the WFA as industry bodies discussing procurement and value-based pricing.
These are citations in Mackay’s argument, not partnerships, and are not listed as relationships.
Salamandra Design & Digital
Disclosed partnership
All three posts were published from a LinkedIn account named Salamandra Design & Digital.
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The posts do not name the business or state Mackay’s role in it. Ask whether they own, work for or contract with the business.
Mackay’s posts form an argument about where human judgement belongs in AI-assisted marketing.
Start with the agency-pricing piece for business models and procurement, then read the AI output post for questions of quality and brand safety, and the client-agency relationship post for how briefs and partnerships shape the work.
The tags group the material by subject, specialty, format and the professional audiences they address.
For adjacent reading, Anthony Hodges makes a directly opposing case about AI absorbing creative and strategist roles, while James Congdon and Anthony Vicino share the first-person LinkedIn commentary format but focus on recruitment and personal development.
AI and marketingAgency pricingValue-based pricingClient-agency relationshipsB2B marketingTechnology marketingCreative strategyShort-form commentaryAgency ownersMarketing procurement
James Congdon
Both write first-person LinkedIn commentary about how agency work is organised and what people inside agencies should understand.
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Congdon starts with a specific recruitment or career observation—inflated job titles in paid media and performance agencies, or a round-up of open roles—and draws out a broader industry point, much as Mackay does with pricing and client relationships.
Congdon focuses on talent and recruitment: job titles, career moves and hiring demand.
Mackay writes about the commercial and creative judgement behind client work, including AI’s effect on pricing and output quality.
Follow Congdon for the careers side of agency life; stay with Mackay for the client and business-model side.
Vicino also turns personal moments into broader arguments in first-person LinkedIn posts.
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The 42nd-birthday list and the account of breaking down on a summit build reflective, motivational points from lived experience, much as Mackay does with the Madeira holiday and the marrow.
Vicino writes about personal development, achievement and mindset.
Mackay applies the reflective format to B2B marketing, agency pricing and AI.
Vicino is a comparator for writing approach, not industry analysis.
Both use LinkedIn posts to argue about what AI does to marketing and creative roles, and ask what remains valuable when production gets faster. Hodges describes an AI system that can research, think, build and route work; Mackay asks who still judges whether the output is accurate, on-brand and worth making.
Hodges argues that a single system can absorb much of the creative strategist, creative director and channel-management work, leaving people to move to an approval layer.
He writes from inside a named ecosystem.
Mackay argues that human judgement is the scarce input and frames the question at industry level, including agency pricing and client relationships.
Their posts make opposing cases that readers can compare directly.
For a campaign aimed at CFOs about AI’s impact on agency pricing and client relationships, the posts support a LinkedIn-first placement.
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The content addresses agency leaders, clients, procurement, and B2B marketing teams, and the account shows 19,155 LinkedIn followers as of 11 October 2026.
Posts cover AI output and judgment, value-based pricing, agency-client collaboration, and procurement’s move beyond cost.
For a keynote or workshop, ask for recent LinkedIn audience demographics and any survey documenting job roles, seniority, and employer type.
Also request post-level engagement for comparable AI, pricing, and agency-relationship topics to compare recent results rather than rely on follower count alone.
Mackay's three posts discuss agency pricing, AI output quality and client-agency relationships, but none outlines a partnership, sponsorship or paid-collaboration offer.
They include no media kit, sponsored format, rate, deliverable specification or booking process.
One post argues that agencies should charge for expertise and outcomes rather than hours as AI speeds up production; that commentary does not establish what a brand could buy.
For a campaign, ask directly about availability, rates, formats and turnaround, and confirm any agreement in writing.
A B2B marketing agency seeking thought leadership about AI's impact on agency pricing and client relationships, including a keynote or workshop on value-based pricing and human-AI collaboration.
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Mackay is a strong fit because three posts address both halves directly.
The pricing post 'If AI makes your agency twice as fast...' sets out the central argument: a time-based model rewards inefficiency, so AI speed can cut fees unless the agency charges for expertise and judgement.
The four-second advice that saves a client £100,000 is a ready on-stage example, while the named ISBA, IPA, CIPS and WFA procurement discussion gives the session an industry hook.
The 'Meet Mr Marrow' post covers human-AI collaboration: AI can produce images, copy, presentations, concepts, video and campaigns quickly, but people still decide whether the output makes sense, is accurate, on-brand, understandable and memorable, has personality, and should have been made at all.
It cites 89% of independent agency leaders expecting AI to shift time to strategy and creative work, 27% reporting substantial human rework from off-brand or unreliable output, and 64% naming output quality and brand safety as barriers.
A post about sitting on both sides of the client-agency table adds material on relationships: vague briefs, client-side complexity, the week-six stakeholder, and solving a problem together rather than buying a product.
The format suits a keynote or workshop: short first-person narratives, named research, everyday analogies, and closing questions that work as audience prompts.
The audience information is a 19,155 LinkedIn follower snapshot, with no demographics or engagement metrics.
Ask for audience data and confirm terms directly; no media kit, rate card, or paid format is documented.
Treat Mackay as a commissioned speaker or workshop contributor, not as a sponsored-post publisher.
When to choose a specialist
A B2B software company seeking a product-led demand-generation campaign using sponsored LinkedIn posts, a product demo, customer case studies, and pipeline metrics tied to target accounts.
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Mackay is a poor fit.
Their posts offer opinion and commentary; none contains a client case study, project outcome, named campaign, product demo, implementation detail, or performance data.
No sponsorship format, rate card, deliverable specification, booking process, or disclosed paid partnership is documented, so a buyer cannot assume a sponsored-post product exists or that Mackay accepts one.
The posts show a follower count but no job roles, seniority, employer type, geography, or engagement metrics, which is not enough to establish that a campaign would reach target accounts or produce pipeline.
They also do not discuss software features, integrations, or buyer pain in a product-specific way; the subjects are agency economics, AI judgement, and client-agency relationships.
Mackay could still suit an industry event as a speaker or workshop leader, but that is a different need from a product-led sponsored campaign.
Ask directly about commercial arrangements and get any terms in writing.
A worked campaign example
For a B2B agency, client-side marketing, and procurement audience seeking thought leadership about AI's impact on agency pricing and client relationships, a worked campaign angle is a 45-minute keynote plus a 90-minute workshop titled 'Four Seconds or Forty Hours?
What Clients Pay For When AI Speeds Up Production.' The keynote opens with the Mr Marrow story to make the production-versus-judgement point concrete: giving something eyes and a mouth does not give it a personality.
It then turns to the pricing paradox in the 'twice as fast' post: under a time-based model, AI efficiency means fewer billed hours and lower fees, while clients are buying the experience to spot a problem quickly, challenge a brief, and say 'I really wouldn't recommend that.' The four-second advice that saves £100,000 becomes the central illustration, with ISBA, IPA, CIPS, and WFA cited as the procurement context for moving beyond cost to long-term value creation.
The second half uses the 89%, 27%, and 64% figures to frame human-AI collaboration as a review and judgement layer: who checks output, against what standard, and where brand safety and legal risk sit.
In the workshop, participants take one real brief, separate the production tasks AI can speed from the judgement tasks that still need human sign-off, then draft a pricing model around output, outcome, or value rather than hours.
The closing prompt comes from Mackay's post: what should agencies be charging for in an AI-enabled world — time, output, outcomes, or value?
The relationship post supplies a second exercise on briefing: each side writes what it wishes the other understood better, using the week-six stakeholder as the scenario.
The campaign is for thought leadership and practical training, not product promotion.
Before booking, ask for recent audience demographics and post-level engagement on comparable AI and pricing topics, and confirm that the arrangement is a commissioned talk or workshop rather than a sponsored post.
Choose Hodges for a session on AI and agency operations if the priority is describing an integrated AI operating system.
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His posts envision AI coordinating research, creative direction, campaign work, approvals, and follow-up, and describe testing a repeatable organic-content system on an internal brand. That gives a buyer material for a session on how AI could change agency operations and execution.
The examples make operational claims, including a roughly 80% reduction in manual management in some workflows, but do not address how agencies should price expertise as production gets faster or explore client-agency relationships as directly as Mackay’s posts do. Ask for evidence that the described results generalize beyond those cases.
Choose Congdon if the need shifts toward growth-marketing recruitment.
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His posts list roles and discuss how inflated job titles can mislead candidates about actual responsibility. That could support a session on hiring and career structures in performance agencies.
The posts focus on recruitment and career titles, not AI’s effect on agency pricing, human-AI collaboration, or client relationships. They offer little evidence for the requested keynote or workshop without changing its subject.
Choose Vicino if the audience needs a personal-development talk about achievement, ambition, and fulfillment.
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His summit reflection gives a first-person narrative about separating self-worth from milestones, which could suit a different leadership session.
The posts center on personal reflection and motivational ideas rather than agency economics, procurement, or practical human-AI collaboration. They are a weaker match for this buyer’s needs than Mackay’s direct treatment of pricing and client-agency work.
Three LinkedIn posts published from the linkedin.com/in/salamandradesigndigital account cover AI output and human judgement, agency pricing when AI speeds production, and client-agency relationships.
The posts do not give publication dates.
The posts do not establish a signature book, show, employer, or documented sponsorship offer.
No audience demographics, engagement metrics, client case studies, or media kit are published in the posts here.
Similar-creator comparisons in the discovery section draw on two-post samples for each named creator and are prompts for further reading, not full assessments.
