David Gut

The proposed fix is to stop thinking in tools and design an ecosystem instead.

• Research feeds content, content connects to distribution, distribution generates data, data returns to strategy, and automation removes the repetitive work between each step.
• Gut keeps a firm line around human judgment: deciding which message matters, which channel to prioritize and which result to pursue stays with people, not with the stack.

The diagnostic question offered for reviewing a marketing stack is not "what tool are we missing?" but "what part of our system is still working in isolation?" That question can be put to a team meeting as written.

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A strong campaign, a working CRM and automated processes can still be undone in minutes by a late reply, a poor delivery or a rigid policy, because the experience happens in the connections: what the company promises, what it delivers, how it responds when something fails, and what it does with what it learned. Technology and AI can connect data, anticipate needs and reduce friction, but Gut states plainly that they cannot compensate for an organization where each area optimizes its own part and ignores the full customer journey.

Gut credits the "Sombrilla de CX" (CX umbrella) framework to CX Latam Group and uses it to show how different areas take part in one customer experience. He then asks readers to comment the number of the piece that feels weakest in their company, and offers to prepare a practical guide if a clear pattern appears in the replies.

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Instructions arrive without context, without a stated objective and without a clear explanation of what is needed; when the answer disappoints, the tool takes the blame for what was a briefing failure.

• The comparison Gut offers is a colleague asked to do a job with too little information.
• The practical side of the argument is that prompting is a learnable skill, taught through reusable templates for orienting instructions, comparing options, questioning ideas and structuring a task.

The post shares a set of 50 prompt shortcuts as an image to keep on hand, and closes by asking readers what they use the tool for most: creating content, researching, analysing information or solving problems.

The same caution appears later in Gut's material on process improvement, so the prompt techniques are presented as a way to work better, not as a substitute for fixing what is broken.

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The team usually knows where the problem is; what is missing is stopping to listen and acting on it.

• Drawing on Kaizen, Gut argues that improvement does not require a large transformation, just a review, a small test and a check of whether it worked.
• Applied to AI, the test before adding a tool or automating a whole process is whether the company is solving a real problem or making something that already worked badly run faster.

The line Gut wants kept is that automating an inefficient process does not turn it into a good process. The post ends with a question teams can use directly: what problem have we tolerated so long that we stopped seeing it as a problem?

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What is harder is understanding the person on the other side: what worries them before a decision, what they expect, what creates distrust, and what needs a patient explanation.

• Listening to a customer teaches things no report shows.
• Asking how to help better, Gut argues, builds a different commercial relationship than asking how to sell more.

The closing comparison makes the point concrete: people forget an ad, a promotion or even the price they paid, but find it much harder to forget how they were made to feel.

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People now ask questions of AI assistants as well as scanning lists of links, and SEO, AEO and GEO are best understood as connected approaches rather than separate strategies.

• The underlying need has not changed: someone asking what a service costs wants a clear explanation, and someone comparing providers needs information that lets them decide.
• Gut warns against producing content only to please algorithms or to win an AI mention, and proposes a harder test for the work.

The test Gut offers is whether the content deserves to be used as a source. Visibility can win an opportunity, he writes, but the trust the published material generates is what makes someone want to know the company better.

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Many companies invest in being visible but rarely stop to review what they are leaving in customers' minds, and when asked why someone should choose them, the answer too often resembles their competitors'. A brand needs clarity about whom it helps and what problem it solves, something it can demonstrate with facts, a point of view it is willing to defend, and the consistency to sustain it over time.

Gut works from the six Ps of positioning and singles out proof as the element that matters most: an attractive promise is easy to build, but results, customer experiences and daily work are what give it credibility.

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The post gathers free courses from providers Gut names as Google, IBM, Cisco and other organizations, covering AI agents, process automation, Power BI, Microsoft tools, customer service, leadership and digital transformation, and frames the post as a shared space for learning opportunities that often go unnoticed.

An image shows 12 selected courses from a longer list of 52. Readers comment with their name or the topic they want to learn, and Gut replies with a direct link, with related recommendations from the longer list.

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Start here because it states the system-level view that the rest of the posts apply to specific problems.

The post argues that companies accumulate an AI research tool, a writing tool, automations, a CRM, analytics, paid media and content platforms, and still end up with the same problem: fragmented processes.

• The proposed fix is to stop thinking in tools and design ecosystems, with a described chain in which research feeds content, content connects to distribution, distribution generates data, data returns to strategy, and automation removes repetitive work between each stage.
• The closing question is not what tool is missing but which part of the system still works in isolation.
• The post also separates technology from judgment: deciding which message matters, which channel to prioritize and which result to pursue stays a human responsibility.

For brands planning a campaign

For a B2B SaaS brief about customer experience and marketing technology integration, this is the most directly on-brief piece supplied. It gives a concrete diagnostic question a brand can reuse in its own review, and it frames integration as a design problem rather than a purchasing problem.

A newcomer should start here because it reframes AI adoption as a thinking and questioning skill rather than a software purchase.

The post opens by saying ChatGPT can give an excellent answer to a badly formulated question, and that this is precisely the problem.

• The argument is that people ask for extraordinary results while giving instructions without context, objectives or a clear explanation of what is needed, then conclude the tool failed.
• The comparison offered is a team member asked to do work with insufficient information.
• The practical resource is a set of 50 prompt shortcuts, described as small templates for orienting instructions, analyzing alternatives, questioning ideas or structuring a task.
• The post ends by asking readers what they use the tool for most: creating content, researching, analyzing information or solving problems.

For brands planning a campaign

Useful for any brief that involves training a team on AI tools, or for content about AI adoption. It supplies a ready-made framing and a named resource that can anchor a workshop or a campaign angle on AI literacy rather than AI features.

Start here because it shows how this creator reasons about the boundary between marketing and service, which is the core tension in customer-experience work.

The post begins from the claim that customers do not know where marketing ends and service begins, and should not have to care.

• It argues that customer experience cannot be treated as an isolated area, because one late response or one absurd policy can undo months of work by other teams.
• The stated view is that experience happens in the connections: what is promised, what is delivered, how the company responds when something fails, and what is done afterward with what was learned.
• Technology and AI are described as valuable for connecting data, anticipating needs and reducing friction, but unable to compensate for an organization where each area optimizes its own part and ignores the full customer journey.
• The reflection credits the 'Sombrilla de CX' concept to CX Latam Group, presented as a useful framework for visualizing how different areas participate in one customer experience.
• The post closes with a call for comments asking which piece feels weakest and offers to prepare a practical guide if a clear pattern appears.

For brands planning a campaign

Directly relevant to a customer-experience brief and to any brand that runs marketing and service separately. It gives a specific external framework to reference and a public engagement device (the comment-driven guide) that a partner brand could replicate.

A good entry point because it is the clearest statement of the creator's recurring argument that sales depends on understanding lived experience.

The post questions how many business opportunities are lost through not listening enough.

• It notes that after years in a market, companies believe they understand their customers: they know what they sell, they know the market, and they know what they want to offer.
• What is harder is understanding the person on the other side: what worries them before a decision, what expectations they hold, what creates distrust, and what needs a patient explanation.
• The claim is that listening to a customer can teach things no report shows, and that being busy selling can crowd out asking how to help better.
• The closing line is that people forget an ad, a promotion or even the price, but find it much harder to forget how they were made to feel.

For brands planning a campaign

Useful for brands selling complex or considered B2B services, where the gap between what a company assumes and what a buyer actually fears is wide. It supports campaign angles built on buyer research and objection handling rather than on offer repetition.

Start here because it treats a current, fast-moving topic without hype and ties it back to content quality.

The post opens with a concern that search positioning is becoming a race to learn new acronyms.

• It notes that people no longer only search a list of links; they also ask AI assistants questions.
• The argument is that tools change how information is found, but the need for useful, trustworthy answers remains: someone asking the price of a service wants a clear explanation, someone comparing providers needs information that lets them decide, and if an AI uses a company's content as a reference, that content should be well supported.
• The post explains that AEO and GEO are useful to understand without treating them as strategies completely separate from SEO, and warns against producing content solely to please algorithms or earn an AI mention.
• The line to hold on to is that visibility can win an opportunity, but the trust what is published generates is what makes someone want to know the company better.

For brands planning a campaign

Relevant for B2B SaaS brands under pressure to react to AI search. It offers a defensible position that keeps content quality central, which can be used in an editorial campaign or an executive point of view.

A strong starting point for positioning work because the test it proposes is simple to apply.

The post asks a direct question: if the company logo disappeared from all its publications tomorrow, would customers still recognize the brand?

• It observes that many companies invest heavily in being visible but rarely stop to review what they are leaving in customers' minds, and that when asked why someone should choose them, the answer too often resembles competitors'.
• The stated requirements are clarity about whom the brand wants to help, what problem it solves, and what it can prove with facts, plus a point of view, a particular way of doing things and the consistency to sustain it.
• The post references the six Ps of positioning, and singles out proof as especially important: an attractive promise can be built, but results, customer experiences and daily work are what give it credibility.
• It closes by repeating the recognition question.

For brands planning a campaign

Useful for brand or positioning campaigns where differentiation claims are weak. The logo-removal test is a compact device a brand can run with its own team, and the emphasis on proof fits campaigns that need to move from claims to evidence.

Start here because it is the clearest statement of the creator's stance on AI and process improvement: small verified changes before large transformations.

The post raises a concern about companies growing accustomed to problems they already know exist: a process that always runs late, a task that has to be repeated, an approval that passes through too many people.

• It notes that the team often already knows where the problem is; what is missing is stopping to listen and acting on it.
• The Kaizen philosophy is presented as the counterweight, alongside the view that continuous improvement cannot be asked of a team if every proposal meets a 'we have always done it this way' response.
• Applied to AI, the argument is that before adding another tool or automating a whole process, a company should ask whether it is solving a real problem or just making something that already worked badly run faster.
• The post ends with the question of what problem has been tolerated so long it stopped looking like a problem.

For brands planning a campaign

Relevant for digital transformation and automation briefs, and useful as a counterweight in campaigns that assume AI adoption is automatically progress. It gives a concrete framing question that fits executive-facing content.

Start here if you want to see the creator's practical side: sharing a usable resource rather than an opinion.

The post questions how many opportunities are lost by assuming learning AI is expensive or too complicated, and shares an offer of free technology and AI courses from providers such as Google, IBM and Cisco.

• The list is explicitly not limited to learning ChatGPT: it covers AI agents, process automation, data analysis with Power BI, Microsoft tools, customer service, leadership and digital transformation.
• The post says a selection of 12 courses is shown in an image, that a longer list of 52 trainings exists, and invites readers to comment with their name or the topic they want to learn in order to receive a direct link.
• The stated interest is reaching people who run businesses and need to understand how to apply technology in daily work.

For brands planning a campaign

Useful for audience-building or community campaigns around AI skills, and as evidence of a resource-sharing, comment-driven distribution format. A brand partner could adapt the format to gate its own training material behind a conversation rather than a form.

Gut describes the common failure as fragmentation: separate tools for research, writing, automation, CRM, analytics, paid media, and content, each working alone, leaving more software and the same broken processes.

• The proposed fix is a loop — research feeds content, content connects to distribution, distribution produces data, data returns to strategy, and automation removes repetitive work between each step.
• Gut also draws a firm line around human judgment: deciding which message matters, which channel to prioritize, and which result to pursue should stay with people.

Gut uses a repeating research-to-strategy loop as a diagnostic and reframes the buyer's question from "what tool are we missing?" to "what part of our system is still working in isolation?"

For brands planning a campaign

For a brief about marketing or AI platforms, this is the strongest available angle: present the product as a connector inside an existing stack, and show one connected workflow end to end instead of a feature list. The reframed buyer question is used here as a teaching device and can be adapted almost directly into campaign copy.

Examples: Source 1

A strong campaign, a good CRM, and automated processes can still be undone in minutes by a late reply, a poor delivery, or a rigid policy.

• Gut locates the experience in the connections between what is promised, what is delivered, how the company responds when something fails, and what it does with what it learns.
• Technology and AI can connect data, anticipate needs, and reduce friction, but Gut states plainly that they cannot compensate for an organization where each area optimizes its own part and ignores the full customer journey.

Gut builds the post around the "Sombrilla de CX" framework from CX Latam Group, which he credits, to show how different areas take part in one experience, then turns it into a diagnostic by asking readers to comment the number of the weakest piece in their company and offering a practical guide if a pattern appears.

For brands planning a campaign

Fits a B2B customer experience or service-design campaign.

• The material supports content about handoffs, recovery after failure, and cross-team ownership rather than a single-channel tool.
• The comment mechanic — asking which piece is weakest — is a ready-made engagement and lead-generation prompt for this audience.

Examples: Source 1

The details that decide a purchase — what someone worries about before deciding, what they expect, what creates distrust, what needs a patient explanation — rarely appear in a report. Gut's point is that asking how to help better produces a different commercial relationship than asking how to sell more, and closes on the idea that people forget an ad, a promotion, or even the price more easily than they forget how they were treated.

He opens with a counterfactual question about lost opportunities, builds the argument through customer-side concerns, and closes with a memorable comparison rather than data.

For brands planning a campaign

Useful for voice-of-customer and CX briefs, or for any campaign that needs to argue for discovery research over assumption. It gives a partner a credible way to talk about listening and service quality without relying on claims about market data.

Examples: Source 1

One post argues that ChatGPT can give an excellent answer to a badly framed question, which is exactly the problem: instructions without context, objectives, or a clear statement of need produce weak output, and the tool gets blamed for a briefing failure.

• Gut shares prompt templates for orienting instructions, analysing alternatives, challenging ideas, and structuring tasks.
• A second post applies Kaizen thinking: teams often tolerate known problems because they have stopped seeing them, and automation aimed at those problems simply makes a bad process faster.
• Gut's test before automating is whether the problem is real.

He pairs ready-to-use prompt templates (shared as an image to keep on hand) with a continuous-improvement cycle, and ends each post with a question to the audience about how they use the tools or which problem they have stopped noticing.

Gut is explicit that automating an inefficient process does not turn it into a good process, so a campaign built on speed alone would run against the message this audience already hears from Gut.

For brands planning a campaign

For an AI enablement or automation brief, lead with prompt literacy and process review rather than raw capability. The templates and the Kaizen framing both give the partner concrete teaching material that matches what Gut already publishes.

Examples: Source 1 · Source 2

He says the image shows 12 courses selected from a longer list of 52, and frames the offer around professionals, entrepreneurs, and business owners who need to apply technology in daily work but lack the time, resources, or guidance to start. Readers are invited to comment a course name or a topic to receive a direct link, with related recommendations offered from the longer list.

Gut uses a curated list plus a comment-to-receive-link distribution mechanic, and positions the post as a shared space for learning opportunities rather than a promotion.

For brands planning a campaign

This is direct evidence of a format Gut already runs: a curated resource list with a link-on-request mechanic. A brand with a training, certification, or enablement offer could fit that structure, but the offer needs to sit credibly inside a free-course list.

Examples: Source 1

He argues that most companies can describe why someone should choose them only in terms that mirror competitors, which signals a problem, and that a brand needs a clear audience, a defined problem, something it can demonstrate with facts, and an opinion it is willing to defend consistently. Gut references the "6 P" positioning exercise and singles out proof as the element he considers especially important: results, customer experience, and daily work are what make a promise credible.

He frames the topic as a set of self-review questions — audience, problem, proof, opinion, consistency — rather than as a definition of branding.

For brands planning a campaign

For brand or positioning work, the usable angle is differentiation paired with demonstrable evidence. A partner can support a campaign that asks buyers to audit their own positioning and back it with proof instead of claims.

Examples: Source 1

He notes that people now ask questions of AI assistants as well as scanning link lists, and that SEO, AEO, and GEO should be understood as complementary rather than as separate strategies.

• The underlying need — useful, trustworthy answers — has not changed: someone asking what a service costs wants a clear explanation, and someone comparing providers needs information that lets them decide.
• Gut warns against producing content only to please algorithms or to earn an AI mention, and proposes a better test: whether the content deserves to be used as a source.

He treats the acronyms as one connected field, shares an image showing how the approaches complement each other, and reframes the goal from visibility to trust.

For brands planning a campaign

For an SEO, content, or AI-visibility brief, the angle is content quality and trustworthiness rather than chasing AI citations. It lets a partner speak to buyers who are being sold acronyms and want a steadier standard.

Examples: Source 1

The feed is not exclusively professional. One post is a photo and reflection from a trip to Washington, standing outside the house where Abraham Lincoln died, about enjoying the present rather than always planning the next thing; Gut states directly that the material usually covers business, marketing, and technology, but that other interests are part of the same life.

Gut alternates between a question-first reflective register for ideas and a list-and-template register for practical resources, and writes in Spanish throughout the supplied material.

The personal post shows the feed mixes reflection with professional content, so a campaign expecting a purely commercial channel would not match the material reviewed.

For brands planning a campaign

Brands should expect a conversational, first-person register and a feed that combines practical marketing material with personal reflection. A campaign tone that is heavily promotional sits against this pattern, while a teaching or discussion-led approach fits it.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6 · Source 7 · Source 8 · Source 9

The post points readers to the training; it does not describe any working or paid relationship between David Gut and Google.

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Mention only; no relationship with IBM is stated.

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The post uses Cisco as an example of available training, not as a partner or client.

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No commercial relationship is stated.

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This is an attribution of an idea, not a disclosed commercial tie.

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The post treats ChatGPT as a product to use, with no affiliation or sponsorship disclosed.

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Lendo writes about energy transition, solar recycling and Mexican electricity policy; a reader who follows Gut for marketing, customer experience or AI practice will not find that subject matter here.

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Garasa's posts are event- and presence-led industrial cybersecurity commentary; they do not develop the customer experience, brand or marketing arguments that carry Gut's feed.

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Quintão's examples come from industrial maintenance, lean manufacturing and rail operations, several tied to named employers and colleagues; Gut stays in marketing and customer experience and names frameworks rather than workplaces.

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The two posts reviewed argue from named international data: IEA figures for installed photovoltaic capacity and roughly 600 GW added in a year, IRENA projections of 78 million metric tons of solar waste by 2050, and Mexico's own targets of 45% renewable electricity by 2030 and a 50% CO2 reduction by 2035.

• The second post reads the IRENA renewable-energy report and Mexico's Plan México package — 622 billion pesos of investment, 22 GW of generation, 6 GW of storage — and draws out what it means for private investment.
• A vendor whose pitch has to sit inside that policy and capital-allocation conversation has a better chance with this material than with a customer-experience argument.

The tradeoff is category distance.

• Neither post discusses customer experience, CRM, marketing operations, or workflow design; both are energy-market and policy briefings with citations rather than teaching formats or comment-driven resources.
• Nothing in the reviewed material shows a sponsored format, a rate, or audience demographics, and the two posts are dated 8 May and 26 July 2026, so a vendor would be buying topical adjacency, not demonstrated customer-experience reach.
• If the brief stays on customer experience and marketing workflows, David Gut remains the closer fit; Lendo is the better choice only when the target buyer moves to energy and sustainability.

Examples: Source 1 · Source 2

The reviewed posts are a speaking announcement and a recap.

• One promotes a session at CYBERTECH LOGISTICS, DISTRIBUTION & TRANSPORT on 28 May 2026 at Auditorio El Beatriz in Madrid and lists the topics: legacy and airgapped system protection, critical-infrastructure security, automation risk, and operational resilience.
• The other recaps the Industry 4.0 Congress inside ADVANCED FACTORIES, where the post describes a talk on resilience in industrial environments delivered alongside a co-speaker from Urbaser.
• Both name the employer, TXOne Networks, and both point readers to the events and to direct messages for invitations.
• For a vendor selling into that sector, that is a route into a technical Spanish-speaking audience and into event amplification, which the customer-experience material does not offer.

The tradeoff is that these posts are event marketing rather than independent argument.

• Neither reviews a customer-experience problem or a marketing-technology stack; both centre on the writer's own speaking slots and employer, so a vendor from a different category would be attaching to an industrial-security context and to one company's calendar.
• The dates are 21 and 22 May 2026, both ahead of the 28 May event, so the material is narrow and time-bound.
• No engagement, audience-job, or commercial data appears in the reviewed posts.
• Where the brief stays on customer experience and marketing workflows, David Gut is the stronger match; Garasa is better only when the buyer and the moment are industrial-security events.

Examples: Source 1 · Source 2

The two posts reviewed argue from a stated operating record: reducing unplanned downtime, raising availability of critical assets, controlling cost, and improving predictability, through reliability discipline, integration between maintenance, operations, and engineering, governance indicators such as availability, MTBF and MTTR, Lean Manufacturing on the shop floor, standardised processes, and visible leadership.

• The older post gives a concrete result — moving a rail wagon line from an average of 12.4 to 18.7 wagons a day by mapping and removing waste — and thanks named colleagues for the work.
• A vendor selling reliability, maintenance, or operations software into heavy industry would find the argument and the vocabulary already in place.

The tradeoff is a double shift: language and category.

• The material is in Portuguese, not Spanish, and it is about industrial availability and Lean practice, not customer experience, CRM, or marketing workflow, so a SaaS vendor in the original brief would be changing both its buyer and its market.
• The two posts are far apart in time — 17 June 2024 and 16 August 2026 — and the 2026 post closes with a direct partnership pitch, a commercial register the David Gut material does not use.
• No audience or engagement data was reviewed.
• Keep David Gut for the customer-experience brief and consider Quintão only when the target is operations leadership in Brazil.

Examples: Source 1 · Source 2