Nick Brackney

In Brackney’s account, organizations get the most from AI when they hand the work to people who already understand the process and customers.

• The roles are shifting accordingly: the “deciders” are becoming the “doers.”
• Brackney says that domain expertise was the bet behind hiring them for a product management role on AI marketing tools — despite having no experience with sprints, no mastery of Kanban boards, and only a rough grasp of agile versus waterfall.
• The claim is that domain expertise compensates, and that a solution built around the business logic will work the way the business already works.

Brackney responds to an article by Shyam Ravindranathan on how the product management function is changing.

• As Brackney describes it, Ravindranathan makes the case for greater technical fluency in the role, while Brackney argues for a more compelling alternative: reducing and abstracting away technical complexity so users don’t have to become more technical.
• Brackney describes themself as fairly technical, but says the biggest wins came from making technology accessible to users, and recommends that most organizations turn to their domain experts to build AI capabilities.
• Ravindranathan’s argument is reported secondhand here; buyers should review the article itself before relying on this characterization.

The tension is that Brackney argues against demanding technical fluency while describing themself as fairly technical. The point isn’t that technical skill is useless, but that it shouldn’t keep domain experts out of the work.

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Deployment isn’t a matter of shipping the tool and hanging the “Mission Accomplished” banner.

• Teams have to work every day to convert users, making adoption a consistent and aggressive part of the strategy if the tool is to deliver value.
• Often, the person who built the tool has to keep presenting it.

Brackney describes a demo to half of the product marketing leadership that went well enough to prompt a question: how could they get Brackney in front of their teams more often?

• That was followed by a debate about whether it was the best use of the time.
• Brackney’s answer: it has to be.
• The account is based on internal meetings and reactions; ask for evidence of measurable results before making a decision.

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Being on team “yes, AI can do that” isn’t enough; you have to be certain. In practice, that means a short investigation, followed by the exact prompt, its output, and a plain explanation of the mechanism so readers can repeat it.

After moving into product management for AI marketing tools, Brackney was new to attribution and tracking URLs, and describes it as a learning opportunity rather than pretending to already know the subject.

• A one-sentence prompt asking for a trackable URL for a Dell NVIDIA landing page produced a link carrying utm_source=bob, utm_medium=referral and utm_campaign=nvidia_ai_promo.
• The post then explains the five main UTM parameters and which are required versus optional.
• Brackney notes the limits: this is probably basic for web teams, and tracking likely still depends on controlling the domain.

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That stance shapes what a sponsored or commissioned piece would look like: an explanation with a product as the worked example, rather than a direct pitch. Brackney also says the one area where they were dinged was not using the platform for outreach campaigns and active engagement with other members — a choice they frame as preferring to offer value and meet people where they are.

Brackney recalls that Pat Moorhead once recognized Brackney at Dell Technologies World with the line "I know Nick he posts all the time on LinkedIn," and describes monitoring small wins, something going viral roughly every six months, and checking a LinkedIn social score after seeing someone discuss it.

• The companion post points to a conversation with Jessica Hreha and Yadin Porter de León about Dell's AI marketing journey, covering how marketers can evolve into AI product management by leaning on domain expertise and how to build and scale AI tooling safely across an enterprise.
• Hreha's and Porter de León's views belong to them; the post is a short pointer and does not discuss the linked conversation's specific claims, so they remain unverified.

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Campaigns, the argument goes, have to start with the why, and marketers should keep working to become better storytellers. It’s a second register alongside the technical work, not a replacement for it.

Brackney says product management has meant less time in marketing, then describes a family law attorney’s advertising campaign spotted on the morning commute as the unlikely inspiration for the post. The post is brief and mostly points to a linked piece, establishing the interest without developing the argument in full.

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Start here to see how the working-example format holds up.

Brackney describes moving from product marketing into product management for AI marketing tools and being new to attribution and tracking URLs.

• Instead of asserting that AI can handle it, they ran a quick check and published the full chain: a one-sentence prompt asking for a trackable URL for a Dell NVIDIA landing page; the resulting link, with utm_source=bob, utm_medium=referral and utm_campaign=nvidia_ai_promo; and a walkthrough of all five UTM parameters, identifying which are required and which are optional.
• Their rule: being on team "yes, AI can do that" does not license an answer unless you are certain.
• They also note that the example is probably basic for web teams and that tracking likely still depends on controlling the domain.

For brands planning a campaign

For a practical AI tooling series, this is a reusable format: a specific business question, the literal prompt, the real output and a short explanation of why it works. A campaign can return to this format for consistent, inspectable pieces rather than general AI commentary.

Read it before the tooling examples to understand what those examples are for.

Brackney opens by admitting the gaps — no mastery of Kanban boards, no experience with sprints and only a rudimentary grasp of agile versus waterfall — then argues that domain expertise compensates.

• They cite a Shyam Ravindranathan article on the evolving product management function, describe leadership as "skating to where the puck was going" in making the hire, and argue that the "deciders" are becoming "doers."
• Their recommendation to organizations is to look to domain experts to build AI capabilities.
• They also push back on the article's call for more technical fluency, arguing that the stronger approach is to reduce complexity and abstract it away so users do not have to become more technical.
• The method: understand the business process and logic, build them into the solution, and it will work the way the business works.

For brands planning a campaign

This offers a defensible angle for a domain-expert-led product management series: marketers need not learn to code, but should own the business logic and hide the technical complexity. Pair it with the tooling examples to demonstrate the claim.

The post states its position directly, making it a useful test of whether the argument can scale to a campaign.

Brackney argues that deploying AI is not "ship it and hang the 'Mission Accomplished' banner"; converting users takes daily work.

• They describe a demo to half of product marketing leadership that went well enough to prompt the question of how to get them in front of more teams, followed by a debate about whether that was the best use of their time.
• Their answer: it has to be, and adoption must be a consistent, aggressive part of the strategy to maximize AI's value.

For brands planning a campaign

Useful for a brief that needs post-launch enablement, not just launch content. The post models a concrete asset — a repeatable internal demo — and explains why the same person should keep delivering it, giving a campaign a case for internal champions.

This is the shortest post here and the least self-contained, so treat it as a signpost to a longer conversation, not an argument in its own right.

Brackney reports a conversation with Jessica Hreha and Yadin Porter de León about the AI marketing journey at Dell.

• The topics are how marketers can evolve their roles to meet the needs of AI product management by drawing on domain expertise, and how to safely build and scale AI tooling across an enterprise.
• The post is a short pointer with the hashtags #AI, #Iwork4Dell, #AITransformation and #DomainExpertise; the substance is in the linked conversation.
• Since that conversation was not part of this review, confirm its specific claims before a campaign relies on them.

For brands planning a campaign

Signals that Brackney will do paired or interview-format content, with recurring themes of domain expertise and safe enterprise scaling. A campaign could commission a longer-form version, but should confirm what was actually said first.

This is the only piece here that directly describes the editorial approach, which matters when deciding whether a sponsored series would feel native or like an ad.

Brackney reflects on whether posting volume has an impact, recalling that Pat Moorhead once recognized them at Dell Technologies World as "Nick, posts all the time on LinkedIn."

• They describe tracking small wins, seeing something go viral roughly every six months, and checking LinkedIn social scores after someone discussed them — landing in what they consider good standing for the industry and network.
• Their approach is an insider's lens: peeling back the curtain to explain what they see and where the market is heading, with value and education ahead of selling.
• They note that they are dinged for not using the platform for outreach campaigns and active engagement with members, and link their move into product management and AI tooling to stronger material.

For brands planning a campaign

This is the clearest signal of how a partnership would land: Brackney puts education first and explicitly avoids outreach-style promotion. Expect an explanatory format with the sponsor's product as the worked example, not a direct-response pitch.

This short marketing-craft post shows Brackney's interest in storytelling alongside the technical work.

Brackney notes that product management has meant less time in marketing, then describes a family law attorney's ad campaign on the morning commute as the unlikely inspiration for a post.

• The point: marketing must start with the why, and the linked piece offers a quick read on becoming a better storyteller.
• The post is brief and mostly a pointer, establishing an interest rather than developing an argument.

For brands planning a campaign

Useful as a second register for a series: Brackney writes about messaging and narrative craft as well as tooling. A campaign can pair a storytelling piece with a build piece to reach marketing and product audiences.

In one post, they describe moving into product management for AI marketing tools without conventional PM fundamentals, but with strong domain knowledge.

• They say their biggest wins came from reducing complexity so tools work the way users already work.
• A second post summarizes a conversation about how marketers can move into AI product management by leveraging their domain expertise.

A first-person account of Nick Brackney’s role transition, framed as a response to an external article they link to.

For brands planning a campaign

For a series on domain-expert-led product management, the central argument is that marketing and business-process knowledge—not engineering fluency—helps AI tools land with users. It speaks to enterprise audiences concerned about AI skills gaps and gives the series a clear point of view beyond a tool tour.

Examples: Source 1 · Source 2

Brackney says teams have to be “in there fighting each day to convert the users,” describes a demo to product marketing leadership that landed well, and reports that the follow-up question was how to get them in front of more teams. They frame adoption as a consistent, aggressive part of strategy.

Ask for evidence of whether this adoption approach produced measurable results; the post reports Brackney’s internal meetings and reactions.

For brands planning a campaign

Supports a series segment on post-launch enablement—repeated internal demos, training, and user conversion—rather than launch-day announcements. A brand selling AI tools to enterprise marketing teams can use this framing to move beyond feature rollout.

Examples: Source 1

The post includes the exact prompt they used, the tracked URL it produced, and a plain explanation of the five main UTM parameters.

• They also state their rule for AI claims: “yes, AI can do that” is not an answer unless you are certain.
• They ran a short investigation first.

A demonstration of the prompt, output, and parameter-by-parameter explanation, with a stated verification step before making the claim.

For brands planning a campaign

An AI tooling series can borrow this format: one concrete task, the prompt, the output, and a short explainer. It works as a copyable tutorial, making it more useful for enterprise enablement than a general claim that AI handles a task.

Examples: Source 1

One post is a conversation about the AI marketing journey at Dell, covering how marketers can evolve their roles and safely build and scale AI tooling across the enterprise.

For brands planning a campaign

For a brand seeking an educational rather than promotional voice on enterprise AI marketing, Nick Brackney’s stated editorial stance fits. Plan for interview and conversation formats alongside written posts: they point readers to recorded discussions as well as text.

Examples: Source 1 · Source 2

It complements Nick Brackney’s AI and product management material.

For brands planning a campaign

Useful for a campaign that needs to pair storytelling and positioning fundamentals with AI adoption content, not just tool demonstrations. It connects classic marketing craft to newer AI product work.

Examples: Source 1

Most are short and include an external link, a hashtag block, or both; one is a longer post built around a prompt example and an explainer. In one post about using the platform, they say they are flagged for preferring to offer value and meet people where they are rather than run active outreach campaigns.

Ask for independently measured platform data if the campaign depends on it; this is Brackney’s own description of their posting habits.

For brands planning a campaign

Plan the series around LinkedIn-native posts and interview or conversation clips rather than long-form articles. If the campaign requires the creator to run outbound activity on the platform, ask whether they would do so; Brackney describes using it without active outreach campaigns.

Examples: Source 1

One post uses a Dell product landing page to show how to generate a trackable referral URL. They also mention Dell Technologies World, where they say they commissioned whitepapers in earlier years.

View example

Both lay out setup steps marketers need to get right before results mean anything: Basu's ChatGPT Ads launch checklist and UTM naming convention cover the same territory as Brackney's tracking-URL walkthrough. Both teach through concrete checks rather than claims that a channel works.

Basu works agency-side on paid media execution and reporting for client accounts, focusing on clean platform setup and CRM numbers. Brackney writes from inside a large enterprise vendor about building AI marketing products and converting internal users after launch—a product and adoption problem rather than a media-buying one.

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Jordan's posts on putting AI search visibility next to paid channels and comparing organic cost-per-click with paid cost-per-click use the same move Brackney makes with the UTM prompt: name the specific number, show the calculation, and let the reader reproduce it.

Jordan focuses on defending organic and AI search budgets, using agency-side reporting, attribution, and ROAS. Brackney focuses on who should build AI marketing tools and how to sustain adoption, giving readers a measurement case from Jordan and a product-management case from Brackney.

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Hadding's half-year ecommerce review and strategic-partnerships post read as insider trend commentary, much like Brackney's insider-lens posts.

Hadding covers DTC and apparel ecommerce, including customer lists, email and SMS revenue, Meta ad spend, and acquisition costs. Brackney focuses on B2B enterprise AI product management, so the comparison is more about format and practitioner voice than subject matter.

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Two posts walk through a ChatGPT Ads launch checklist—pixel firing on the landing and thank-you pages, UTMs on every ad, deciding which event counts as a conversion, giving the landing page a clear next step, comparing platform reporting against the CRM from day one, and keeping spend small while learning—and a UTM convention that keeps source, medium, campaign, and content consistent so one channel does not split into four lines in a report. For a software company that wants an enterprise audience to see campaign mechanics rather than product building, this is closer to the daily work than Brackney's product-management material.

The tradeoff is scope and altitude.

• Basu's posts cover operator-level paid media—launch hygiene and reporting conventions—so they suit a team already running campaigns.
• They do not cover product management, building AI tooling, or how a domain expert moves into an AI product role, the other half of the need.
• Two recent posts offer a narrow basis for judging range.
• Ask for audience and availability information.

Examples: Source 1 · Source 2

The posts offer a repeatable argument: put LLM visibility data in the same slide and format as paid, show conversion-rate parity with paid channels, track branded search and direct traffic as proxies for AI recommendations, and use the paid team's vocabulary—ROAS, not ROI.

• A second post applies the same approach to a retainer by putting organic cost-per-click next to paid cost-per-click.
• For a software company whose marketing leadership has to justify AI-search spend to a CFO, this is more useful than an AI-adoption narrative.

The tradeoff is that the material is about winning budget, not building or teaching AI products.

• A team seeking a series on how domain experts shape AI product management would lose that thread.
• Both posts are short and argument-driven, with no product or tooling walkthrough to reuse.
• Ask who reads them.

Examples: Source 1 · Source 2

The posts show aggregate performance across an agency's active e-commerce accounts—revenue growth, returning-customer rates, email and SMS share of revenue, ad spend, and platform mix—and a partnership idea for two brands in the same category to pool existing customer lists as acquisition costs rise. For a DTC brief about retention, list strategy, or cross-brand partnerships, this is a closer match than Brackney's enterprise AI material.

The tradeoff is audience and subject.

• The posts cover consumer e-commerce and agency client reporting, not B2B enterprise software, AI marketing tooling, or product management.
• Choosing Hadding for a campaign about those topics means changing the buyer's category as well as the creator.
• There are two posts plus one half-year summary.

Examples: Source 1 · Source 2