Kevin Nanney works in B2B software product and writes about product management, AI-enabled product work, and measuring AI’s business impact. Nanney’s posts connect product decisions to the problems customers face and the work teams need to deliver.
Kevin Nanney is most useful for connecting AI product work to customer problems and measurable business outcomes.
Best for
A B2B SaaS brand that needs AI product claims framed honestly and tied to business outcomes.
Not for
A poor fit is broad reach brand awareness, consumer, lifestyle, or multi-format campaigns.
Key takeaways
Tempo example reports 61% faster work from its rollout, not a result others should expect.
AI can speed early product work but shifts the bottleneck to coordination.
AI spend can be tied to the work item then traced back to strategy.
Check first: Confirm audience breakdown, recent post results, and whether paid placements are available.
Main ideas
Shipping software and solving the problem are not the same thing · AI moves the product bottleneck instead of removing it · Standardize the tools and process, or the speed just adds confusion
Kevin Nanney works in B2B software product and writes about product management, AI-enabled product work, and measuring AI’s business impact.
Their posts connect product decisions to what customers actually need: their account of moving from implementation consulting into product management centers on software that shipped but made users’ work harder.
They also examine how AI changes team workflows and how to connect AI spending to the work and strategy it supports.
Follow their work for practical questions about whether software solves a real problem, how teams should organize AI-assisted work, and how to assess its results.
Nanney’s posts link customer experience, team operations, and business measurement rather than treating product delivery or AI speed as success by themselves.
They explain this through concrete contrasts: enterprise software can meet delivery targets yet worsen users’ processes; AI can accelerate early product work while shifting the bottleneck to coordination; and AI costs become more meaningful when traced to work items and strategy.
In the Tempo example, they report a 61% faster-work result from the company’s own rollout and explicitly do not present it as a result others should expect.
Their posts also include a short hiring update, so they are not exclusively analysis.
Together, these examples make their work useful to readers considering how product teams can turn technology into work that helps customers and can be evaluated against business goals.
Kevin Nanney writes about building B2B software in the AI era, drawing on a career that began in enterprise implementation consulting and moved into product management.
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Two concerns run through the posts reviewed here.
First, delivering software on schedule is not the same as solving the customer's problem; people who design tools should account for the people who have to use them.
Second, AI compresses the early work of research, requirements, and business cases, shifting the bottleneck to building and coordination as roles blur.
The payoff depends on whether teams standardize their tools and process instead of letting each group assemble its own.
A fourth post applies the same instinct to cost: an AI budget becomes defensible when spending is tied to the specific work it funded and traced up to the strategy that work serves.
Shipping software and solving the problem are not the same thing
Nanney's position, grounded in firsthand implementation work, shapes how they say they evaluate product decisions.
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Software can be delivered on time, sometimes on budget, and still be wrong: the people who built it never asked the people who had to use it what they needed.
Nanney argues this is a design and process failure, not a technical one.
Watching a customer realize the tool meant to help them made things harder should shape every later decision.
As an implementation consultant at Compaq Computers, Phillips Petroleum, and Union Carbide, Nanney installed ERP software that offered configuration rather than customization, replacing long-standing processes with standardized ones that were "slower, clunkier, and fit about as well as a rental suit."
Customers blamed the consultant rather than the executives.
Nanney describes sitting with those users and listening instead of deflecting.
The post closes with the principle: "Shipping software and solving the problem are not the same thing.
I learned that early on."
The post traces a deliberate career move: Nanney left implementation for product management because of this experience, describing the frustration as "the chip on my shoulder" that still drives the work—not a phase that ended.
AI moves the product bottleneck instead of removing it
Nanney argues that AI has dramatically sped up the front end of product work—researching the problem, writing requirements, building a business case, and aligning stakeholders—but that this is not pure upside.
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The bottleneck shifts to building and coordination.
Teams now go straight into building, with prompts feeding one system where documents once did.
The old structure no longer fits: product, design, product marketing, and engineering blur together, and ownership becomes unclear.
Nanney describes a single body of work reused across everything, where one system can generate prototypes, suggest front-end code, create sales and enablement materials, and build competitive analysis. The consequence is that "it's no longer separate teams producing separate outputs," and teams have to rethink how they operate because roles and ownership are less defined.
Standardize the tools and process, or the speed just adds confusion
Nanney identifies fragmentation as the risk companies underestimate.
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When every team uses its own tools, gains do not compound: each group builds its own version of the system, with nothing connecting them.
The advantage, in their argument, comes from standardizing how the team works—the same tools, the same process, one shared system.
Without that, teams do the same work faster, with more confusion, rather than gaining real leverage.
The post contrasts a shared system—producing prototypes, code suggestions, sales materials, and competitive analysis from a common source—with separate teams assembling their own stacks. Nanney's conclusion is clear: the real advantage comes from standardizing how you work; without it, speed turns into fragmentation.
Tie AI spend to the work item, then trace it back to strategy
Nanney treats "is our AI bill worth it?" as a question most teams cannot answer with evidence.
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The common answer, in this account, is guesswork: manual reports, partial readings from developer productivity tools that cover only the engineering share of the spend, and hope that the story holds up when finance asks. Nanney argues that AI costs become defensible when tied to the actual work item and traced up to the strategy it serves, making the most expensive AI-assisted work visible rather than estimated.
Nanney describes Tempo Software's Workforce Intelligence tying AI spend to the work item—visible down to an epic—and tracing it back to strategy.
They contrast this with other solutions that scrape prompts running through a team's tools and estimate spend.
Nanney cites Tempo's own rollout as shipping work 61% faster with AI than without it, and states plainly that the figure is the company's own result, "not a promise about anyone else's results."
This post gives newcomers a concrete explanation of the customer-problem focus in Nanney’s work: it comes from seeing what happens when software is delivered successfully but fails to help its users.
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Nanney describes implementing enterprise software that met delivery targets but made established processes slower and clunkier.
Users blamed the consultant responsible for training them and managing the rollout.
They say the experience pushed them toward product management, with a lasting focus on whether software solves the problem it was meant to address.
For brands planning a campaign
For a campaign about product leadership or customer-centered software, this offers a useful angle: measure success by whether a product improves the work customers need to do, not just by whether it ships.
This post explains how Nanney thinks AI affects product teams beyond task speed, making it a useful first read for anyone interested in the organizational consequences of AI-enabled product work.
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Nanney argues that AI can compress early product tasks such as research, requirements, and business cases, shifting the bottleneck to building and coordinating the work. They describe prompts and shared systems producing prototypes, code suggestions, sales materials, and competitive analysis, while warning that separate tools and processes can fragment the work.
For brands planning a campaign
A company evaluating Nanney for a product-marketing brief could explore a campaign about connecting AI-assisted research and production to shared team workflows, rather than presenting AI as a standalone productivity shortcut.
This is the clearest example of Nanney connecting AI product work to a business measurement question: what work does the spending support, and what result followed?
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In a post about Tempo Software’s Workforce Intelligence, Nanney describes linking AI spend to work items and the strategy they support.
The approach can show costs down to an epic or work item and identify expensive features.
They report that Tempo’s own rollout shipped work 61% faster with AI than without it, framing the figure as the company’s result rather than a promise for other organizations.
For brands planning a campaign
For a brief about connecting AI product work to measurable business outcomes, this is a strong discussion prompt. Keep the 61% figure in context as Tempo’s own result; it does not predict what other companies should expect.
This offers a secondary example of the professional updates Nanney shares, alongside posts on product marketing and AI.
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This short post shares two product-marketing openings at Tempo and points readers to the job posting and the company’s other openings. It provides a concrete but limited example of Nanney using LinkedIn to share a hiring opportunity.
For brands planning a campaign
It may help frame a recruiting or team-building conversation, but the post alone does not establish a broader hiring strategy or campaign format.
How AI is moving the product bottleneck instead of removing it
Nanney argues that AI has drastically sped up the front end of product work — researching the problem, writing requirements, building a business case, aligning stakeholders — but the bottleneck has moved rather than disappeared.
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Once teams get past those steps, they go straight into building, and what used to be documents are now prompts fed into one system that generates prototypes, front-end code, sales and enablement materials, and competitive analysis.
One body of work now serves everything, blurring the lines between product, design, product marketing, and engineering and making roles and ownership less clear.
They name a second risk that most companies underestimate: if every team uses its own tools, nothing compounds, and fragmentation replaces leverage.
In this account, the advantage comes from standardizing tools and process into a shared system; otherwise, teams do the same work faster, with more confusion.
Names the change people welcome, then the consequence they are not planning for (ownership blur, fragmentation), and closes on the condition that makes the change pay off: standardizing how the team works.
For brands planning a campaign
A vendor selling AI product tooling, product operations platforms, or workflow consolidation can ask Nanney to frame the shift as an ownership and operating-model problem, not a speed story. The standardization argument fits a message about adopting one shared system across product, design, and marketing instead of letting teams assemble their own stack.
Tying AI spend to the work and the strategy behind it
Nanney takes the CFO question directly — whether the AI bill is actually worth it — and describes the common state as guesswork: manual reports, partial answers from developer productivity tools that cover only part of the spend, and hoping the story holds up under finance scrutiny.
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Their pitch for Tempo Software's Workforce Intelligence is to tie AI spend to the actual work item, then trace that item back to its strategy, so costs can be seen down to the epic and the most expensive AI-assisted features being shipped.
They explicitly contrast this with other solutions that scrape prompts running through tools and estimate spend against them.
Nanney cites Tempo's own rollout as shipping work 61% faster with AI than without, and flags that figure as their own result, not a promise about anyone else's numbers.
Sets up the competing approach, states a crisp differentiator, then supports it with a single first-party number and a clear boundary around what that number proves.
For brands planning a campaign
For companies in AI cost governance, engineering intelligence, or FinOps, this is a concrete, measurable use case: attributing AI cost to work and strategy rather than tool meters. The contrast with prompt-scraping competitors gives a brand a ready-made positioning hook, and Nanney's description of the 61% figure as internal data signals that vendor claims should be framed honestly.
Implementation failure as the reason to build better software
Nanney's career story starts as an implementation consultant at large enterprise companies — Compaq Computers, Phillips Petroleum, Union Carbide — installing ERP software that offered configuration rather than customization and replaced processes that had worked for years with standardized ones that were slower and clunkier.
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The projects were delivered on time and sometimes on budget, but were wrong.
The people who built the software had not asked the people who had to use it, and the blame landed on the consultant rather than the executives.
Nanney describes choosing to sit with frustrated customers and listen instead of deflecting, hitting a wall as the face people yelled at for tools they did not build, and moving into product management with that experience.
The closing line states the position plainly: shipping software and solving the problem are not the same thing.
Uses a firsthand account with specific employers and project outcomes, then turns the experience into an operating principle that shapes later product decisions.
For brands planning a campaign
A vendor whose pitch centers on solving the customer's problem rather than shipping features can use this narrative to build credibility: the cost of ignoring users during design, and what that looks like from the implementation side. It suits content on product discovery, implementation, or customer-centric development more than feature-benefit material.
Hiring for product marketing and pointing the audience at open roles
One post is a short notice sharing two product marketing openings at Tempo, working for a leader Nanney describes positively, with a link and a pointer to tempo.io for other openings.
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It functions as a distribution channel for a careers page.
A short link post, included so the topics map reflects the actual range of what was published, not just the argument-led posts.
For brands planning a campaign
Nanney uses this account for hiring and employer-brand distribution. A brand could consider this kind of placement for recruiter or talent-facing content; before making it part of a campaign, ask to see an example of a sponsored version and how Nanney evaluates product marketing work.
Kevin Nanney’s posts document employment rather than sponsorship.
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They work at Tempo Software, where their posts describe work on AI-spend measurement and share open product-marketing roles. They also name three large-enterprise employers from an early career as an implementation consultant: Compaq Computers, Phillips Petroleum and Union Carbide.
Tempo Software
Employment
Nanney writes about Tempo Software’s Workforce Intelligence product, describing how it ties AI spending to individual work items and strategy.
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They cite the company’s own rollout figure: work shipped 61% faster with AI.
A separate post shares product-marketing job openings at Tempo.
The posts establish a current connection to the company but do not specify Nanney’s role or contract length; ask for those details if they matter to a campaign.
Original disclosure
Find out what’s coming at Tempo Software through the link in the comments
Nanney names this as one of the large enterprise companies where they worked early in their career as an implementation consultant, installing software, training users and driving go-lives.
Kevin Nanney's reviewed work consists of short-form LinkedIn posts, most following one shape: a firsthand situation, the pattern underneath it, and a principle at the end.
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The clearest entry point is the career post about installing enterprise ERP software that was delivered on time, sometimes on budget, and still wrong.
It explains why they keep returning to the gap between shipping software and solving the customer's problem.
The AI posts then split into two threads.
One asks where the bottleneck goes when AI speeds up research, requirements, and business cases: into building and coordination, where roles blur and the payoff depends on standardizing tools and processes rather than letting each team assemble its own stack.
The other answers the CFO's question about whether the AI bill is worth it by tying spend to the actual work item and tracing it up to the strategy that work serves.
One item points to product marketing openings at Tempo Software and carries no argument; readers looking for ideas rather than job notices can skip it.
Read in that order, the posts move from personal history to operating argument to measurement, and the tags below follow those strands.
Greenfield starts from the same failure Nanney describes and teaches the lesson in much the same way.
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The post follows a company that spent $80,000 on an enterprise AI platform, trained IT and a few power users, and watched adoption settle at 11 percent—not because the technology failed, but because the people closest to the work got a tool with no context for their jobs.
It echoes Nanney's argument from the implementation years in a different setting: the people who built or bought the thing never asked the people who had to use it.
Both writers narrate a specific case with numbers, draw one principle from it, and close with a question to the reader.
Greenfield focuses on training and adoption inside mid-market companies.
They argue that education should be built around the decisions people make all day, not software features.
Nanney writes about the product side: how AI reshapes development ownership and how AI costs are attributed to work and strategy.
For how organizations get people to use AI tools, Greenfield is the closer match; for how AI changes what product teams build and how they account for it, stay with Nanney.
Rao writes the same kind of first-person career reflection Nanney does, and reaches a similar conclusion about where the work should point.
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One post traces a path from programming games as a child and studying math and computer science to development, professional services, QA, and finally customer-facing work.
The realization is plain: what they enjoy most is getting people unstuck—listening, understanding what is actually happening, then helping the team move forward.
The other post shows that instinct under pressure, when a routine software upgrade knocked a large client's customers out of their portal and the technical engineer was on leave.
Rao's focus is customer success and technical account work: handling escalations, providing frequent updates before an answer is available, and explaining in a root cause analysis how a failure will be prevented.
Nanney turns the equivalent experience into product and buying arguments.
Follow Rao for how to work with a customer during an outage; follow Nanney for how that experience informs decisions about what to build and what AI spend has to prove.
Huber takes a firsthand sales anecdote—being written up for talk time as a top-five-percent rep, then quitting along with six other high performers over a metric unrelated to sales—and turns it into a short numbered list readers can act on: write down every rule reps must follow, ask whether each one helps sell more or protects the company, keep the non-negotiables, and cut the rest.
Nanney does the same with the AI bill and standardizing product tools.
Huber also makes Nanney's central move of defining the operating system before scaling, arguing that owners hire a sales manager without ever writing down what the job hands back each week.
Huber writes for owners of contracting and field-sales businesses; the numbers he cares about are rep scorecards, forecasts, appointment rates, and close rates.
Nanney writes for product, engineering, and marketing teams in software.
Huber is the better follow for running a sales team and its process; Nanney is the better follow for what happens to product work and the budget behind it once AI enters product development.
The only measured audience figure is Kevin Nanney’s LinkedIn following: 7,739 followers, recorded 9 October 2026.
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Four posts dated 18 May 2026 to 1 October 2026 point to an audience interested in practical product and AI-operations commentary.
Nanney writes about product management practice, including the shift from requirements documents to prompt-driven building and the ownership problems that follow; AI-enabled product work; and tying AI spend to specific work items and strategy at Tempo Software.
The topics suggest potential overlap with product management, product marketing, engineering, and finance or operations readers concerned with AI costs, but do not establish audience composition.
Before committing, ask Nanney for recent results on comparable posts—same topic, depth, and format—and for an audience survey or other documented breakdown of job function, seniority, and industry.
LinkedIn does not publish that composition by default, so a survey or creator-provided report is the realistic source.
If the campaign depends on video, ask for video performance data too.
Without audience data, treat relevance as unverified rather than assuming the readership matches the topic mix.
Kevin Nanney's posts do not include a sponsorship policy, rate card, media kit, or disclosed paid partnership.
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The four posts here are first-person writing about product management, AI-enabled product work, and measuring AI spend, plus one post sharing open product marketing roles at Tempo Software.
Their brand mentions all point to the same employer, Tempo Software, and to a product described to a professional audience rather than a purchased placement; buyers should not read them as sponsorships.
Partnership availability is unverified.
Before committing budget, ask Nanney whether sponsored posts, newsletter placements, event appearances, or co-created content are available, and what disclosure practice, turnaround, and review steps to expect.
The strongest match is a B2B SaaS company looking for a product marketing leader who can connect AI product work to measurable business outcomes.
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Three of Nanney's four posts address that need.
The Tempo Software post on AI spend tackles the finance question head-on: it describes the usual state as guesswork built from manual reports and partial data from developer productivity tools, then outlines a mechanism that ties AI cost to the actual work item and traces it back to its strategy, making the most expensive AI-assisted features visible.
Nanney supports the argument with one first-party number and states plainly that it comes from Tempo's own rollout, not a promise for other companies.
That boundary-setting suits a vendor that needs its claims framed honestly.
The post on the AI product bottleneck adds a forward-looking argument: research, requirements, and business cases are faster, but ownership blurs across product, design, product marketing, and engineering.
The payoff comes only if teams standardize on shared tools and processes instead of fragmenting.
The career post adds a customer-problem orientation that fits product marketing and discovery content.
Audience fit remains unverified: the only audience data is a LinkedIn follower count of 7,739, recorded 9 October 2026, with no breakdown by job function, seniority, or industry.
Nanney's posts also provide no sponsorship policy or rate card; ask directly whether a paid placement is available before setting a budget.
When to choose a specialist
A broad-reach brand awareness campaign is a poor fit for structural reasons.
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The only audience fact available is a LinkedIn following of 7,739, with no geography, seniority, job function, or industry data, so a campaign targeting a defined consumer segment cannot be planned from Nanney's posts.
The content is written for working professionals: it covers product management practice, AI-enabled product work, and cost measurement, with one short post pointing readers to product marketing job openings at Tempo.
There is no consumer-facing material, lifestyle or entertainment format, or reported video or long-form work, so a campaign requiring a multi-format or multichannel creator footprint would rest on assumptions.
Campaigns that gauge creators by engagement volume, impressions, or follower scale are also a mismatch: nothing here reports post performance, and follower count alone says nothing about whether the readership matches a campaign's audience.
Nanney's posts provide no rate information, media kit, disclosure practice, or disclosed paid partnership; the only brand referenced is their own employer, Tempo Software, described to a professional readership rather than placed for a fee.
Ask directly whether Nanney accepts sponsorship and what paid format is available before planning a campaign.
A worked campaign example
Campaign need: a vendor selling AI cost governance or engineering intelligence to product and finance leaders wants a credible voice to start a conversation about what AI spending is actually buying.
Angle: use the CFO question as the spine.
Open with the problem every finance team raises — whether the AI bill is worth it — and the common answer: a manual estimate assembled under pressure.
Then explain why the spend is hard to attribute: developer productivity tools cover only part of the picture.
Land on the vendor's mechanism, which ties cost to the work item and traces it up to strategy, and finish with what that measurement produced for one company.
Sequence and format: three text posts over roughly three weeks.
Post one frames the finance question and the gap.
Post two carries Nanney's argument that AI moved the bottleneck rather than removing it, and that standardization turns speed into leverage.
Post three sets a boundary around what one internal result does and does not mean for another company, then points to a practical next step, such as a short assessment or benchmark in the comments.
Each post should stand alone for a reader who sees only one of them.
What to keep honest: label any figure used as the source company's own result, not an expected outcome. Describe the vendor's differentiator as attribution to work and strategy, the contrast Nanney's posts draw with tools that scrape prompts and estimate spend.
Success measure: qualified conversations and meeting requests from product operations, engineering, and finance readers, not impressions.
Before spending: ask Nanney directly whether a paid format exists, what disclosure practice applies, what the turnaround and review steps are, and how any performance data would be shared.
When the campaign's reader is a mid-market operations or IT leader rather than a product or finance leader, and the argument is about why AI rollouts stall, Greenfield is a fit.
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The post opens with the pattern: leadership approves an AI initiative, IT gets trained, then the rollout quietly dies in departments that were not in the room.
It grounds the story in a manufacturing vice president's account: $80,000 on an enterprise AI platform, adoption at 11% six months later.
The post then names the fix: map the decisions a person makes repeatedly and the work they hand off, then build training around that instead of features.
A vendor selling implementation services, onboarding, or workforce training gets a specific, first-person case to work from, plus hashtags pointing at the same audience.
Greenfield also runs this same passage as both a LinkedIn post and an Instagram copy, demonstrating a pattern for sponsors who want one message carried in two places.
The material reviewed for Nanney is LinkedIn text only.
The subject is adoption and education, not product marketing, positioning, or tying AI spend to business results.
The argument is that education is the implementation and rushed training keeps adoption low; nothing here describes how to measure what AI work produced.
For a brief seeking a product marketing leader who can connect AI product work to measurable business outcomes, swapping in Greenfield trades product-and-finance substance for change-management substance.
The two pieces are the same body text on two channels, so they offer one argument, not two.
When the need is to build trust after a failure among customer success, support, or technical account leaders, Rao is a strong fit.
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The outage post is a complete service-recovery story: an upgrade left a large client's customers unable to log in, the company rolled back, the outage made the news, and the sales engineer who normally covered the account was on vacation.
The work then involved escalating internally, meeting with customer stakeholders, sending frequent updates, and delivering a root-cause analysis with prevention steps.
A vendor selling customer success platforms, incident tooling, or managed services can use that narrative directly; for a campaign about handling problems well, it is a stronger match than Nanney's posts.
The career post — programming games as a child, studying math and computer science, moving from development into customer-facing work, and settling on getting people unstuck — tells an origin story for a customer-facing role.
Rao's material is about being the person who shows up when something breaks, not about how a product decision is justified to finance.
There is no AI product work, cost or outcome measurement, positioning, or launch content, so a brief centered on connecting AI work to business results gains little from the swap.
Two saved posts are a thin basis for a campaign; ask for more examples before committing.
When the buyer task shifts to sales process and sales hiring in an owner-operated or field services business, Huber is a fit.
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The first post argues that contractors hire a sales manager without defining what that person should hand over each week, then lays out the list: a weekly rep scorecard covering leads, appointments sat, close rate, and average ticket; a 30-day forecast with stated confidence; a fixed meeting cadence; CRM hygiene with every lead in a stage; and a recruiting pipeline built before someone quits.
It explains that acting as the sales manager for a while is how the job gets written down before the real hire arrives.
The second post argues that when top performers ignore a process, the process is usually the problem, and gives a five-step test for cutting rules that do not help sell more or protect the company.
A CRM, sales enablement, or sales-management vendor selling to trades and contractor owners could use both posts without translation.
Huber's audience does not match a campaign aimed at product and finance leaders.
Huber writes for contractor and home-services owners; the posts do not address AI, product marketing, or software product audiences.
A vendor choosing a voice for product and finance leaders cannot make that decision with these posts.
Huber is worth naming as a contrast that shows how much of a match depends on the reader, and would be the right swap for a sales-process campaign aimed at trades owners rather than this one.
Three carry the substance: a first-person account of early enterprise implementation work and the move into product management, posts on how AI moves the product bottleneck and why standardizing tools matters, and a post on tying AI spend to the work item and the strategy behind it.
The fourth is a short notice about product-marketing openings at Tempo Software, a range note rather than an idea.
Comparisons with similar creators involve three other writers: Zack Greenfield, Kishor Rao, and Steve Huber.
The posts here do not give dates; the only audience figure stated is a LinkedIn following of 7,739 recorded on 9 October 2026.
Kevin Nanney's posts do not say whether there are other channels, longer-form work, a rate card, or a sponsorship policy, so those remain open questions rather than documented facts.
