# Kevin Nanney

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.

- Profile: https://b2binfluencer.marketing/creators/kevin-nanney/
- LinkedIn: https://www.linkedin.com/in/kevin-nanney
- LinkedIn followers: 7,739
- Topics: artificial-intelligence, product-management
- Based on 10 dated posts, latest 2026-10-10

## Summary

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.
- Check first: Confirm audience breakdown, recent post results, and whether paid placements are available.

### 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.

## Main ideas

- **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. 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.
- **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. 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.
- **Standardize the tools and process, or the speed just adds confusion**: Nanney identifies fragmentation as the risk companies underestimate. 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.
- **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. 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.

## Work to read

- [Why Nanney moved from implementation consulting into product](https://www.linkedin.com/posts/kevin-nanney_i-used-to-walk-into-rooms-where-nobody-was-activity-7477737802063876098-2Tu2)
- [How AI changes the product team’s bottleneck](https://www.linkedin.com/posts/kevin-nanney_a-few-months-ago-a-product-team-would-spend-activity-7462155185461649408-nYfQ)
- [Tying AI spend to work and strategy](https://www.linkedin.com/posts/kevin-nanney_is-our-ai-bill-actually-worth-it-every-activity-7506379042355470336-r_xt)
- [Tempo product marketing openings](https://www.linkedin.com/posts/kevin-nanney_portfolio-management-software-for-the-ai-activity-7511460371174576128-dgAG)

## Brand relationships

- **Tempo Software** (employment)
- **Compaq Computers** (employment)
- **Phillips Petroleum** (employment)
- **Union Carbide** (employment)

## Partnerships

Kevin Nanney's posts do not include a sponsorship policy, rate card, media kit, or disclosed paid partnership. 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.

## Alternatives

- Zack Greenfield: 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. 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.
- Kishor Rao: When the need is to build trust after a failure among customer success, support, or technical account leaders, Rao is a strong fit. 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.
- Steve Huber: When the buyer task shifts to sales process and sales hiring in an owner-operated or field services business, Huber is a fit. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/