# Keith Dunnell

Keith Dunnell leads global go-to-market work for ServiceNow’s manufacturing industry. Dunnell’s posts focus on connecting manufacturing operations, technology, security, and transformation work to business outcomes.

- Profile: https://b2binfluencer.marketing/creators/keith-dunnell/
- LinkedIn: https://www.linkedin.com/in/keithdunnell
- LinkedIn followers: 4,389
- Topics: artificial-intelligence, leadership, cybersecurity
- Based on 14 dated posts, latest 2026-10-05

## Summary

Keith Dunnell is useful for practical manufacturing OT/IT security and transformation execution insights from a platform-side go-to-market view.

- Best for: Manufacturing software company campaign on OT/IT security or transformation execution using an industry advisor or keynote speaker.
- Not for: A campaign needing an independent or vendor-neutral voice, comparison-led buyer's guide, or paid sponsorship terms.
- Check first: Ask for audience analytics and speaking attendee profiles before booking.

### Takeaways

- A controller swap left a firmware gap and a six-week security blind spot.
- Security, maintenance, and production each had a reason to wait.
- Transformation programs fail on cadence, not strategy.

## Main ideas

- **Plant shutdowns come from split records, not missing tools**: The most repeated argument across the posts is that manufacturing's security and safety problems are architectural. Safety and security can be looking at the very same machine and neither can see what the other has done to it, because the work order and the vulnerability list sit in different systems. Dunnell's reasoning is that this is not a technology gap that a better tool closes — it is a structural gap in how information about a piece of equipment is recorded and shared. The posts support the point with two outside figures: manufacturing as the most-attacked industry five years running, and a Siemens estimate of $2.3 million per hour for an idle automotive line.
- **The fix is one record both functions can read, not merging the disciplines**: Dunnell's prescription follows directly from the diagnosis: give safety and security one record to read rather than folding the two disciplines into one team. The same fragmentation shows up as a decision standoff, where exposure, maintenance schedules, and customer deadlines are each owned by a different function and nobody moves first. The delay is manufactured by the structure, not by any one team being unreasonable, so a shared view of the machine and its state is what shortens it.
- **AI agents need oversight because they now make decisions, not just read data**: Dunnell's position on AI in manufacturing is about governance, not adoption. Shared from the IMTS 2026 show in Chicago, the point is that AI agents have always been accessing data, but now they are also making decisions. Manufacturers need to monitor the agents and keep security in place while AI adds value — oversight running alongside deployment rather than after it. This is the emerging-technology edge of the same visibility argument Dunnell makes about plant equipment.
- **Transformation programs fail on cadence, not strategy**: Away from security, Dunnell's recurring execution argument is that large change efforts drift for unglamorous reasons. Across five transformations, the near-failure point was the same every time, and it was never the strategy, the technology, or the organizational design — it was the cadence: the weekly sales reviews, biweekly supply chain reviews, and monthly budget accountability sessions that nobody puts on a roadmap or celebrates, but which hold the effort together. These routines feel boring and redundant precisely when they matter most; sustaining them is the actual work of transformation.
- **Manufacturing transformation as a platform orchestration problem**: The posts take a commercial vantage point, which shapes how the other arguments land. Announcing a move into a global manufacturing go-to-market role at ServiceNow, Dunnell set out the themes underpinning the work — AI, IT/OT convergence, supply chain resilience, and connecting plant-floor performance to enterprise outcomes — and described ServiceNow as an AI control tower that connects and orchestrates across the manufacturing value chain in ways no point solution can. They describe the job as building the commercial architecture that turns agentic AI and platform capability into measurable business outcomes for large manufacturers. Weigh the security and convergence arguments with that frame in view: Dunnell works for a company that sells the orchestration layer. The cadence argument is the exception; it does not depend on a platform.

## Work to read

- [A controller swap, a two-version-old firmware gap and a six-week blind spot](https://www.linkedin.com/posts/keithdunnell_manufacturing-security-clocks-make-production-activity-7508294157061242880-po3x)
- [When security, maintenance and production all refuse to move first](https://www.linkedin.com/posts/keithdunnell_one-system-one-answer-how-manufacturing-activity-7512927490445631488-i4Zn)
- [One machine, two systems, zero visibility](https://www.linkedin.com/posts/keithdunnell_manufacturings-been-the-most-attacked-industry-activity-7511455671507169281-9WmM)
- [AI agents that do not just read data but make decisions](https://www.linkedin.com/posts/keithdunnell_manufacturers-increasingly-need-to-monitor-activity-7511666317851049984-327O)
- [Five transformations, one near-failure point: the cadence](https://www.linkedin.com/posts/keithdunnell_five-transformations-same-near-failure-point-activity-7454717465043046401-JcLH)
- [Starting as VP, Global Head of Manufacturing Industry GTM at ServiceNow](https://www.linkedin.com/posts/keithdunnell_servicenow-manufacturing-gtm-activity-7481076033220173824-0grQ)

## Brand relationships

- **ServiceNow** (employment)
- **IMTS 2026** (mention)
- **Great Question: A Manufacturing Podcast** (mention)

## Partnerships

Keith Dunnell's posts do not describe a sponsorship, paid-collaboration or partnership offering. Their manufacturing commentary comes from an employed executive role — global manufacturing go-to-market at ServiceNow — rather than a personal media business. There is no media kit, rate card or published audience demographic to plan a paid placement against. The only third-party media appearance mentioned is a conversation with Tom Wilk on Great Question: A Manufacturing Podcast, recorded at IMTS 2026; it is editorial commentary, and no fee, format, booking terms or availability are stated. For a potential partnership, approach them directly and ask about formats, fees and availability, framing the proposal as an industry-advisory or speaking arrangement rather than a standard content sponsorship. Two specifics in their posts could anchor that conversation: OT/IT/security convergence and the cost of fragmented records on the plant floor, and transformation execution as a cadence discipline rather than a technology purchase.

## Alternatives

- Bob Gafvert: Switch to Gafvert when the campaign is about design and production iteration in robotics and additive manufacturing, not plant-floor security or transformation execution. Gafvert's two posts work through one concrete problem: a robotics team needs a better gripper or a redesigned bracket; a new geometry or durometer normally means a new tool and weeks of waiting. The pitch is that a part can go from CAD to a testable, engineering-grade part in the same day, on the same material, without redesign. A brief seeking a speaker or advisor to explain why tooling lead times stall a robotics programme, or to walk an audience through material selection under heat, saltwater, and load, gets a tested narrative here. The second post extends that argument to defence programmes, construction robotics, drone delivery, underwater drones, and race cars, all with the same requirement: parts with no porosity that hold up under stress and extremes. For robotics hardware development or additive materials, Gafvert is closer to the buyer's question than Dunnell.
- Rizwan Khan: Consider Khan when the campaign is about how manufacturers sequence industrial AI adoption, rather than securing the plant floor. Khan's two closing takeaway posts from a manufacturing technology show offer a repeatable decision path: shop-floor signals become context, insight, a recommended action, execution, and a business result. That chain has to reach ERP, planning, inventory, purchasing, supply chain, cloud, and finance because the business problem usually spans the operation. The advice is practical and specific: start from the outcome rather than what AI can do, use process knowledge, machine history, quality patterns, and operator experience as competitive advantage, and finish a small number of high-value use cases instead of attempting everything. Khan also raises a question Dunnell only touches: who owns the operational data and institutional knowledge converted into AI systems. For a campaign about use-case selection, data foundations, or where intelligence belongs at the edge versus the cloud, Khan offers a coherent, teachable structure.
- Anoop Mohan: Choose Mohan when the campaign is specifically about agentic AI and industrial knowledge capture on the plant floor. Mohan's posts describe an open framework that turns thirty years of operator judgement into plain files a process engineer can review: operator heuristics with human sign-off, trust rules for incoming readings, and cause-and-effect relationships. The problem is precise and quotable for the right audience: intuition about how a line behaves on a cold start, or what a specific pressure signature meant the last three times it spiked, exists but is trapped in email threads and people's heads—and retires when they do. A second post pairs reasoning models with machine telemetry and operational context to deliver role-based agents to manufacturing teams. A brief seeking a speaker on governing AI agents, capturing retiring expertise, or why vertical-specific context is where foundation models earn their value will find more usable substance here than in Dunnell's posts, which raise agent decisions once without developing them.

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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/