# Devin Bhushan

Devin Bhushan builds AI tools at Squint for factory operations, including agents that study manufacturing work and help teams improve processes.

- Profile: https://b2binfluencer.marketing/creators/devin-bhushan/
- LinkedIn: https://www.linkedin.com/in/devinbhushan
- LinkedIn followers: 5,473
- Topics: artificial-intelligence, operational-excellence, continuous-improvement
- Based on 11 dated posts, latest 2026-10-06

## Summary

Devin Bhushan is most useful for concrete factory AI time studies and continuous improvement content aimed at manufacturing operations leaders.

- Best for: Industrial technology brands selling plant-floor software, seeking co-marketing or pilot partnerships with continuous-improvement leaders.
- Not for: General enterprise AI platforms or non-manufacturing brands wanting paid sponsored posts for broad business audiences.
- Check first: Confirm whether they accept sponsorship and request audience demographics and comparable results.

### Takeaways

- One post reports 126 time studies across 32 processes from 144 hours of video.
- AI can analyze the wrong step if people do not identify the actual bottleneck.
- Greg Brue joins Squint as advisor to build Lean Six Sigma into the agent.

## Main ideas

- **Stop getting ready for AI and start using it**: Devin Bhushan casts readiness and transformation projects as delay. In their account, AI on the factory floor is manufacturing's low-hanging fruit, and it works with a plant's existing systems out of the box. Integration efforts and multi-year rollout planning only push the gains further out. The reasoning comes down to a mismatch between supply pressure and process capability: demand for what factories make is rising faster than the systems and processes built to serve it. The constraint is how work gets done, not whether the tools are mature.
- **The tool is only as good as the bottleneck it is pointed at**: Devin Bhushan separates automation speed from process choice. A time-and-motion study is worthless if the recorded step was never the constraint: the line gets optimized around a problem that was not costing the plant money. Bhushan says the industry has never had to build bottleneck judgment into a product because no product needed it before. That judgment means knowing which process actually costs money, a skill built over years on the floor rather than through model quality.
- **Proof comes from the continuous-improvement work the agent replaces**: The posts measure value against the manual study the team no longer had to do: hours of footage in, classified time studies out, compared with the engineer-weeks the same work previously took. The claim also extends to outcomes on the line: findings on waste, safety and ergonomics, and a rebalance that changes how many operators the line needs.
- **Get the data, understand the actual work, then improve the process**: Bhushan presents this as the method behind the product and as common ground between statistics and AI models: both depend on ground truths. The sequence is observation first, judgment second, change third—not the reverse. Bhushan also describes what the agent removes from the job—the stopwatch and clipboard—and why it suits the task: it brings more patience, memory and context to repetitive studies than a person working by hand, freeing manufacturing engineers for higher-order work.
- **Pair AI builders with people who have walked the factory floor**: Bhushan treats floor experience as part of the team, not a customer-side precondition. The posts describe a group of AI builders learning manufacturing quickly and needing someone who knows where constraints live—a role the hiring post calls a sherpa. The stated commercial priority is to partner with continuous-improvement experts rather than displace them, positioning the tool as an accelerant for their work.

## Work to read

- [A reported one-day AI time study on a large manufacturing line in Singapore](https://www.linkedin.com/posts/devinbhushan_does-anyone-know-how-you-get-in-touch-with-activity-7504196038292062208-g9CM)
- [Greg Brue joins Squint as advisor and helps build Lean Six Sigma into the agent](https://www.linkedin.com/posts/devinbhushan_greg-brue-has-joined-squint-as-an-advisor-activity-7507844265415180288-S5jp)
- [Squint is hiring a Head of Lean Six Sigma to bridge factories and AI](https://www.linkedin.com/posts/devinbhushan_open-positions-at-squint-activity-7509254400922525696-o9TW)
- [Why AI is the low-hanging fruit in US manufacturing](https://www.linkedin.com/posts/devinbhushan_whole-industries-are-exploding-right-now-activity-7508542945676021760-RYMC)
- [Community webinar reaction to Squint's Lean Manufacturing Agent](https://www.linkedin.com/posts/devinbhushan_since-we-launched-our-lean-manufacturing-activity-7511170608680869889-1wnj)

## Brand relationships

- **Squint** (employment)

## Partnerships

Across the five posts, Devin Bhushan promotes Squint's manufacturing AI products and recruits for Squint; they do not offer creator partnerships. The posts mention no sponsored post, brand collaboration format, rate, or sponsorship policy. Their only calls to action invite readers to contact Squint about its Lean Manufacturing agent or link to a Squint job posting—company product and hiring routes, not an official channel for proposing a partnership with Devin. Ask whether they accept sponsorships and, if so, for their partnership terms.

## Alternatives

- Benjamin Reynolds: Consider Benjamin for a campaign about applying spatial computing and AI to factory work, especially ergonomics or time-and-motion analysis. In a post about taking a product to the Azure Marketplace, Benjamin describes using phone-camera data to analyze physical work and says early users reported productivity gains in ergonomics analysis and time-motion studies. This offers a closer angle on spatial technology and product deployment than Devin Bhushan’s posts, which focus more directly on AI-led continuous improvement and line studies.
- Prasad Velaga, PhD: Consider Prasad for a campaign explaining production scheduling in high-mix, low-volume job shops, rather than promoting an AI continuous-improvement agent. One post uses city traffic as an analogy for how jobs move through a job shop; another argues that scheduling and simulation can help estimate how improvement efforts affect throughput, work in progress, and lead time. These examples offer a specialized angle for a scheduling or production-planning brief.
- Paul D'Souza: Consider Paul for a campaign about preparing enterprise teams to deploy agentic AI with process discipline and simulated testing. In one post, Paul argues for mapping processes with Lean Six Sigma before deploying agents and using a digital twin to test decisions before they affect live operations. That framing may suit a campaign aimed at leaders evaluating deployment risk rather than practitioners assessing a specific factory-line study.

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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/