# Sushanth Raman

Sushanth Raman works at Pallet on AI tools for supply-chain and logistics operations, and shares perspectives on product development and building the company.

- Profile: https://b2binfluencer.marketing/creators/sushanth-raman/
- LinkedIn: https://www.linkedin.com/in/sushanth-raman
- LinkedIn followers: 15,853
- Median of last 8 posts: 115 reactions, 4 comments (checked 2026-11-02)
- Topics: artificial-intelligence, supply-chain
- Based on 10 dated posts, latest 2026-10-01

## Summary

Sushanth Raman is most useful for concrete examples of AI agents in supply chain and logistics operations.

- Best for: A B2B logistics software company wanting content on AI agents in supply chain operations.
- Not for: A broad business audience video campaign or high volume publishing commitment.
- Check first: Confirm audience demographics and recent post results for comparable topics before contacting them.

### Takeaways

- An agent may retrieve rates and prepare a quote, but should not create an order.
- EDI connections can create durable network effects, making EDI one of the strongest standards.
- AI coordination could make vertical integration more attractive in delivery.

## Main ideas

- **AI agents should work across the systems operations teams already use**: Raman focuses on the friction between business workflows and the software around them, rather than presenting AI as a reason to replace existing systems. The posts describe agents gathering scattered information, handling exceptions and updating operational tools. The examples make interoperability—not just the model’s capability—a central part of automation.
- **Enterprise agents need permissions matched to the actions they take**: Raman argues that agents can affect inventory, customer commitments, costs and downstream operations, so teams need to decide what each agent may do. The emphasis is on setting boundaries around specific actions, not treating an agent as an employee with open-ended access.
- **Old standards can become more valuable as agents connect more partners**: Raman challenges the idea that network effects belong mainly to consumer platforms. A long-established business standard can also be hard to displace when each new connection takes time and money to set up. In this view, AI’s role is to work across the varied ways partners send information, not to make those differences disappear.
- **AI could make controlling more stages of a supply chain more attractive**: Raman uses a logistics acquisition to explore a broader business question: whether AI can reduce the coordination burden that has made vertical integration difficult. The argument is that coordinating decisions across a whole order could matter more than optimizing fulfillment and transportation separately. This is Raman’s analysis of the deal, not a detailed financial case.
- **Company building means giving people room to own ambiguous work**: In a hiring post, Raman presents the Founder's Office as a place to learn company building through responsibility from an early stage. The team is described as bringing together people with different strengths and backgrounds, rather than following one standard career path.

## Work to read

- [Defining what an enterprise AI agent is allowed to do](https://www.linkedin.com/posts/sushanth-raman_as-ai-agents-become-active-participants-in-activity-7493727840790601728-fFtZ)
- [Why AI could make vertical integration more attractive in delivery](https://www.linkedin.com/posts/sushanth-raman_doordashs-acquisition-of-instacart-shows-activity-7522050966272274432-LXxE)
- [The case that EDI has one of the strongest network effects](https://www.linkedin.com/posts/sushanth-raman_people-talk-about-the-network-effects-of-activity-7514006782738890752-lSj-)
- [What made CargoWise integration hard: XML schemas and LLMs](https://www.linkedin.com/posts/sushanth-raman_many-of-our-customers-run-their-freight-forwarding-activity-7493352367397036032-cfS_)
- [Agents that handle CAPTCHAs and browser-based workflows](https://www.linkedin.com/posts/sushanth-raman_some-of-the-most-important-workflows-in-supply-activity-7516214283370766336-1S4h)
- [The repeating integration post: Pallet and NetSuite](https://www.linkedin.com/posts/sushanth-raman_many-of-our-customers-run-their-business-activity-7503859886955499520-1mhZ)
- [What Raman looks for when growing the Founder's Office](https://www.linkedin.com/posts/sushanth-raman_im-growing-the-founders-office-at-pallet-activity-7497704557079113728-f_PM)

## Brand relationships

- **Pallet** (employment)
- **NetSuite** (mention)
- **CargoWise** (mention)
- **SAP** (mention)
- **Morgan Stanley** (mention)
- **DoorDash** (mention)

## Partnerships

Sushanth Raman's reviewed LinkedIn posts contain no published sponsorship policy, media kit, rate card, or disclosed paid brand collaboration. What they do show is a consistent direct-response habit: several posts close by asking readers to message directly to see Pallet's agents automate a workflow or to talk through a use case. Those invitations are sales and product conversations, not documented partnership offerings, and they should not be read as a sponsorship format or as a rate card. The posts also show the kind of work a brand would be buying access to: hands-on commentary on AI agents in supply-chain operations, integration specifics for systems such as NetSuite and CargoWise, and named role changes at Pallet. A brand planning a collaboration should treat commercial terms as unresolved and confirm format, deliverables, pricing, exclusivity, and disclosure directly with Sushanth Raman before committing budget or building a timeline around an assumed package. No official partnership or sponsorship inquiry page was verified in the reviewed material, so no inquiry link is shown here.

## Alternatives

- Josh LaSov: LaSov is the stronger choice when the collaboration is about agents doing finance and accounting work inside NetSuite rather than agents running supply-chain operations. The reviewed posts are built around named processes and measured time savings: a five-agent build covering bills of lading matched to sales orders, payroll accruals for 1,000 employees across 18 subsidiaries and all 50 states, revenue analysis, order entry and cash application, with each agent tied to a specific weekly task and a before-and-after number. The revenue-analysis post is the clearest single demonstration for a finance audience — a CFO at a $250M company spending three to four days a month digging through transactions, cut to about 30 minutes, and an argument that the value is not speed alone but surfacing variances the CFO had not known to look for. A brief that wants finance-leader readers and a NetSuite accounting frame fits this material closely.
- Brian Chess: Chess fits when the collaboration is about how people talk to business software and how finance analysis gets packaged for large language models, rather than about agents executing logistics work. One reviewed post reports on Ask Oracle, a NetSuite feature through which customers have had thousands of conversations, and it lays out what those questions actually look like — overdue invoices and order status at first, then cash shortages, margin pressure, month-end anomalies and manufacturing forecasts — and the progression from what happened to why it happened to what deserves attention to what can be done about it. The other announces a NetSuite Finance Analyst Skill for Claude and ChatGPT, with named guardrails around period status, materiality and source authority, and executive-ready narratives as the output. A brief aimed at NetSuite customers, finance leaders or product teams working on AI in the enterprise would find a question-led frame here that has already been tested with real users.
- Sohaib Khan Durrani: Khan Durrani is the better choice when the campaign is aimed at D2C food and consumer-brand founders and the argument is operational rather than marketing-led. The two reviewed posts make the same case from different angles: growth breaks in fulfillment, packaging and inventory planning rather than in ad creative, and systems that hold at 20 orders a day fail at 200. Repeat customers are framed as the real growth engine, with product, packaging, fulfillment and customer experience as a chain where one broken step loses the buyer. The posts are short and plain, closing with a direct question to the reader — a format a consumer-brand collaboration could adopt without changing register.

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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/