# Juan Jaysingh

Juan Jaysingh writes about AI agents in customer service, focusing on how businesses can keep decisions consistent across channels by combining AI reasoning with rules and human judgment.

- Profile: https://b2binfluencer.marketing/creators/juan-jaysingh/
- LinkedIn: https://www.linkedin.com/in/juanjaysingh
- Topics: artificial-intelligence, crm
- Based on 10 dated posts, latest 2026-09-28

## Summary

Juan Jaysingh is most useful for decision orchestration in customer service, keeping AI reasoning, rules, and human judgment consistent across channels.

- Best for: B2B SaaS AI customer service tools; thought leadership on agentic CX decision orchestration.
- Not for: B2B software vendors outside customer service seeking sponsored short-form video demos or audience composition and engagement figures.
- Check first: Confirm format, scope, and requirements, and request recent LinkedIn audience analytics.

### Takeaways

- Deciding what to do is the hard part, not understanding the request.
- Permissions are ahead of procedures, and a procedure without enforcement is only a request.
- Complex customer service is several different problems, each needing its own control.

## Main ideas

- **Deciding what to do is the hard part, not understanding the request**: Jaysingh argues that AI can already read intent and produce straightforward replies. The harder task is deciding whether a customer qualifies for a particular outcome when policy, value thresholds and missing evidence all apply. They propose moving that business logic out of the model and into a decision orchestration layer reachable through MCP, so chat, voice, text and human agents reach the same answer. Their point: the right answer should not depend on the channel; everyone should sing from the same sheet music.
- **Permissions are ahead of procedures, and a procedure without enforcement is only a request**: Writing about Salesforce and Anthropic putting the CRM inside Claude, Jaysingh separates what is known from what remains unknown. An admin connects Salesforce once, permissions are managed centrally, and actions route back through Salesforce so business rules are enforced. But which rules, and how they are enforced, remain unclear. That gap is the argument. Permissions govern what a person may access; procedures provide rules-based guidance on what to do. Without enforcement, a procedure is a polite request, not a control. Jaysingh argues that the model cannot serve as the procedure layer: procedures cannot be hard-coded deterministically inside a model, and a procedure in a prompt can be interpreted differently depending on context. Their closing instruction: verify rather than trust.
- **Complex customer service is several different problems, each needing its own control**: In a post about an article for CRM Magazine, Jaysingh pushes back on treating complex customer service as a single problem. They distinguish at least three forms of complexity, argue each calls for a different kind of control, and frame the real questions: where should AI reason, which decisions must follow defined rules, and when should a person step in? Their recommendation is a hybrid: give AI room to reason, enforce the rules that matter, and involve people where judgment is needed.
- **Headless optionality is the shift worth watching, not integrations or APIs**: Jaysingh argues that SaaS spent twenty years competing over who owns the screen, and that agentic headless optionality changes the equation. They rate headless above integrations or APIs because one decision layer can serve many interfaces, and read the Salesforce–Anthropic release as a signal of where headless is heading.
- **Naming what is still unknown is part of the argument**: Jaysingh lists what is known about a vendor connection, states plainly what remains unknown, and closes with an instruction to verify rather than trust. Brent Thomas of Zingtree offers a specific point about models producing plausible answers without an inspectable reason. Jaysingh uses the quote to support moving logic out of the model, not to pad the post.

## Work to read

- [What changes when the CRM stops being the destination](https://www.linkedin.com/posts/juanjaysingh_saas-spent-twenty-years-competing-over-who-activity-7507808809508220928-3mA1)
- [Permissions versus procedures in the headless CRM](https://www.linkedin.com/posts/juanjaysingh_salesforce-and-anthropic-put-the-crm-inside-activity-7498809882045931520-HLoB)
- [Why complex customer service is not one problem](https://www.linkedin.com/posts/juanjaysingh_complex-cx-is-where-ai-agents-still-struggle-activity-7509258147480440833-Vac0)

## Brand relationships

- **CRM Magazine** (disclosed_partnership)
- **CX Today** (mention)
- **Salesforce** (mention)
- **Anthropic** (mention)
- **Zingtree** (mention)

## Partnerships

Juan Jaysingh's three posts analyze AI agents in customer service, CRM decision workflows, and decision orchestration. They don't describe partnership formats, announce a sponsorship, publish a media kit or rate card, or set out terms for working with a brand. Buyers should confirm format, scope, and requirements directly before planning around them.

## Alternatives

- Akhilesh Perla: Choose Perla when the article needs an enterprise-platform argument: which layers must exist before an agent can safely act on a customer-service decision, and why integration, governed data, and metering belong in the same stack. Both posts approach the same buyer question from the platform side. One walks through Salesforce's acquisitions as layers of an AI operating system — Informatica for governed data, Fin for autonomous service resolution, Contentful for a headless content layer, m3ter for billing metered agent interactions — and argues that systems which only reply to text are not agents; the ones that matter start workflows, update systems, trigger transactions, and enforce policy as they act. A sponsor selling the orchestration or enforcement layer gets an enterprise-architecture section and a 2026 roadmap hook from this framing.
- Max Votek: Choose Votek when the deliverable is a comparative market scan rather than a single argument. The agent-readiness scoring covers 22 software companies across five named dimensions — API openness, agent-native features, data portability, real-time access, and ecosystem flexibility — and reports scores including Shopify 8.6, Stripe 8.4, Salesforce 8.4, Oracle 7.2, and SAP 6.6, with a note that both Salesforce and SAP are implemented at Customertimes. A sponsor article that places agentic customer service in a ranked landscape, or gives buyers a repeatable vendor-evaluation frame, gets more from this than from one case study. The second post adds the professional-services angle: IBM Consulting's move toward 'service as a software', $3.5bn in savings across 70 corporate functions, and the cited finding that 95% of generative AI projects failed to deliver financial returns.
- Sushil Barwal: Choose Barwal when the need is a practical checklist for a mid-market operations reader. The automation post sets out five processes worth automating first — lead qualification and routing, tier-1 support and order inquiries, cross-system data synchronisation, document verification and client onboarding, and real-time operational monitoring — each with a short before-and-after description. The second post applies the same approach to eCommerce performance, naming heavy assets and script creep, slow checkout database queries, and slow server rendering, with fixes for each. It notes that moving from 3.8 seconds to under 1.2 seconds can change conversion. A sponsor wanting a how-to piece, a numbered framework, or a storefront-performance angle gets a ready structure.

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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/