# Rajesh Viswanath

Rajesh Viswanath writes about finance planning and transformation, including how finance teams can use operational data, AI, and clearer investment priorities to improve budgeting, forecasting, and order-to-cash work.

- Profile: https://b2binfluencer.marketing/creators/rajesh-viswanath/
- LinkedIn: https://www.linkedin.com/in/rajeshviswanath
- LinkedIn followers: 3,105
- Topics: artificial-intelligence, ai-in-finance
- Based on 15 dated posts, latest 2026-10-09

## Summary

Useful for finance software thought leadership on budgeting, forecasting, data quality, AI, and order-to-cash.

- Best for: B2B finance software brands needing CFO and FP&A thought leadership.
- Not for: Consumer budgeting, retail fintech, video ads, or guaranteed conversion campaigns.
- Check first: Confirm media kit, sponsorship terms, and recent per-post performance data.

### Takeaways

- Rolling forecasts fail when pipeline, inventory, and customer data remain outdated.
- An AI agent on broken spreadsheets automates confusion rather than fixing unclear processes.
- The hours spent double-checking an AI model belong in its ROI calculation.

## Main ideas

- **The annual budget is the wrong architecture, not a discipline problem**: Rajesh Viswanath argues that the annual budget is built to measure a fixed target, then used to fund a business that keeps moving. A number set in December assumes the market holds still until the next December; by February, most FP&A teams already know it is stale, and the rest of the year becomes a negotiation around a plan that stopped being true. The proposed replacement is dynamic capital reallocation: track eight to ten core drivers, such as rate moves, input costs and supply-chain shifts, instead of defending hundreds of line items nobody remembers by the third quarter. Scale reserve capacity with sales volume rather than with what was negotiated in the last cycle. A companion post makes the same case from the allocation side: budgeting starts with departmental requests — marketing wants more budget, sales wants headcount, product wants engineers, customer success wants to expand — and finance ends up refereeing a contest instead of funding value. The better question, in their telling, is which investment moves the company's most important value drivers.
- **Rolling forecasts are a data transformation, not a finance transformation**: Having argued that annual budgeting is flawed, Rajesh Viswanath says the usual remedy often fails for a predictable reason: finance teams replace the budgeting process without fixing the data underneath it. If sales pipeline data is outdated, inventory numbers lag by weeks, or churn is not captured in real time, a rolling forecast does not make planning more agile; it updates bad assumptions more often. The companies that make rolling forecasts work, they write, start with operations rather than finance, connecting the forecast to drivers that change daily — pipeline conversion, production output, customer retention, inventory levels and pricing — instead of monthly financial reports.
- **Do not deploy an AI agent on top of a broken spreadsheet**: Rajesh Viswanath distinguishes passive copilots, which summarize and chart when asked, from agents, which are meant to watch data continuously, catch anomalies and execute workflows on their own. Agents work only if what they watch is reliable. Most enthusiasm about agentic AI skips that step. The unglamorous work of becoming AI-native is tackling the process debt underneath: inconsistent categorization, undocumented manual overrides, and three versions of the same number depending on who pulled it. An agent inherits that debt rather than repairing it. The prescription is to clean the data model and define ownership first; a real-time command center with no close-week scramble and no board surprises is the reward, not something a company can buy around.
- **The hours spent double-checking an AI model belong in its ROI calculation**: This is the sharpest counterweight in the material to enthusiasm about automation. Rajesh Viswanath argues that finance teams count the hours a model replaces but not the hours someone now spends re-verifying a number they cannot trace back to a reason. A model that saves ten hours of manual work and costs eight hours of re-checking has, by that arithmetic, saved nothing. Explainability belongs in the return math, not just in compliance, because every hour spent rebuilding trust in an opaque output cancels an hour the model claimed to save. For models such as propensity-to-pay and credit-risk scoring, explainability has to be built into the architecture from the first design decision, not generated as a report afterward.
- **Working capital is a data problem, not a treasury problem**: Rajesh Viswanath argues that CFOs have long treated the cash conversion cycle as a working capital metric, while the best ones treat it as a competitive advantage. The gap between strong and weak performers is not just better liquidity but extra days to hire, invest, replenish inventory or respond to market shifts before competitors can. They attribute the difference not to stricter collections or delayed supplier payments but to visibility: high-performing finance teams connect data across procurement, operations, suppliers and finance to create a real-time view of how cash moves through the business. The conclusion: CFOs gaining the biggest advantage are building systems that make cash move faster.
- **Order-to-cash is becoming one connected process rather than a series of handoffs**: The shortest piece in the material sets out a position rather than a worked argument: AI can bring data across the order-to-cash cycle together to identify anomalies, trends and emerging risks, but the bigger shift is O2C moving from a series of manual handoffs to a more connected, intelligent process. They argue this shift matters more than automating individual finance tasks one at a time. It shows the same data-first instinct applied beyond planning and budgeting, and is the only piece in the set focused on receivables and order-to-cash.

## Work to read

- [A budget built in December assumes the market holds still until next December](https://www.linkedin.com/posts/rajeshviswanath_a-budget-built-in-december-assumes-the-market-activity-7493663916477308928-HGlE)
- [If the budget is obsolete three months into the year, the problem isn't the budget](https://www.linkedin.com/posts/rajeshviswanath_if-the-budget-is-obsolete-three-months-into-activity-7483879454667104256-htfi)
- [One of the biggest flaws in traditional budgeting isn't how companies build budgets. It's how they allocate capital.](https://www.linkedin.com/posts/rajeshviswanath_one-of-the-biggest-flaws-in-traditional-budgeting-activity-7486053763695157248-UcGE)
- [For years, CFOs have treated the cash conversion cycle as a working capital metric](https://www.linkedin.com/posts/rajeshviswanath_for-years-cfos-have-treated-the-cash-conversion-activity-7485328988370935810-i6eX)
- [A model that saves ten hours of manual work and costs eight hours of re-checking its output hasn't saved anything](https://www.linkedin.com/posts/rajeshviswanath_a-model-that-saves-ten-hours-of-manual-work-activity-7497650180759134208-XuR0)
- [An agent deployed on top of a broken spreadsheet doesn't fix the mess. It just runs it faster, with more confidence.](https://www.linkedin.com/posts/rajeshviswanath_an-agent-deployed-on-top-of-a-broken-spreadsheet-activity-7495113479687688192-ayVe)
- [AI can bring data across the O2C cycle together to identify anomalies, trends and emerging risks](https://www.linkedin.com/posts/rajeshviswanath_ai-can-bring-data-across-the-o2c-cycle-together-activity-7514320100921892865-sgG7)

## Brand relationships

- **upsta analytics** (disclosed_partnership)
- **McKinsey & Company** (mention)
- **PYMNTS Intelligence** (mention)

## Partnerships

The posts do not say whether Rajesh Viswanath offers partnership formats, sponsored placements, or paid collaborations. The eight posts are standalone analytical write-ups about finance planning, AI in finance, and order-to-cash work; several cite outside sources such as McKinsey and PYMNTS, and none carries a sponsorship disclosure, rate card, media kit, or offer description. One post closes by asking readers to follow a company account (upsta analytics), a follow prompt rather than a paid format. A B2B finance software company cannot evaluate a published package, price, or specification here. Contact them through their LinkedIn profile to ask whether they accept sponsorships and, if so, request campaign terms.

## Alternatives

- Bojan Radojicic: Choose Bojan Radojicic when the deliverable needs to be a working FP&A artifact rather than an argument: a template pack, a build-along tutorial for a custom reporting app, or practitioner-level process documentation. The posts get into the mechanics: an FP&A team working 70-hour weeks with static spreadsheets, broken VLOOKUPs and variance comments that take three days; and a walkthrough of building a reporting app with Claude, Cloudflare, Git and GitHub. It includes control rules (code calculates, AI explains; line owners review; MFA and audit trail) and a 10–14 week first-version estimate. If the software company's objective is to show finance teams how to do something on a Monday morning, this is a closer fit than Rajesh Viswanath's strategic framing.
- Michel D’Abranches: Choose Michel D’Abranches when the campaign is about working capital, treasury or liquidity rather than FP&A and AI. The posts argue that working capital is now a board-level priority, with weekly liquidity updates; that single-bank AR/AP programs may constrain capacity rather than expand it; and that multi-funder AR/AP structures change pricing and risk appetite. The second post details how to sell to CFOs without asking for a rip-and-replace: start with the tail the banks don't cover, run the two side by side for six months, and let the CFO ask to move the rest. For a working-capital platform campaign that needs a partner able to narrate buyer behaviour inside the CFO's office, that specificity is absent from Rajesh Viswanath's posts.
- Heather Crowley: Choose Heather Crowley when a large bank or enterprise trade-finance platform needs global-markets framing, risk language and the executive question of whether a constraint is a lever. The posts describe volatile trade dynamics, tightening credit conditions and compressed payment cycles; name the failure modes of fragmented tools and slow implementations; and present a unified platform with real-time visibility and coverage across 55+ markets as the fix. Addressed to CFOs and treasurers, the posts carry institutional weight—suited to a campaign where the software company wants to borrow institutional credibility and a global footprint rather than argue for a new operating model.

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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/