Rajesh Viswanath

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.

The most concrete version names drivers by sector.

• For a SaaS business: net revenue retention, CAC payback, pipeline conversion and gross margin.
• For manufacturing: capacity utilization, yield, inventory turns and on-time delivery.
• The supporting claim is attributed to McKinsey research on planning maturity, which they say shows that whether the forecast actually rolls forward with the business predicts CFO satisfaction with the planning process better than any tool or template.

The two posts make the same case from different angles: who gets funded, and when the plan stops being true. The allocation version explains why departmental budgeting misfires; the timing version explains why the fix is a rolling architecture, not a better budget cycle.

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If sales pipeline data is outdated, inventory numbers lag by weeks, or churn is not captured in real time, a rolling forecast does not make planning more agile; it updates bad assumptions more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.

The closing line distills the argument into one image: without reliable operational data, a rolling forecast is just a static budget with a new refresh rate.

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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 concrete symptoms are familiar to finance teams: inconsistent categorization, undocumented manual overrides, and three versions of the same number depending on who ran the report. Their one-line verdict on skipping the cleanup: the agent automates the confusion at a faster clock speed.

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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.

The test they offer is blunt and portable: a prediction a finance team cannot explain to its own CFO is one it will not act on. The real return of a finance AI model, they write, is how quickly someone stops double-checking it.

Read original · Original 2

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.

The figures come from a PYMNTS Intelligence report they cite: top-performing companies convert cash in 24.2 days against 44.4 days for lower performers, a gap of more than twenty days.

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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.

The post closes by pointing readers to Upsta Analytics for more on FP&A optimization and order-to-cash intelligence.

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The other pieces in this set develop the same argument.

The argument: annual budgeting is an architecture problem, not a discipline problem.

• It is built to measure a fixed target, then used to fund a moving business.
• Once the plan stops matching the market, usually by February, the rest of the year becomes a negotiation around a number nobody believes.
• 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; scale reserve capacity directly with sales volume, not with what was negotiated in the last planning cycle.
• The piece attributes to McKinsey research on planning maturity the claim that whether the forecast actually rolls forward with the business predicts CFO satisfaction better than any tool or template.

For brands planning a campaign

For a finance software partner, this is the strongest opener: it frames the problem as one of architecture and data flow, where planning and FP&A products compete.

• A collaboration built around driver-based reallocation rather than budget templates would extend the argument Rajesh Viswanath already makes.
• The quantitative backbone comes from third-party research, so the partner would need to supply original benchmark data.

It explains why the fix keeps disappointing people.

Rolling forecasts are presented as the standard remedy for stale budgets, then as a remedy that often fails.

• The problem, the piece argues, is sequence: finance teams replace the planning process without fixing the underlying data.
• If pipeline data is outdated, inventory lags 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.
• Companies that make it work start with operations and connect the forecast to daily-moving drivers — pipeline conversion, production output, customer retention, inventory levels, pricing — rather than monthly financial reports.
• The closing line casts rolling forecasts as a data transformation, not a finance transformation.

For brands planning a campaign

This is the natural companion to the budgeting argument and a strong anchor for a joint piece on forecast reliability.

• It lets a data or planning vendor position its product as a prerequisite for better forecasting, not an alternative.
• The argument rests on experience and reasoning rather than a cited study, so a partner should add quantified evidence.

Read it to see how the abstract argument translates into specific metrics.

Budgeting is described as starting with departmental requests — marketing wants more, sales wants headcount, product wants engineers, customer success wants to expand — and finance then arbitrates.

• The better question: which investment creates the most enterprise value?
• The piece cites a McKinsey observation that eight to ten operational drivers explain most performance variance in most organizations, then names them for two sectors.
• For SaaS: net revenue retention, CAC payback, pipeline conversion, gross margin.
• For manufacturing: capacity utilization, yield, inventory turns, on-time delivery.
• It concludes that a CFO's job is to allocate capital where it creates the most value, not to fund departments, and that driver-based planning is spreading across FP&A teams as volatility rises.

For brands planning a campaign

These driver lists provide ready-made scaffolding for a co-authored piece, webinar, or industry benchmark survey.

• Since the specific drivers vary by sector, this is also the easiest place to split a partnership into vertical tracks.
• The sector examples are illustrative, not drawn from named companies; a partner would need to supply real cases.

The claim: the cash conversion cycle has moved from a treasury metric to a competitive advantage.

• A PYMNTS Intelligence report finds that top-performing companies convert cash in 24.2 days, compared with 44.4 days for lower performers — a gap of more than twenty days.
• The piece frames that gap not as better liquidity but as twenty extra days to hire, invest, replenish inventory, or react to market shifts before competitors can.
• It attributes the difference not to stricter collections or slower supplier payments but to visibility: high-performing finance teams connect procurement, operations, suppliers, and finance in a real-time view of how cash moves.
• Its conclusion is that working capital is now a data problem, and CFOs gaining the biggest advantage are building systems that make cash move faster.

For brands planning a campaign

This is the most quote-ready item in the set: two named figures, a named source, and a clear shift from treasury problem to data problem.

• For a finance software partner, it supports an argument for connected data across procurement and operations and offers a measurable outcome for a campaign.
• The figure comes from a third-party report, which should be credited in any reuse rather than presented as original research.

It is the set's sharpest counterweight to enthusiasm about automation.

The piece opens with a cost comparison most finance teams are said not to make: a model that saves ten hours but costs eight hours of re-checking has saved nothing.

• Teams count the hours a model replaces, not the hours someone spends re-verifying a number they cannot trace to a reason.
• Explainability is part of the ROI arithmetic, not just a compliance requirement: every hour spent rebuilding trust in an opaque output cancels an hour the model claimed to save.
• The examples are propensity-to-pay and credit risk scoring, where explainability, the piece argues, must be built into the architecture from the first design decision, not generated as a report afterwards.
• The closing claim: the real ROI of a finance AI model is how quickly someone stops double-checking it.

For brands planning a campaign

Lead with this piece if the partnership goal is credibility with sceptical finance buyers rather than general AI enthusiasm.

• It supports content about model governance, auditability, and adoption metrics, and gives a partner a way to differentiate on explainability rather than raw speed.
• The ten-hours-versus-eight-hours framing is reusable, though presented as an example rather than a measured case study.
• The same text also appears at a second LinkedIn URL.

The piece draws a distinction: copilots are passive — they summarise and chart when asked — while agents are meant to watch data continuously, catch anomalies, and execute workflows on their own.

• That only works if the data they watch is reliable.
• The unglamorous work of becoming AI-native is described as paying down process debt: inconsistent categorisation, undocumented manual overrides, and three versions of the same number depending on who pulled it.
• An agent inherits that debt rather than fixing it.
• The recommendation is to clean the data model and define ownership first; otherwise, the agent simply automates the confusion at a faster clock speed.
• The real-time command centre CFOs want is the reward for doing that work, not a feature they can buy around it.

For brands planning a campaign

This piece argues directly against buying an agent as a shortcut, making it useful for a partner selling the data foundation layer and a poor fit for one selling autonomous agents as a standalone product.

• A joint piece could honestly pair the positions: what must be true of the data before agent deployment makes sense.
• The examples of overrides and duplicate numbers are concrete enough to reuse as diagnostic questions in a campaign asset.

The bigger shift in order-to-cash is moving from manual handoffs to a connected, intelligent process.

• AI can bring data together across the cycle to spot anomalies, trends and emerging risks.
• The piece offers no specific company, metric or process example, and points readers to an analytics account for more on FP&A optimisation and order-to-cash intelligence.

For brands planning a campaign

This is evidence of range: cite it when a campaign covers order-to-cash or receivables rather than planning.

• It is thin on specifics, so treat it as a topic signal, not a source to quote.
• For an O2C partnership, ask the partner to supply the process detail and case studies.

Departmental requests — marketing wants more budget, sales wants headcount, product wants engineers — set up finance to referee a contest rather than fund value.

• The better question is which investment moves the company's most important value drivers.
• The post cites McKinsey's finding that roughly 8 to 10 operational drivers explain most performance variance, then names drivers by sector: net revenue retention, CAC payback, pipeline conversion and gross margin for a SaaS business; capacity utilization, yield, inventory turns and on-time delivery for manufacturing.
• A second post makes the timing version of the same case: a budget built in December assumes the market holds still until the next December, so by February teams are negotiating around a plan that stopped being true.
• The proposed replacement is dynamic capital reallocation across 8 to 10 core drivers such as rate moves, input costs and supply-chain shifts, with reserve capacity scaled to sales volume rather than last year's negotiation.
• This post points to McKinsey research on planning maturity, which it says ties CFO satisfaction to whether the forecast actually rolls forward with the business.

Uses named third-party research as an anchor, then turns it into sector-specific metric lists a finance team could act on.

For brands planning a campaign

This is the strongest spine for a thought leadership collaboration on FP&A transformation.

• A finance software brand could pair Rajesh Viswanath's argument about line-item budgeting with tooling or benchmark data showing driver-based planning in practice.
• The sector-specific driver lists make the material easy to localize for SaaS or manufacturing audiences instead of writing one generic piece.

Examples: Source 1 · Source 2

Rolling forecasts are the common response, but many finance groups replace the budgeting process without fixing the underlying data: if pipeline data is stale, inventory numbers lag by weeks, or churn is not captured in real time, a rolling forecast just updates bad assumptions more often.

• Companies that make rolling forecasts work, they write, start with operations rather than finance, connecting forecasts to drivers that change daily — pipeline conversion, production output, customer retention, inventory levels and pricing — instead of monthly financial reports.
• Their conclusion is that rolling forecasts are a data transformation rather than a finance transformation.

For brands planning a campaign

Good material for a data-integration or FP&A platform angle: the argument gives a vendor a reason to talk about operational data plumbing rather than another planning template. A collaboration could turn this into a diagnostic checklist of the operational feeds a rolling forecast needs.

Examples: Source 1

Agents only work, they argue, if the data they watch is reliable.

• Much of the excitement around agentic AI skips the unglamorous layer 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 fixing it, automating confusion at a faster clock speed.
• Their prescription is to clean the data model and define ownership first; the real-time command center with no close-week scramble and no board surprises is the reward for that work, not a feature that can be bought around it.

For brands planning a campaign

Useful for a finance software brand that wants to position governance and data ownership as prerequisites for AI adoption. It also gives a collaboration a credible reason to discuss implementation sequencing rather than model capabilities.

Examples: Source 1

A model that saves ten hours of manual work but costs eight hours of re-checking has saved nothing: teams count the hours the model replaced but not the hours someone spends re-verifying a number they cannot trace back to a reason.

• They argue that explainability is not a compliance requirement but part of the return math, because every hour spent rebuilding trust in an opaque output cancels an hour the model claimed to save.
• They apply this to propensity-to-pay and credit-risk scoring, arguing that explainability has to be built into the architecture from the first design decision rather than generated as a report afterward.
• The test: a prediction a finance team cannot explain to its own CFO is one it will not act on.

For brands planning a campaign

Directly relevant to an AI-driven finance transformation pitch. A vendor could build a collaboration around how explainability changes adoption, using the ten-hours-saved-eight-hours-checking framing as a concrete hook rather than an abstract governance message.

Examples: Source 1 · Source 2

They cite a PYMNTS Intelligence report finding that top-performing companies convert cash in 24.2 days while lower-performing peers take 44.4 days, a gap of more than 20 days.

• That means 20 extra days to hire, invest, replenish inventory or respond to market changes before competitors can.
• The report attributes 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 get a real-time view of how cash moves.
• Their reframe is that working capital is no longer just a treasury problem but a data problem, and that the biggest advantage goes to CFOs building systems that help cash move faster.

For brands planning a campaign

Strong hook for finance software marketing beyond FP&A: the number pair gives a collaboration a specific, quotable claim to build content around. It supports a data-visibility narrative connecting cash performance to the operational-data argument found across their other posts.

Examples: Source 1

They argue this is where AI has more impact than automating individual finance tasks. The short post functions as a positioning statement rather than a worked example, and closes by directing readers to follow Upsta Analytics for more content on FP&A optimization and order-to-cash intelligence.

The closing follow prompt points to a named business. Verify it as an affiliation in the brand-relationships section; it does not establish sponsorship or payment.

For brands planning a campaign

A ready-made bridge between order-to-cash and AI transformation, useful if the brand's product touches receivables, collections or cash application. The post states the argument without evidence, so ask for case detail before building a collaboration around it.

Examples: Source 1

An arresting claim opens — the budget is obsolete, an agent on a broken spreadsheet, a model that saves ten hours — followed by a short reframe that redirects blame from process to data or architecture.

• Arguments are broken into short lines and occasionally numbered lists, then close with a sentence that restates the point in sharper language, such as a rolling forecast being a static budget with a new refresh rate.
• Where third-party evidence is used, the source is named in the text (McKinsey, PYMNTS Intelligence), with fuller links placed in comments.
• Posts are short, roughly a few hundred words.

Argument-first short-form posts built around a counterintuitive opening, a named research anchor and a closing reframe.

For brands planning a campaign

Easy to brief into a collaboration: the format suits a short series of opinion pieces or LinkedIn-native thought leadership with one idea each. Ask the partner brand to provide worked examples and case data.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6 · Source 7 · Source 8

The post does not identify the relationship with the business. Ask whether the promotion reflects employment, ownership, a client relationship or an unpaid recommendation.

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One uses the finding that eight to ten operational drivers explain most performance variance to argue that budgets should allocate capital by value driver rather than department. The other cites McKinsey’s planning-maturity research to argue that whether a forecast rolls forward with the business predicts CFO satisfaction better than any tool.

View example

The post credits the report for the source and data and attributes the performance gap to data visibility rather than collections or payment terms. The full link is said to be in the comments.

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Radojicic describes a team working 70-hour weeks on static spreadsheets and broken lookups; Rajesh Viswanath argues that bad operational data defeats a rolling forecast. Both identify the quality of templates and data models—not the software purchased—as the real lever.

Radojicic teaches through working assets: downloadable FP&A templates and a walkthrough of a finance team building its own reporting app with Claude, Cloudflare and Git/GitHub. Rajesh Viswanath takes an analytical approach, arguing from named third-party research and reframing the problem rather than shipping tools or models.

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D’Abranches's point that an active single-bank programme is not necessarily optimized echoes Rajesh Viswanath's view of budgets and forecasts: the inherited structure is the problem.

D’Abranches writes as a commercial operator, closing each post with the case for Monkey Global's multi-funder AR/AP model, its cash flow figures and an offer to send a snapshot. Rajesh Viswanath's posts are diagnostic, ending with a reframe rather than a product pitch.

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Both link better cash visibility to AI-enabled tools that flag risk and optimize terms at scale.

Crowley writes from inside J.P.

• Morgan, describing the bank's own Trade and Working Capital platform and its coverage across more than 100 markets, and inviting readers to see what the team is building.
• Rajesh Viswanath writes independently and cites research reports rather than promoting a platform or product.

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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.

The tradeoff is altitude and independence.

• Bojan Radojicic's posts target FP&A practitioners and analysts and end with a downloadable template offer, putting a partnership closer to enablement marketing than CFO-level thought leadership.
• The vendor would need to decide how much of its product story to fold into the template.
• Rajesh Viswanath's posts present the executive argument and named research anchors—McKinsey on operational drivers and planning maturity, and PYMNTS on cash conversion—that a CFO audience expects; Bojan Radojicic's posts deliver the artifacts.
• Before choosing either creator, ask for a sponsorship offer, rate card and audience demographics; the posts don't provide them.

Examples: Source 1 · Source 2

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.

The tradeoff is promotional proximity and scope.

• Michel D’Abranches' posts present a vendor's account of its own model and include performance claims for Monkey Global (more than 55% supplier participation, 25% cash-flow expansion in four months, outcome-based pricing), so a software company buying thought leadership would share the page with another commercial offer.
• The subject matter also stops at treasury and supply-chain finance; there is no rolling-forecast, AI explainability or FP&A data-quality thread to connect to an AI-driven finance transformation campaign.

Examples: Source 1 · Source 2

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.

The tradeoff is that the posts read as institutional product messaging tied to J.P.

• Morgan's working-capital solutions and campaign links.
• They demonstrate the house position rather than an independent practitioner's point of view, and imply an enterprise treasury audience.
• For a finance software company selling FP&A and AI-driven transformation to mid-market finance teams, that scale and subject matter are a mismatch: there is no FP&A process content, AI model governance or explainability argument, or template or diagnostic asset to reuse.

Examples: Source 1 · Source 2