Work in Progress

This document is a draft — strategic decisions are still pending. Do not reference for decision-making yet.

Beyond Forecasting v2: Strategic Bets¶

Authors: Gurjit & Emma | Date: 2026-02-18 | For: Product Planning Session (Feb 25)

Purpose: Define Palm's expansion beyond core cash forecasting to reach $150K+ ACVs. Present strategic bets, what we know, what we need to validate.

1. Strategic Positioning¶

The Thesis¶

The shift (per BVP): AI is disrupting traditional enterprise software. "Systems of Action" will win - AI agents that automate workflows and decision-making, delivering 10x UX improvements. Traditional SoR moats (implementation costs, data centralization) are eroding.

Our take: True, but execution alone isn't defensible. The moat is the institutional knowledge that tells you WHAT to execute and WHY it's safe.

The Problem¶

Treasury teams need to make confident decisions (move cash, hedge FX, invest surplus), but today's tools force a choice:

TMS: Connectivity + reporting, no intelligence
AI forecasting: Predictions, but no institutional knowledge (policies, patterns, governance)
Execution platforms: Can move money, but require manual instruction and don't know if it's safe/compliant

The gap: No one combines sophisticated forecasting with the institutional knowledge that makes decisions trustworthy.

Palm's Position: System of Intelligence¶

We're building both sides of the equation:

1. Incredible forecasts (ML architecture moat) - Factor-based modeling (not just time series) - Covariance/ripple effects (adjust revenue → suggests payroll, tax, IC impacts) - Hierarchical forecasting (entity-level roll-ups) - Hyper-customization (institution-specific patterns) - Data connectivity (ingest internal data beyond bank statements: ERP, CRM, payroll, debt schedules)

2. Institutional knowledge that compounds (thick middle moat) - Policy enforcement (which entities can invest in what, approval thresholds) - Pattern recognition (Customer X pays day 60, Q4 inventory extends AP 15 days) - Decision provenance (audit trail of every change, assumption, outcome) - Confidence scoring (learn which forecasts are 95%+ accurate vs. need review)

3. UX & agents that make it actionable (workflow moat) - Dual interface (natural language (in app, MCP servers, slack etc) + control room UI) - Proactive recommendations (agent suggests actions, human approves/corrects) - Cross-system orchestration (works across distributed systems)

Concrete example:

Approach	What You Get
TMS	Bank connectivity + balance reporting
AI Forecasting	"Collections will be €2.3M next week"
Execution Platform	Button to move €2.3M to overnight deposit
Palm	"Collections forecast €2.3M, but Customer X pays day 60 (learned pattern), so available cash is €2.1M. UK entity policy allows overnight only (no term deposits). Recommend €2.1M to overnight deposit. Requires approval for >€2M movements. [Agent can coordinate execution via two steps: (1) notify fund via phone/email/portal, (2) initiate payment via TMS/bank]"

Why this is defensible: - The execution layer might get commoditized or distributed - The model layer is already commoditizing (multiple LLM providers, open weights) - The defensible layers are: (1) ML architecture that delivers accurate, sophisticated forecasts, and (2) institutional knowledge that makes any decision safe

After 2 years with Palm, switching means losing both: forecast sophistication tuned to your business AND accumulated expertise (policies, patterns, corrections).

Won't institutional knowledge commoditize like the "context problem" in ML?

No, because: - Proprietary, not universal: Foundation models can't pre-train on YOUR business rules ("Customer X pays day 60", "UK entity overnight-only policy") - Deterministic, not probabilistic: Enterprises need enforceable constraints ("you cannot do this because covenant X"), not suggestions - Compounding, not static: 2 years of corrections/patterns compound per customer — competitors start at zero - Governance layer: Audit trails, policy enforcement, explainability that models alone can't provide

Even as models improve, the structured knowledge layer remains valuable because it's customer-specific, enforceable, and auditable.

The Open Question: Do We Own Execution?¶

We don't need to decide today whether we own execution (payment initiation, FX trades, investment execution) or partner with systems that do.

The bet is: Own the intelligence layers (forecast sophistication + institutional knowledge) that make any execution safe. If owning execution creates strategic value (higher ACVs, better UX, TMS independence), we explore it. If partnering is faster/better, we do that.

2. Strategic Bets: ACV Multipliers¶

These are the areas where we see opportunity to expand beyond core cash forecasting and justify $150K+ ACVs.

1. FX Risk Intelligence¶

Outcome: Progressive depth on FX capabilities — starting with visibility, moving toward decision support, eventually full hedging workflows.

Three levels:

Level 1: Exposure Visibility (baseline - partially exists) - Show forecasts by currency (not just functional currency) - Multi-currency balance visualization - FX scenarios (rate movements impact on forecasts) - Bottom-up forecast-driven exposure analysis (vs consolidated balance views)

Level 2: Decision Intelligence (next depth) - Move from "here are your exposures" to "here are the decisions/options you should take" - P&L impact analysis of rate movements - Hedging recommendations: "If EUR/USD moves 5%, you need to hedge €X by [date]" - Acquisition funding optimization (hold USD vs local currency for 6-8 month planned acquisitions) - IC loan FX centralization decisions (borrower pays in rupees vs lender holds FX exposure)

Level 3: Execution & Governance (eventual) - Full hedging workflows (hedge execution, hedge accounting, measuring hedge effectiveness) - Integration with FX brokers/banks for trade execution - Hedge tracking and maturity management - Regulatory reporting (hedge effectiveness tests)

Why it matters: Multi-currency customers (ON, Personio, Euroports, Ocado) struggle with FX visibility. Legacy tools (Fire Apps/Kyriba, Reval/Ion) show consolidated balances, not bottom-up forecast exposure. Palm's differentiation: leverage existing forecast data to show forward-looking exposure by entity, category, and time horizon. ON actively kicking off risk management program (2027 go-live).

What we know: - Level 1 partially exists (can show some FX in forecasting, but can do more) - Gurjit's strategic hypothesis: Natural progression after cash forecasting matures - Market pricing estimates (not validated): Standalone FX tools $80-150K/yr; combined cash + FX could reach $150K+ ACV - Data already exists in Palm (forecasts by account carry currency, FX rates feed available) - Competitive landscape: Fire Apps (acquired by Kyriba), Reval (acquired by Ion), Unbound (execution-focused, not forecasting) - Proposed differentiation: Bottom-up forecasting approach vs. traditional consolidated balance views - Multi-currency customers identified: ON, Personio, Euroports, Ocado (need to validate broader pipeline %)

What we need to validate: - How many sources explicitly request FX risk management (currently have 3 deep transcript sources; claim of "11 sources" needs validation) - ACV potential for our ICP (not just market comps) - Build effort estimate from engineering for each level - What % of current pipeline is multi-currency - Which level drives most ACV value: exposure visibility, hedging recommendations, or execution

Prioritization Assessment:

[ ] ACV Impact: [High/Medium/Low] — $[X]K [standalone/add-on/bundle component]
[ ] Evidence Strength: Moderate-to-Strong — 6 transcript sources (ON x3, Levi's, IHG, Treasury industry); emerging job-to-be-done across operational, strategic, and global liquidity use cases
[ ] Path to Revenue: [New deals/Expansion/Strategic] — [which prospects/customers]
[ ] Competition: [Differentiated/Parity/Unknown] — [who offers this, at what price]

Transcript Evidence

**Sources:** - ON (2025-10-07): Currency dashboard feedback - ON (2026-02-18): FX scenarios and investments - ON (2025-08-07): Cash pool visibility across currencies - Levi's expert interview (2025-12-11): Dette on FX and acquisition planning (25+ years treasury, managed $3B FX portfolio) - IHG expert interview (2025-04-07): Multi-currency subsidiary funding - Treasury Dragons conference (2025-12-09): Industry view on multi-currency forecasting **Three Distinct FX Workflow Types Identified:** 1. **Operational visibility** (ON) - Day-to-day currency position monitoring 2. **Strategic planning** (Levi's) - Major corporate events (acquisitions) with FX impact 3. **Global liquidity management** (IHG, Treasury industry) - Managing cash across multiple currencies and entities **Key quotes & pain points:** **Multi-currency visualization challenge:** > "Visualizing currency flows is quite hard because to visualize something you really need all to be in functional currency but then it becomes kind of not very useful" — ON **Currency exposure monitoring:** > "Monitor currency exposures and cash pool positions...Minimize time required to identify currency shortfalls/surpluses" — ON **FX scenario modeling:** > "Once you see your scenario, it would be nice to see what you said was your best case, your baseline, and your worst case, and then with the actuals laid on top of that so it doesn't wipe out the history... similarly for fx, if it's called one way versus another way versus the point when you set your scenario. Would be nice to see them layered on top of each other" — ON (Jennifer) **Acquisition FX planning (6-8 months ahead):** > "An acquisition. Timing of when you close your acquisition. Could be also very influential if you have this data... most acquisitions. You know this. You're working in an acquisition, maybe six or eight months ahead of time. Then you can say, Let's just say also the acquisition is in foreign currency. Then you're able to then plan ahead. Because you have a cash flow forecast by currency. Work the region. And then now you also know your outflows from a US dollar standpoint. And then you can then trade. How much you're going to hold so that you can pay for an acquisition in US Dollars or in currency." — Levi's (Dette) **FX centralization for IC loans:** > "Some companies will say, well, we want to centralize the FX exposure to the lending company. So the borrower should not be exposed. They borrow a million rupees, they pay back a million rupees. FX is with [the lender]." — Levi's (Dette) **Industry validation:** > Multi-currency forecasting is "obviously important for global liquidity management" — Treasury Dragons conference **Desired outcomes identified:** - Show FX hedges in the forecast - View forecasts by individual currencies (not just functional currency) - Track historical FX scenarios vs actual FX movement - Minimize FX impact on acquisition costs through advance currency planning - Optimize acquisition funding strategy (hold USD vs local currency) **Gap:** Evidence shows FX visibility needs but lacks depth on specific FX risk management workflows (hedging decisions, P&L impact analysis, exposure reporting formats). Need more discovery on what "FX risk intelligence" means to customers vs. just multi-currency forecasting.

2. Bridging & Working Capital Intelligence¶

Outcome: Explain to CFO why actual cash differs from budget/forecast. Start with bridging (variance explanation), then go deeper on working capital drivers (AR/AP movements, DSO/DPO shifts, customer concentration risk).

The natural progression:

Step 1: Direct/Indirect Bridging (baseline variance explanation) - Automate monthly T+1 cash burn analysis: "What happened last month and why?" - Bridge treasury (direct method) and FP&A (indirect method) forecasts - Three-way variance analysis: actual vs direct forecast, actual vs indirect forecast, forecast vs forecast - Waterfall visualization showing variance breakdown by category - Working capital breakdown (AR vs AP timing shifts)

Step 2: Working Capital Intelligence (deeper analysis of what drives variances)

Once you can explain variances, the natural question is: "Why did working capital swing?" Going deeper means: - How sensitive is liquidity to DSO shifts? (If collections slip 7 days, what happens?) - Customer concentration risk: What % of runway depends on top 5 customers paying on time? - Pattern recognition: Customer X pays day 60, Q4 inventory extends AP by 15 days - Proactive recommendations: "If DSO doesn't improve by 5 days, you need to borrow €2M more" - Covenant risk alerts: "If top 3 customers delay, you breach covenant"

Why they're coupled: Bridging answers "what changed?" Working capital answers "why and what's at risk?" Both explain the gap between the budget forecast (indirect, FP&A) and the cash forecast (direct, treasury).

Why it matters: - Bridging: Personio does T+1 analysis manually day 1 of every month (hours of work). Euroports spends significant time asking local entities "what does this mean?" US companies with strong FP&A culture care a lot. - Working Capital: Working capital swings affect ability to meet debt covenants, pay dividends, fund growth. CFOs need this visibility, not just treasurers. Expands buyer from treasury to CFO office.

What is T+1 cash burn analysis?

**T+1** = Trade date + 1 day (the first business day after month-end closes) On day 1 of the new month, treasury teams need to explain what happened in the prior month: - How much cash did we burn? - Why did actual differ from forecast/budget? - What drove the variance? **Personio's current process (manual, takes hours):** 1. Month closes (Jan 31) 2. Feb 1st morning: Pull bank statements, categorize transactions, reconcile against forecasts 3. Create variance explanation for CFO/management 4. Feed into board reporting and investor updates **The pain:** This is hours of manual Excel work every month, under time pressure, just to answer "what happened and why?" **Three types of variance analysis needed (Personio):** 1. **Actual vs Direct Forecast** - How did real cash differ from treasury's transactional forecast? 2. **Actual vs Indirect Forecast** - How did real cash differ from FP&A's budget? 3. **Forecast vs Forecast** - How does treasury's view differ from FP&A's view? Each requires different reconciliation logic and tells a different story to stakeholders.

What we know: - Bridging: 4 deep customer sources (Personio, Euroports, Levi's, Instacart) with explicit bridging workflows and T+1 reporting pain - Working Capital: 3 sources with explicit working capital needs (ON, Levi's, Euroports) - AR/AP movements, DSO/DPO tracking, customer-level drill-down - The thick middle opportunity: learn patterns (Customer X pays day 60, Q4 inventory extends AP by 15 days) - Need: waterfall visualization, working capital breakdown (AR vs AP timing), drill-down, exportable reports - Strengthens "single pane of glass for CFO" positioning

What we need to validate: - ACV impact (gut: part of $100K+ bundle, but not standalone for bridging; working capital depth could add $50K+) - Build effort estimate for bridging baseline vs working capital intelligence layer - How to get budget data (file upload first, then planning system integrations: Anaplan, Adaptive Planning, Hyperion, BPC, etc.) - Budget data typically lives in: OneStream, Anaplan, Workday Adaptive Planning, SAP BPC/Analytics Cloud, IBM Planning Analytics, Oracle Hyperion - How to get ERP data for AR/AP drill-down (Dynamics, NetSuite, SAP, Oracle integrations)

Prioritization Assessment:

[ ] ACV Impact: [High/Medium/Low] — $[X]K [standalone/add-on/bundle component]
[ ] Evidence Strength: Strong — 5 unique transcript sources with explicit bridging workflows (4 sources), working capital tracking needs (3 sources), T+1 reporting pain, and treasury-FP&A reconciliation
[ ] Path to Revenue: [New deals/Expansion/Strategic] — [which prospects/customers]
[ ] Competition: [Differentiated/Parity/Unknown] — [who offers this, at what price]

Transcript Evidence

**Sources:** - Personio (2025-12-04): Direct/indirect bridging deep-dive session with Tom Thorn (Treasury) - Euroports (2025-10-27): Bridging session with Matthias Depoorter (Treasury Manager) - both bridging and working capital needs - Levi's expert interview (2025-12-11): Dette's deep dive on direct vs indirect bridging, working capital hedge, AR patterns (25+ years treasury experience) - Instacart expert interview (2025-07-01): David Watt on variance attribution and budget reconciliation - ON (2026-02-18): Standup discussing inventory and working capital scenarios **Key quotes & pain points:** **BRIDGING EVIDENCE:** **T+1 reporting pain (Personio):** > "T+1 reporting on first day of each month takes hours of manual work" — Tom Thorn **Three-way variance analysis needed (Personio):** > "There's the actual versus direct forecast, there's the actual versus indirect forecast, and then there's the forecast versus forecast. So there's various different variance points." — Tom Thorn **Central hub requirement (Personio):** > "What I'm aiming to achieve is being able to use the system as like, a true reporting hub." — Tom Thorn **Manual entity interrogation (Euroports):** > "It's a lot of time that we spend a lot of time asking the different local entities. What does this mean?" — Matthias Depoorter **Budget deviation analysis (Euroports):** > "We want to know the deviation of the short term forecast versus the budget... I want to understand the drivers." — Matthias Depoorter **No Excel workaround (Euroports):** > "I really don't want to go back to Excel for nothing. Everything needs to be done through the tool." — Matthias Depoorter **Treasury-FP&A timing gap (Levi's):** > "This is an fpna, right? There's a forecast...And there's this treasury forecast, right? This forecast for the fpna doesn't get refreshed as often with actuals, right?...How do we bridge that?" — Dette **Variance grows over time (Levi's):** > "It's when you come closer to the end of a period that you're so far apart because they haven't refreshed. And I have." — Dette **Tracking permanent vs timing differences (Levi's):** > "We talk about what's in common. That is already stipulated in the FP&A model. And in treasury model, we talk about things that are not in the FP&A model" — Dette **Reconciling treasury vs FP&A forecasts (Instacart):** > "I'd say reconcile it to other forecasts too, right? That's a big part of what we do. Is you've got your base forecast, but somebody else has a different end result, and you have to figure out why they're different" — David Watt **Budget conformance requirement (Instacart):** > "Typically, I have to conform to the budget, right? So when they set the budget... my 13 week view has to kind of end at where the first quarter of the budget says, it's going to be" — David Watt **Extending 13-week to year-end for reconciliation (Instacart):** > "extend your ad 35 weeks right now, so take it to the end of the year... you can reconcile that to the budget... The 13 weeks should have been built out to go to the end of the budget year... you try to tie out the whole year to the budget, right? That's kind of your starting assumptions" — David Watt **WORKING CAPITAL INTELLIGENCE EVIDENCE:** **Working capital movement derivation (AR/AP breakdown) (Euroports):** > "basically the derived movement and working capital...We'll just probably do movement and AR movement and AP" — Matthias Depoorter **Customer-level AR drill-down (Euroports):** > "drill down, a little bit more saying. Okay, this is the AR movement for example, because of, I don't know. Fried forwarding revenues, or this is from thermal revenues...I think for my understanding, we do have specific customers, very large customers...so I want to see more what's happening there" — Matthias Depoorter **DSO, DPO tracking capabilities (Euroports):** > "those working capital Mac directs, like DSO, DPO, etc. On the weekly basis and you can track that out" — Matthias Depoorter **Inventory and networking capital scenarios (ON):** > "I think I can see us using it for different reasons. Like, for example, for inventory. Like more this kind of networking capital. Like account receivable, accounts payables, inventory. Maybe we can do some tests. We don't really do this for our forecast yet, so it will be new." — ON **Working capital hedge concept (Levi's):** > "Most FP&A groups would have what they call working capital hedge...They'll have a cushion. They always have a range...working capital they usually have...They'll identify. Just say, I have a hedge of $50 million. Well, I'm off 50 million. There we go. We're offset" — Dette **Cash conversion cycles and DSO (Levi's):** > "depending on the company...sometimes going down to the very minutiae of each customer might be helpful for AR because then what influences...depending again on the product. If you have AR they have top 10 and those top 10 customers can sway payment behavior" — Dette **Explicit feature needs:** - Waterfall visualization showing variance breakdown (Euroports, Personio) - FP&A budget upload and overlay on Palm forecast views (Personio) - Multiple variance comparison tables: actual vs forecast, forecast vs forecast (Personio) - Working capital breakdown (AR vs AP timing) with drill-down (Euroports) - Entity-by-entity variance with group consolidation (Euroports) - 13-week forecast extended to year-end for budget reconciliation (Instacart) - Reconcile short-term treasury forecast to annual P&L budget (Instacart) - DSO/DPO tracking on weekly basis (Euroports) - Customer-level AR drill-down (Euroports, Levi's top 10 customers) - Working capital hedge / buffer tracking (Levi's) - Exportable reports for CFO/board - Weekly view with button to publish instantly - Permanent vs timing differences tracking (Levi's)

3. Tokenized Treasury Products & Cross-Border Payments¶

Outcome: Investment execution into yield-bearing tokenized products (money market funds, yield stablecoins). Faster intercompany settlements, reduced FX conversion costs, 24/7 settlement vs banking hours.

Why it matters: New category of treasury products blend traditional yields (~4% from US Treasuries) with blockchain settlement speed. Could position Palm as "future of treasury" vs legacy TMS.

Use Case 1: Surplus Cash Investment (Yield-Bearing Products)

**BlackRock BUIDL** (USD Institutional Digital Liquidity Fund) A tokenized money market fund - essentially US Treasury bills wrapped as blockchain tokens. - **$2.85B AUM** (as of Feb 2026) - launched March 2024 - **~4% APY** paid as daily dividends (minted as new tokens) - **Backed 1:1** by US Treasury bills, cash, and repurchase agreements - **8+ blockchains** (Ethereum, Polygon, Solana, BNB Chain, etc.) - **Feb 2026:** Listed on Uniswap (first DeFi integration) for institutional trading **Circle USYC** (USD Yield Coin) A yield-bearing stablecoin (unlike USDC which pays no yield). - **$1.6B in assets** (as of Jan 2026) - **Backed by** short-term US Treasury bills and reverse repos - **Yield mechanism:** Token price rises automatically (trading at $1.12 in 2026 vs $1.00 base) - no staking/claiming needed - **Instant redemptions** with continuous access **Franklin Templeton BENJI** First traditional asset manager to launch tokenized money market fund (2022). Tokenizes the shareholder registry itself - one share = one BENJI token, with transfer and record-keeping maintained on-chain. Expanding across multiple blockchains including Avalanche, Canton, VeChain. **Ondo OUSG** (Short Duration US Government) DeFi-native issuer of tokenized short-term US Treasuries. Offers instant 24/7 minting and redemptions in USDC or PYUSD. Targets institutions seeking treasury exposure with immediate liquidity.

Use Case 2: Cross-Border Payments & IC Settlements

**USDC** (Circle) The dominant payment stablecoin - $1 peg, backed 1:1 by USD reserves, no yield. Used for instant cross-border transfers without FX conversion delays. **Bridge** (acquired by Stripe, 2025) Stablecoin infrastructure platform now powering Stripe's global payment rails. Key features: - **Stablecoin Financial Accounts** in 100+ countries - businesses hold balances in fiat or stablecoins - **Global payouts** - now processing from 120+ countries (70+ countries within first week) - **Corporate card settlement** - stablecoin-funded card programs - **Cross-border netting** using USDC for IC settlements Stripe is embedding stablecoin functionality directly into APIs for payouts, wallet balances, and card issuing - making stablecoins as accessible as traditional card or bank rails.

Why we should care:

This represents a new category of treasury products that combine: - Traditional money market yields (~4%) - Stablecoin-like instant settlement (24/7, not banking hours) - Blockchain infrastructure (programmable, composable) - Global reach (120+ countries via Stripe/Bridge)

For Palm, this opens: 1. Investment recommendations that include tokenized products: "Move €2M surplus to BUIDL for 4% yield" 2. Cross-border recommendations: "Settle IC payment to APAC via USDC (instant) vs SWIFT (2-3 days)" 3. Execution layer that can move funds into these products (not just suggest it) 4. Positioning as "future of treasury" vs "legacy TMS"

Sources

- [BlackRock BUIDL Overview](https://www.ccn.com/education/crypto/blackrock-buidl-fund-tokenized-money-markets-explained/) - [BlackRock's Uniswap Integration](https://fortune.com/2026/02/11/blackrock-uniswap/) - [Circle USYC Details](https://www.circle.com/usyc) - [Binance adds USYC](https://fortune.com/crypto/2025/07/24/binance-circle-usyc/) - [Stripe's Bridge and Stablecoin Infrastructure](https://www.americanbanker.com/news/payment-fintechs-push-stablecoin-tech-for-2026) - [Bridge's Invisible Stablecoin Payments](https://www.theasianbanker.com/updates-and-articles/bridge-and-the-rise-of-invisible-stablecoin-payments) - [Franklin Templeton BENJI Expansion](https://www.avax.network/about/blog/franklin-templeton-launches-tokenized-money-market-fund-benji-avalanche) - [Ondo OUSG Details](https://ondo.finance/ousg) - [Tokenized Treasuries Market Growth](https://cryptoslate.com/tokenized-us-treasuries-silently-replaced-defis-foundation-and-you-missed-the-critical-9-billion-shift/)

What we know: - Zero customer mentions to date for stablecoins/cross-border (no evidence in transcripts, signals, or conversations) - Tokenized treasury products scaling rapidly: BUIDL ($2.85B), USYC ($1.6B) - Infrastructure providers exist (Circle, Blackrock, Bridge/Stripe, Wise, Airwallex) - Regulatory landscape still shifting but major institutions entering (Blackrock, Circle)

What we need to validate: - Which treasury teams are actively exploring tokenized products vs stablecoins for payments (probe in discovery calls) - ACV potential for investment execution vs cross-border payments (market still forming) - Build vs partner approach (likely partner for rails, build intelligence + recommendation layer) - Investment execution as broader opportunity beyond tokenized products (traditional money market funds, term deposits)

Prioritization Assessment:

[ ] ACV Impact: [High/Medium/Low] — $[X]K [standalone/add-on/bundle component]
[ ] Evidence Strength: Very Weak — Zero customer mentions in all transcripts searched
[ ] Path to Revenue: [New deals/Expansion/Strategic] — [which prospects/customers]
[ ] Competition: [Differentiated/Parity/Unknown] — [who offers this, at what price]

Transcript Evidence

**No evidence found** in current transcript base. **Searched for:** stablecoin, blockchain, tokenized, USDC, crypto, cross-border payments (beyond standard international transfers) **Observation:** This bet may be too early-stage or not validated by current customer base. No customer has mentioned interest in tokenized treasury products, yield stablecoins, or blockchain-based settlement. **Action needed:** Probe discovery calls explicitly to validate if this is a real need or purely speculative.

4. Deeper IC Intelligence¶

Outcome: IC loan tracking with interest accrual forecasting, in-house bank capabilities, forecast timing for periodic IC. Beyond current IC categorization work.

Why it matters: Gurjit tracked 60+ IC loans manually at Uber. Kyriba IC module is a "complete mess." ON needs IC interest accrual forecasting (daily 1.25% rate, quarterly settlements). Personio needs forecast timing fixes (monthly IC averaged weekly instead of pinned). Could be $20-30K add-on (gut estimate).

What we know: - Current work: IC categorization improvements, basic IC flows in forecasts - ON: Daily IC interest accrual tracking (1.25% rate), multiple cash pools (UBS, Deutsche Bank, JP Morgan), ~800k discrepancies - Personio: Forecast timing errors (monthly IC averaged weekly instead of pinned to correct week) - AP-driven cost-plus settlements ($5-20M/month) invisible to treasury - needs ERP visibility

What we need to validate: - Commercial signal (is there ACV potential beyond categorization fixes?) - How many prospects have 50+ entities (qualify IC complexity in pipeline) - Build effort for IC loan tracking vs. full in-house bank capabilities - COBO/POBO workflows (no transcript evidence - validate or remove)

Prioritization Assessment:

[ ] ACV Impact: [High/Medium/Low] — $[X]K [standalone/add-on/bundle component]
[ ] Evidence Strength: Strong — 5+ transcript sources with IC loan tracking, ZBA balance display blocking adoption, interest accrual forecasting, Kyriba module failure
[ ] Path to Revenue: [New deals/Expansion/Strategic] — [which prospects/customers]
[ ] Competition: [Differentiated/Parity/Unknown] — [who offers this, at what price]

Transcript Evidence

**Sources:** - ON (2026-02-02): IC sync - interest accrual, cash pool tracking - Personio (2026-02-02): IC sync - ZBA balance display blocker, forecast timing issues - Internal (2025-12-02): Gurjit's IC loan workbook walkthrough (Uber) - ON (2026-02-18): IC in scenarios discussion - ON (2025-11-17): Kyriba walkthrough and IC issues - ON (2025-10-07): IC categorization problems **Key quotes & pain points:** **Forecast timing errors (Personio):** > "Forecast timing incorrect for monthly IC transactions (averaged weekly instead of pinned to correct week)" — Personio IC sync > "They have very specific once a month transfers, which should be quite easy to forecast. But the forecasts are not being pinned to the correct week. They're just being attributed over the year." — Personio IC sync **IC interest accrual tracking (ON):** > "Tracks IC loans actively with daily interest accrual (1.25% between On Holding and On AG)" — ON IC sync > "Interest forecasting: extrapolate accrued interest into next two months" — ON IC sync > "Quarterly settlement workflows" — ON (interest paid quarterly) **Multiple cash pools (ON):** > "Multiple IC positions across cash pools (UBS, Deutsche Bank, JP Morgan)" — ON IC sync > "~800k discrepancy in JP Morgan pool due to categorization issues" — ON IC sync **AP-driven settlements invisible to treasury:** > "Large amounts ($5-20M/month), kicked off by AP not treasury, flow from one entity to many" — IC sync (2026-02-02) > "Treasury doesn't have visibility on AP-initiated cost plus payments between entities" — IC sync **Kyriba IC module failure (Uber):** > "We try to take this and put it into Kyriba. We did go through an RFP with Kyriba to get their intercompany loan module. It was a complete mess." — Gurjit **IC loan tracking needs (Uber):** > "borrower, lender, amount, interest rate, how much is borrowed, how much interest is owed, how much interest is paid - great report...because most treasury teams are building these and sending these out on a daily basis." — Gurjit **Manual benchmark rate updates pain (Uber):** > "This is where things started getting really manual and tricky...pulled from Bloomberg...updating every month" — Gurjit **Complex withholding tax tracking (Uber):** > "We need to calculate what the withholding tax is between and between the two countries" — Gurjit **Thin capitalization complexity (Uber):** > "We couldn't figure out a way to really track it properly" — Gurjit **IC categorization issues (ON):** > "I think it's not in this screenshot, it's super helpful to have this view. I can see 360 here caching and then 347 cash out. So I know I have to fix some transactions" — ON **IC in scenarios (ON):** > "would it also be possible to include like non business categories? Or...for intercompany, would it also be possible to include like non business categories?" — ON **Explicit feature needs:** - Forecast timing: pin monthly IC to correct week, don't average (Personio) - IC interest accrual forecasting: daily rate calculation, quarterly settlement projection (ON) - IC balance tracking: sum all IC transactions for running position with "as of date" filter - IC counterparty identification: rule-based + LLM narrowed to IC categories - AP-driven cost-plus settlement visibility: surface ERP-initiated IC flows to treasury - IC loan tracking: borrower, lender, amount, rate, interest owed/paid (Uber/Gurjit) - Monthly reports for accounting teams - Maturity alerts (30 days before expiration) - Benchmark rate integration (avoid manual Bloomberg updates) - Notional vs actual borrowed amount tracking

5. Direct Bank Connectivity¶

Outcome: Own the connection layer instead of sitting on top of Kyriba/TMS. Control full data pipeline (quality, freshness, semantics). Can extend beyond banks to investment platforms, FX brokers, tokenized treasury providers.

Why it matters: Could justify higher ACVs. Eventually enables cutting loose from TMS dependencies (long-term, not near-term - we're nowhere near feature parity). Strategic enabler: If we want to own execution (payment initiation, investment execution, FX trades), owning connectivity is a prerequisite. Connectivity beyond banks (investment platforms, FX brokers, tokenized treasury) enables broader execution capabilities.

What we know: - Currently jack into existing connectivity (Kyriba, other TMS) - works fine, not a blocker - Our ICP mostly has connectivity in place already, though some prospects (Euroports, Personio, Dunelm, Discogs) lack reliable connectivity - Owning connectivity unlocks execution layer optionality (can build or partner for execution, but need connectivity either way)

What we need to validate: - Does owning connectivity justify higher ACVs? - Which customers would pay for direct connectivity vs. TMS integration? - Build effort (significant - not just API integrations, but data normalization, error handling, reconciliation) - Middle-ground options like Fides (banking connectivity aggregator) as partner vs. full build - Execution strategy: build vs. partner (connectivity is a prerequisite for either path)

Note on agent orchestration: As AI agents mature, they may coordinate execution across distributed systems (TMS, banks, investment platforms) without requiring Palm to own connectivity. In this scenario, the moat shifts entirely to the intelligence layer (institutional knowledge, policies, decision provenance) rather than the pipes. This reinforces the "thick middle" positioning - connectivity is valuable near-term, but institutional knowledge is the durable moat.

Prioritization Assessment:

[ ] ACV Impact: [High/Medium/Low] — $[X]K [standalone/add-on/bundle component]
[ ] Evidence Strength: Strong — 3+ transcripts with persistent integration blockers, data quality issues, TMS limitations
[ ] Path to Revenue: [New deals/Expansion/Strategic] — [which prospects/customers]
[ ] Competition: [Differentiated/Parity/Unknown] — [who offers this, at what price]

Transcript Evidence

**Sources:** - ON (2026-02-18): Dynamics API integration readiness discussion - ON (2025-11-17): Kyriba walkthrough revealing limitations - Internal (2025-12-02): IC loan workbook and data pipeline challenges **Key quotes & pain points:** **Dynamics API integration readiness:** > "We have a new IT product manager for Dynamics who is excited about the integration. Ready to kick off, may get resources in Q2. Wants specs/documentation from Palm." — ON **BigQuery integration planned but blocked:** > "Discussion about feeding Anaplan capex budgets into Palm for better scenario coverage...ideally we will connect with BigQuery and I have this ERP data as well, but for now, no, we're just using the Palm forecast and the manual addings" — ON **Kyriba limitations in forecasting:** > "What Kyriba does is grabbing our initial balance, adding all of the expected outflows, expected inflows. Nothing super smart, nothing of AI, no nothing fancy." — ON **Data timing issues with Dynamics:** > "Paid items still show as open until accounting closes them...the system cannot match...If open item is there and approved, technically the automatic pay run should pick it up. However, if that pay run fails for whatever reasons...it's canceled. The whole batch will be canceled." — ON **HighRadius integration data flow:** > "There is no connection for all of the enhanced data or insights from high radius flowing to Dynamics or to the data lake. It stays within high radius." — ON **Need for better bank statement matching:** > "The bank statement is your key for your actuals and it's probably the best machine learning that you can actually use" — Levi's (Dette on Kyriba limitations) **Explicit feature needs:** - Dynamics API integration specs/documentation - BigQuery data lake integration - Bank statement parsing and matching - Payment run batch tracking - Transaction-level visibility of which payments went through - Anomaly detection for unusual payments/failed batches

2.5. Prioritization Summary¶

Once the assessments above are complete, sort bets into tiers based on the 4-axis framework:

Tier 1: High-Confidence ACV Multipliers¶

Strong evidence + clear path to $100K+ ACVs

[ ] TBD

Tier 2: Strategic Depth Builders¶

Medium ACV impact but compounds with other bets OR enterprise prerequisite

[ ] TBD

Tier 3: Tactical Add-Ons¶

Lower standalone ACV but addresses specific pain

[ ] TBD

Tier 4: Validate Before Prioritizing¶

Unvalidated assumptions or unclear ACV path

[ ] TBD

Decision Criteria¶

For each bet, evaluate:

ACV multiplier: Does this get us from $X to $X+50K?
Evidence: Do we have 4+ sources asking for this?
Deal impact: Does this close an active prospect or expand a customer?
Bundle synergy: Does this make other bets more valuable?
Competition: Does this beat competitors or achieve table stakes?
Build cost: Can we ship V1 in 2026 or is this 2027+?

3. Enterprise Readiness: Table Stakes¶

These are hygiene, not differentiation. Needed to sell upmarket, but not strategic bets.

Advanced Scenario Modelling¶

Outcome: Stress-test forecasts without exporting to Excel. Answer "what if collections drop 10%?" instantly. Compare base vs pessimistic vs optimistic side-by-side.

Why it matters: Universal demand. Differentiates us from "reporting tool" to "decision tool." Currently in progress (skateboard stage).

What we know: - Every customer exports to Excel for scenarios today (Personio, ON, Instacart, Levi's) - Percentage-based adjustments validated with Personio (Feb 2026) - Need: multi-scenario comparison, FX rate overlays, save & share, covariance/ripple effects

What we need to validate: - ACV impact (gut: supports $100K+ ACVs, but not tested) - Which scenario types matter most (OPEX vs FX vs structural funding)

Prioritization Assessment:

[ ] ACV Impact: [High/Medium/Low] — $[X]K [standalone/add-on/bundle component]
[ ] Evidence Strength: Strong — 3+ transcripts with explicit requests and high excitement (ON rated 8-9/10)
[ ] Path to Revenue: [New deals/Expansion/Strategic] — [which prospects/customers]
[ ] Competition: [Differentiated/Parity/Unknown] — [who offers this, at what price]

Transcript Evidence

**Sources:** - ON (2026-02-18): Standup on scenarios and investments - Personio (2026-02-18): Weekly sync discussing scenarios (Tom Thorn) - Levi's expert interview (2025-12-11): Dette on bridging and scenario needs **Key quotes & pain points:** **ON - High excitement (8-9/10):** > "Super cool, actually. It's part of our goals, actually, for this year to do more scenario and stress testing." **Percentage-based adjustments validated:** > "You change the percentage. It was exactly like this...very intuitive and very fitting for Treasury" — ON **Need to compose assumptions into events:** > "treating these like little Lego pieces...combine different assumptions...Swedish store opening" — ON **FX scenarios explicitly mentioned:** > "I think FX is always relevant...how the currency development happens" — ON **Scenario comparison issue identified:** > "Once you set a best/worst/baseline scenario, the rolling forecast re-baselines to actuals, wiping out the comparison. Need ability to 'fix' a scenario and overlay actuals on top" — ON **Personio - Pain of spreadsheet-based process:** > "This is being developed... all of this analysis, anything like this stays outside the system. And it's always like the most painful... you have to take all the data out of the system, you have to generate a whole new spreadsheet." — Tom > "As soon as you shut that spreadsheet, it's out of date. It will never be looked at or used again. It will only be used as a reference to say, oh, you said this was going to happen and it didn't happen." — Tom **Personio - Worst case focus:** > "Usually worst case scenario from a Treasury side is what you focus on. And if all of those things happen together — like you have to think of the worst, worst, worst case." — Tom **Personio - Validation of prototype:** > "I can probably go away and say we need to apply this to our balances in six months time with some planned activities there. With this, we would be able to do it, I think." — Tom **Personio - One-off absolute values:** > "is there a way to input not necessarily a percentage, but a different type of impactful activity... Say we do some capital markets activities and we say, oh, we're going to have a loan. Or are we going to have a, you know, we're going to acquire an entity in six months time." — Tom **Personio - Save, share, and apply workflow:** > "the process for something like that would probably be to save it, share it with the management team. You know that that's why you're doing the scenario analysis in the first place. And then once you have that, if there's a transition, like click to click to apply." — Tom **Levi's - Disruptor adjustments:** > "So let's just say your inputs are all machine learning. It's all machine learning, as in looking at the past. Right. But you have disruptors like a Covid, tariffs, all these things. How do you adjust that?" — Dette **Levi's - Percentage-based adjustments with explanations:** > "And then you can have a description so that the storytelling is clear. I just had a Tiffany here [likely: epiphany]. Maybe that's what I want built into the system. I don't really need manual input. Maybe I just need percentages of how I want to adjust the number." — Dette > "And an explanation. For. For the next poor soul who goes into the system and tries to understand what went on there." — Dette on adjustment tracking for audit **Explicit feature requests:** - Layering multiple assumptions (percentages + one-off absolute values) into a single named scenario - One-off scenario items with absolute values (acquisitions, capital markets activities) and custom labels - Scenario save & share workflow - save, share with management, then click-to-apply to forecast - Forecast explainability - see why forecast landed at a value (e.g., "supplier payments trending up 10%") - Assumption override - challenge/correct model assumptions at category level - Best/worst case from historicals - combine max outflows + min inflows from past 6 months - Confidence bands as scenarios - use forecast confidence intervals as built-in best/worst - Account-type grouping for scenarios (operations, tax, collections accounts) not just entities - Ability to fix scenarios and overlay actuals - Manual input for scenarios without historical data - Intercompany and non-business categories in scenarios - Outlier marking - manually mark transactions to exclude from forecast learning - Percentage-based adjustments with explanation fields for audit trail

Decision & Governance Intelligence¶

Outcome: Make AI trustworthy through institutional memory. Policy enforcement, audit trails, approval workflows, risk tracking, scenario libraries.

Why it matters: This IS the thick middle. Without it, Palm is "better forecasting UI." With it, Palm becomes institutional memory that compounds. Enterprise customers need governance to enable automation.

What we know: - Finance teams require explainability ("why did you recommend this?") - High-stakes decisions need audit trails and approvals - ANZ flags "explainability and trust" as adoption barrier (industry source)

What we need to validate: - ACV impact (gut: drives higher ACVs for enterprise, especially when combined with other bets) - Build effort (some governance basics needed in 2025-2026, full platform by 2027-2028) - What governance features matter most (policy enforcement? audit trail? approval workflows?)

Prioritization Assessment:

[ ] ACV Impact: [High/Medium/Low] — $[X]K [standalone/add-on/bundle component]
[ ] Evidence Strength: Strong — 3 transcript sources (Personio, Levi's, ON) with explicit policy enforcement, approval workflows, explainability, and institutional memory needs
[ ] Path to Revenue: [New deals/Expansion/Strategic] — [which prospects/customers]
[ ] Competition: [Differentiated/Parity/Unknown] — [who offers this, at what price]

Transcript Evidence

**Sources:** - Personio (2024-11-26, 2026-02-18): Policy compliance scenario planning, approval workflows, treasury policy framework - Levi's expert interview (2025-12-11): Explainability, trust requirements, institutional memory (25+ years treasury experience) - ON (2025-11-11): ML trust and variance validation **Key quotes & pain points:** **Policy enforcement (Personio):** > "Minimize the risk of policy breaches when liquidating investments for funding" — Tom Thorn > "Increase visibility into how funding decisions affect policy metrics (liquidity %, tenor mix)" — Tom Thorn **Documented treasury policies (Personio):** > "We should have x amount of funds available at any one time... 30% mature over one month period and then the rest over a three month period" — Tom Thorn (tenor mix policy) > "Personio maintains defined FX policy: always keeping three months worth of foreign exchange in any currency" **Approval workflows (Personio):** > "Process...would probably be to save it, share it with the management team...then once you have that transition, like click to apply" — Tom Thorn (scenario approval workflow) > "Need to check. For example, if you liquidate it, are you within policy?" — Tom Thorn (compliance verification gate) **Variance accountability (Personio):** > "From a forecast point of view, it could be by the following month... that's an additional week where we funded for that payment and it has an impact on our interest" — Tom Thorn (showing cost impact to business teams for accountability) **Explainability & trust requirements (Levi's):** > "What would make you trust? Because, I mean, you use it to drive decisions that are quite high stakes" — Emma to Dette > "There's a different dimensions to it. One building trust. Having it been explainable, making sure it's transparent, making sure it's clear. What are the building blocks of this forecast?" — Dette **Institutional memory (Levi's):** > "If you have that line of adjustments...And maybe that adjustment line would already be 20%, 10%, 15%. All I have to do is check the box" — Dette (system remembers recurring adjustments) **Audit trail for adjustments (Levi's):** > "You can have a description so that the storytelling is clear...And an explanation...for the next poor soul who goes into the system and tries to understand what went on there." — Dette **Accountability & performance governance (Levi's):** > "That level of granularity...really helps you to feed back into the business and hold some of those teams accountable...you need to set some good boundaries...if you can get that level of granularity then that's a huge [impact]" — Dette **Trust through validation (ON):** > "Can I trust it? Machine learning can be such a big thing. So I think this will be really cool to have that tap." — Lucia (ON) > "I love how this looks and it just feels very, very useful, but then you have to validate it to say, Hey, yeah, we trust it." — ON **Explicit feature needs:** - Policy compliance verification before executing funding/investment decisions (Personio) - Approval workflow: save scenario → share → approve → apply (Personio) - Policy metrics dashboard (liquidity %, tenor mix) (Personio) - Adjustment explanations and audit trail (Levi's, Personio) - Institutional memory: remember recurring adjustments with check-box reapply (Levi's) - Forecast explainability: show building blocks and assumptions (Levi's) - Variance accountability reporting with cost impact (Personio) - Ability to mark specific model configurations per account (ON)

Other Table Stakes Items¶

Basic debt visibility & forecasting (facility tracking, repayment schedules)
Basic investments visibility & forecasting (maturity ladders, rate tracking)
Cash structures visibility (ZBA, notional pools, COBO/POBO)
Accurate IC categorization & forecasting (entity-level balance accuracy)
Reporting & shareability (export, stakeholder views, audit logs)
Enterprise-grade ACL & governance controls