Capital allocation & corporate development
Investment requests come back scored against the same return framework, the capital budget tracks committed against available in real time, build-versus-buy gets modeled instead of argued, and the diligence data room assembles itself. Where the money goes stops being a function of who lobbies hardest.
Overview
Capital allocation is the decision that determines whether a company compounds or stalls, and in most companies it is made by whoever builds the most persuasive spreadsheet. Every department wants budget. Every project's ROI model is built by the person who wants the project funded, on their own assumptions, in their own template, which means none of them are comparable. The CFO is asked to choose between investments measured on different rulers. The result is not allocation. It is negotiation with decimal places.
This agent imposes a common ruler. It scores every investment request against the same return framework (NPV, IRR, payback, strategic fit) using consistent assumptions, tracks the capital budget as committed-versus-available in real time, models build-versus-buy and make-versus-acquire scenarios on demand, and assembles the diligence materials when a deal goes live. It does not make the call. Capital allocation is a judgment that belongs to the people accountable for it. It makes sure the call gets made on comparable numbers instead of competing narratives, with the assumptions visible and the math checkable.
Capabilities
- Scores investment and budget requests against a single, consistent return framework (NPV, IRR, payback period, strategic fit) so proposals become directly comparable
- Tracks the capital budget in real time, committed versus available versus pipeline, by department, initiative, and time period
- Models build-versus-buy, lease-versus-own, and make-versus-acquire scenarios on demand, with the assumptions surfaced and adjustable
- Stress-tests proposal assumptions against actuals and benchmarks, flagging where a model's inputs diverge from what the business has historically delivered
- Assembles the diligence data room for active deals (financials, contracts, cap table, customer and cohort data) organized to the standard buyer or investor request list
- Presents the analysis for human decision with assumptions visible and traceable, and recommends nothing it cannot show the math for
Example Output
Capital request scorecard, Q3 allocation cycle
Available capital this cycle: $2.4M discretionary, against $5.1M in requests. Six proposals scored on the common framework.
- Proposal | Requested | NPV (3yr) | IRR | Payback | Strategic fit | Rank
- Sales headcount (+4 AEs) | $720K | $1.84M | 71% | 14 mo | High | 1
- Data infrastructure rebuild | $540K | $1.12M | 44% | 22 mo | High | 2
- New-market expansion (UK) | $1.30M | $1.95M | 38% | 28 mo | Medium | 3
- Marketing automation platform | $180K | $310K | 52% | 19 mo | Medium | 4
- Office expansion | $640K | ($120K) | n/a | n/a | Low | 5
- Speculative R&D initiative | $1.72M | unmodelable | n/a | n/a | High (uncertain) | 6
Agent notes (where the math needs a human):
- The +4 AE request ranks first, but its NPV assumes the AEs ramp to quota in 6 months. The trailing-12-month actual ramp for the last four hires was 9 months. Re-run at a 9-month ramp, NPV drops to $1.21M and IRR to 48%. Still rank 1, but the gap to the data rebuild narrows. Assumption flagged.
- The office expansion is NPV-negative on the numbers. If it gets funded, it is a culture and retention decision, not a return decision, and it should be argued on those terms rather than on a financial model that does not support it.
- The R&D initiative cannot be scored on a discounted cash flow; the outcomes are too uncertain to model honestly. Recommend evaluating it as an option (a small staged commitment with a kill gate), not a lump capital request.
Status: Scorecard staged for the allocation committee. Assumptions documented and adjustable in the model. No allocation decision made; the decision is the committee's.
Agent Workflow
Intake the request on a standard template
The agent receives each investment or budget request on one standard template, so every proposal arrives with the same inputs rather than in whatever format its sponsor preferred.
Normalize to the common return framework
The agent recomputes each request against the same return framework (NPV, IRR, payback, strategic fit) using consistent assumptions, converting incomparable spreadsheets into a single ranked view.
Stress-test assumptions against actuals
The agent checks each model's key inputs against what the business has historically delivered (ramp times, conversion rates, cost curves) and flags where a proposal's assumptions are more optimistic than the evidence.
Update the capital budget
The agent maintains the capital budget in real time, tracking committed against available against pipeline by department, initiative, and period, so the room always knows how much is actually left.
Model scenarios on demand
When the question is build-versus-buy, lease-versus-own, or make-versus-acquire, the agent models the scenarios with the assumptions exposed and adjustable, so the debate is about the inputs rather than about whose spreadsheet to trust.
Assemble diligence materials for live deals
When a deal goes live, the agent assembles the data room (financials, contracts, cap table, cohort data) organized to the standard request list, so diligence starts assembled instead of scrambled.
Present for human decision
The agent presents the ranked analysis with every assumption visible and traceable. It recommends only what it can show the math for, and the allocation decision stays with the people accountable for it.
Track funded initiatives against their model
After funding, the agent tracks each initiative's actuals against the model that justified it, so next cycle's scoring is calibrated by what the last cycle's bets actually returned.