For a life sciences company, reliable financial planning software keeps runway current, shows what each program costs and holds up in front of the board and investors. Abacum is the AI-native business planning platform for scaling enterprises. It brings all your data, business logic and business context into one governed place. This guide gives you a selection checklist and compares Abacum with Jirav, Anaplan, Pigment, Workday Adaptive Planning, Planful and Cube. In life sciences, runway is the plan: 33% of public US and EU biotechs had under 12 months of runway at the end of 2025 (EY, via BioSpace, 2025).
What reliable means in life sciences
Runway you can re-forecast as often as the science changes. A rolling cash forecast, planned week by week, that stays board-ready.
Program cost you can defend. Spend by program and phase, without a data team.
Scenarios around milestones and financing events. Trial readouts, delays and the next raise on the live plan.
A budget that matches the ledger. Budget and actuals use the same accounts, so budget versus actuals reconciles without mapping Excel to the ledger by hand.
Traceable outputs. Every figure links back to its source data and assumptions.
Governed AI. Agents propose, the planning engine calculates, and finance approves every change to reported figures.
Selection checklist
Check | What to ask | Why it matters |
|---|---|---|
Weekly runway forecast | Can I plan cash week by week? | Runway changes with every study decision |
Program and phase spend | Can I see cost by program and phase? | Programs drive the budget |
Milestone scenarios | Can I model a readout, a delay and a raise? | Boards ask what-if questions |
Ledger match | Is the budget built on the same accounts as actuals? | Reconciliation without manual mapping |
Traceability | Does each number link to its source? | Investors ask where numbers come from |
Ownership | Who builds and maintains the model? | Finance should own it |
Reviews | What do independent reviews say? | A second view beyond the demo |
Platforms compared
Platform | Approach | Confirm in a demo |
|---|---|---|
Abacum | AI-native business planning platform on one governed layer of data, logic and context | Weekly runway, program cost and milestone scenarios |
Jirav | Dashboards and planning connected to accounting systems, with a life sciences solution page | Program and phase detail |
Anaplan | Multi-dimensional planning for large, complex models | Who builds and maintains the models |
Pigment | Flexible modeling platform for teams willing to build and maintain their own planning models | Who owns the model after go-live |
Workday Adaptive Planning | Planning connected to the Workday suite | Fit with your current systems |
Planful | Planning and reporting with pre-built templates | Templates for R&D budgets |
Cube | Planning that runs on Excel or Google Sheets | Version control across program leads |
Pigment is a flexible modeling platform for teams willing to build and maintain their own planning models. Your team or an implementation partner designs it and keeps it running, so confirm who will own the model after go-live.
Accounting systems such as Sage's biotech and pharmaceutical suite record actuals. A planning platform works above them.
How Abacum meets the checklist
Runway on a rolling basis. A cash forecast planned week by week that stays board-ready.
Program cost. Spend by program and phase without a data team.
Scenarios. Model trial readouts, delays and the next raise on the live plan.
Budget that matches the ledger. Build the budget on the same accounts as your actuals.
AI Modeler. Describe the model you need in plain language, or bring the spreadsheet you already built, and get a working model in minutes. Every model explains itself, line by line, before the board meeting. Nothing saves until you approve.
Included, not metered. No credits, no meter, no hidden fees.
We position Abacum on the finance layer, above your ledger. Abacum holds a 4.7 rating on G2 (as of October 2026), and more than 85% of our customers use AI in Abacum every month (Abacum, October 2026). Implementation runs six to nine weeks depending on data complexity, entity count and workflows.
