Strategic allocation
Capacity by economic purpose
72%
Aligned
Executive operating system
Command
Command center
Engineering intelligence across the AI SDLC.
Executive narrative
What changed this period
AI economics
Assistant usage is accretive but uneven.
Teams with explicit eval gates show the strongest cycle-time compression; unmanaged agent usage is driving 16% avoidable token leakage.
Finance treatment
Capital review is concentrated in platform runtime work.
$812K of Q4 work is ready for evidence review; 61% has approvals and demonstrable coding-and-testing support.
Delivery risk
Two commitments are at risk.
Identity refactor and enterprise audit trail carry the highest schedule-risk weighted margin exposure.
Resource allocations
Where engineering capacity is being spent.
Team allocation
Capacity mix by team
Allocation drift
Maintenance pressure is 7 pts over plan.
Interrupt-driven work is highest in Data Platform and Core Runtime. Scenario planner recommends shifting 8% capacity from reactive support to reliability automation.
Operating action
Fund the platform runtime lane.
The platform lane carries the highest reusable asset score, strongest AI enablement dependency, and clearest capitalization evidence path.
AI investment intelligence
Adoption, spend, and impact across coding assistants, agents, and model APIs.
Tool comparison
Assistant ROI by workflow
Spend mix
Provider concentration
AI investment thesis
Route expensive cognition to proven work.
Dummy data shows frontier-model spend is productive for architecture and eval generation but poor for repetitive remediation. Local specialist models and cached tool calls are the largest gross-margin preservation levers.
DevFinOps ledger
Cost, treatment, margin, and action in one operating file.
Ledger sample
Dummy records by initiative
COGS exposure
$1.09M
Customer-facing AI workload and support automation run-rate mapped to gross margin.
Waste / leakage
$136K
Uncached retries, experimental agent loops, and untagged sandboxes with no durable business purpose.
Capital action
$812K
Runtime and eval assets with enough evidence for technical accounting review.
Delivery intelligence
Roadmap execution, risk, and release economics.
Initiative health
Strategic commitments
Cycle-time shape
Idea to production
Release narrative
Delivery risk is not evenly distributed.
Most roadmap work is within tolerance. The customer audit trail has low engineering uncertainty but high compliance review drag; identity refactor has the opposite profile and should receive architecture review before adding capacity.
Capitalization evidence
Treat AI software work as assets only when evidence clears the gate.
Candidates
Technical accounting review queue
Thought leadership
Preparing for the transition.
Agents as customers will turn software CapEx into the core growth engine: more capitalized internal software, more revenue per employee, higher margins.
Policy guardrail
Expense novel uncertainty.
Prototype loops, unresolved performance requirements, and churned specifications remain expensed until coding-and-testing evidence supports probable completion.
Benchmarks
Performance and reliability signals against target operating bands.
Scenario planner
Model investment moves before committing capital.
Scenario inputs
Capacity and AI routing levers
Forecast output
Modeled P&L and delivery effect
Recommended capital move
Fund runtime automation and expand eval gates.
The selected case preserves margin while creating a cleaner capitalization evidence file.
Integrations