AEGIS · Module 3 · Signal
Artifact 3.3
AI Value & Spend Tracker
Quarterly accounting of where AI investment is going and what it returns — so governance decisions are made on data, not vendor pitches.
- Client
- [CLIENT NAME]
- Engagement
- [ENGAGEMENT ID]
- Version
- v1.0
- Issued
- 2026-05-18
Delivered by TechFides under the AEGIS Governance Operating Services engagement. This document is proprietary to the client named above. Redistribution beyond the engagement steering committee requires written consent.
Purpose
Intent — Every AI governance program is asked the same two questions at the board: how much are we spending, and what are we getting back? This tracker answers both with evidence.
The discipline this enforces
Spend is observable — licenses, API bills, labor charges, renewal dates. Value is only observable if someone has set up the measurement before the work starts. Half of this artifact exists to force that conversation: no new initiative goes into production without a value hypothesis and a measurement method.
Two ledgers, one view
- Spend ledger — fully loaded AI cost, sourced from finance + the inventory (Artifact 3.1).
- Value ledger — claimed and measured value across six categories, with the evidence method named for each.
Neither ledger is useful alone. Spend without value is a budget line item; value without spend is marketing.
Spend Ledger
Intent — Six categories, quarterly cadence, sourced from finance and the inventory. Totals drive the executive dashboard and the board pack.
| Category | Typical items | Q1 | Q2 | Q3 | Q4 | Annualized |
|---|---|---|---|---|---|---|
| Model & platform licenses | ChatGPT Enterprise, Claude for Work, Copilot | $78,400 | $81,200 | $84,900 | $89,500 | $334,000 |
| API / usage | OpenAI API, Anthropic API, embeddings, image gen | $22,800 | $31,400 | $38,600 | $46,200 | $139,000 |
| Compute & inference infra | GPU capacity, vector DBs, caching layer | $14,200 | $17,600 | $19,100 | $22,400 | $73,300 |
| Integration & engineering labor | Internal build, contract dev, integration consulting | $44,000 | $52,000 | $38,000 | $36,000 | $170,000 |
| Governance & assurance | AEGIS retainer, legal review, external audit | $28,000 | $28,000 | $28,000 | $32,000 | $116,000 |
| Training & enablement | Role-based curricula, certifications, vendor training | $8,400 | $6,200 | $9,800 | $5,100 | $29,500 |
| Total | $195,800 | $216,400 | $218,400 | $231,200 | $861,800 | |
Value Framework
Intent — Six value categories, each with a definition, a measurement method, and an illustrative example. Anything claimed must fit one of these six and cite its method.
Productivity uplift
$122,740 / yrHours reclaimed by AI-assisted work, measured against a baseline workflow time study.
Method · Workflow-level time samples before and after, held to a 20-tool baseline quarterly. Converted to $ at fully loaded labor cost.
Example — RFP response drafting: 14 hrs → 4.5 hrs (median). 38 RFPs / qtr × 9.5 hrs × $85/hr = $30,685 / qtr.
Throughput gain
$88,000 / yrAdditional work produced without adding headcount — e.g. more sales touches, more support tickets resolved, more deals processed.
Method · Delta in output volume at constant FTE, valued at contribution margin or per-unit revenue.
Example — Support tickets: 420/wk → 580/wk at same FTE. Contribution ≈ $11/ticket × 160 × 50 wks.
Error & rework reduction
$102,000 / yrDefects, misstatements, or compliance misses avoided. Measured against the historical incidence rate.
Method · Count of issues caught pre-release × historical cost to remediate post-release.
Example — Contract review: 4 redlines missed per quarter → 0.6. Avoided avg. remediation cost $7,500.
Revenue acceleration
$84,000 / yrShortened cycle times on revenue-linked work: proposals, contracts, customer research, pricing.
Method · Cycle-time delta × weighted average deal value × probability uplift.
Example — Proposal turnaround 8 days → 3 days. Close-rate lift +4 pts on $2.1M pipeline slice.
Customer experience
Tracked qualitativelyCSAT / NPS / resolution-time improvements tied directly to AI-assisted channels.
Method · Pre/post CSAT on identified channels, with segment controls. Not a dollar value on its own — reported alongside retention.
Example — Tier-1 CSAT 82 → 89 in 2 quarters on AI-assisted support desk.
Risk avoidance
Modeled, not bookedEstimated exposure removed by governance controls — data-leak incidents avoided, regulatory fines avoided.
Method · Incident-rate baseline × estimated loss per incident, inclusive of remediation and reputational cost.
Example — One P0 data exposure avoided via DLP + approved-tools enforcement — est. cost avoided $450K–$1.2M.
Per-Initiative Scorecards
Intent — One row per live AI initiative. Net value and ROI are the summary — but the status column is what drives decisions.
| Initiative | Sponsor | Invested | Measured Value | Net | ROI | Status |
|---|---|---|---|---|---|---|
| Contract Review Assistant | General Counsel | $78,200 | $186,400 | +$108,200 | 2.4× | Active · quarterly review |
| Proposal Response Copilot | VP Sales | $54,000 | $206,700 | +$152,700 | 3.8× | Active · scaling to EU segment |
| Tier-1 Support Assistant | VP Customer | $96,000 | $148,000 | +$52,000 | 1.5× | Active · CSAT uplift tracked |
| Engineering Copilot Rollout | VP Engineering | $122,400 | $210,000 (est.) | +$87,600 | 1.7× | Active · model in revision |
| Meeting Notes Automation | Chief of Staff | $28,000 | $41,200 | +$13,200 | 1.5× | Conditional · legal/privacy review |
| Marketing Imagery Pilot | Head of Marketing | $14,400 | — | — | — | Under Review · synthetic-data only |
Decision Thresholds
Intent — Thresholds turn the ledger into decisions. The governance committee reviews these against the scorecards every quarter.
Scale — 2.0× ROI sustained for two quarters
Expand seats, extend to adjacent use cases, request budget uplift. The initiative has proven the model; invest in throughput.
Hold — between 1.2× and 2.0×, or too new to score
Keep running, invest in measurement, review in 90 days. Do not expand scope or seats. If still below 2.0× at two-quarter mark, move to Rework.
Rework — below 1.2× with identified cause
Owner submits a rework plan: usually model choice, prompt library, or workflow redesign (Artifact 4.1). 60-day window. New scorecard at the end.
Retire — below 1.0× after one rework, or value unmeasurable
Sunset the initiative, retire the tool in the inventory (3.1), reallocate budget. Retirement is not failure — it is evidence the governance process works.
Cadence
Intent — The tracker is a discipline, not a document. These cadences keep it honest.
Monthly
- Spend ledger reconciled against finance close + vendor bills.
- New initiatives registered with value hypothesis + method.
Quarterly
- Full scorecard refresh. Decisions at every threshold applied.
- Value categories re-tested against measurement method integrity.
- Spend vs. budget, with forward-looking quarter forecast.
Annually
- Full-year ROI report to the board (Artifact 6.2).
- Refresh the value framework — categories that produced no signal get retired.
- Budget plan for the next year based on measured returns.
Regulatory & Audit Notes
Intent — The tracker is not itself a compliance control, but the evidence it produces is what SOC 2 / ISO / board auditors increasingly ask for when testing AI oversight.
- Demonstrates financial accountability for AI investments under SOX / SOC 2 governance tests.
- Satisfies ISO 42001 Clause 9 (performance evaluation) and Clause 10 (improvement) with measurable outcomes.
- Supports board-level oversight expectations for material technology spend and strategic initiatives.