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
- 2026-09-22
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 to $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.