AI TCO Worksheet: The Seven-Sheet Model

Key takeaways

• Evidence

  Most AI business cases price only the model invoice, missing 60-80% of the true operating cost in integration, governance, review time, and infrastructure.

• Evidence

  The seven-sheet TCO worksheet maps directly to the canonical seven-category AI cost taxonomy used by FinOps Foundation and enterprise practitioners.

• Interpretation

  A finance analyst and platform engineer can complete the worksheet together in an afternoon per use case, producing three numbers a CFO can repeat: fully loaded unit cost, naive-versus-loaded gap, and attribution status.

• Evidence

  The pilot-to-production bridge sheet addresses the most common business case failure: assuming pilot economics scale linearly to production.

Why this matters

The seven-sheet structure

Sheet 1: Use case header

| Field | Notes | |---|---| | Use case name and owner | One named person accountable for economics | | Workflow description | What the AI does, who consumes the output | | Stage | Pilot / scaling / production | | Business case reference | Link, or "none" recorded as a finding | | Baseline captured? | Y/N, date, where stored | | Unit of work | The denominator everything divides by: ticket resolved, change merged, document processed, workflow completed |

Sheet 2: Direct AI costs (monthly)

Model and API consumption:

• Input tokens, output tokens, blended £/unit by provider

• Evidence

  Agentic multiplier: tokens per task for agent-driven work versus interactive work (tracked separately; this is where forecasts break)

Seat licences and SaaS tiers:

• Tool licences for this workflow

• Evidence

  Embedded SaaS AI tier allocation (the AI-attributable share of the relevant SaaS contract, even if estimated)

Training and hosting:

• Fine-tuning or training runs, amortised over expected life
• GPU, hosting, vector store, orchestration platform share

Sheet 3: The costs business cases forget

Human review and rework:

• Evidence

  Minutes per output × volume × loaded rate of the reviewer

• Interpretation

  For coding tools, review of AI-generated changes; for service AI, QA sampling and escalation handling

• Evidence

  Share of AI outputs corrected or redone, × cost of correction

Quality gates and governance:

• Senior sign-off, compliance review steps that didn't exist pre-AI
• Security and compliance assessments (one-off and recurring)

• Evidence

  Evaluation harness and monitoring: building and running the thing that tells you quality held

Integration and enabling infrastructure:

• Evidence

  Integration and pipeline build: one-off, amortised over an assumed life (state the assumption)

• Evidence

  Data preparation and retrieval infrastructure: the enabling layer - cleaning, permissions, indexing

Organisational costs:

• Change management and training
• Vendor management overhead (meaningful once tool count grows)

Failure-state consumption:

• Evidence

  For agentic workflows, tokens burned by retries, loops and abandoned runs - tracked as its own line, not blended into the average

Sheet 4: Unit economics

Calculations:

• Fully loaded monthly cost (Sheets 2+3) ÷ units of work = fully loaded unit cost
• Same calculation with Sheet 2 only = naive unit cost

• Interpretation

  The gap between the two numbers is itself a finding worth reporting

Comparators:

• Pre-AI unit cost from the baseline, or the human-only alternative at its full loaded cost

• Evidence

  Threshold: the unit cost at which this use case is agreed to be working (set in advance, signed by finance and the use-case owner)

Sheet 5: Pilot-to-production bridge

For each assumption, pilot value → production value → reason it changes:

Volume and case mix:

• Evidence

  Volume (and whether case mix changes with it - the hard 20% usually arrives at scale)

Consumption patterns:

• Tokens per unit (retrieval depth, context growth, agentic intensity)

• Interpretation

  Review rate (pilots skip the quality gate production requires)

Edge cases and build costs:

• Edge-case share and cost
• One-off build costs not incurred in pilot

Output:

• Evidence

  Production fully loaded unit cost versus pilot unit cost, as a multiple. If the multiple is unknown, the scaling decision is being made on fiction.

Sheet 6: Forecast and variance

Forecast structure:

• 12-month consumption forecast by line, with stated adoption and intensity assumptions
• Actual versus forecast, monthly, with variance triggers

• Interpretation

  Suggested triggers: investigate at 15%, restrict at 30%

Sensitivity analysis:

• Evidence

  Vendor price change ±25% (see Cursor, June 2025)
• Agentic intensity ×2
• Adoption ±20%

Sheet 7: Value side (paired, so the sheet never ships cost-only)

Value tracking:

• The measured outcome metric for this use case and its current value versus baseline
• Value realised versus business case, quarterly

• Evidence

  Attribution status: outcome-linked / activity-linked / unattributed

Net position:

• Interpretation

  Value evidenced minus fully loaded cost, with confidence noted

Worked example: Customer service AI agent

Use case header (Sheet 1)

• Use case: Customer service AI agent for tier-1 support
• Owner: Head of Customer Operations
• Workflow: Agent handles incoming support tickets, escalates complex cases to human agents
• Stage: Scaling (pilot complete, moving to full production)
• Baseline: Pre-AI resolution time 4.2 hours, cost per ticket £18.50
• Unit of work: Ticket resolved

Direct AI costs (Sheet 2) - Monthly

• Model consumption: 2.5M input tokens, 800K output tokens at £0.015/1K tokens = £49.50
• Agentic multiplier: 3.2× tokens per ticket versus interactive chat (agent retrieves context, checks knowledge base, formats response)
• Orchestration platform: £2,400/month (shared across 8 use cases, allocated 15% = £360)
• Vector database: £180/month
• Total Sheet 2: £589.50/month

Hidden costs (Sheet 3) - Monthly

• Human review: 12% of tickets reviewed, 8 minutes per review, 2,400 tickets/month × 12% × 8 min × £45/hour loaded rate = £1,728
• Rework: 4% of tickets require human correction, 15 minutes per correction = £270
• Quality gate: Weekly QA sampling, 4 hours/week × £55/hour = £880
• Integration build: £24,000 one-off, amortised over 24 months = £1,000/month
• Data preparation: Ongoing knowledge base curation, 20 hours/month × £50/hour = £1,000
• Monitoring and evaluation: £400/month
• Change management: Training and adoption support, 10 hours/month × £60/hour = £600
• Total Sheet 3: £5,878/month

Unit economics (Sheet 4)

• Volume: 2,400 tickets/month
• Fully loaded unit cost: (£589.50 + £5,878) ÷ 2,400 = £2.69 per ticket
• Naive unit cost: £589.50 ÷ 2,400 = £0.25 per ticket
• Gap: 10.8× (the naive cost understates the true cost by a factor of 11)
• Pre-AI baseline: £18.50 per ticket
• Savings: £15.81 per ticket (85% reduction)

Pilot-to-production bridge (Sheet 5)

• Volume: Pilot 200 tickets/month → Production 2,400 tickets/month (12× increase)
• Case mix: Pilot handled curated easy cases → Production includes full case mix (hard 20% drives review rate from 5% to 12%)
• Review rate: Pilot 5% → Production 12% (quality gate requirement)
• Tokens per ticket: Pilot 2.1× → Production 3.2× (deeper context retrieval at scale)
• Production multiplier: Fully loaded unit cost increased 2.4× from pilot to production

Forecast and variance (Sheet 6)

• 12-month forecast: £77,616 (assumes 2,400 tickets/month steady state)
• Sensitivity: Vendor price +25% = £84,993; agentic intensity ×2 = £91,344
• Variance trigger: Investigate if monthly cost exceeds £7,100 (15% over forecast)

Value side (Sheet 7)

• Outcome metric: Customer satisfaction score (CSAT)
• Baseline CSAT: 3.8/5.0
• Current CSAT: 4.1/5.0 (8% improvement)
• Resolution time: 4.2 hours → 0.8 hours (81% improvement)
• Attribution: Outcome-linked (measured before/after with control group)
• Net position: £37,944/month value (£15.81 savings × 2,400 tickets) minus £6,468 cost = £31,476/month net value (high confidence)

Design rules for implementation

1. Label every input cell with provenance

2. Produce three numbers a CFO can repeat

• Fully loaded unit cost
• Naive-versus-loaded gap
• Attribution status

3. Ship with a worked example

Mapping to the AI TCO Framework

Seven-category taxonomy mapping:

1. Model and inference → Sheet 2 (model consumption, API calls)
2. Data and pipeline → Sheet 3 (data preparation, retrieval infrastructure)
3. Compute and infrastructure → Sheet 2 (GPU, hosting, vector store)
4. Tooling and platform → Sheet 2 (orchestration platform, development tools)
5. Integration and orchestration → Sheet 3 (integration build, pipeline work)
6. Governance, risk and compliance → Sheet 3 (quality gates, security assessments, monitoring)
7. Change, adoption and human → Sheet 3 (review time, rework, training, change management)

Lifecycle stage mapping:

• Prepare: Sheet 1 (baseline capture, unit of work definition)
• Build: Sheet 3 (integration build, data preparation, one-off costs)
• Run: Sheets 2, 3, 4, 6 (operating costs, unit economics, variance tracking)
• Scale/Retire: Sheet 5 (pilot-to-production bridge), Sheet 7 (value validation for continuation decisions)
• Govern (cross-cutting): Sheet 3 (governance costs), Sheet 7 (attribution and value evidence)

What to do next

For finance leaders:

• Interpretation

  Start with one high-visibility use case and complete the worksheet with the platform team.

• Evidence

  Use the naive-versus-loaded gap as the lead finding in the next AI investment review.

• Interpretation

  Establish the worksheet as the standard template for all AI business cases going forward.

For platform and engineering leaders:

• Evidence

  Instrument the top three AI workflows so Sheet 2 and Sheet 6 can be populated from telemetry rather than estimates.

• Interpretation

  Work with finance to establish the loaded rates for review time and rework that populate Sheet 3.

• Evidence

  Build the pilot-to-production bridge (Sheet 5) into every scaling decision.

For operating-model leaders:

• Interpretation

  Use the worksheet as the economic foundation for AI governance, not a finance-only exercise.

• Evidence

  Track attribution coverage (Sheet 7) as the lead metric for AI value management maturity.

• Interpretation

  Publish completed worksheets internally to build shared understanding of what AI really costs to run well.