Build an AI ROI Scorecard for Your Agentic Deployments Using the OpenAI Framework

The task

You're advising an enterprise client on a portfolio of agentic AI deployments — support triage, contract review, sales research — and the CFO wants to know what's actually paying off. This piece walks you through turning raw deployment notes into a defensible ROI scorecard aligned to OpenAI's newly published framework, without spinning up a dashboard tool.

Before AI

Today this is a two-day slog: pull usage exports, chase pilot leads for outcome anecdotes, argue with finance about how to value "time saved," then hand-build a slide in PowerPoint. The scorecard usually lands as opinion because the categories differ per workflow. Consultants either over-engineer a framework of their own or lean on whatever the vendor sent — neither survives the steering committee.

The workflow

The reference framework: OpenAI outlined a five-part framework emphasizing that the key metric is not token price, but "useful work per dollar" — tasks completed, time saved, and decisions improved. The OpenAI enterprise guide organizes this around visibility, outcome-based ROI measurement, and dynamic governance, plus efficiency and scaling. We'll turn each into a scorecard column.

Step 1 — Normalize the deployment inventory. Paste your raw notes (usage stats, pilot descriptions, cost figures — however messy) and get a clean table back.

You are a management consultant preparing an AI ROI scorecard for a Fortune 500 client. The framework is OpenAI's "Managing AI investments in the agentic era," which measures useful work per dollar across five dimensions: (1) Visibility into usage, (2) Useful work per dollar (tasks completed, time saved, decisions improved), (3) Efficiency (routing to the right model/tool), (4) Scaling high-value workflows, (5) Governance.

Below is a raw dump of the client's current agentic deployments. Your first job: normalize it into a markdown table with these columns exactly — Deployment | Business Owner | Monthly Cost (USD) | Monthly Tasks | Avg Human Minutes Saved / Task | Stated Outcome | Data Gaps.

If a field is missing from the input, write "NOT REPORTED" — do not invent numbers. Preserve every deployment mentioned. Output the table and nothing else.

RAW DEPLOYMENT NOTES:

Client: Meridian Global Logistics (fabricated). Fiscal Q2 2026 review.

1) "ContractHawk" — agentic contract review copilot in Legal. Owner: Priya Ramanathan, AGC. Cost: ~$18,400/mo in model + infra. Volume: ran on 612 vendor MSAs last month. Legal team says it saves "about 40 min per contract" on first-pass redlines. No formal tracking of override rate. Rolled out to 22 of 34 attorneys.

2) "TriageBot" — customer support classification + draft-reply agent. Owner: Marco DeLuca, VP Service Ops. Cost figures unclear — finance says between $9k and $12k monthly, model spend has been climbing. Handles roughly 14,000 tickets/month. CSAT unchanged. Average handle time down from 8.2 to 6.1 minutes per ticket per the Zendesk report.

3) "DealScope" — sales research agent that builds account briefs. Owner: Janine Okafor, RevOps Director. Cost: $6,200/mo. AEs run it ~430 times/month. Reps informally say it saves "an hour or two" per brief but nobody's timed it. Sales leadership loves it, wants to double seats.

4) "PolicyPilot" — HR policy Q&A bot, retrieval-augmented. Owner: unclear, sits under IT. Cost: $2,100/mo. Usage has dropped from 3,200 to 900 sessions/month over Q2. HR says employees don't trust the answers after two wrong PTO responses in April.

5) "InvoiceMatch" — AP three-way match agent. Owner: Sam Chen, Controller. Cost: $4,800/mo. Processes ~7,500 invoices/mo, auto-clears 71%. Saves approximately 3 minutes per auto-cleared invoice vs. manual matching. Governance: no formal audit trail review yet.

Step 2 — Score each deployment against the five OpenAI dimensions. This is where the framework earns its keep — every workflow gets rated on the same axes.

Using the normalized table above, score each deployment on the five OpenAI framework dimensions. Use a 0-3 scale where 0 = absent, 1 = weak/anecdotal, 2 = measurable, 3 = instrumented and governed.

Dimensions:
- Visibility: can we see usage, cost, and who is invoking the agent?
- Useful Work per Dollar: is there a task-completed / time-saved / decision-improved metric tied to spend?
- Efficiency: any evidence of model routing, caching, or right-sizing?
- Scaling Readiness: is this workflow high-value and ready to expand, or should it be contained?
- Governance: overrides logged, audit trail, clear owner, accuracy checks?

Output two things:
(A) A markdown table: Deployment | Visibility | Useful Work/$ | Efficiency | Scaling | Governance | Total (/15)
(B) One-sentence rationale per deployment explaining the lowest score.

Be strict. If the input says "not tracked" or "informal," that is not a 2 or 3. Do not pad scores to be polite.

Step 3 — Compute useful-work-per-dollar and recommend portfolio moves. Convert the softest metric — time saved — into dollars using a stated assumption, then give the client three concrete decisions.

Now translate time savings into dollars and produce a portfolio recommendation.

Assumptions to use explicitly (state them at the top of your output):
- Fully-loaded hourly cost: attorneys $180/hr, support agents $45/hr, sales reps $95/hr, HR/IT staff $60/hr, AP clerks $40/hr.
- Only count time savings that were measured, not "informally reported." For informal claims, show the dollar figure in [brackets] and label it "unverified."
- Useful Work per Dollar ratio = (monthly value created) / (monthly cost).

For each deployment produce:
- Monthly value created (USD), with the math shown in one line.
- Useful Work per Dollar ratio.
- A single recommendation from this set: SCALE, HOLD & INSTRUMENT, FIX GOVERNANCE, or SUNSET. Justify in one sentence.

Then output a final "Portfolio Actions" section with exactly three bullets the client's steering committee should approve next quarter. Each bullet must name the deployment, the action, and the expected impact.

Step 4 — Package it for the client read-out. Turn the analysis into a one-page executive summary the client can drop into their board deck.

Compose a one-page executive summary titled "AI Investment Scorecard — Q2 Review." Structure:

1) Headline finding (1 sentence, lead with the useful-work-per-dollar leader and the laggard).
2) Portfolio snapshot: bulleted list of deployments with their score /15 and recommendation, sorted by ratio descending.
3) Three portfolio actions (from the previous step, tightened to board language).
4) "What we still don't know" — list the data gaps that would change the recommendations if closed. Reference the specific deployments.
5) Framework footnote: one line citing OpenAI's five-dimension framework as the scoring basis, with the URL https://openai.com/index/managing-ai-investments-in-agentic-era/.

Tone: senior partner briefing a CFO. No hedging adverbs. No em-dashes. Under 400 words total.

Gotchas

• Assumption sensitivity. The dollar figures in Step 3 swing hard on the hourly-rate assumption. Before showing the client, ask them for their own fully-loaded cost table and re-run Step 3 with those numbers — otherwise the CFO will spend the meeting arguing about $180/hr instead of the portfolio decisions.
• "Informal" claims inflate ratios. The model will sometimes score a 2 on Useful Work per Dollar when the underlying evidence is a rep saying "an hour or two." The bracketed/unverified convention in Step 3 helps, but audit every non-instrumented row before it leaves your laptop.
• Framework drift. OpenAI's guide emphasizes measuring useful work per dollar, improving efficiency, and scaling high-value workflows — but the specific five labels have been paraphrased across coverage. If a client asks for the canonical list, point them to the source page and don't defend the exact wording your prompt used.
• This scorecard is a snapshot, not a monitoring system. For continuous tracking, the client will still need instrumentation on the vendor platform itself. Say that out loud in the read-out.
• PHI, PII, real contract text. Never paste actual deployment logs with client data into the prompt. Redact to counts and categories first, the way the sample input does.

Time saved

Manually assembling a five-dimension scorecard across five deployments — normalizing notes, scoring, monetizing, drafting the one-pager — is a solid 4-5 hour block for an experienced consultant. This chain compresses it to about 45 minutes including a careful review pass, so 3-4 hours per client scorecard is the conservative claim. It scales roughly linearly with deployment count: a 15-deployment portfolio saves closer to a full day. The saving comes from the framework being pre-loaded, not from the model being smarter than you about the client.

What we got when we ran it

We executed every prompt above, in order, against the sample input (model: claude-sonnet-4-6). Outputs below are real and unedited apart from length truncation.

Step 1 output

| Deployment | Business Owner | Monthly Cost (USD) | Monthly Tasks | Avg Human Minutes Saved / Task | Stated Outcome | Data Gaps | |---|---|---|---|---|---|---| | ContractHawk | Priya Ramanathan, AGC (Legal) | $18,400 | 612 vendor MSAs | 40 minutes | First-pass redline acceleration across 22 of 34 attorneys; faster contract turnaround | Override/acceptance rate not tracked; no formal time-saving validation; partial attorney rollout (22/34) unexplained | | TriageBot | Marco DeLuca, VP Service Ops | $9,000–$12,000 (range; climbing) | ~14,000 tickets | 2.1 minutes (derived: 8.2 → 6.1 min AHT per Zendesk) | Reduced average handle time; CSAT unchanged | Exact monthly cost not pinned; root cause of cost increase not documented; no quality/accuracy metric; CSAT flat with no explanation | | DealScope | Janine Okafor, RevOps Director | $6,200 | ~430 account briefs | NOT REPORTED (reps estimate "1–2 hours"; untimed) | Faster account brief creation; high user satisfaction; expansion requested | Minutes saved per task never formally measured; no revenue attribution or win-rate impact data; seat expansion business case not yet quantified | | PolicyPilot | NOT REPORTED (nominally under IT) | $2,100 | ~900 sessions (down from 3,200 in Q2) | NOT REPORTED | HR policy self-service Q&A via RAG | Business owner unassigned; no accuracy/hallucination tracking; no remediation plan documented after April PTO errors; sharp usage decline unexplained beyond trust erosion | | InvoiceMatch | Sam Chen, Controller (AP) | $4,800 | ~7,500 invoices (5,325 auto-cleared at 71%) | ~3 minutes per

…output truncated at 250 words.

Step 2 output

(A) Scoring Table | Deployment | Visibility | Useful Work/$ | Efficiency | Scaling Readiness | Governance | Total (/15) | |---|---|---|---|---|---|---| | ContractHawk | 2 | 2 | 1 | 2 | 1 | 8 | | TriageBot | 1 | 2 | 1 | 1 | 1 | 6 | | DealScope | 2 | 1 | 1 | 2 | 1 | 7 | | PolicyPilot | 1 | 0 | 1 | 0 | 0 | 2 | | InvoiceMatch | 2 | 2 | 1 | 2 | 1 | 8 | --- (B) Lowest-Score Rationale Per Deployment - ContractHawk — Governance scores 1 because override and acceptance rates are entirely untracked, meaning there is no way to verify whether the agent's redlines are accurate or silently ignored at scale across 22 attorneys. - TriageBot — Visibility scores 1 because the monthly cost cannot be stated as a single figure, finance holds only a range that is actively widening, and no cost-per-ticket or invocation-level attribution exists. - DealScope — Useful Work/$ scores 1 because the only time-saving evidence is informal rep estimates of "an hour or two" that have never been measured, making it impossible to compute a defensible value-per-dollar figure or justify the requested seat expansion. - PolicyPilot — Scaling Readiness and Governance both score 0: usage has collapsed 72% following known accuracy failures, there is no assigned business owner to authorize or oversee changes, and no remediation, accuracy audit, or trust-recovery plan has

…output truncated at 250 words.

Step 3 output

Assumptions (Stated Explicitly) - Fully-loaded hourly rates: Attorneys $180/hr | Support Agents $45/hr | Sales Reps $95/hr | HR/IT Staff $60/hr | AP Clerks $40/hr - Time savings are only counted as verified if formally measured or system-reported. Informally estimated savings are shown in [brackets] and labeled **unverified**. - Useful Work per Dollar = Monthly Value Created ÷ Monthly Cost - For TriageBot, monthly cost is taken as the midpoint of the reported range: $10,500 - For InvoiceMatch, value is calculated only on the 71% of invoices that are auto-cleared (5,325 invoices), as the savings claim applies exclusively to that subset - No value is credited to PolicyPilot because no time-saving metric of any kind was reported --- ## Per-Deployment Analysis ### 1. ContractHawk **Monthly value created:** 612 contracts × 40 min × ($180/hr ÷ 60) = 612 × 40 × $3.00 = **$73,440** **Useful Work per Dollar:** $73,440 ÷ $18,400 = **3.99×** **Recommendation: FIX GOVERNANCE** The value ratio is strong, but the absence of override tracking means Meridian cannot confirm whether those 40 minutes saved reflect accurate work or 22 attorneys quietly correcting AI errors — instrument acceptance and override rates before scaling to the remaining 12 attorneys. --- ### 2. TriageBot **Monthly value created:** 14,000 tickets × 2.1 min × ($45/hr ÷ 60) = 14,000 × 2.1 × $0.75 = **$22,050** **Useful Work per Dollar:** $22,050 ÷ $10,500 = **2.10×** **Recommendation: HOLD & INSTRUMENT** The ratio is positive but thin given an uncontrolled and rising cost base,

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Step 4 output

# AI Investment Scorecard — Q2 Review ### Meridian Global Logistics | Fiscal Q2 2026 | Prepared for CFO Review --- Headline Finding ContractHawk leads the portfolio at a verified 3.99x return on spend; PolicyPilot returns zero measurable value against $2,100 monthly cost and is damaging employee trust in AI across the enterprise. --- Portfolio Snapshot | Deployment | Score (/15) | Useful Work/$ | Recommendation | |---|---|---|---| | ContractHawk | 8/15 | 3.99x verified | FIX GOVERNANCE, THEN SCALE | | DealScope | 7/15 | Unverified | HOLD AND INSTRUMENT | | InvoiceMatch | 8/15 | 2.22x verified | FIX GOVERNANCE | | TriageBot | 6/15 | 2.10x verified | HOLD AND INSTRUMENT | | PolicyPilot | 2/15 | 0.00x | SUNSET | Sorted by verified ratio descending. DealScope holds second position by potential; placement is contingent on time-study results. --- Three Portfolio Actions for Steering Committee Approval 1. ContractHawk: Mandate override and acceptance-rate logging within 60 days, then expand to all 34 attorneys. Projected impact: $18,360 in additional verified monthly value from the 12 currently excluded attorneys. 2. PolicyPilot: Approve immediate shutdown. Redirect the $25,200 annual budget to a re-procurement contingent on an assigned business owner and documented accuracy benchmarks prior to relaunch. 3. DealScope: Authorize a 30-day structured time-study before approving the requested seat expansion. At the low end of current estimates, this deployment would exceed 6.5x return, making it the strongest scaling candidate in the portfolio once verified. --- What We Still Do Not Know -

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