Measurable ROI, Use‑Case Selection, and the Leap from Pilots to Scale

The question I’m hearing everywhere

In the last 72 hours, I’ve been asked the same question by a statewide chamber of commerce, a room full of telecom leaders during an AI talk, a public radio interviewer, and an executive team at a large telehealth company: “What’s the ROI?” If you’re asking it too, you’re in good company—and you’re right to insist on a clear, defensible answer.

The short version: the ROI is real, but it shows up first in well‑chosen, well‑measured use cases—not in sprawling pilots. Organizations that treat AI as an operating‑model change (with governance, data, evaluation, and change management) are already seeing unit‑level revenue gains and cost reductions; those that don’t are stuck in “pilot purgatory.” McKinsey & Company

What the latest data actually says (and what it doesn’t)

• Adoption is mainstream. In McKinsey’s 2025 State of AI survey, 78% of leaders say their organizations use AI in at least one function, and 71% report regular use of generative AI in at least one function. McKinsey & Company+1
• Value is showing up inside business units. Compared with early 2024, a larger share of respondents now report revenue increases within the functions using gen‑AI, and most report cost reductions—but more than 80% still do not see a tangible enterprise‑wide EBIT impact yet. Translation: value is local unless you wire it for scale. McKinsey & Company
• Budgets are surging because of agents. In PwC’s May 2025 AI Agent Survey, 88% of executives plan to increase AI budgets over the next 12 months specifically due to agentic AI; 73% expect agents to deliver a significant competitive edge this year. PwC+1
• Field evidence sets realistic targets. A large NBER study of contact‑center agents found a 14% average productivity lift (and 34% for novices) with a gen‑AI assistant. In coding tasks, a randomized trial showed developers complete tasks 55.8% faster with an AI pair programmer. In consultant‑style knowledge work “inside the frontier,” performance gains approach ~40%—but fall when tasks are outside the model’s competence. Benchmarks are context‑dependent. NBER+2arXiv+2
• Reality check on hype. Gartner expects a large share of agentic projects to be abandoned by 2027 due to unclear business value—underscoring why use‑case selection and measurement matter. Reuters

The value tree: how to quantify AI’s ROI

ROI formula

ROI = (Annualized Benefits – Annualized Costs) ÷ Annualized Costs

Three value levers (measure all three):

1. Efficiency (time & throughput)
   • Time saved per unit × volume × fully loaded labor cost × adoption rate × quality factor (1 – rework/defect rate).
2. Growth (revenue & margin)
   • Incremental conversion or upsell lift × traffic/volume × AOV × margin.
   • For sales enablement or next‑best‑action, run controlled trials (A/B or stepped‑wedge) and attribute uplift to the intervention.
3. Risk (loss avoidance & quality)
   • Fewer errors, leaks, compliance breaches, or write‑offs × cost per incident × probability reduction × detection/catch rate.

Cost model (be honest, upfront):
Licenses + model/compute + data engineering + integration & API work + eval/monitoring + change management + oversight time. Depreciate setup costs; show per‑use‑case run‑rate.

Pro tip: Add adoption and guardrail multipliers to every estimate. A beautiful model with 30% adoption delivers 30% of the value.

Selecting use cases that hit the P&L

Use this five‑factor scorecard (0–5 each; weight in parentheses). Prioritize the top 6–10 into a near‑term portfolio:

1. Business value (×4) — direct line to revenue, cost, or risk KPI?
2. Friction price (×3) — how much pain exists today (cycle time, backlog, error cost)?
3. Feasibility (×2) — policy fit, data access, integration complexity, change scope.
4. Data readiness (×1) — coverage, freshness, permissions, ground‑truth availability for evals.
5. Operate & own (×1) — clear process owner, support model, and budget line.

Patterns that usually score high early:

• Service operations: assisted agents, retrieval‑augmented responses, triage & summarization. (Evidence: 14%+ productivity lift in field.) NBER
• Software engineering: code assistance for boilerplate/tests/migration; guard for quality & technical debt. (RCT: 55.8% faster for scoped tasks; caution on scale debt.) arXiv+1
• Marketing & sales: knowledge retrieval, brief‑to‑content, next‑best‑action with human review.
• Back office: document understanding (AP, claims, credentialing) with deterministic checks.

From pilot to P&L: a 30‑60‑90 day play

Days 0–30 — Baseline, design, and guardrails

• Pick 2–3 high‑scoring use cases. Define one north‑star KPI and 3–5 operational KPIs per use case.
• Instrument baseline (e.g., average handle time, first‑contact resolution, error rate, queue age).
• Draft the acceptance tests and evals (accuracy/groundedness, safety, latency, cost).
• Establish access controls and an oversight plan (who reviews what; sampling strategy). McKinsey’s 2025 survey shows tracking well‑defined KPIs correlates most strongly with EBIT impact—make this non‑negotiable. McKinsey & Company

Days 31–60 — Controlled pilot

• Roll to one team per function. Keep humans‑in‑the‑loop.
• Run A/B or before–after for at least two business cycles.
• Publish a pilot scorecard weekly: adoption, acceptance rate, quality, latency, unit cost, and business KPI movement.

Days 61–90 — Scale & wire‑in

• If thresholds are met, ramp to the next 2–3 cohorts.
• Integrate with systems of record (CRM, EHR, ERP) and embed into workflows.
• Create a playbook (prompts, patterns, failure modes), capability training, and an ops runbook.
• Bring the board a one‑page value pack: baseline → delta → annualized benefit; cost curve; risks & mitigations; next use cases.

What to measure: the definitive KPI set

Business KPIs

• Revenue/growth: conversion rate, AOV, pipeline velocity, renewal/upsell rate.
• Efficiency: average handle time, resolution rate per hour, cycle time, SLA hit rate.
• Risk/quality: error/rework rate, complaint rate, audit findings, leakage/write‑offs.

AI‑specific KPIs

• Adoption: weekly active users / eligible users, sessions per user.
• Quality: acceptance rate (human kept vs. edited vs. rejected), groundedness/accuracy score, hallucination rate, red‑team findings.
• Reliability & cost: latency (p50/p95), unit cost per task, outage minutes, token/compute burn.
• Safety/compliance: PII incidents, policy violations, access exceptions.

Tip: Keep two dashboards—operator‑level (daily) and C‑suite‑level (monthly). The latter should ladder into EBIT/EBITDA impact by function. McKinsey finds most value shows up within functions first; wire your dashboards accordingly. McKinsey & Company

Two quick worked examples (with conservative assumptions)

1) Contact center (telecom or public services)

• 500 agents × 40 hours/week = 20,000 hours/week.
• Using NBER’s measured +14% productivity lift from AI assistance, regained capacity ≈ 2,800 hours/week (20,000 × 0.14).
• At a fully loaded cost of $45/hour, weekly benefit ≈ $126,000 (2,800 × 45); annualized over 50 weeks ≈ $6.3M.
• If your adoption is 60%, expected benefit ≈ $3.78M; if program costs (licenses/compute/integration/enablement) are $1.2M/year, then ROI ≈ 215% (2.58 ÷ 1.2). Field uplift varies; run your own A/B to confirm. NBER

2) Engineering (product teams)

• 120 developers; assume 40% of time is on tasks amenable to AI assistance.
• Annual coding hours ≈ 120 × 1,800 × 0.40 = 86,400 hours.
• RCT shows 55.8% faster completion on scoped tasks → time saved ≈ 48,211 hours (86,400 × 0.558).
• At $120/hour, gross benefit ≈ $5.79M per year; at 50% adoption and a 0.7 quality factor to account for integration/technical‑debt risks, net ≈ $2.03M. Guide your targets using controlled trials; beware scale risks flagged by MIT Sloan (technical debt in brownfield systems). arXiv+1

Avoiding pilot purgatory: five failure modes (and fixes)

1. Ambiguous success criteria → Fix: lock KPIs and acceptance thresholds before kickoff; publish weekly. (McKinsey links KPI tracking to EBIT impact.) McKinsey & Company
2. Hobby projects without owners → Fix: every use case has a single accountable process owner and budget line.
3. Data permission gaps → Fix: design for retrieval with least‑privilege access and auditable trails from day one.
4. Scaling before proving → Fix: two clean cycles of evidence (or an A/B) before enterprise rollout.
5. Agent hype without a case → Fix: insist on a costed workflow, measurable outcomes, and de‑risked handoffs. (Gartner expects many agentic projects to be scrapped for lack of value clarity.) Reuters

A board‑ready “ROI pack”

1. Use‑case snapshot: problem, owner, target KPI(s), baseline, thresholds.
2. Measurement plan: design, population, time window, control.
3. Results: KPI deltas, confidence, unit cost curve, adoption.
4. Financials: annualized benefit, cost breakdown (setup vs. run rate), ROI.
5. Risk & compliance: controls, incidents (if any), mitigation plan.
6. Scale plan: cohorts, training, system integrations, next candidates.

Industry notes: telecom & telehealth

• Telecom: start in service ops (assisted agents, RAG over knowledge bases), field ops (dispatch recommendations), and retention (save‑offer copilots). KPIs: AHT, first‑contact resolution, truck rolls per 1k subs, churn. McKinsey’s survey shows service operations among the functions with the most frequent gen‑AI use and value—match the pattern, measure tightly. McKinsey & Company
• Telehealth & provider groups: target intake, pre‑charting, coding, and patient messaging with human oversight. KPIs: clinician after‑hours time, days in A/R, denial rate, readmission risk, patient satisfaction. Prove safety with sampling and clinician acceptance rates before scaling.

What this means for leaders right now

• Start with six to ten high‑scoring use cases that are close to revenue, cost, or risk.
• Instrument baselines now; treat evals and acceptance tests as production assets.
• Publish a weekly scorecard and tie it to incentives.
• Invest where evidence is strongest (contact centers, engineering, ops) while maintaining guardrails.
• Lean into agents—carefully. Budgets are rising for a reason, but value comes from cross‑functional orchestration, not point solutions. PwC

About Superior Communications

We help organizations move from AI pilots to measurable P&L impact—with the governance, data plumbing, evaluation, and change management to make it stick.

Sources & further reading

• McKinsey, The State of AI: How organizations are rewiring to capture value (March 2025): adoption across functions; revenue/cost effects; KPI tracking correlation with EBIT. McKinsey & Company+2McKinsey & Company+2
• PwC, AI Agent Survey & 2025 AI Predictions (Midyear Update): budgets up 88% due to agentic AI; competitive edge expectations. PwC+1
• NBER, Generative AI at Work: contact‑center field experiment, +14% average productivity, +34% for novices. NBER
• GitHub/Microsoft, RCT on Copilot: developers 55.8% faster on scoped tasks. arXiv
• MIT Sloan Management Review: productivity “inside the frontier” (~40%), and cautions on technical debt at scale. MIT Sloan+1
• Reuters (Gartner outlook): many agentic projects will be scrapped by 2027 without clear business value. Reuters