AI Adoption and Use Cases: How is AI being used today across education, work, and everyday life? What differentiates high-impact applications from low-impact ones?

Labor Market Impact: How are jobs evolving in an AI-first economy? What will AI-driven job transformation look like in the future?

Investment and Growth: How do investments in AI—across infrastructure, education, and institutions—shape macroeconomic growth and productivity?

Economic Value of AI: Which metrics and models best capture AI’s economic contribution at both firm and national levels?

AI Adoption & Use Cases

Where It’s Working Now

• Education: AI tutors for personalized practice/feedback; drafting IEPs/syllabi; auto-grading short answers; accessibility (real-time captions, text-to-speech, reading level adjustments).
• Work: Code copilots for faster development; customer support triage with suggested replies; document drafting/summarization; data prep/SQL generation; meeting notes to actionable tasks; contract review; analytics copilots embedded in BI tools.
• Everyday Life: Planning (trips, meals, budgets); automated form filling; image cleanup/enhancement; real-time language translation; assistive tech for low vision, dyslexia, or hearing impairments.

What Separates High-Impact from Low-Impact

• Embedded vs. Sidecar: Integrated into core systems (CRM, EMR, IDE, ERP) at workflow checkpoints vs. standalone chat interfaces.
• Closed Loop: Models suggest, trigger, and track actions with guardrails, learning from outcomes (user feedback, A/B test results).
• High-Frequency, High-Cost Tasks: Targets repetitive, time-intensive tasks (ticket resolution, coding, claims processing) vs. occasional creative tasks.
• Tight Data Flywheel: Domain-specific data, retrieval, and evaluation pipelines improve outputs; clear ownership of prompts, evals, and metrics.
• Clear Decision Rights & Risk Controls: Defined roles for accepting AI outputs, human-in-the-loop requirements, and logged provenance. Low-impact pilots are often disconnected chatbots lacking data integration or KPI alignment.

Sample KPIs for AI Adoption

• Education: Student engagement rate (% interacting with AI tutor), IEP draft time reduction, caption accuracy (% correct), student outcome improvement (% grade uplift).
• Work: Code commits per developer, ticket resolution time, document draft time, SQL query accuracy (% error-free).
• Everyday Life: Task completion rate (e.g., % plans executed), form-filling time reduction, translation accuracy (% correct), user satisfaction (CSAT).

Impact/Feasibility Matrix

Use Case	Impact (KPI Improvement)	Feasibility (Ease of Integration)	Priority
AI Tutors (Education)	High (20%+ grade uplift)	Medium (requires LMS integration)	High
Code Copilots (Work)	High (30%+ dev productivity)	High (IDE plugins available)	High
Customer Support Triage (Work)	High (40%+ faster resolution)	Medium (CRM integration needed)	High
Form Filling (Everyday)	Medium (20% time savings)	High (standalone apps)	Medium
Novelty Chatbots	Low (<5% KPI uplift)	High (no integration needed)	Low

Labor Market Impact

What’s Happening

• Task Reallocation > Job Elimination: Routine cognitive tasks (summaries, drafts, lookups) reduced; judgment, stakeholder engagement, and domain oversight grow.
• Productivity Uplift Varies: Largest gains for less-experienced workers and documentation-heavy roles; experts see quality/throughput improvements but less time savings.
• New Roles: AI product managers, prompt engineers, evaluators/red-teamers, data stewards, model ops, governance specialists, and “AI team leads” embedded in functions.

What’s Next (2–5 Years)

• Agentic Workflows: Multi-tool agents handle multi-step processes (e.g., intake → triage → draft → submit) under policy constraints.
• Smaller, Leveraged Teams: One senior + AI tools replaces larger teams; focus on orchestration, review, and exception handling.
• Skill Premium Shifts: Higher wages for complementary skills (domain expertise, data literacy, process design, communication); routine cognitive roles face wage pressure.
• Licensure/Compliance Integration: Auditable AI usage in SOPs (health, legal, finance) with “AI use attestation” in records.

Sample KPIs for Labor Market Impact

• Task Reallocation: % of routine tasks automated, % time spent on high-value tasks.
• Productivity Uplift: Output per worker-hour, throughput for novices vs. experts.
• New Roles: # of AI-related job postings, % teams with embedded AI roles.

Investment & Growth

Where to Invest

• Infrastructure: Compute (GPU/CPU), vector/RAG stores, event buses, observability, secure data access; prioritize latency and reliability alongside model performance.
• Data Assets: Standards for collection, labeling, ontology, retention, and governance; secure data usage rights (privacy, IP).
• Human Capital: AI literacy programs, role-specific copilot training, advanced tracks (MLOps, safety, evals); incentives tied to adoption (OKRs, time targets).
• Institutions: Clear liability frameworks, procurement policies, competition rules, privacy/security standards; regulatory sandboxes for high-stakes sectors.

Macroeconomic Lens

• J-Curve Effect: High initial capex/opex and learning costs, with productivity/TFP gains post-diffusion and process redesign.
• Spillovers: General-purpose technology drives growth via intangibles (software, data, org capital), not just models.

Sample KPIs for Investment

• Infrastructure: Compute uptime (%), latency (ms), data access speed (queries/sec).
• Data Assets: % data labeled, compliance with privacy standards (% audits passed).
• Human Capital: % workforce trained, adoption rate (% using AI tools weekly).

Measuring AI’s Economic Value

Firm-Level KPIs (Tie to Dollars)

• Throughput & Cycle Time: Units per staff-hour, time-to-resolution, time-to-first-draft.
• Quality: Error/defect rate, rework %, compliance hits, customer sentiment (CSAT/NPS), win rates.
• Conversion & Revenue: Uplift from AI-assisted variants (A/B), attach/cross-sell, retention/churn.
• Cost-to-Serve & Margin: Tickets per agent, cost per claim/case, gross margin impact.
• Risk: Incident rate, policy violations, PII leaks, hallucination frequency (eval suite).
• Financials: ROI/NPV, payback, ROM (Return on Model = (Value created – Model+Ops cost)/Model+Ops cost), TCO of AI stack.

Evaluation Mechanics

• Task-Level Offline Evals: Accuracy, calibration, toxicity, bias; domain-specific rubrics with gold sets.
• Online Causal Measurement: A/B or switchback tests (AI vs. control); CUPED or diff-in-diff for non-randomized settings.
• Attribution: Shapley-style or incrementality models to apportion gains across AI, data, and process changes.

Sector Examples (Impact Barometers)

• Software: Lead time for changes, PRs per dev, escaped defects, incident MTTR.
• Customer Ops: First-contact resolution, average handle time, backlog, QA pass rate.
• Sales/Marketing: SDR outreach per day, reply/booking rates, CAC/LTV shifts.
• Healthcare: Documentation time per encounter, HEDIS/quality measures, denial rates, prior auth turnaround.

Economy-Wide Metrics/Models

• Growth Accounting: AI as capital deepening + TFP (augmented Solow or KLEMS with “AI capital” and data/intangible stocks).
• Task-Based Models: Share of tasks automated/augmented by occupation → wage/employment effects (Acemoglu-Autor framework).
• Endogenous Growth: AI boosts idea production (Romer/Aghion-Howitt); track R&D productivity, patent/knowledge outputs.
• Diffusion S-Curves: Adoption across firms/sectors; measure gaps between frontier and laggards.
• National Stats: Labor productivity growth, TFP, AI/compute investment (% of GFCF), AI adoption rates, AI-related price indices, digital/AI intangible capital formation.

Sample KPIs for Economic Value

• Firm-Level: % reduction in cycle time, % uplift in CSAT, ROM (% return on model investment).
• Economy-Wide: TFP growth rate, % GDP from AI-driven sectors, AI adoption rate (% firms using AI).

Practical Playbook

1. Prioritize: Use Impact × Feasibility matrix to select high-value, achievable use cases.
2. Design for the Loop: Build retrieval → generation → action → feedback → eval pipelines.
3. Ship Guardrails: Implement policy checks, human-in-the-loop thresholds, and observability.
4. Prove Value Early: Target one KPI, one team, four-week A/B test.
5. Scale: Expand only after securing data rights, evaluation pipelines, and owner teams.