Divya van Mahajan

Introducing Maturity Maps — A New Way to Measure AI Adoption

ai-daily-brief-podcast

Maturity Maps: A New Framework for Measuring AI Adoption

Overview

This talk introduces AI Maturity Maps, a new benchmarking framework developed by Nathaniel Whittemore (host of the AI Daily Brief and founder of enterprise AI strategy firm Super Intelligent / besuper.ai) to help organizations understand where they stand in AI and agent adoption relative to peers. The core argument is that existing benchmarking tools (e.g., Gartner’s Magic Quadrant) are poorly suited to the current AI era, and that organizations are navigating adoption decisions without meaningful comparative data. Maturity Maps attempt to fill that gap by assessing AI readiness across six dimensions and ten business functions. This episode is presented as Day 2 of “Build Week.”

Source video: (URL not provided)

Prerequisites

  • Basic familiarity with enterprise AI adoption concepts (use cases, pilots, workflows, agents)
  • Understanding of what AI assistants vs. autonomous agents are
  • Awareness of common enterprise functions (sales, marketing, engineering, HR, finance, legal, etc.)
  • General knowledge of business ROI measurement and change management
  • Familiarity with terms like “capability overhang,” “upskilling,” and “AI governance”

Main Points

1. The Benchmarking Gap in Enterprise AI

  • Organizations are adapting rapidly to AI but doing so “without a map” — no reliable comparative benchmarks exist.
  • Example: A company that grows content output 30% YoY via AI may feel successful, until they learn competitors grew 50% — a gap invisible without benchmarking.
  • Existing tools like Gartner’s Magic Quadrant are designed around vendor selection, which is largely irrelevant to the actual challenge of AI adoption.
  • The speaker’s prior AI ROI Benchmarking Survey (end of 2023) gathered self-reported use case impact across eight dimensions but was limited by self-reporting bias, an advanced-user sample skew, and a narrow focus on use cases only.

2. Two Precursor Frameworks: ROI Surveys and AI Opportunity Radars

  • The AI ROI Benchmarking Survey asked practitioners to rate impact across dimensions like time savings, cost savings, new capabilities, and output — finding generally positive self-reported results.
  • AI Opportunity Radars organize use cases by function and readiness level:
    • Prime Time: Most organizations can extract value now.
    • Emerging: Value is achievable but requires some setup or infrastructure.
    • Frontier: High value possible, but most organizations lack the prerequisites.
  • Radars are maintained by an agentic system that continuously ingests and assesses new resources to update use case placement.

3. The Six Dimensions of AI Maturity Maps

The framework assesses AI maturity along six categories:

  • Deployment Depth: Not just how many use cases, but whether they are AI-assisted tasks, full workflow automations, or agentic systems with meaningful autonomy.
  • Systems Integration: How deeply AI is embedded into existing enterprise systems (e.g., an agent running through a CRM vs. employees using ChatGPT in a separate tab).
  • Data: Quality, volume, and accessibility of company data to AI systems — from manual PDF uploads to MCP server-hosted knowledge bases.
  • Outcomes: Whether deployments are still pilots/experiments or have demonstrated, measured, trackable results.
  • People: Upskilling, AI capability development, and crucially, employee attitudes toward AI — a major and underinvested adoption barrier.
  • Governance: Clarity, communication, and enforcement of AI policies, permissions, and issue-resolution mechanisms.

4. Scoring and Visualization

  • Each of the six dimensions is scored on a five-point scale for each of ten functions:
    • 1 = Significantly behind | 2 = Behind | 3 = On track | 4 = Ahead | 5 = Significantly ahead
  • Score of 3 (“On Track”) is a normative target — where an average organization should be — not where most organizations actually are.
  • The gap between “on track” and “where organizations actually are” is described as a visualization of the capability overhang.
  • The ten functions covered: Customer Service, Engineering, IT, Sales, Marketing, HR, Operations, Finance, Legal, Product.

5. Data Sources Behind the Maps

  • Draws from 480+ studies and surveys from the last quarter.
  • Combined respondent base: 150,000+ professionals across 50+ countries.
  • Source categories include:
    • Big Four and top-tier consulting firm research (20+ sources)
    • Major platform earnings statements
    • Analyst firm research (Gartner, Forrester, IDC)
    • Function-specific annual surveys (e.g., Stack Overflow engineering study)
    • Academic and government research
    • Behavioral data (e.g., Jellyfish’s AI coding benchmark: 200,000+ engineers, 700 companies, 20 million PRs)
    • Practitioner reports and vendor case studies (rated with skepticism)
  • Supplemented by Super Intelligent’s own frontline data from thousands of voice agent AI readiness interviews per month.

6. Key Q2 Findings: Cross-Functional Patterns

  • Adoption-Embedding Gap: High claimed adoption rates but low depth and utilization — the dominant pattern across all functions.
  • Leader-Worker Perception Gap: Leaders consistently overestimate AI training adequacy and organizational readiness vs. worker-level reports.
    • Example (Customer Service): 72% of leaders say training is adequate; 55% of employees disagree.
    • Example (HR): Majority of leaders call AI a priority; over two-thirds of HR staff say their org is not proactive on upskilling.
  • People is the bottleneck: 7 of 10 functions scored 1 (significantly behind) on the People dimension. Deloitte data cited: 93% of AI spend goes to infrastructure, only 7% to people.
  • Data is the ceiling: 8 of 10 functions scored 1 or 1.5 on Data. Without proprietary context (codebase, customer history, deal data), organizations cannot advance beyond basic assisted usage.
  • Outcomes measurement is universally weak: Speed-of-adoption pressure has meant few organizations paused to develop ROI measurement frameworks. Speaker predicts this will improve most in coming quarters.

7. Function-Specific Highlights

  • Customer Service: On track for Deployment Depth and Systems — but 87% of workers report high stress, 75% of leaders acknowledge AI may be increasing stress. Identified as a “canary in the coal mine” for under-investing in people alongside AI deployment.
  • Engineering & IT: On track for Deployment Depth, Systems, and People — structural advantages include mature tooling, technical practitioners, and measurable workflows.
  • Sales: “Adoption mirage” — 88% report using AI, but only 24% have it embedded in actual revenue workflows. Most usage is ChatGPT in a separate tab for email drafts.
  • Operations: Conflation of legacy automation (statistical forecasting, rules-based inventory) with new Gen AI maturity inflates perceived adoption. Only 23% have a formal AI strategy. The Gen AI layer is often thin.
  • Finance: Only non-technical function to score “on track” in any pillar — specifically Governance — because of existing regulatory discipline (audit trails, fiduciary duty). However, rated significantly behind in every other category. Raises the “tortoise and hare” question: will strong governance enable finance to deploy more safely and effectively later?
  • IT/Governance: Only 54% of organizations have centralized AI governance frameworks; 50% of AI agents are unmonitored; 88% have had security incidents.

8. How to Access and Future Roadmap

  • A public-facing 18-question quiz is available at besuper.ai/quiz that maps an individual’s organization relative to the on-track and average lines.
  • This is explicitly a quiz, not a full audit or assessment — intended to gather broad participation and refine the framework.
  • Planned improvements:
    • Differentiated on-track and average lines by organization size (e.g., 10-person startup vs. 10,000-person enterprise).
    • Segmentation by industry.
    • More granular function-level breakdowns beyond the current 10 broad categories.

Key Concepts

  • AI Maturity Maps: A framework assessing AI and agent readiness across six dimensions (Deployment Depth, Systems Integration, Data, Outcomes, People, Governance) for ten business functions, scored on a five-point scale.
  • Capability Overhang: The gap between what AI systems can do and what organizations are actually using them for.
  • On-Track Line (Score of 3): A normative benchmark representing where an average organization should be — not where it typically is — across each maturity dimension.
  • Deployment Depth: A measure of not just how many AI use cases are in play, but the sophistication level of those deployments (assisted → automated → agentic).
  • Systems Integration: The degree to which AI workflows are embedded into existing enterprise systems rather than used in isolation.
  • Adoption-Embedding Gap: The pattern of high claimed AI adoption rates co-existing with low depth of actual integration or utilization.
  • Adoption Mirage: A situation where surface-level AI usage statistics suggest broad adoption, but actual workflow integration is minimal (illustrated by the sales function).
  • AI Opportunity Radars: A visual framework organizing use cases by function and readiness tier (Prime Time, Emerging, Frontier).
  • People Bottleneck: The finding that human upskilling and attitude change — the largest barrier to converting AI adoption into AI value — receives disproportionately little organizational investment.
  • MCP Servers (Model Context Protocol): Infrastructure for making company knowledge bases accessible to AI systems in a structured way.
  • AIDB Intel: The research and surveying arm of the AI Daily Brief providing proprietary data on leading organizations.
  • Super Intelligent (besuper.ai): The speaker’s enterprise AI planning and strategy firm, which conducts AI readiness assessments via voice agent interviews.

Summary

The speaker argues that organizations adopting AI are operating without meaningful comparative benchmarks, and that this absence causes real strategic harm — making it impossible to know whether adoption efforts are ahead, on pace, or behind peers. To address this, the speaker’s organizations (AI Daily Brief and Super Intelligent) have developed AI Maturity Maps: a framework that scores AI readiness across six dimensions (Deployment Depth, Systems Integration, Data, Outcomes, People, and Governance) for ten business functions on a five-point normative scale, drawing on over 480 studies representing 150,000+ professionals. The dominant Q2 finding is that most organizations score behind the “on-track” line in most dimensions, particularly in People (chronically underinvested despite being the primary barrier to value conversion) and Data (which functions as a ceiling constraint on all other dimensions). Functions with technical practitioners and measurable workflows (Engineering, IT, Customer Service) are furthest along, while Finance presents an unusual case — strong governance but negligible deployment. The framework is nascent and publicly accessible via a short quiz at besuper.ai, with future iterations planned to introduce segmentation by organization size and industry.