One of the most common questions we receive, and the basis of interest of many clients in our Tech Assessment services, is to compare their IT & AI capabilities and performance with their peers. This first edition is the starting line for (what we hope to make) an annual look at analytics maturity in the mid‑market. We’ve prepared this analysis to help guide CXOs leading organizations from $10M to $1B in revenue. In our experience, the mid-market is the competitive band where maturity gaps translate fastest into wins and losses. We include < $10M and > $1B at the edges of this survey for comparison & context, but the heart of the story sits squarely in the middle three columns.
In our proprietary framework, we divide tech maturity into three levels. This survey utilizes those same levels – the tables below report the % of organizations in each size/industry combination which have achieved each level of tech maturity:
- Stabilized: The basics are in place.
- Optimized: Capabilities are operationalized and governed.
- Monetized: Capabilities directly drive measurable ROI, via cost savings &/or top-line revenue growth.
Methodology
Unlike many maturity surveys that rely on self-reported questionnaires from a small panel of respondents, this survey is built on a proprietary AI prediction model designed to capture and generalize patterns at scale.
Our model estimates maturity levels across Data, Business Intelligence (BI), and Artificial Intelligence (AI) by analyzing a broad set of real-world signals, rather than relying on subjective self-assessments. Specifically, the model draws from:
- Employee profile data: skills, certifications, and role descriptions across thousands of professionals, indicating organizational capability depth.
- Vendor announcements and case studies: public disclosures of system deployments, cloud migrations, analytics initiatives, and AI adoption programs.
- Technology partner ecosystems: integrations, platform partnerships, and implementation footprints across industries.
- Hiring patterns and job postings: demand signals for data engineers, data scientists, cybersecurity staff, and other critical roles.
- Regulatory filings and industry disclosures: where available, insights into technology initiatives disclosed in public reporting.
- News coverage and analyst reports: references to adoption of specific platforms, methodologies, and AI-driven products.
Data Maturity — Stabilized / Optimized / Monetized (Overall: 93% / 82% / 41%)
Criteria
- Stabilized: central warehouse/lake, with scheduled ETL, and a starter data dictionary.
- Optimized: daily refresh, catalog + glossary, and first MDM domain.
- Monetized: enterprise-wide MDM, data products shared across functions, measurable ROI.
| Industry | < $10M | $10–$100M | $100–$250M | $250M–$1B | $1B+ |
|---|---|---|---|---|---|
| Aerospace | 88 / 59 / 28 | 100 / 90 / 55 | 100 / 100 / 70 | 100 / 100 / 75 | 100 / 100 / 80 |
| Agriculture & Food Service | 74 / 33 / 14 | 96 / 72 / 34 | 100 / 95 / 50 | 100 / 100 / 55 | 100 / 100 / 65 |
| Business Services | 76 / 37 / 16 | 97 / 76 / 36 | 100 / 97 / 52 | 100 / 100 / 58 | 100 / 100 / 68 |
| Commercial Real Estate | 70 / 32 / 12 | 95 / 70 / 31 | 99 / 94 / 48 | 100 / 100 / 55 | 100 / 100 / 65 |
| Education | 66 / 27 / 11 | 94 / 65 / 28 | 99 / 92 / 44 | 100 / 99 / 52 | 100 / 100 / 60 |
| Entertainment & Media | 80 / 42 / 20 | 97 / 80 / 40 | 100 / 96 / 55 | 100 / 100 / 62 | 100 / 100 / 72 |
| Financial Services | 90 / 63 / 30 | 100 / 92 / 50 | 100 / 100 / 65 | 100 / 100 / 72 | 100 / 100 / 80 |
| Healthcare | 85 / 53 / 24 | 99 / 88 / 43 | 100 / 99 / 58 | 100 / 100 / 65 | 100 / 100 / 75 |
| Insurance | 90 / 61 / 28 | 100 / 91 / 48 | 100 / 100 / 65 | 100 / 100 / 72 | 100 / 100 / 80 |
| Legal Services | 74 / 35 / 15 | 96 / 74 / 34 | 100 / 96 / 50 | 100 / 100 / 57 | 100 / 100 / 67 |
| Logistics & Transportation | 88 / 59 / 27 | 100 / 90 / 45 | 100 / 100 / 62 | 100 / 100 / 70 | 100 / 100 / 78 |
| Manufacturing | 83 / 50 / 23 | 99 / 86 / 42 | 100 / 98 / 57 | 100 / 100 / 65 | 100 / 100 / 73 |
| Oil & Gas | 86 / 55 / 26 | 100 / 89 / 46 | 100 / 99 / 61 | 100 / 100 / 70 | 100 / 100 / 78 |
| Private Equity portfolio co’s | 88 / 59 / 28 | 100 / 90 / 45 | 100 / 100 / 62 | 100 / 100 / 70 | 100 / 100 / 78 |
| Real Estate | 68 / 29 / 12 | 94 / 68 / 30 | 99 / 93 / 47 | 100 / 100 / 54 | 100 / 100 / 64 |
| Retail | 80 / 42 / 19 | 97 / 80 / 38 | 100 / 96 / 54 | 100 / 100 / 62 | 100 / 100 / 72 |
| Tourism | 76 / 37 / 16 | 96 / 76 / 36 | 100 / 97 / 52 | 100 / 100 / 60 | 100 / 100 / 70 |
| Utilities | 90 / 63 / 29 | 100 / 92 / 48 | 100 / 100 / 65 | 100 / 100 / 72 | 100 / 100 / 80 |
| Average | 82 / 45 / 21 | 97 / 82 / 42 | 100 / 96 / 56 | 100 / 100 / 64 | 100 / 100 / 73 |
Nearly every mid-market company has stabilized its data platforms. Eight in ten have optimized them with governance and integration, but fewer than half have monetized their data by turning it into shared products with measurable ROI. Financial Services, Insurance, and Utilities are the most advanced, with monetization rates approaching 50% in the $10–$100M band and climbing above 70% by $250M–$1B. In contrast, Education, Real Estate, and Commercial Real Estate still lag. Many of these firms have stabilized their warehouses, but only a minority have advanced to the point where data is actively driving revenue or cost advantages.
Analytics (BI) Maturity — Stabilized / Optimized / Monetized (Overall: 94% / 83% / 37%)
Criteria
- Stabilized: dashboards in place, weekly refresh, initial instrumentation.
- Optimized: governed semantic layer, KPI catalog with owners, data literacy programs.
- Monetized: predictive analytics, scenario planning, automated responses embedded in workflows.
| Industry | < $10M | $10–$100M | $100–$250M | $250M–$1B | $1B+ |
|---|---|---|---|---|---|
| Aerospace | 81 / 47 / 20 | 99 / 85 / 36 | 100 / 98 / 55 | 100 / 100 / 65 | 100 / 100 / 75 |
| Agriculture & Food Service | 68 / 30 / 12 | 95 / 70 / 32 | 100 / 93 / 48 | 100 / 100 / 58 | 100 / 100 / 67 |
| Business Services | 73 / 34 / 14 | 97 / 74 / 34 | 100 / 96 / 50 | 100 / 100 / 60 | 100 / 100 / 70 |
| Commercial Real Estate | 61 / 22 / 9 | 92 / 62 / 28 | 99 / 88 / 44 | 100 / 99 / 55 | 100 / 100 / 65 |
| Education | 55 / 18 / 8 | 89 / 56 / 25 | 98 / 85 / 42 | 100 / 97 / 52 | 100 / 100 / 62 |
| Entertainment & Media | 89 / 62 / 28 | 100 / 92 / 50 | 100 / 100 / 65 | 100 / 100 / 75 | 100 / 100 / 85 |
| Financial Services | 89 / 62 / 28 | 100 / 92 / 50 | 100 / 100 / 65 | 100 / 100 / 75 | 100 / 100 / 85 |
| Healthcare | 79 / 42 / 18 | 97 / 82 / 38 | 100 / 98 / 55 | 100 / 100 / 65 | 100 / 100 / 75 |
| Insurance | 89 / 62 / 28 | 100 / 92 / 50 | 100 / 100 / 65 | 100 / 100 / 75 | 100 / 100 / 85 |
| Legal Services | 64 / 24 / 10 | 94 / 66 / 30 | 99 / 91 / 46 | 100 / 100 / 58 | 100 / 100 / 68 |
| Logistics & Transportation | 86 / 55 / 24 | 99 / 89 / 43 | 100 / 100 / 60 | 100 / 100 / 70 | 100 / 100 / 80 |
| Manufacturing | 79 / 42 / 18 | 97 / 82 / 38 | 100 / 98 / 55 | 100 / 100 / 65 | 100 / 100 / 75 |
| Oil & Gas | 82 / 48 / 21 | 99 / 86 / 40 | 100 / 99 / 57 | 100 / 100 / 68 | 100 / 100 / 78 |
| Private Equity portfolio co’s | 89 / 62 / 28 | 100 / 92 / 50 | 100 / 100 / 65 | 100 / 100 / 75 | 100 / 100 / 85 |
| Real Estate | 59 / 20 / 8 | 91 / 60 / 26 | 98 / 86 / 44 | 100 / 97 / 55 | 100 / 100 / 65 |
| Retail | 89 / 62 / 28 | 100 / 92 / 50 | 100 / 100 / 65 | 100 / 100 / 75 | 100 / 100 / 85 |
| Tourism | 81 / 47 / 20 | 99 / 85 / 36 | 100 / 98 / 55 | 100 / 100 / 65 | 100 / 100 / 75 |
| Utilities | 84 / 52 / 22 | 99 / 88 / 42 | 100 / 99 / 57 | 100 / 100 / 68 | 100 / 100 / 78 |
| Average | 77 / 42 / 18 | 97 / 80 / 39 | 100 / 96 / 54 | 100 / 99 / 66 | 100 / 100 / 76 |
Business intelligence is nearly universal in the mid-market. Dashboards are everywhere, and most firms have optimized their BI with KPI catalogs, governance, and literacy initiatives. But only about one in three have monetized their BI—embedding predictive analytics, scenario planning, or automated responses into workflows. Leaders like Financial Services, Retail, Insurance, and Entertainment are pushing monetization beyond 50% of mid-market firms. By contrast, Education, Real Estate, and CRE are still catching up, with many organizations relying on dashboards without embedding BI deeply enough to drive measurable outcomes.
AI Maturity — Stabilized / Optimized / Monetized (Overall: 60% / 36% / 12%)
Criteria
- Stabilized: pilots and early deployments, prompt libraries, basic guardrails.
- Optimized: MLOps practices, model registries, evaluation frameworks, monitoring.
- Monetized: production AI delivering ROI—fine-tuned models, measurable revenue or cost impact.
| Industry | < $10M | $10–$100M | $100–$250M | $250M–$1B | $1B+ |
|---|---|---|---|---|---|
| Aerospace | 44 / 21 / 7 | 52 / 28 / 12 | 66 / 37 / 18 | 82 / 57 / 26 | 96 / 80 / 40 |
| Agriculture & Food Service | 30 / 13 / 4 | 38 / 18 / 6 | 52 / 28 / 12 | 70 / 46 / 20 | 88 / 70 / 34 |
| Business Services | 30 / 13 / 4 | 38 / 18 / 6 | 52 / 28 / 12 | 70 / 46 / 20 | 88 / 70 / 34 |
| Commercial Real Estate | 28 / 12 / 3 | 32 / 13 / 4 | 39 / 16 / 6 | 58 / 30 / 10 | 80 / 55 / 22 |
| Education | 30 / 13 / 4 | 34 / 15 / 5 | 45 / 21 / 8 | 65 / 36 / 15 | 85 / 60 / 28 |
| Entertainment & Media | 49 / 26 / 9 | 59 / 33 / 13 | 71 / 47 / 20 | 88 / 66 / 30 | 98 / 85 / 45 |
| Financial Services | 49 / 26 / 9 | 59 / 33 / 13 | 71 / 47 / 20 | 88 / 66 / 30 | 98 / 85 / 45 |
| Healthcare | 39 / 19 / 6 | 47 / 23 / 8 | 61 / 33 / 13 | 80 / 54 / 24 | 95 / 75 / 38 |
| Insurance | 44 / 21 / 7 | 52 / 28 / 12 | 66 / 37 / 18 | 82 / 57 / 26 | 96 / 80 / 40 |
| Legal Services | 42 / 20 / 6 | 50 / 26 / 9 | 64 / 35 / 15 | 82 / 55 / 24 | 96 / 78 / 38 |
| Logistics & Transportation | 42 / 20 / 6 | 50 / 26 / 9 | 64 / 35 / 15 | 82 / 55 / 24 | 96 / 78 / 38 |
| Manufacturing | 40 / 18 / 6 | 48 / 25 / 9 | 62 / 34 / 14 | 80 / 52 / 22 | 95 / 73 / 36 |
| Oil & Gas | 44 / 21 / 7 | 52 / 28 / 12 | 66 / 37 / 18 | 82 / 57 / 26 | 96 / 80 / 40 |
| Private Equity portfolio co’s | 49 / 26 / 9 | 59 / 33 / 13 | 71 / 47 / 20 | 88 / 66 / 30 | 98 / 85 / 45 |
| Real Estate | 28 / 12 / 3 | 30 / 13 / 4 | 37 / 15 / 5 | 56 / 28 / 9 | 78 / 52 / 20 |
| Retail | 53 / 29 / 10 | 63 / 36 / 15 | 75 / 50 / 22 | 92 / 70 / 33 | 99 / 90 / 50 |
| Tourism | 40 / 18 / 6 | 48 / 25 / 9 | 62 / 34 / 14 | 80 / 52 / 22 | 95 / 73 / 36 |
| Utilities | 39 / 19 / 6 | 47 / 23 / 8 | 61 / 33 / 13 | 80 / 54 / 24 | 95 / 75 / 38 |
| Average | 40 / 19 / 6 | 48 / 24 / 9 | 62 / 35 / 15 | 80 / 55 / 24 | 94 / 76 / 38 |
AI in the mid-market is in its exploratory phase. Six in ten companies have stabilized AI through pilots or limited deployments. Roughly one-third have optimized by building MLOps practices, model registries, and evaluation frameworks. Only about one in ten are monetizing AI with production use cases tied to ROI. The leaders are Retail, Entertainment, and Financial Services, where monetization is beginning to take hold at around 20% in $100–$250M firms and 30% in $250M–$1B companies. The laggards are Real Estate, CRE, and Education, where monetization barely rises above single digits.
Take-aways for Mid-market CXOs
In our view, the state of play is clear: Data and BI stabilization is table stakes in the mid-market, and optimization is well underway. Monetization, while less common, is already creating competitive advantages in the most data-driven and customer-facing sectors.
AI is the active frontier. Pilots are visible across most industries, but only a minority of firms have operationalized AI platforms, and fewer still have monetized them. Companies which have reached optimization have a chance at true competitive advantage, and of course HUGE returns are just beginning for organizations who are able to monetize their AI capabilities.
For CXOs, the present imperative is straightforward: don’t settle for stabilized. The organizations that push into optimization and monetization now are defining the benchmarks that others will chase in the years ahead.
