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. 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.
As an update on one of the more popular blog articles we’ve posted, this second installment in our annual review of Data, BI & AI maturity captures a mid-market that is moving forward with urgency. This second annual survey updates our baseline for how mid-market organizations ($10M–$1B in revenue) are progressing in their use of Data, BI, and AI. We continue to measure tech maturity into three levels:
- 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.
Key Differences Between 2023 and 2024
One year into this survey, the numbers tell a clear story: stabilization is nearly universal across Data and BI, optimization is firmly entrenched, and monetization is beginning to separate leaders from laggards. Here are the ten most important differences from 2023 to 2024:
1. Data monetization rose most sharply
Average monetization in the $10–$100M band jumped from 42% in 2023 to 46% in 2024, and from 56% to 57% in the $100–$250M tier. That four-point swing at the low end is significant—firms that hesitate are falling behind peers who are already turning data into products and measurable ROI.
2. Optimized Data adoption expanded in smaller firms
Among <$10M companies, optimized Data moved from 45% to 48%, showing that even resource-constrained firms are now formalizing catalogs, MDM, and integration. The shift pressures laggards to catch up or risk losing credibility with partners and investors.
3. Analytics monetization became mainstream in mid-market bands
In $10–$100M firms, BI monetization moved from 39% to 42%; in $100–$250M companies, from 54% to 56%. These numbers indicate predictive analytics and automation are no longer experiments—they are becoming expectations.
4. Education is still the weakest BI sector
Education’s BI monetization rose only slightly, from 25% to 27% in $10–$100M firms. With peers in Finance, Retail, and Insurance already at or above 50% monetization, Education risks structural disadvantages in efficiency and service delivery.
5. AI stabilization surged across the mid-market
Average AI stabilization in $10–$100M companies climbed from 48% to 54%, and in $100–$250M firms from 62% to 66%. That 6–8 point leap shows pilots are spreading rapidly across industries.
6. AI optimization followed close behind
Optimization rates in $10–$100M companies moved from 24% to 31%, and in $100–$250M from 35% to 41%. This is the single biggest year-on-year jump in the survey. Firms without MLOps and monitoring in place are now clearly behind the curve.
7. AI monetization doubled in some bands
In $10–$100M companies, monetization advanced from 9% to 12%, and in $250M–$1B firms from 24% to 29%. Even though the absolute numbers remain modest, the growth rate is dramatic. The implication: monetization is moving from outlier to trend.
8. Retail widened its leadership in AI
In $250M–$1B Retail firms, monetization leapt from 33% to 38%, outpacing other sectors. This underscores how consumer-facing industries are embedding AI into pricing, personalization, and operations faster than peers.
9. Real Estate and CRE are the clear laggards
Despite incremental gains, Real Estate’s AI monetization barely moved from 4% to 5% in $10–$100M firms, while Commercial Real Estate stayed flat at 4–5%. These sectors are at greatest risk of disruption if competitors—or new entrants—use AI to digitize workflows and client experiences.
10. The spread between leaders and laggards is widening
Across Data, BI, and AI, the top quartile of industries (Financial Services, Retail, Insurance, Entertainment & Media) are now 20–30 points ahead of the bottom quartile (Education, Real Estate, CRE). That gap is where disruption risk lives. Firms stuck in “stabilized-only” mode are most vulnerable, as optimized and monetized peers compound their advantage.
Where laggards are most at risk:
The greatest risk of disruption is in AI, especially in Real Estate, Commercial Real Estate, and Education, where monetization remains in single digits. These industries face a widening gap against sectors already scaling AI-driven efficiency and customer engagement. But even in Data and BI, laggards that remain “optimized but not monetized” risk being commoditized by peers who have already made the leap to measurable ROI.
And now, for the full report. Each cell of these tables shows Stabilized / Optimized / Monetized percentages for that industry/size cohort. < $10M and > $1B are included for context, but the spotlight remains on the mid-market.
Data Maturity — Stabilized / Optimized / Monetized (Overall: 96% / 86% / 46%)
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 | 90 / 61 / 30 | 100 / 92 / 58 | 100 / 100 / 75 | 100 / 100 / 80 | 100 / 100 / 85 |
| Agriculture & Food Service | 76 / 36 / 15 | 97 / 76 / 37 | 100 / 97 / 53 | 100 / 100 / 60 | 100 / 100 / 70 |
| Business Services | 78 / 40 / 17 | 98 / 79 / 39 | 100 / 98 / 55 | 100 / 100 / 62 | 100 / 100 / 72 |
| Commercial Real Estate | 73 / 34 / 14 | 96 / 74 / 34 | 100 / 96 / 51 | 100 / 100 / 58 | 100 / 100 / 68 |
| Education | 69 / 29 / 12 | 96 / 68 / 32 | 100 / 95 / 49 | 100 / 100 / 56 | 100 / 100 / 66 |
| Entertainment & Media | 82 / 44 / 21 | 98 / 83 / 43 | 100 / 98 / 59 | 100 / 100 / 66 | 100 / 100 / 76 |
| Financial Services | 92 / 66 / 32 | 100 / 94 / 54 | 100 / 100 / 70 | 100 / 100 / 76 | 100 / 100 / 85 |
| Healthcare | 88 / 56 / 26 | 100 / 90 / 46 | 100 / 100 / 62 | 100 / 100 / 70 | 100 / 100 / 80 |
| Insurance | 92 / 64 / 31 | 100 / 94 / 52 | 100 / 100 / 70 | 100 / 100 / 76 | 100 / 100 / 85 |
| Legal Services | 76 / 36 / 15 | 97 / 76 / 36 | 100 / 97 / 53 | 100 / 100 / 60 | 100 / 100 / 70 |
| Logistics & Transportation | 90 / 61 / 29 | 100 / 92 / 48 | 100 / 100 / 65 | 100 / 100 / 73 | 100 / 100 / 82 |
| Manufacturing | 86 / 53 / 24 | 100 / 88 / 44 | 100 / 100 / 60 | 100 / 100 / 68 | 100 / 100 / 78 |
| Oil & Gas | 89 / 58 / 26 | 100 / 91 / 49 | 100 / 100 / 65 | 100 / 100 / 74 | 100 / 100 / 83 |
| Private Equity portfolio co’s | 90 / 61 / 28 | 100 / 92 / 48 | 100 / 100 / 65 | 100 / 100 / 74 | 100 / 100 / 83 |
| Real Estate | 71 / 32 / 13 | 96 / 72 / 33 | 100 / 96 / 50 | 100 / 100 / 57 | 100 / 100 / 67 |
| Retail | 82 / 44 / 20 | 98 / 83 / 42 | 100 / 98 / 58 | 100 / 100 / 66 | 100 / 100 / 76 |
| Tourism | 78 / 40 / 17 | 98 / 79 / 39 | 100 / 98 / 55 | 100 / 100 / 63 | 100 / 100 / 73 |
| Utilities | 92 / 66 / 31 | 100 / 94 / 52 | 100 / 100 / 70 | 100 / 100 / 76 | 100 / 100 / 85 |
| Average | 84 / 48 / 20 | 99 / 84 / 42 | 100 / 98 / 57 | 100 / 100 / 65 | 100 / 100 / 75 |
Data maturity in the mid-market continues to strengthen. Almost all companies have stabilized, most have optimized, and nearly half are now monetizing. Leaders like Financial Services, Insurance, and Utilities are driving monetization past 50% in the $10–$100M band, while Education, Real Estate, and Commercial Real Estate continue to lag. The monetization frontier is widening: sectors like Entertainment & Media and Retail are embedding data products and ROI measures more quickly than before.
Analytics (BI) Maturity — Stabilized / Optimized / Monetized (Overall: 96% / 85% / 42%)
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 | 84 / 50 / 22 | 100 / 88 / 38 | 100 / 99 / 58 | 100 / 100 / 70 | 100 / 100 / 80 |
| Agriculture & Food Service | 71 / 32 / 13 | 96 / 74 / 35 | 100 / 94 / 50 | 100 / 100 / 60 | 100 / 100 / 70 |
| Business Services | 76 / 36 / 15 | 98 / 79 / 37 | 100 / 96 / 52 | 100 / 100 / 62 | 100 / 100 / 72 |
| Commercial Real Estate | 64 / 24 / 10 | 94 / 66 / 31 | 100 / 90 / 47 | 100 / 100 / 58 | 100 / 100 / 68 |
| Education | 58 / 20 / 9 | 92 / 60 / 27 | 100 / 87 / 44 | 100 / 99 / 55 | 100 / 100 / 65 |
| Entertainment & Media | 92 / 64 / 30 | 100 / 94 / 52 | 100 / 100 / 68 | 100 / 100 / 78 | 100 / 100 / 88 |
| Financial Services | 92 / 64 / 30 | 100 / 94 / 52 | 100 / 100 / 68 | 100 / 100 / 78 | 100 / 100 / 88 |
| Healthcare | 82 / 44 / 19 | 98 / 85 / 41 | 100 / 99 / 58 | 100 / 100 / 70 | 100 / 100 / 80 |
| Insurance | 92 / 64 / 30 | 100 / 94 / 52 | 100 / 100 / 68 | 100 / 100 / 78 | 100 / 100 / 88 |
| Legal Services | 66 / 26 / 11 | 96 / 68 / 32 | 100 / 92 / 48 | 100 / 100 / 60 | 100 / 100 / 70 |
| Logistics & Transportation | 89 / 58 / 25 | 100 / 92 / 45 | 100 / 100 / 63 | 100 / 100 / 74 | 100 / 100 / 84 |
| Manufacturing | 82 / 44 / 19 | 98 / 85 / 41 | 100 / 99 / 58 | 100 / 100 / 70 | 100 / 100 / 80 |
| Oil & Gas | 84 / 48 / 21 | 99 / 86 / 42 | 100 / 99 / 60 | 100 / 100 / 72 | 100 / 100 / 82 |
| Private Equity port-co’s | 92 / 64 / 30 | 100 / 94 / 52 | 100 / 100 / 68 | 100 / 100 / 78 | 100 / 100 / 88 |
| Real Estate | 61 / 22 / 9 | 94 / 62 / 28 | 100 / 91 / 46 | 100 / 99 / 57 | 100 / 100 / 67 |
| Retail | 92 / 64 / 30 | 100 / 94 / 52 | 100 / 100 / 68 | 100 / 100 / 78 | 100 / 100 / 88 |
| Tourism | 84 / 50 / 22 | 100 / 88 / 38 | 100 / 99 / 58 | 100 / 100 / 70 | 100 / 100 / 80 |
| Utilities | 86 / 53 / 23 | 100 / 92 / 45 | 100 / 100 / 62 | 100 / 100 / 73 | 100 / 100 / 83 |
| Average | 80 / 46 / 20 | 98 / 83 / 40 | 100 / 97 / 56 | 100 / 100 / 68 | 100 / 100 / 78 |
Business intelligence has reached full penetration in the mid-market. Almost every company has stabilized, and most have optimized. Monetization is rising: now more than 40% of mid-market firms are embedding predictive analytics and automation into workflows. Leaders include Financial Services, Retail, Insurance, and Entertainment, which have pushed monetization beyond half of their mid-market peers. Education, Real Estate, and CRE remain behind, showing slow progress toward monetization despite strong adoption of dashboards and governance.
AI Maturity — Stabilized / Optimized / Monetized (Overall: 69% / 48% / 18%)
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 | 52 / 28 / 9 | 61 / 37 / 15 | 74 / 48 / 22 | 88 / 68 / 32 | 98 / 90 / 48 |
| Agriculture & Food Service | 38 / 18 / 6 | 46 / 24 / 8 | 60 / 34 / 14 | 78 / 52 / 24 | 94 / 79 / 40 |
| Business Services | 38 / 18 / 6 | 46 / 24 / 8 | 60 / 34 / 14 | 78 / 52 / 24 | 94 / 79 / 40 |
| Commercial Real Estate | 32 / 14 / 4 | 34 / 14 / 5 | 41 / 18 / 6 | 60 / 32 / 12 | 85 / 61 / 28 |
| Education | 34 / 15 / 5 | 38 / 17 / 6 | 48 / 24 / 8 | 68 / 40 / 16 | 90 / 70 / 32 |
| Entertainment & Media | 54 / 31 / 11 | 64 / 39 / 17 | 76 / 52 / 22 | 90 / 70 / 34 | 98 / 91 / 50 |
| Financial Services | 54 / 31 / 11 | 64 / 39 / 17 | 76 / 52 / 22 | 90 / 70 / 34 | 98 / 91 / 50 |
| Healthcare | 44 / 23 / 7 | 54 / 30 / 10 | 67 / 41 / 18 | 83 / 60 / 28 | 96 / 85 / 42 |
| Insurance | 52 / 28 / 9 | 61 / 37 / 15 | 74 / 48 / 22 | 88 / 68 / 32 | 98 / 90 / 48 |
| Legal Services | 49 / 27 / 8 | 59 / 34 / 12 | 72 / 46 / 20 | 86 / 66 / 30 | 97 / 88 / 44 |
| Logistics & Transportation | 49 / 27 / 8 | 59 / 34 / 12 | 72 / 46 / 20 | 86 / 66 / 30 | 97 / 88 / 44 |
| Manufacturing | 46 / 25 / 7 | 57 / 32 / 11 | 70 / 44 / 19 | 85 / 63 / 27 | 97 / 86 / 41 |
| Oil & Gas | 52 / 28 / 9 | 61 / 37 / 15 | 74 / 48 / 22 | 88 / 68 / 32 | 98 / 90 / 48 |
| Private Equity port-co’s | 54 / 31 / 11 | 64 / 39 / 17 | 76 / 52 / 22 | 90 / 70 / 34 | 98 / 91 / 50 |
| Real Estate | 32 / 14 / 4 | 32 / 14 / 5 | 38 / 17 / 6 | 58 / 30 / 11 | 84 / 58 / 26 |
| Retail | 58 / 36 / 12 | 68 / 44 / 19 | 80 / 57 / 24 | 92 / 76 / 38 | 99 / 93 / 52 |
| Tourism | 46 / 25 / 7 | 57 / 32 / 11 | 70 / 44 / 19 | 85 / 63 / 27 | 97 / 86 / 41 |
| Utilities | 44 / 23 / 7 | 54 / 30 / 10 | 67 / 41 / 18 | 83 / 60 / 28 | 96 / 85 / 42 |
| Average | 46 / 25 / 8 | 54 / 31 / 12 | 66 / 41 / 19 | 82 / 60 / 29 | 95 / 83 / 44 |
AI in the mid-market is beginning to tip from experimentation into operations. Nearly 70% of firms have stabilized through pilots or early deployments, and almost half have optimized with MLOps practices. Monetization remains rare but is rising: about one in five mid-market firms now generate measurable ROI from AI. Retail, Entertainment, and Financial Services continue to lead, with monetization approaching 20–30% in the mid-market. Real Estate, CRE, and Education remain far behind, with monetization rates in the single digits.
Take-aways for Mid-market CEOs
Tech investment and advancement are accelerating to levels never seen before. This year’s survey shows a mid-market that has finished the climb to optimized Data and BI and is now accelerating into monetized AI. Stabilization is almost universal, optimization is table stakes, and the decisive battleground is monetization – where leaders are pulling further ahead and laggards are drifting into danger.
CXOs should take note: the gap between those merely stabilized and those already monetizing is widening quickly, and firms that don’t make the leap soon risk being left behind by competitors who are already compounding ROI from their investments.
