Data & AI Adoption Radar

Data first. AI next. — ADAPT
Last updated:
Sources: Eurostat · FOD Economy · VLAIO · Gartner · RAND · MIT · Deloitte

Belgian companies adopt AI faster than almost all of Europe

Especially mid-market firms — exactly ADAPT's target audience — are accelerating. But adoption says nothing about results.

Belgium — all companies 10+ emp
34.5%
use at least one AI technology · +150% in 2 years (FOD Economy, 2025)
Mid-market (50–249 emp)
54.5%
growth +52.6% in one year — the strongest climber
EU Average
20.0%
+6.5 pp vs. 2024 (Eurostat, 2025)

AI Adoption by Company Size — Belgium (FOD Economy, 2025)

Large (250+ emp)
76.4%
Mid-market (50–249 emp)
54.5%
Small (10–49 emp)
28.8%
Flanders (all, VLAIO)
58.8%

But… (VLAIO AI Barometer 2025)

AI use emerges "bottom-up, experimental, and fragmented". A clear AI strategy is often missing, leaving potential untapped. And less than half of companies know about the AI Act. — VLAIO AI Barometer 2025
Adoption without foundation is the fastest route to the failure statistics on the next tab. — why order matters

Four in five AI projects deliver no business value

Technology doesn't fail — the approach does. Governance, data foundation, and leadership alignment make the difference.

No Business Value
~80%
of AI projects (RAND 2025, confirmed Gartner 2026)
Stumbles on Data
60%
abandoned due to non-AI-ready data, through 2026 (Gartner)
Stopped After PoC
≥50%
GenAI projects abandoned after proof-of-concept (Gartner)

Why Projects Fail (Analysis of Failed Implementations)

No leadership alignment on success criteria
73%
Underinvestment in data governance
68%
AI treated as IT project
61%
Loss of C-level sponsorship < 6 months
56%

Illustrative figures from failed enterprise implementation analysis (2026) — pattern consistent with Gartner/RAND.

How an AI Project Ends (RAND, 2025)

80%no value
33.8% — abandoned before production
28.4% — in production, no expected value
~20% — delivers business value (including partial)

With sustained CEO involvement, success rate climbs from 11% to 68%.

Failure is not free — here's the price tag

The biggest cost is not the failed pilot, but the bad data beneath it — which runs every day.

Per Stopped AI Initiative
$7.2 mln
average sunk cost, 2025 (Deloitte) · 42% of companies stopped at least one
Poor Data Quality
15–25%
of annual revenue is lost (MIT Sloan)
Per Large Organization
$12.9 mln
average annual cost of bad data (Gartner)

What This Means for an SME (Calculation Example, Illustrative)

Revenue €20 mln · data loss 15%
€3.0 mln
Revenue €20 mln · data loss 25%
€5.0 mln
Cost of a Readiness Program
<1%

Illustrative proportion — exact figures per organization come from the scan.

The BI Failure Nobody Calls a Failure

Dashboards get built and ignored. Adoption has stagnated for seven years — the problem is rarely the tool.

Use BI Effectively
20–25%
of employees — barely any growth in 7 years (BARC/Eckerson)
With Access, Actually Use
45%
the rest feeds the "dashboard graveyard"
Dashboard Failure via Data
40%
traceable to data quality, not technology

Power BI Adoption in Organizations (Real-World Data)

< 25% adoption
58%
25–99% adoption
26%
Full adoption
16%

The Lesson for Those Starting with AI Now

BI taught us: tooling without adoption is a cost, not an investment. AI repeats the same mistake at larger scale — unless data, governance, and people are ready first. That's the PRACTICE step in the ADAPT method: train, embed, repeat. — Acknowledge · Diagnose · Articulate · Practice · Trust

Know if your organization is ready for AI?

The Data & AI Readiness Scan shows where your data, processes, and people stand today — and what the logical first step is. Accessible, no heavy programs.

Request the Data & AI Readiness Scan →