Opinion
AI readiness isn’t about models - it’s about maturity
Organizations overestimate their AI readiness because they underestimate governance complexity.
According to Gartner, 85% of all AI models/projects fail to reach production. This statistic is often attributed to technical complexity or talent shortages. But for CEOs across global enterprises, it’s a missed opportunity to unlock competitive advantage that stems from a lack of data maturity in the organization.
Organizations have invested billions into advanced analytics platforms, data science teams, and generative AI tools. Yet many of these initiatives stall at the pilot stage if they even make it that far. The issue is rarely the algorithm, but rather the data’s lack of readiness to be used for AI coupled with the enterprise’s inability to operationalize AI safely at scale.
For global organizations, this isn’t just an operational issue - it’s a strategic risk.
Many companies overestimate their readiness because they equate activity with maturity: Hiring data scientists, launching innovation labs, deploying copilots, running pilots in different departments. But these are signals of ambition - not readiness.
In one construction firm, a workforce analytics model performed well in pilot. Yet when deployed enterprise-wide, inconsistencies in employee data definitions across regions made the results unreliable. Concepts such as “Headcount” meant different things in Europe versus the U.S. Access permissions varied by country. The enterprise was not aligned with accurate and consistent data. AI didn’t fail. The culprit was a lack of governance maturity.
For CEOs focused on a global scale, AI is not about experimentation. It is about unlocking value across regions. AI maturity begins with governance discipline, and this means clarity in five areas:
- Data Ownership: Every critical data domain must have a defined owner accountable for quality, access, and change management.
- System-of-Record Discipline: Organizations must define where truth lives. AI cannot reconcile ambiguity at scale.
- Enterprise Glossary Alignment: Consistent definitions across business units are essential for reliable outputs.
- Role-Based Access and Auditability: Especially in regulated industries, transparency and traceability are non-negotiable.
- Integration Accountability: AI initiatives often fail when no single function owns cross-platform integration.
Global enterprises face added complexity: multiple jurisdictions, regulatory frameworks, and legacy architectures that create fragmentation. What accelerates performance in one region may violate policy in another. Scaling AI requires governance models that are global.
Organizations that gain competitive advantage from AI treat readiness in the way they treat financial maturity or cybersecurity resilience. It is an enterprise capability, not a technical experiment.
Before funding or scaling the next AI initiative, CEOs should ask:
- Do we have clear data domain ownership?
- Is our system-of-record architecture defined and enforced?
- Are business terms consistent across the domains covered?
- Are governance controls designed for scale, not pilots?
- Who is responsible for cross-platform integration at the executive level?
The enterprises that successfully translate AI vision into measurable impact must have disciplined operating foundations. AI is a maturity test. And maturity begins long before the first model is deployed.
Gilad Barash is Head of Data & Business Transformation at Matrix USA.
