"We wrote policies for data governance and bought the tools; why is it still not taking hold?" In many organizations that want to speed up their AI projects, this question is back on the agenda. AI has made the quality, ownership and rules of use of data impossible to ignore. But a new data point shows that the problem often lies not in technology or budget, but in how people approach data.
According to a forecast Gartner published on September 21, 2026, 60% of organizations that fail to address the cultural challenges of data and analytics governance will fail to govern AI successfully by 2027. In this article, we look at what that finding tells us, why AI-ready data is not just a technical matter, and what steps can be taken to make culture part of governance.
What the data says: resistance outweighs budget
According to a survey Gartner conducted with 223 data and analytics leaders in March 2026, cultural resistance stands out as the primary reason governance initiatives fail, at 60%, while the share citing budget constraints is 40%.
Gartner defines culture here as the mindsets, behaviors and organizational norms that influence how governance is adopted and sustained within an organization. The main cultural problems that derail programs are listed as low data-focused maturity, stakeholders not fully grasping the value of governance, and weak participation from business units.
The press release stresses one point: while organizations focus on producing policies and rolling out technology, they overlook the cultural side that would ensure those policies are applied at scale and made to last. According to Gartner, AI-ready data also requires stakeholders who understand the value of trusted data, take part in policy management and sustain a culture of accountability.
Why now: AI is making the data gap visible
The culture problem is not new; AI is making it more expensive. According to a survey Gartner conducted with 1,203 data management leaders in July 2024, 63% of organizations either do not have the right data management practices for AI or are not sure whether they do. In the same source, Gartner predicts that 60% of AI projects not supported by AI-ready data will be abandoned by the end of 2026.
Two points in Gartner's assessment bear directly on the culture debate. First, in traditional data management, where and how data is used is often not documented; data accumulates in silos across different systems. Second, AI-ready data is not a one-off project but a practice that must be improved continuously. Yet documentation habits and continuity come not from tools but from the day-to-day behavior of teams.
The framework: governance is also a matter of culture
The idea that culture sits at the center of governance is not unique to Gartner. The Artificial Intelligence Risk Management Framework of the US National Institute of Standards and Technology (NIST) defines the GOVERN function as the one that cultivates and implements a culture of risk management within the organization. According to the framework, senior leadership sets the tone for risk management and, with it, organizational culture, while documentation strengthens transparency and accountability.
The subcategories of the same function also offer a concrete checklist: roles, responsibilities and lines of communication being documented and clear to everyone in the organization; employees and business partners receiving enough AI risk management training to carry out their duties; and senior leadership taking responsibility for decisions about AI risks. These items can be read as the counterpart of the problems Gartner points to, "weak business-unit participation" and "not grasping the value of governance."
A practical path: embedding culture in governance
To improve governance outcomes, Gartner recommends three areas to data and analytics leaders: tying governance to business outcomes, repositioning it as a business enabler and as "teamwork," and embedding governance and culture in daily workflows. The steps below are a general roadmap derived from these recommendations and from the NIST framework; each organization needs to adapt it to its own structure.
- Start governance with an AI use case. Gartner recommends prioritizing governance efforts according to strategic business goals and AI expectations, so that measurable value is produced and senior leadership's support is maintained. Instead of "Let's clean up all of the organization's data," the question "What data, at what quality, does this use case need?" offers a more manageable starting point.
- Give data ownership to the business units. Gartner recommends that governance not be seen as IT's responsibility alone and that shared accountability be established between business and technology stakeholders. Designating one business owner and one technical lead for each critical data domain is the practical counterpart of this principle.
- Put roles in writing and make them visible. As NIST suggests, the answers to "who decides what?" and "to whom is a problem reported?" should be documented and open to everyone concerned.
- Put the rules inside the workflow. Gartner recommends integrating data governance, data literacy, AI literacy and change management into daily operations. For example, data definitions and quality checks can be part of the step of creating a record or preparing a report, rather than a separate approval meeting.
- Plan literacy by role. Short training sessions with different content for management, data owners and end users speak directly to the problem of "not grasping the value of governance." We also covered how to make employees' use of AI tools visible in our article titled From Unchecked Use to Governance.
- Raise the maturity of your data inventory and metadata. Gartner states that organizations with basic or manual metadata management will struggle to prepare their data for AI, and that, as a first step, this area should be brought to maturity now.
- Measure culture too. Track not only data quality indicators but also data owners' attendance at meetings, how often definitions are updated, and the data-issue reports coming from business units. An increase in reports can be a positive sign at first; it shows that problems are becoming visible.
Conclusion
Gartner's data shows that the biggest barrier to data governance is culture, not budget, and that this barrier carries over directly into AI governance. Policies and tools are necessary but not enough on their own; without teams that understand the value of data, take ownership of it and follow the rules in their daily work, AI-ready data cannot be sustained. Governance should therefore be treated not as an IT project but as a management practice in which senior leadership sets the tone and business units take shared responsibility.
Why logistics experience matters here. A well-run warehouse shows what this culture looks like in practice. Item master data has a named owner (step 2), the rules sit inside the scan and the goods-receipt posting rather than in a separate approval meeting (step 4), and routine cycle counts show how closely the records match what is on the shelves (step 7). Because logistics data crosses warehouse, transport and order systems, shared definitions and clear ownership are a daily operating need rather than a policy exercise. One of Orient Global's consultants brings more than 30 years in operations, logistics and supply chain: at a technology distribution company of roughly one billion dollars in scale, he ran logistics, warehousing, technical service and administrative operations end to end, building and leading teams of 100+ people and delivering lasting gains in operational performance through KPI-driven programs. He has also managed nationwide warehouse and transport operations across leading retail, logistics and distribution organizations. An industrial engineer, he now brings that supply-chain discipline to SMEs through AI and automation. Meet our consultants.
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