Why AI adoption outpacing governance poses real risks

AI pilots turn into production use fast, and spend follows. In December 2025, enterprise spending on OpenAI reached a new record, with business adoption climbing to 36.8%, signaling that AI is moving from experiments to everyday work.

Yet governance is lagging behind.

Most firms still lack clear controls, roles, accountability structures, and runbooks for safe use. McKinsey’s 2025 survey finds that while AI tools are common, most companies have not embedded governance deep enough to deliver enterprise-level outcomes. 

This mismatch creates real risk. Netskope reports that data policy violations tied to generative AI have doubled year over year, with many involving regulated data and the use of personal, unmanaged accounts.³

Your teams are shipping code and content with AI, while governance is still catching up. Why does the gap exist, why does it matter, and why must governance be designed alongside AI adoption, not after?

Why AI adoption spreads faster than organizational governance

AI adoption is moving quickly for one simple reason. The tools are easy to access and mostly immediately useful.

Most modern AI tools are:

• Cloud based
• Low cost or free to start
• Easy to integrate into existing workflows
• Marketed directly to developers and businesses. 

An employee can start using an AI assistant in minutes. No procurement process. No architecture review. No formal approval. This leads to a pattern seen across many organizations what security teams now call shadow AI. The value is immediate, especially for repetitive or boilerplate tasks.

This leads to a pattern seen across many organizations:

• Individual employees experiment with AI tools
• Teams begin relying on them for daily work
• Usage becomes embedded before leadership is aware

By the time governance conversations start, AI is already part of the daily workflow.

Why AI governance moves slower than AI adoption

Governance requires coordination. AI governance in particular touches multiple teams:

• Security teams
• Legal and compliance
• Data protection officers
• Architecture and platform teams
• Engineering leadership

Each group has different concerns, incentives, and risk tolerances. Aligning them takes time.

In contrast, adopting an AI tool can take minutes.

This imbalance creates a predictable outcome. Adoption happens first. Governance becomes a reaction, not a design input.

The most common AI governance blind spots in enterprise teams

When AI tools are adopted without structure, several risks often go unnoticed.

Shadow AI and data exposure

Employees frequently paste internal content into AI tools. This may include:

• Proprietary source code.
• Internal documentation.
• Configuration files.
• Customer or client data.

Even when this is done to improve productivity, it can expose sensitive information outside the organization.

Many AI providers collect user interactions, and in some cases, this data may be used to train models. This creates a real risk that proprietary IP or customer data leaves the organization without formal consent.

Loss of intellectual property control

Source code is intellectual property. When developers share it with external AI tools, ownership and usage rights may become unclear.

Without governance, organizations may not know:

• Which tools are approved
• Where proprietary code is being shared
• Whether that code is stored or reused

Model drift and reliability risks

AI systems can change behaviour over time as models are updated. Outputs that were reliable last month may not behave the same way today.

Without governance, teams may:

• Treat AI outputs as deterministic
• Use them in critical paths without validation
• Miss changes that affect accuracy or bias

Without clear guidance, teams may trust AI output more than they should. This creates operational risk that is difficult to detect after deployment.

Compliance and regulatory exposure

Regulators are paying attention to AI usage. The EU AI Act entered into force in 2024, and similar frameworks introduce obligations around transparency, risk classification, and accountability³. If models touch personal data, GDPR duties also apply. 

However, many existing privacy and compliance processes do not yet account for how LLMs access customer data or how vendors handle training data.

Governance does not mean blocking AI

One of the biggest misconceptions is that governance equals restriction.

Good governance enables faster adoption as it:

• Defines which tools are safe to use
• Clarifies what data can and cannot be shared
• Provides safe environments for experimentation
• Reduces uncertainty for teams

Teams move faster when rules are clear. The problem is not governance itself. The problem is introducing it too late.

The role of platforms in enforcing AI governance

AI governance is also a platform challenge.

Without the right infrastructure capabilities, governance relies on trust and manual checks. That does not scale.

Stable platforms support governance by design through:

• Isolated environments for testing and experimentation
• Clear separation between production and non-production data
• Auditable deployment workflows
• Visibility into services and dependencies

Platforms that standardise how applications are built, deployed, and operated make it easier to enforce governance consistently without slowing teams down. This is especially true when platforms expose clear, predictable configuration and deployment models that both humans and automation can rely on.  It allows teams to experiment and innovate without bureaucracy slowing them down.

This is even more true when technological advances like the Model Context Protocol (MCP) have expanded what AI agents can do and access by enabling them to connect to databases, file systems, APIs, and internal tools with ease.

This further heightens the need for governance. The more capable AI agents become, the more urgent the need for clear boundaries, approved tooling, and auditable trails.

Designing governance alongside AI adoption

The safest organizations are not waiting for perfect regulations or final frameworks.

They are:

• Mapping where AI is already used
• Defining lightweight usage policies
• Providing approved tooling and environments
• Building governance into developer workflows

This approach keeps teams moving while reducing risk.

Governance becomes part of how software is built, not an external checkpoint.

The cost of ignoring the AI governance gap

The longer the governance gap persists, the harder it becomes to close. AI usage spreads quickly. Once embedded, it is difficult to unwind without disruption.

The risks compound:

• Security incidents become harder to trace
• Compliance gaps widen
• Trust with customers and partners erodes
• Teams lose confidence in AI outputs

None of these outcomes is inevitable. They are the result of delaying structure until after scale.

Closing the governance gap intentionally

AI adoption is not slowing down. That is a given.

The real choice is whether organizations adopt AI deliberately or allow it to spread without structure.

The strongest teams design governance alongside adoption. They create safe paths for experimentation rather than trying to control behaviour after the fact.

Sources

1. Business Insider: Enterprise spending on OpenAI hit a record in December 2025  (https://www.businessinsider.com/openai-business-spending-ai-models-jumps-record-ramp-data-2026-1)
2. McKinsey, The State of AI 2025 (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)
3. ITPro summary of Netskope Threat Labs, 2025 report on generative AI data violations (https://www.itpro.com/technology/artificial-intelligence/generative-ai-data-violations-more-than-doubled-last-year)
4. Journal of Medical Internet Research, 2024 study on hallucinated references (https://www.jmir.org/2024/1/e53164)
5. European Parliament Research Service, AI Act implementation timeline, June 2025 (https://www.europarl.europa.eu/RegData/etudes/ATAG/2025/772906/EPRS_ATA%282025%29772906_EN.pdf)
6. Goodwin Procter, EU AI Act implementation timeline explainer, Oct 2024 (https://www.goodwinlaw.com/en/insights/publications/2024/10/insights-technology-aiml-eu-ai-act-implementation-timeline)