Gartner’s research shows that organizations with strong AI and data risk management are 12% further along in adopting advanced technologies. McKinsey’s latest findings reinforce this: companies seeing the highest returns from AI are significantly more likely to have centralized governance models that coordinate efforts across the enterprise.

The message is clear: governance accelerates adoption and amplifies value, while poor governance slows progress and increases risk.

Where organizations must focus to build strong AI governance

1. Building practical, actionable AI governance frameworks

Many organizations struggle to define clear roles, responsibilities, and decision paths. Service providers can help design governance models that are aligned with the company’s structure, regulatory obligations, and ambitions.

2. Establishing AI-ready data foundations

Gartner predicts that most AI projects through 2027 will fail to meet expectations due to weaknesses in data governance. IT service providers help organizations implement the data quality, lineage, privacy, and access controls needed for trustworthy AI.

3. Creating safe, repeatable AI development and deployment processes

Transitioning from prototypes to production requires discipline. Service providers can design and implement:

• Standardized development workflows

• Model validation and documentation

• Monitoring and lifecycle management

• Secure MLOps practices

This ensures AI systems are scalable, explainable, and auditable.

4. Embedding risk, compliance, and security controls

Gartner warns that by 2029, legal claims related to harmful AI outcomes will double, largely due to missing guardrails. Providers help organizations integrate risk assessments, content validation, and automated controls directly into AI operations.

5. Identifying and managing shadow AI

With GenAI widely accessible, unauthorized AI tools pose growing risks. McKinsey reports that more than half of organizations using AI have experienced at least one negative consequence, including inaccuracies and security exposures. Service providers can help detect shadow AI, set guardrails, and formalize safe alternatives.

6. Supporting cross-functional collaboration

Effective AI governance touches legal, cybersecurity, compliance, HR, IT, and business operations. Providers help orchestrate alignment across these groups, ensuring consistency and reducing fragmentation.

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AI Governance: The foundation for trust, confidence, and scalable value

AI is transforming industries but without a robust governance model, organizations struggle to maintain accuracy, security, and compliance. McKinsey finds that 27% of organizations manually review all AI-generated content, while another 25% review almost none – a sign of how uneven governance remains.

By investing in strong AI governance, organizations can achieve:

• Higher trust in AI-driven outcomes

• Lower operational and compliance risk

• Better control of costs and complexity

• Consistency across business units

• Faster, safer adoption at scale

And with the right IT service provider such as Trask, companies don’t have to build these capabilities alone. Providers bring the expertise, proven frameworks, and cross-industry experience needed to ensure AI remains secure, responsible, and aligned with business strategy.

Governance isn’t a constraint, it’s the enabler of sustainable, enterprise-grade AI!

Author

Jan Burian, Head of Industry Insights at Trask

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