Analysis of key findings and implications for mid-market technology companies
The rapid adoption of artificial intelligence across business operations has created a critical gap between organizational policy and operational practice. Recent survey data from Pacific AI provides quantitative evidence of this disconnect and offers important insights for mid-market technology companies evaluating their AI governance strategies.
Survey Findings: The Current State of AI Governance
The Pacific AI 2025 AI Governance Survey, conducted by Gradient Flow in April and May 2025, examined how organizations manage the risks and responsibilities associated with deploying generative AI systems. The results reveal significant gaps in governance implementation across organizations of varying sizes.
While 75% of surveyed organizations report having AI usage policies in place, only 59% have established dedicated governance roles, and merely 54% maintain incident response playbooks specifically designed for AI-related risks. This data indicates a fundamental misalignment between policy creation and operational implementation.
The survey identifies three critical areas of concern that have particular relevance for mid-market technology companies:
Organizational Scale and Governance Maturity: Small companies demonstrate consistently lower levels of governance maturity compared to larger enterprises. Only 36% of small companies have designated governance officers, compared to 62-64% for larger organizations. Similarly, just 41% of small companies provide annual AI training programs, while 59-79% of larger organizations have implemented such programs.
Regulatory Framework Awareness: Familiarity with established frameworks such as NIST AI Risk Management Framework (RMF) remains concentrated primarily within large enterprises. Small companies report only 14% familiarity with major industry standards, creating potential compliance vulnerabilities.
Incident Response Capabilities: Many organizations lack protocols specifically designed for AI failure modes, including prompt injection attacks and biased outputs. This suggests that current incident response capabilities extend little beyond traditional IT operational playbooks.
Why These Findings Matter for Mid-Market Technology Companies
Mid-market technology and technology-enabled companies face particular vulnerability in the current AI governance landscape. Unlike large enterprises with dedicated compliance departments and substantial regulatory affairs budgets, mid-market organizations must navigate complex governance requirements while maintaining operational efficiency and competitive speed.
These companies often serve as technology providers to other businesses, making them responsible not only for their own AI governance but also for ensuring that the AI-enabled solutions they deliver to clients meet evolving regulatory and risk management standards. This dual responsibility creates amplified exposure to governance failures.
Furthermore, mid-market technology companies frequently operate in regulated industries or serve clients in sectors such as healthcare, financial services, and government, where AI governance failures can result in significant legal and financial consequences. The survey data indicates that organizations in this segment are particularly unprepared for these challenges, with limited governance expertise and insufficient incident response capabilities.
Technical and Business Implications
Mid-market technology companies face unique challenges in the AI governance landscape. Unlike larger enterprises with dedicated compliance teams and regulatory affairs departments, mid-market organizations must balance governance requirements with resource constraints while maintaining competitive agility.
The survey findings have several implications for this segment:
Risk Management: Organizations deploying AI systems without adequate governance frameworks expose themselves to legal, financial, and reputational risks. The pressure to innovate and deploy AI capabilities rapidly can outpace an organization's ability to implement appropriate safety and compliance measures.
Regulatory Compliance: As regulatory frameworks continue to evolve, organizations with limited familiarity with standards such as NIST AI RMF may find themselves unprepared for compliance requirements. This knowledge gap becomes particularly problematic as regulations become more prescriptive and enforcement mechanisms are established.
Operational Resilience: The absence of AI-specific incident response protocols creates operational vulnerabilities. Traditional IT incident management processes may prove inadequate when addressing AI system failures, data bias issues, or model performance degradation.
Competitive Positioning: Organizations that establish robust governance frameworks early may develop competitive advantages in markets where trustworthy AI becomes a differentiating factor for client acquisition and retention.
Strategic Recommendations
Based on these survey findings, mid-market technology companies should consider implementing governance frameworks that address both current operational needs and future regulatory requirements. Key areas for investment include establishing clear accountability structures for AI oversight, developing incident response protocols specific to AI systems, and building organizational competency in relevant regulatory frameworks.
The data suggests that successful AI governance requires more than policy documentation. Organizations must invest in the operational capabilities, training programs, and technical infrastructure necessary to implement these policies effectively in production environments.
How Forte Group Addresses AI Governance Challenges
At Forte Group, we recognize that effective AI governance requires both technical expertise and strategic business alignment. Our approach focuses on implementing governance frameworks that integrate seamlessly with existing development processes while providing the transparency, auditability, and risk management capabilities that regulatory compliance demands. We work with mid-market technology companies to establish governance structures that support innovation while mitigating operational and compliance risks, enabling our clients to deploy AI systems with confidence in their safety, reliability, and regulatory compliance.
