State of AI Governance Across Industries: Overall Readiness, Gaps, and Future Priorities

AI adoption is accelerating across industries, but scaling AI responsibly requires more than technical performance. As AI moves from experimentation into business-critical operations, governance is becoming a strategic capability rather than a compliance exercise. However, the current governance landscape also reveals an important gap: organizations have made more progress in establishing rules for AI adoption than in operationalizing oversight throughout the AI lifecycle.

As AI becomes increasingly embedded in operational processes and business decision-making, organizations are treating governance as a core component of adoption rather than a post-deployment compliance exercise. Sustainable AI implementation depends not only on model performance but also on an organization's ability to manage risk, assign accountability, protect data, control costs, and maintain stakeholder trust. To achieve this, enterprises are building governance considerations directly into employee adoption policies, data management practices, cybersecurity controls, cost oversight, and ongoing system monitoring. As a result, governance is becoming an operational discipline that shapes how AI solutions are selected, deployed, and managed throughout their lifecycle.

The strongest governance efforts today focus on establishing practical guardrails around AI adoption. Rules governing employee use of public AI tools are the most widely implemented measure. According to the Forrester research commissioned by FPT, nearly eight in ten organizations also monitor AI-related costs across models, training, inference, and infrastructure, while 77% have privacy and security protocols designed to address emerging AI use cases. Together, these measures indicate that organizations are prioritizing governance in four key areas: controlling access to AI technologies, evaluating external providers, safeguarding information, and improving visibility into the costs and risks associated with AI deployment.

Organizations are making substantial strides in governing how AI enters the enterprise.  Rules for employee use of public AI tools are now the most widely established governance capability, reported by 86% of organizations and followed by provider evaluation standards (79%) and privacy and security protocols for new AI initiatives (77%) are also widely implemented. This reflects growing business demand to scale AI adoption while maintaining control over risk, compliance, and operational costs. As organizations move AI from isolated experiments into broader enterprise use, they increasingly need clear guardrails that enable innovation without compromising security and regulatory risks.

However, governance maturity declines once AI moves into production. Although many organizations have established controls for AI adoption and procurement, more than one-third still lack processes to monitor model drift, bias, third-party data use, and ongoing alignment with business objectives.

One reason for this gap is that operational governance is significantly more complex than establishing policies. Usage guidelines and privacy controls can often be integrated into existing compliance and cybersecurity frameworks. Monitoring AI in production, however, requires ongoing performance tracking, clear ownership, regular validation, and specialized expertise to address issues as they arise.

Healthcare demonstrates a relatively mature approach to AI governance across investment, current capabilities, and future planning. According to the Forrester Consulting research, governance and compliance readiness account for 41% of planned AI investments, while 71% of organizations report advanced AI governance maturity, the highest among industries surveyed. Healthcare organizations are also among the most optimistic about future governance outcomes, with 92% reporting realized governance benefits within the next two years.

This maturity strongly reflects the sector's long history of operating within highly regulated environments. Healthcare organizations already maintain established processes for compliance, patient safety, data privacy, clinical oversight, auditability, and risk management. As AI adoption expands across clinical decision support, administrative automation, patient engagement, and research, governance programs can build upon these existing structures rather than creating entirely new oversight frameworks. 

For the manufacturing industry, governance and compliance readiness represent the industry's largest AI investment priority at 46% of planned investments. However, many governance initiatives remain in relatively early stages of implementation, with only 5% of enterprises reporting realized governance benefits today. Manufacturing organizations may face a longer path from governance investment to measurable outcomes, as controls must be integrated into production environments where AI influences operational efficiency, product quality, and supply chain performance. Despite limited outcomes, future expectations are strong, with 91% reporting realized or expected governance benefits within the next two years. 

Insurance: Moderate progress with the highest values expected in the future 

Insurance shows solid governance progress, even though it has not yet reached the maturity levels compared to healthcare or manufacturing. The Forrester research revealed that 64% of insurers reported advanced governance maturity, while 75% say they are prepared to govern AI effectively. More importantly, the industry is already seeing tangible results from its governance investments, ranking second in realized benefits (17%) behind only manufacturing (19%). Yet, the industry's strongest signal lies in its future expectations: insurance has the highest proportion of organizations expecting governance benefits within one to two years (28%) compared to all industries. 

Automotive organizations face governance challenges that extend beyond internal enterprise applications. AI increasingly supports vehicle engineering, driver-assistance systems, connected services, fleet management, and manufacturing operations, requiring governance across complex ecosystems of suppliers, software providers, and regulatory requirements. 

Specifically, only 58% of automotive companies report advanced governance maturity. Despite recording the lowest governance readiness score among the five industries (70%), 94% of automotive organizations expect governance benefits to be realized within the next two years. This indicates strong confidence that current investments and initiatives will translate into measurable values.

Oil & Gas: Strong outcomes despite lower maturity

Oil and gas presents one of the most interesting governance patterns in the survey. While the sector reports the lowest governance maturity level at 57%, it also reports both the highest governance investment priority, at 55%, and the highest realized governance benefits, at 19%. Rather than indicating weak governance overall, the Forrester research revealed that some organizations have been highly effective at adapting long-established safety, compliance, operational-risk, and audit disciplines to AI governance. 

The next phase of AI governance is likely to focus less on establishing governance frameworks and more on translating those frameworks into business outcomes. Governance initiatives remain a major priority, with 81% of organizations reporting governance and control capabilities in their future pipeline. While only 14% of organizations report realized governance and control benefits today, 95% have either already realized benefits or expect to do so within the next two years. 

Governance priorities differ across industries, but the objective remains the same: ensuring AI systems are trusted, secure, accountable, and aligned with business outcomes. Through our AI governance, security, and trust framework, FPT helps organizations establish responsible AI practices grounded in internationally recognized standards such as ISO 42001, ISO 27001, SOC 2, GDPR, and the EU AI Act. 

FPT introduced FleziPT, an AI-first platform that brings together safeguards for ethical and secure AI, an agentic development life cycle that embeds AI from planning through testing and deployment, and a workforce model that pairs human experts with digital workers and AI agents, accelerating execution without losing governance. FleziPT creates a practical path from work visibility to digital execution, agentic orchestration, and ultimately to measurable impact, including up to 60% faster deployment, over 50% reduction in rework, and 30% boost in productivity.

Combined with industry-specific expertise across healthcare, automotive, manufacturing, and energy, FPT helps organizations turn AI strategies into operational capabilities. By combining secure-by-design architectures, centralized controls, clear accountability, and ongoing monitoring, FPT enables enterprises to scale AI confidently while maintaining trust, compliance, and business resilience.

Discover the full Forrester Consulting report here: https://fptsoftware.com/enterprise-ai-executive-study