For CIOs and CISOs, driving AI adoption and managing its risks are the same job. Here's how to build programs that satisfy both.
OVERVIEW
Enterprise AI initiatives rarely fail because the technology didn’t work.
More often, they stall when an organization can't provide clear answers to the questions that governance, legal and risk teams are asking: How was this decision made? What data was used? Who reviewed the exceptions, and what controls were in place?
For CIOs and CISOs, navigating that accountability is now central to the job. Driving AI adoption and managing its compliance implications aren't separate workstreams — they're the same responsibility, and the pressure on both sides is intensifying.
This blog post draws on findings from a 2026 Forrester Consulting study commissioned by Hyland to examine why governance must evolve alongside AI adoption, what that looks like in practice and how organizations can build AI pipelines that satisfy regulators and the business alike.
H2: The enterprise reality: high ambition, low scale
According to Forrester, 45% of decision-makers say their organization is already using AI agents, with another 25% actively piloting them.
That's a significant share of the market in motion. Even so, only 17% have achieved enterprise-wide deployment.
Most organizations are running AI at a team or individual level, which means the harder work of scaling is still ahead. Getting from a successful pilot to enterprise rollout requires satisfying a different set of stakeholders: compliance teams, legal, risk officers, internal auditors and more.
These groups want to understand exactly how the system makes decisions, what data it draws on and how consistently those rules are applied.
Without clear answers, projects get paused. Not because the technology failed, but because the organization can't yet demonstrate control over it.
H2: Why governance must evolve alongside AI adoption
Three priorities tend to define how CISOs approach AI in regulated environments:
- Traceability: Without an audit trail, there's no meaningful way to investigate errors, respond to regulatory inquiries or demonstrate consistent behavior over time.
- Chain of custody: Knowing where content came from, who had access to it and whether it was altered in transit is foundational to any compliance argument, particularly in regulated industries like financial services and healthcare.
- Supervised autonomy: Autonomous decisions need a defined escalation path where a human can review, override or intervene.
Most organizations aren't consistently meeting these standards yet.
The same Forrester study found that 41% of organizations currently have ad hoc or inconsistent governance practices, and only 18% report advanced governance capabilities.
With frameworks like the EU AI Act beginning to take effect and sector-specific requirements tightening across financial services, healthcare and government, that pressure is only likely to increase.
H2: Practical ways to scale governance without losing it
A common response to governance pressure is to centralize control under IT. That can work in the short term, but it creates a different problem. Governance becomes a bottleneck, and business units start working around it.
A more sustainable approach distributes oversight across business units while maintaining consistent standards and centralized visibility. When teams get the flexibility they need to deploy AI in their context, the audit requirements and compliance rules stay uniform across the organization.
This matters most for unstructured data — contracts, case notes, clinical records, emails — where the lack of standard structure makes consistent rule application harder.
For example, an AI-enabled agentic process automation solution, like Hyland Automate, addresses this directly. It transforms document-intensive workflows into intelligent automations with consistent, repeatable outcomes that can be applied for end-to-end business processes.
Its rules engine enforces compliance across diverse process types, so governance requirements are applied at the point of execution rather than added as a manual step afterward.
For organizations managing large volumes of legacy content, that consistency matters as much as the technology itself. The goal is reducing the surface area for compliance gaps — not by adding more manual review, but by ensuring the automation is doing the right thing reliably the first time.
H2: Finding the right partner
Most organizations are not set up to solve the governance, integration and context challenges of enterprise AI deployment on their own — and the data reflects that.
The Forrester study found that 81% of organizations successfully using AI agents rely on an external partner, most commonly a trusted software vendor or systems integrator.
What makes a partner effective in this context is familiarity with both the regulatory environment and the content landscape the organization is already operating in.
Hyland Content Innovation Cloud™ is designed around that constraint. It federates access across multiple content repositories while maintaining a chain of custody.
This means that AI agents can draw on the full breadth of enterprise content without compliance teams losing visibility into where data came from or how it was used.
H2: Building from the right foundation
Scaling AI across an enterprise is achievable. What it requires is building a foundation where trust, transparency and control are embedded in how the system works, not added on top of it.
The organizations pulling ahead right now are the ones that treat governance as a capability, not a constraint.
Download the full Forrester report to explore how leading organizations are approaching enterprise AI adoption. Reach out to Hyland to learn how to build a governed, AI-ready content foundation.
