Research reveals governance and maturity matter in building AI Confidence™

Every technology transformation brings a hype cycle — a race to be first, to demonstrate you're ahead of the game, to prove you're not losing ground. Caught up in the fever of this moment, many otherwise cautious organizations may fail to ask themselves the deeper questions. How should we proceed? What can we expect to gain? And most importantly, what safeguards must be taken?

In knowledge work — an industry built on careful judgment, wisdom, and considered analysis — we’re witnessing a mad rush into AI with a curious deficit of those things applied. A subtle irony, perhaps, but it feels tangible. In a race to be first, reason comes last.

Even in the cleverest of industries, the shiny object commands attention.

When we commissioned the iManage Knowledge Work Benchmark Report 2026, we already saw this moment coming. Our prior research in the Knowledge Work Maturity Model, published in 2022, demonstrated the need for frameworks to help organizations transform thoughtfully, not just quickly. And this research builds on that foundation, providing real data to help leaders apply the same rigor to their AI decisions that they bring to client work.

Our approach has always been pragmatic and thoughtful. At times, that might have made us appear less quick to move than others. But advancement grounded in sound reasoning ensures something more valuable than speed — it ensures confidence. The confidence that we can continue to support our customers’ growth, to prosper as a technology leader and partner, and above all, to enable us to give back to the industry that underpins everything iManage is today.

We were delighted that industry leader, Reena SenGupta, Executive Director of RSGi, wrote the foreword to the research report. She called the findings “Ballast for change and future investment.” And she's right — this research isn't just data points. It's a roadmap for organizations navigating the most significant technology transition in a generation.

What the iManage Knowledge Work Benchmark Report 2026 reveals

Investment is broad-based. 72% plan to upgrade document management systems (DMS), with roughly 1 in 4 making significant digital transformation investments and about half making moderate ones. 

Near-term roadmap. Over the next three years, 46% plan moderate improvements to existing tools, and 32% will actively modernize or upgrade. 

AI is widespread — but uneven. 22% piloting, 46% implementing, 17% fully integrated — that’s 85% at some stage of adoption. Yet maturity matters: 27% of the most mature organizations are fully integrated vs. 3% of the least mature. 

Governance pressure is intensifying. 36% have experienced a policy violation, and 20% have delayed adoption due to security concerns. 70% expect AI and data privacy regulations to have a transformational or significant impact within three years. 

Shadow AI is real. 25% say end users are using public AI with little oversight.

Maturity drives performance. Organizations at the highest knowledge work maturity level are four times more likely to report top-quartile financial performance (28% vs 7%). 

Source: iManage Knowledge Work Benchmark Report 2026

AI itself isn’t the problem. Uncontrolled AI is.

Artificial intelligence has moved from experimentation to expectation. Across the legal industry and beyond, organizations know AI is no longer optional. Yet, as adoption accelerates, confidence lags behind. The result is a widening gap between ambition and execution — one defined not by technology limitations, but by trust, governance, and control.

The research data highlights this tension clearly. Nearly half of organizations are actively implementing AI, but only a small fraction have fully integrated it into daily work. At the same time, professionals are increasingly turning to public AI tools on their own, creating new risks for firms that operate in highly regulated, confidentiality-driven environments.

The confidence gap at the heart of AI adoption

The pace of AI experimentation tells one story. Confidence in AI tells another. While 46 percent of organizations report actively implementing AI, just 17 percent say it is fully embedded into their operations. That disconnect points to a deeper issue: many firms are testing AI without the governance structures needed to scale it safely.

But here's what makes this confidence gap so critical: it's not just internal. Fifty-seven percent of respondents told us that client needs directly influence their organization’s AI adoption decisions, and 30 percent report that clients frequently restrict AI usage. The bottleneck isn't technology capability — it's trust. And organizations can't build trust while they're still sorting out governance.

The stakes are already visible. One in four professionals admits to using public AI tools with little or no oversight. More than a third of firms have experienced AI policy violations, and one in five has delayed or paused AI initiatives altogether due to security concerns.

This isn’t reluctance to innovate — it is caution born from risk. For organizations where client trust and data integrity are non-negotiable, unmanaged AI adoption exposes them to risks that leadership simply cannot ignore. 

The path forward requires reframing the conversation. AI is not inherently risky. Risk emerges when AI operates outside the firm’s control framework.

When governance fails, trust follows

Governance used to be where innovation went to slow down. In 2026, it's where competitive advantage is won or lost.

Seventy percent of leaders say global AI and data privacy regulations will significantly impact their organizations. In this environment, innovation is no longer just about what technology can do — it’s about what firms can defend, explain, and audit. Leadership increasingly recognizes that competitive advantage won’t come from stacking new apps, but from the quality of the data and AI systems that sit beneath them. Nearly half of leaders now see data and AI quality as a decisive differentiator.

This shift underscores a crucial truth: without governance, AI erodes trust. With governance, AI becomes a strategic asset.

That’s why keeping AI inside ethical walls — with clear auditability, controlled prompts, and traceable outputs — is no longer optional. It’s foundational to responsible adoption.

The market is moving toward trusted AI ecosystems

Looking ahead, the direction of travel is clear. Organizations aren’t just investing in AI — they’re investing in ecosystems that allow AI to operate safely across tools, teams, and workflows.

Over the next 12 to 18 months, the largest AI investment areas reflect this shift. AI-powered knowledge management tops the list, followed closely by autonomous workflows and predictive analytics. These use cases depend on high-quality content, integrated systems, and confidence that AI outputs can be trusted in real-world decision-making. 

Yet barriers remain. A third of leaders say AI will increase the need for cross-role collaboration, and nearly the same number cite system integration as the single biggest obstacle to scaling AI.

This is where the conversation shifts from tools to infrastructure.

Organizations don't just need more AI features — they need a governed platform that can support AI at scale. A foundation that connects systems, enforces policy, and enables AI to operate safely across the organization. A system where autonomous agents can work within boundaries, and every action is auditable.

That's not about limiting what AI can do. It's about ensuring you can trust what it does. Renowned for its robust governance and deeply committed to the potential of Model Context Protocol (MCP), iManage is dedicated to turning safe, multi-vendor AI ecosystems into a reality for its customers.

Building AI Confidence™, not just AI capability

The future of AI isn’t about unchecked autonomy — it’s about building AI Confidence at scale.

Agentic AI, automation, and advanced analytics all depend on a secure, governed platform that understands context, permissions, and provenance.

Agentic AI needs a governed platform. Firms that succeed will be those that embed trust into their AI architecture from day one, rather than retrofitting governance after risk appears. 

iManage is built for what’s next. That’s the difference between experimenting with AI and operationalizing it. 

By keeping AI inside secure boundaries, applying governance at every stage, and enabling responsible integration across the ecosystem, iManage remains the trusted partner that organizations can rely on to help them navigate beyond hesitation and into confident AI adoption.

Because AI isn’t the risk. Uncontrolled AI is.

And the next frontier of AI-enabled knowledge work will belong to the firms and their partners who understand the difference.

Please download the full iManage Knowledge Work Benchmark Report 2026 and get the complete research dataset, segment cuts, and practical frameworks to turn insight into action.