Limited AI integration amid high experimentation
It’s clear that leaders across industries are interested in AI. The latest research proves that. In fact, 85% of organizations are running pilots, implementing, or actively using AI in some way. But most of that energy isn’t making it into daily business operations. Only 17% of these companies have actually woven AI into their routine workflows. That’s a huge gap between ambition and reality.
What this tells us is important. Companies are willing to try AI. They’re not afraid to experiment. But experimentation is only the first step. Turning those pilots into real, scalable systems that improve how a business works, that’s where things fall short. And it always comes down to the same issue: foundational readiness.
Getting AI into production requires more than a prototype or a flashy vendor. It means the basics, like having your data structured, your systems clean, and your people aligned. Without that, AI remains stuck in labs and slides. If you’re an executive looking to be taken seriously about AI, stop thinking about short-term pilots and start thinking about scale and structure.
The Knowledge Work 2026 Benchmark Report surveyed over 3,100 business and tech leaders across 26 countries. These numbers aren’t just suggesting a trend, they’re showing a major gear mismatch. People want outcomes, not experiments.
Knowledge governance as a critical enabler for AI success
If your organization doesn’t have solid knowledge governance, your AI isn’t going to work well. You can deploy AI faster than anyone else, but if the foundation underneath it is unstable, the whole thing breaks. That’s the core message from the data.
Organizations that have mature knowledge systems, ones where information is structured, secured, and reusable, are simply performing better with AI, legally and operationally. They aren’t just using AI more effectively; they’re doing it with fewer errors, fewer risks, and stronger trust from clients and employees.
Almost one-third of executives in the survey said their companies ran into policy problems because someone used unregulated AI tools. That’s not some remote risk, that’s happening in real businesses. Another 30% delayed adoption altogether due to similar concerns. That’s wasted momentum.
What does this mean for you? It’s simple. Governance doesn’t slow innovation, it enables it. Companies that take governance seriously are in a position to move faster because they know their risk posture. They’ve done the internal work to make AI sustainable, not just exciting.
Laura Wenzel, Global Insights Director at iManage, frames it clearly: “AI success isn’t about who experiments fastest, it’s about who has done the foundational work.” That foundational work is governance. It’s systems. It’s security. Ignore that, and AI becomes risk, not reward.
Client demands driving AI adoption in knowledge-mature organizations
Right now, clients are pushing harder. They want AI. They expect faster service, better insights, and more consistency, and they assume you already have the tools in place. That external pressure is becoming a major driver of AI adoption, especially for organizations that already manage their knowledge effectively.
The Knowledge Work 2026 Benchmark Report shows that 57% of organizations say client expectations shape their AI plans. But when you look at companies with mature knowledge systems, that number jumps to 74%. That means these firms aren’t just listening, they’re acting. They’re pulling AI deeper into client-facing and operational workflows. That’s a strategic move based on readiness.
C-suite leaders shouldn’t ignore this. If your AI roadmap isn’t aligned with evolving client demands, someone else’s is, and that’s a competitive risk. Clients are watching how quickly and responsibly AI is deployed. They want to trust your systems, especially in core sectors like financial services, legal, and asset management.
So if your environment is structured correctly, with smart knowledge capture, good permissions, and clear internal access, you’re already ahead. If it’s not, improving that should move to the top of the list. AI adoption doesn’t just depend on internal factors, it’s tied to external expectations that are scaling fast.
Strong knowledge governance correlates with improved business outcomes
Companies that manage knowledge well perform better. That’s not opinion, that’s data. The iManage survey makes it clear. Organizations with more mature knowledge environments reported stronger business outcomes across key metrics: revenue growth, profitability, and financial strength.
And this isn’t about perceptions. These are self-reported numbers from executives, based on their actual year-over-year performance. Mature organizations were nearly twice as likely to report revenue growth compared to less mature peers. Results like that aren’t random, they’re the outcome of consistent internal systems that support decisions, reduce friction, and accelerate execution.
This is critical for executives to understand. It’s not enough to just look at AI as a tool. You need to create fertile ground for it, and knowledge governance is that ground. Without it, scaling becomes expensive, slow, and risky.
Too often, companies invest in performance outcomes without fixing foundational problems. Solid governance maximizes the return from AI, because it ensures the systems are in place to support it across business units. That’s where intelligent growth happens, not just smarter inputs, but better results.
If you’re serious about financial performance, knowledge maturity isn’t a side project, it needs to be a strategic priority.
AI as a tool to enhance existing job roles
The data settles a key debate: AI isn’t about replacing people. It’s about enabling them to do more. Among the 3,185 professionals surveyed, 57% reported that AI primarily enhances existing roles. That’s the majority. And in firms with mature knowledge environments, that number translates into clear productivity gains.
AI is being applied to streamline workflows, automate repetitive tasks, and surface insights faster. It’s not eliminating the value of human expertise, it’s increasing the impact. Knowledge-mature organizations are seeing this already, because they’ve created conditions where AI can operate efficiently within business processes, not on the outside of them.
If you’re in leadership, this changes how you think about workforce planning. It’s not about cutting roles, it’s about shifting responsibilities. People do more strategic work, and AI takes over predictable, time-consuming functions. But for that shift to work, the tools need context. That’s where structured data and internal knowledge systems matter. They give AI a clear view of what’s relevant.
Expecting results from AI without preparing your teams and systems is short-sighted. But when you get that alignment right, you unlock higher performance from both people and technology.
Persistent challenges in information retrieval impact AI effectiveness
There’s a gap between confidence and performance when it comes to knowledge search. According to the report, 86% of decision-makers say they’re confident in their ability to find and reuse internal knowledge. But the same professionals spend an average of 37 minutes every day just looking for information. That’s nearly four hours a week, per employee, lost to inefficiency.
That search time signals a real problem. It’s not just about productivity. AI systems rely on access to well-organized, permissioned, and up-to-date data. If your teams are struggling to locate documents, prior communications, or internal analysis, your AI won’t deliver meaningful output. It will mirror the gaps in your internal systems.
Executives need to factor this into digital strategy immediately. AI results are only as strong as the data foundation behind them. Poor organization, redundant storage, and lack of access controls don’t just affect users, they severely limit any AI’s ability to function with precision.
So if you want real AI value, start where the friction is. Improve how professionals access knowledge and reduce wasted time. That effort will compound, because AI processes information faster than people can. But it still needs the right baseline to start with.
Strategic investment in knowledge platforms is essential for future success
Across industries, leaders are making serious moves when it comes to knowledge infrastructure. According to the Knowledge Work 2026 Benchmark Report, 72% of organizations plan to invest in a new document or knowledge management platform within the next two years. That number alone signals a shift, one that prioritizes structured information environments as part of long-term strategy, not just IT upgrades.
But here’s the key point: investment in tools isn’t enough unless it’s backed by consistent governance. The platform itself solves nothing without clear policies around how information is captured, shared, secured, and used. Without that structure behind it, the tech can easily become underutilized or misaligned with business needs.
This matters especially for AI. Whether the goal is operational efficiency, compliance, or innovation, AI performance relies heavily on access to well-maintained, permissioned data. Companies that treat knowledge systems as standalone IT projects won’t get the full ROI. Strategic advantages go to organizations that connect platform investments with governance, adoption, and productivity goals.
Reena SenGupta, Executive Director at RSGi, summed it up clearly. She said, “Investment in knowledge systems, architecture and AI is non-negotiable. Law firm strategy cannot be a wait and see, or be a second follower.” She’s right. Organizations winning today didn’t wait for certainty, they built stronger systems ahead of demand. In today’s environment, delayed investment just opens the door for competitors to move faster.
If knowledge maturity hasn’t been prioritized yet, now’s the moment. The tech is available. Your clients are watching. The internal upside is real, and measurable.
Recap
AI isn’t a magic switch. It doesn’t outperform without structure, and it won’t scale without trust. The difference between experimentation and impact comes down to how well your organization manages its knowledge, securely, consistently, and with purpose.
If you’re in the C-suite, this is your call to rethink the foundation. Investing in AI without fixing knowledge governance is like pushing acceleration with no steering. Alignment, clarity, and control are what enable AI to drive real outcomes, for your teams, your clients, and your bottom line.
The companies pulling ahead aren’t doing so because they deploy faster. They’re doing the groundwork others overlook. Strong information systems. Thoughtful governance. Clean processes that support confidence at scale. That’s what market leadership looks like now.
It’s not about being first. It’s about building right.
