Success through knowledge maturity

As AI continues to dominate strategic conversations across boardrooms worldwide, a growing body of evidence suggests that the organisations that have been able to operationalise AI successfully are not those focused on the latest tools, but those that have achieved a high level of knowledge maturity.

 

According to a recent research report of more than 3,000 professionals across 26 countries, organisations with strong knowledge foundations behave, think, and operate fundamentally differently than their less mature peers, resulting in more confident, more advanced, and more effective use of AI. Less mature organisations will struggle to capture the full value of their AI investments — and that gap is quickly becoming a defining factor in future competitiveness.

 

 

Translating knowledge maturity into AI success

Mature organisations have invested in process, technology, and culture to manage their knowledge effectively. 

 

They treat knowledge as a strategic asset, not a byproduct, and have invested in creating knowledge ecosystems where information is structured, contextualised, and trusted – with centralised repositories, clear repeatable processes, and a culture that rewards the democratisation of knowledge and reuse of their best work.

 

This investment in knowledge maturity pays off when it comes to AI. The report highlights a sharp divide in how organisations experience AI depending on their knowledge maturity.

 

Mature organisations report smoother implementation – delaying projects rather than abandoning them – greater customer-driven adoption with fewer restrictions, and a broader range of use cases, from accelerating document review to supporting client-facing interactions.

 

The data puts a finer point on these trends. Some 55 percent of the most advanced organisations use AI to spot risks, errors, or anomalies in documents, compared with 34 percent of the least advanced. Businesses at higher maturity levels are also nearly twice as likely to integrate AI into client-facing tools: 46 percent versus 24 percent.

 

One of the most striking findings is how much customer needs shape AI usage among advanced organisations. While customers influence AI usage among 57 percent of businesses overall, that share ranges from just 20 percent among the least mature organisations to 74 percent among the most mature.

 

Critically, the most mature businesses are far less likely to face customer pressure to limit their use of AI, while the least mature report such requests far more often – suggesting a greater level of customer trust in more mature knowledge work organisations.

 

The bottom line? Knowledge maturity is pivotal – and fundamental to AI success.

 

 

Governance as a strategic enabler

Another emerging trend is the shifting role of governance. Far from being a bureaucratic burden, strong governance frameworks – including role‑based access and defined AI usage policies – are proving essential for building organisational trust.

 

Governance is especially critical given that a quarter of surveyed respondents in the report admit to allowing employees to use public AI tools without oversight, and more than a third of respondents have suffered policy violations. As AI comes under closer regulatory scrutiny, such risks will carry even greater consequences.

 

Fortunately, organisations with high knowledge maturity tend to have more disciplined governance practices, helping to neutralise these risks and enabling more confident use of AI. These organisations experience fewer incidents related to data leakage or policy violations and report stronger alignment with client expectations.

 

As AI accelerates, governance is quickly becoming a key differentiator that will propel those organisations most adept at institutionalising it in their workflows and knowledge ecosystem.

 

Maturity prepares organisations for the next era of AI-enabled work

The study also reveals a striking difference in how mature organisations view the future. Rather than focusing solely on task automation, they see AI as a catalyst for new roles, new business models and new forms of value creation.

 

The most mature organisations point to data quality and the ability of their organisation’s AI to learn and adapt in real time as the true drivers of future differentiation — underscoring the limits of LLMs and the primacy of underlying data.

 

By contrast, organisations at lower maturity levels seem to be thinking about how AI can fix existing problems and enhance existing roles versus solving net new problem or building new value streams. In this way, knowledge maturity level creates a fundamentally different posture toward the future – one that ultimately shapes what organisations can unlock next.

 

 

Knowledge maturity is the only path forward

As AI becomes more embedded in daily operations and long‑term planning, the knowledge maturity gap is expected to widen. Organisations that prioritise knowledge maturity today by establishing centralised knowledge systems, improving governance, aligning processes and investing in culture will be better prepared to scale AI strategically.

 

Those that do not may find themselves unable to truly leverage the full value of their AI investments, regardless of how aggressively they invest in AI tools. In effect, the performance of AI systems will be determined not by the sophistication of the model alone but by the quality of the ecosystem surrounding it.

 

For organisations seeking to move beyond experimentation and into meaningful transformation, the message is clear: AI success starts long before AI is deployed. It starts with knowledge maturity. 

 

---

 

Laura Wenzel is Global Marketing and Insights Director, iManage

 

Main image courtesy of iStockPhoto.com and Dilok Klaisataporn