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Knowledge Management Research Report with APQC
Knowledge isn’t power when you can’t find it. Access APQC and Sinequa’s latest global market research
Organizations’ search problems intensified in 2020. Remote work put more strain on the technologies that connect employees to information, knowledge, and one another. Many newly remote workers scrambled to find important documents stowed away on personal desktops, on-premise servers, and dusty corners of the company intranet. At the same time, a flurry of emails and messages made the enterprise information landscape even more complex and harder to navigate.
APQC partnered with Sinequa, a leader in Intelligent Search, to survey more than 200 high-level knowledge management (KM) and IT leaders (director level and above) in large organizations (those with 10,000 or more employees) across North America and Europe. We specifically targeted people with a deep understanding of the current state of search inside their organizations and who were either responsible for or key stakeholders in knowledge and information management strategy.
Our research goals were to assess:
- current business priorities for information management,
- the solutions in place and under development, and
- how organizations define success for search and findability.
This paper outlines the major trends we see in terms of how organizations think about search, the investments they’re making, and the results they are seeing and expect to see.
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What the Research Covers
The State of Enterprise Search: What’s Working and What Isn’t
Survey respondents provided direct assessment of their organization’s current search capabilities where enterprise search is meeting expectations, where it is falling short, and how the gap between available information and findable information is affecting operational performance. The research identifies the patterns that distinguish organizations with high-performing knowledge environments from those where search dysfunction is a recognized operational problem.
Current Business Priorities for Information Management
The survey asked KM and IT leaders to rank their current priorities for information management investment, what they are being asked to solve, where executive attention is focused, and how those priorities have shifted as organizational knowledge environments have grown more complex. The priority data provides a benchmark for CKOs, CIOs, and KM leaders evaluating whether their own investment focus is aligned with peers.
Solutions in Place and Under Development
What technologies and approaches are large enterprises actually deploying for enterprise search and knowledge management and which ones are producing results? The research maps the technology investment landscape across the survey population, identifying the solution categories that correlate with improved findability outcomes and those that have not delivered on their promise.
How Organizations Define and Measure KM Success
One of the persistent challenges in knowledge management investment is the measurement problem: how do you demonstrate that KM improvements have produced business value? The research examines how senior KM and IT leaders are defining success for search and findability, which metrics they track, which ones they report to leadership, and the significant variation in measurement maturity across the survey population.
Why This Research Matters More in 2026 Than in 2020
The enterprises that participated in this survey were describing a knowledge access problem. The solution they were looking for, AI-powered knowledge management that makes enterprise information reliably findable, synthesizable, and actionable at scale, now exists. Sinequa’s enterprise AI platform, including AI-powered search, advanced RAG, and AI agents that operate on governed enterprise knowledge, is the architectural answer to the findability and KM effectiveness gaps this research documents.
The APQC findings provide the independent evidence base for the business case: if your organization’s KM and search challenges match the patterns this research identifies, you are operating in the mainstream of large enterprise experience and the AI-powered solutions that have closed those gaps at Alstom, Crédit Agricole, UCB, and TotalEnergies are available at your scale.
Who Should Download This Report
- Chief Knowledge Officers and KM Program Leaders benchmarking their organization’s search and knowledge management maturity against peer enterprises and building the investment case for AI-powered KM improvements
- CIOs and IT Directors responsible for enterprise search infrastructure and evaluating the current state of KM technology investment across large organizations
- Digital Transformation and Innovation Leaders building the strategic case for AI-powered knowledge management and needing independent research to anchor that case beyond vendor claims
- Heads of HR, L&D, and Organizational Effectiveness responsible for knowledge retention, expertise location, and the workforce productivity implications of poor enterprise findability
Frequently Asked Question
The research surveyed 200+ director-level and above KM and IT leaders at enterprises with 10,000 or more employees across North America and Europe. The study examined three core dimensions: the current business priorities for information management investment among large enterprises, the search and KM solutions deployed and under development, and how organizations define and measure success for search and findability. Findings documented the persistent gap between enterprise information availability and employee findability, the disconnect between what organizations know and what their people can actually access, and identified the technology and process factors that correlate with stronger knowledge management outcomes.
The operational context for the research — remote work disruption exposing enterprise information fragmentation — has changed since 2020. The underlying KM and search challenges it documents have not. Enterprise information environments have grown significantly more complex since 2020: more data sources, more unstructured content, more AI tools that require high-quality knowledge retrieval to function reliably, and greater competitive pressure to make institutional knowledge a strategic asset. The findability gap, the KM investment uncertainty, and the measurement challenges that survey respondents described in 2020 are more operationally consequential in 2026 than they were then — because the cost of poor knowledge management now includes degraded AI performance on top of the workforce productivity impact. The research provides the foundational evidence base for enterprise KM investment decisions that AI-powered search and knowledge management solutions are now positioned to address.
Enterprise knowledge management and AI-powered search are not separate disciplines that happen to overlap, they are co-dependent capabilities. KM programs that invest in content organization, taxonomy development, and information governance without addressing search and retrieval findability are building knowledge assets that employees cannot effectively access. AI-powered search without a governed, well-structured knowledge environment produces hallucinations, incomplete synthesis, and unreliable outputs because the quality of AI retrieval depends directly on the quality and accessibility of the underlying knowledge. The APQC research documents the search and findability gap from the KM practitioner perspective. Sinequa’s enterprise AI platform, AI-powered search, advanced RAG, and AI agents operating on governed enterprise knowledge addresses that gap architecturally, connecting the KM investment to the AI capability layer that makes knowledge accessible, actionable, and continuously valuable.
The APQC research explicitly examines how organizations define and measure success for search and findability, a question that KM practitioners consistently identify as one of the most challenging in the discipline. The most common measurement approaches include time-based metrics (average time employees spend searching for information, time-to-answer for common knowledge queries), adoption metrics (search usage rates, knowledge base engagement), and satisfaction metrics (employee ratings of search effectiveness and findability). The organizations with the most mature KM measurement programs connect these operational metrics to business outcomes: engineering cycle time reduction, customer resolution time improvement, compliance documentation completeness, and training time reduction for new employees.
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