ARC Advisory Group’s research on Knowledge Transfer (KT) reveals that the imperative for robust KT solutions and services has never been more critical across the global landscape of asset-intensive infrastructure and process industries. The market is being driven by the need to strategically address a new fundamental transformation in workforce dynamics: The promise of AI-driven operational excellence and autonomous manufacturing cannot be realized on a foundation where expert knowledge is siloed, messy, or walking out the door due to retirement. Leading industrial organizations see that effective knowledge transfer is now correctly understood as being critical to sustain and build the value associated with Industrial AI and Data Fabrics.
"Buyers must view knowledge transfer not merely as an IT function, but as a core component of a holistic organizational strategy. This strategy must be designed to enable scalable, un-siloed deployments of Artificial Intelligence of Things (AIoT) platforms. A culture that explicitly fosters Knowledge Management (KM) is the bedrock for true team agility, proactively closing critical workforce competency gaps and providing the essential human-centric layer to guarantee the usability and scalability of enterprise systems,” according to Peter Manos, Director of Research and key author of ARC's Knowledge Transfer and Enterprise Information Systems Market Research.
Knowledge Transfer Market Trends
In addition to providing detailed competitive market share data, the research also addresses key market trends as follows:
Shifting Solution Focus from Data Volume to Enterprise-Wide Unifying Intelligence
There is a clear market trend moving away from "one size fits all" data platform approaches. Instead, the focus is on achieving an enterprise-wide unifying organizational intelligence as a cultural and procedural norm. Solutions are being prioritized that allow organizations to serve diverse user personas with a tailored mix of industrial data fabrics, effectively democratizing data access and AI development to foster seamless IT/OT/ET and data science collaboration.
Increased Urgency to Address Critical Workforce Competency Gaps
The need for comprehensive knowledge transfer solutions is magnified by demographic and operational factors, including the imminent retirement of experienced employees and the increasing velocity of turnover among newer cohorts. This trend is establishing the market for solutions that capture, codify, and disseminate expertise as a vital, high-growth sector because it directly addresses the fundamental requirement for optimally leveraging people, process, and platforms in the face of ever-greater operational complexity.
The Convergence of KT, AIoT, and Data Fabrics Drives New Investment
The market is characterized by a strategic shift where the optimization of knowledge transfer emerges as the central component for the deployment of Artificial Intelligence of Things (AIoT) platforms built on optimized industrial data fabrics. This deep intertwining is driving significant investment, as global organizations recognize that excellence stems not only from technological infrastructure but also from the codification and dissemination of expertise to drive safe, scalable, and “boots-on-the-ground” transformation.
Leading Suppliers to the Knowledge Transfer Market Identified
In addition to providing specific market data and industry trends, this ARC market research also identifies and positions the leading suppliers to this market and provides and summarizes their relevant offerings. An alphabetical list of key suppliers covered in this analysis includes: AVEVA, Confluence 3, Docebo, KPMG, McKinsey & Company.
About the Knowledge Transfer Research
The Knowledge Transfer and Enterprise Information Systems research explores the current and future market performance and related technology and business trends and identifies leading technology suppliers. This new research is based on ARC’s industry-leading market research database, extensive primary and secondary research, and proprietary economic modelling techniques. The research includes competitive analysis, plus five-year market forecasts. The research segmentation includes World Region, Application and Asset Type, Revenue Category, Industry, Customer Tier, Sales Channel.
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