As knowledge professionals, we’ve long wrestled with the question: is knowledge something you manage with tools, or something you develop through processes?
The short answer is: both. However, the nuance matters more than ever.
In today's workplace, knowledge is not merely an asset to be stored, it’s a strategic process that underpins decision-making, innovation, and organizational learning. And while tools play a critical role in enabling this process, they’re not the solution by themselves. In this blog we explore the importance of knowledge automation.
In a knowledge economy where speed, accuracy, and innovation define competitive edge, the way we manage and use knowledge has to evolve. Manual, fragmented knowledge processes are no longer sustainable. Knowledge workers spend up to 41% of their day on low-value tasks that could be automated. Meanwhile, AI and regulatory demands are pushing firms to govern knowledge with more structure and intelligence than ever before.
Knowledge automation isn't just a productivity play, it's a strategic imperative.
Definition
Knowledge automation refers to the use of AI, machine learning, and digital workflows to manage and apply knowledge with minimal human input.
Knowledge automation streamlines processes that traditionally required extensive human intervention. By leveraging advanced technologies such as AI and machine learning, organizations can now automate routine, repetitive tasks, freeing up valuable time for professionals to focus on more complex and impactful work.
As Forrester puts it: “Knowledge work automation is the future of employee efficiency”.
For instance, automating tasks like document reviews and research can significantly reduce the time and cost associated with these activities. This not only boosts productivity but also improves accuracy and reduces the risk of human error. As a result, organizations can deliver higher quality services to their clients, enhancing client satisfaction and loyalty.
Intelligent vs standard automation
Standard automation (e.g. using Microsoft Power Automate) handle routine, rule-based tasks. Intelligent automation, on the other hand, applies cognitive tools such as natural language processing (NLP), semantic search, and AI-powered summarization to high-value knowledge work.
Time savings and ROI
For knowledge workers, automating just document handling and content retrieval can unlock hours per week for higher-value activities. Forrester found that firms adopting knowledge work automation report improved productivity (52%), better customer experience (48%), and enhanced compliance (47%). ROI isn't just in cost savings, it's in speed, quality, and reduced risk.
Effective knowledge automation involves several key components:
Most organizations struggle to capture knowledge from informal sources such as conversations, chats, and lived experience. Automation should extract insights from emails, meetings, and messaging tools, turning them into usable knowledge assets alongside traditional documents and reports.
Manual tagging is slow, inconsistent, and unreliable. This is where automation is possible and essential, so that knowledge becomes searchable, structured, and accessible at scale.
Speed is everything. Knowledge automation must support contextual, intelligent search, surfacing relevant content based on role, location, or task. This could include personalized results, semantic search, and auto-suggested content.
With AI in the mix, accuracy is paramount. A modern knowledge system should identify authoritative “gold standard” content and maintain traceability from source to output—especially important for regulatory compliance (e.g., EU AI Act, ISO 42001).
Knowledge should meet users where they are—inside Microsoft Teams, Outlook, CRM tools, and project workspaces. This requires tight integration and automation that ensures the right knowledge appears when it’s needed most.
Knowledge automation should also include the tracking of usage, contributions, gaps, and if possible, reuse rates. Data-driven insights allows leaders to iterate quickly and demonstrate ROI on their knowledge strategy, as well as for continuous improvement.
When implementing knowledge automation, it is important to follow best practices to ensure success. Start by identifying the tasks that are most suitable for automation. These are typically repetitive tasks that require minimal human judgment, such as document review and data entry.
Next, choose the right technology that fits your organization's needs. Look for solutions that offer seamless integration with your existing systems and have a user-friendly interface. It is also important to train your staff on how to use these new tools effectively. Providing ongoing support and training will help ensure that your team can fully leverage the benefits of knowledge automation.
Despite its many benefits, implementing knowledge automation in the law industry can come with challenges. One common challenge is resistance to change. Legal professionals may be hesitant to adopt new technologies, fearing that it could replace their jobs or disrupt their workflow.
To overcome this, it is important to communicate the benefits of knowledge automation clearly. Highlight how it can enhance their work by taking over mundane tasks, allowing them to focus on more strategic and rewarding activities. Additionally, involving employees in the implementation process and providing adequate training can help ease the transition and increase buy-in.
Knowledge automation will play an increasingly important role in helping organizations stay competitive. By adopting these technologies, organizations can future-proof their operations and stay ahead of the curve.
Continuous investment in advanced technologies will enable organizations to automate even more complex tasks in the future.
Platforms like Atlas lead the way by blending intelligent automation with human-centric design.
Rather than relying on heavy curation or static repositories, Atlas:
Turns pages, news, conversations, and documents into structured, contextual knowledge
Integrates with Microsoft 365 to deliver knowledge where work happens
Automatically applies metadata and validates authoritative sources
Powers Retrieval-Augmented Generation (RAG) pipelines to feed AI with trusted insights
Supports compliance and frameworks like the EU AI Act and ISO 42001
In short, Atlas automates the manual parts of knowledge management, so that your people can focus on using knowledge, not managing it.
Forrester, Unlocking Efficiency – The Inevitable Rise of Knowledge Work Automation.
Atlas by ClearPeople, Knowledge Productivity 2.0 eBook.
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