How to Improve Your Knowledge Management Strategy with Gen AI

Generative AI and large language models (LLMs) have captured the imagination of businesses worldwide. Their immense potential to drive transformative commercial and operational gains promises to revolutionise how organisations operate. From hyper-personalised customer experiences to streamlined operations, they are brimming with potential. One key focus area is the use of AI in knowledge management.

Yet, for enterprises to experience the benefits, finding the right use cases is critical. We have extensive experience delivering custom AI solutions for some of the UK’s leading businesses. This means our Data and AI consultants have developed a deep understanding of when generative AI should and shouldn’t be used, particularly in knowledge management.

Below, we explore how generative AI can help enterprises scale the knowledge management mountain, overcome the tidal waves of data modern businesses are lashed by, and tap into its significant value.

What is AI in Knowledge Management?

Knowledge management – the process of gathering, organising, and distributing information within an organisation – incorporates employee knowledge, processes, records, feedback, product information, and so much more. When generative AI is applied to knowledge management systems and processes, significant portions are essentially automated.

The result is knowledge management that automatically catalogues and categorises information. Users can access information far quicker and easier. With every piece of data added, query submitted, and piece of information served, the system learns. It becomes smarter, faster, and more efficient. It ultimately becomes tailored to the specific nature of the business using it, driving knowledge retention, employee collaboration, customer service, and performance.

According to IBM research, 38% of enterprise-scale companies (those with over 1,000 employees) reported using generative AI in their business in 2024, and another 42% were exploring the technology. Knowledge management was stated as a centre of attention, but with competition for better practices ramping up, what use cases can companies new to the technology focus on?

Key Generative AI Knowledge Management Use Cases & Benefits: Document Mountain And Information Tsunami

Generative AI has emerged as a game-changing tool for addressing two of the biggest challenges in modern knowledge management: the Document Mountain and the Information Tsunami. These two problems, while related to the overwhelming amount of data businesses generate, present distinct challenges that generative AI is uniquely suited to solve.

What is the Document Mountain Knowledge Management Problem?

One of the most prevalent and complex challenges we’ve encountered is what we call the Document Mountain – vast repositories of knowledge that lack management and are seemingly unmanageable.  Solving this challenge with generative AI can unlock the value held in data such as ‘lessons learned’ documents to proactively generate insights that can hone your business’s operations and prove genuinely transformative.

In many large enterprises, effective knowledge management is a critical yet elusive goal. It’s a classic “big problem” that affects many business areas but is notoriously difficult to solve. 

Health and safety, risk and compliance, procurement, bid management, quality control, and project management: all key functions , but all heavily reliant on the efficient sharing of knowledge. Without it, mistakes are repeated, risks go unnoticed, and opportunities slip away.

These failures in knowledge management are all too common in large organisations. Learning loops are often broken, leading to ineffective knowledge transfer and, ultimately, costly errors. But why is this the case? 

For most businesses, the challenge isn’t a lack of documentation. On the contrary, many organisations have invested significant resources in creating detailed records of their observations, learnings, and processes. The problem lies in the overwhelming volume of documentation they now generate – the Document Mountain.

Examples of these documents include:

• Safety incident reports
• Internal or external policies and guidelines
• Project review “lessons learned” documentation
• Customer correspondence and complaints
• Invitations to Tender (ITTs), Requests for Proposals (RfPs), bid responses, and feedback
• Non-compliance reports
• Project documentation, such as status reports
• Maintenance manuals and reports.

These documents are often stored in vast, fragmented libraries that are difficult to navigate and access. The insights users need are in there, but finding them within the available time can seem almost impossible. And to make matters worse, in some cases, it’s a mountain range, as documentation is scattered across different repositories and storage systems. 

The result? 82% of organisations report their data is siloed and 24% don’t trust it. Their employees then spend, on average, 2 hours a day searching for the information they need.

What makes this problem particularly challenging is the nature of the documentation itself. Much of it is unstructured free text, which traditional search methods or analytical techniques struggle with if documents lack metadata.

As a result, the valuable insights contained within these documents remain trapped and inaccessible to those who need them. 

The scale of this problem is truly staggering. According to International Data Corporation analysis, 80% of the world’s data (140 zettabytes in all) will be unstructured by 2025. 

The impact of these broken learning loops can be just as enormous. Project overruns, safety incidents, regulatory failures, and lost business opportunities – all of which can have significant reputational and financial consequences. A recent analysis cited by Fast Company found that, in the US, Fortune 500 companies lose around $31.5 billion each year from the effects of their knowledge siloes.

Generative AI: the Solution to the Document Mountain

The powerful natural language processing capabilities of generative AI and large language models allow enterprises to unlock the insights hidden within their Document Mountains.

Generative AI excels at reading, interpreting, and summarising large volumes of free text and can support easy, natural-language interactions with users. 

Imagine being able to ask questions of your vast repositories of unstructured data and having the AI respond with relevant, actionable insights. What would that data reveal if it could talk back?

Scaling the Knowledge Management Mountain: Key Use Cases

Let’s explore some specific use cases where generative AI can help enterprises solve the problem for good.

1. Customer Service

Delivering exceptional customer service requires having the right information at your fingertips. This can be particularly challenging in sectors like banking or insurance, where customers expect quick answers to complex questions about their terms and conditions or coverage details.

Generative AI can create virtual assistants that assist customer service agents in real time, providing them with accurate and relevant information from vast stores of documents. By augmenting customer-facing teams with AI-powered tools, organisations can significantly improve response times and service quality.

2. Complaint Management

Handling customer complaints and feedback is another area where Generative AI can shine. 

Complaints and feedback often arrive as unstructured data at high velocity, making them difficult to manage and analyse effectively. They also often need to be read and compared with complex, nuanced policies and regulations. As such, human teams are overwhelmed by the sheer volume of work, and simple, rules-based complaint-handling software often struggles with the complexity of the task.

Generative AI is well-suited to reading, summarising, and categorising this information quickly. This capability allows organisations to handle new complaints at scale while also extracting valuable insights from the broader dataset. By equipping generative AI with relevant policies and operating procedures, companies can even build AI agents that not only process complaints but also respond appropriately.

3. Bid Management and Bid Development

In industries where companies bid on major contracts, vast libraries of bid responses, case studies, credentials, service specifications, and similar documents often accumulate over time. 

Effectively utilising this information remains a long-standing challenge. Best practices are only sometimes shared, and content is frequently duplicated or recreated from scratch. Additionally, bid assessment feedback is often under-analysed, leading to missed opportunities for improving future responses or service design.

Generative AI can assist by analysing and summarising previous submissions, highlighting best-practice examples for reuse, and speeding up the bid development process. By improving the quality of tender responses, AI can help organisations win more business while reducing the time and effort required to prepare bids. 

In large organisations competing for multi-million-pound tenders, even small gains in bid management effectiveness can reap huge ROI on the investment in Gen AI solutions.

4. Lessons Learned

Effective knowledge management is a classic challenge for many enterprises, regardless of whether they have a dedicated Knowledge Management team. Businesses often accumulate knowledge in the form of “lessons learned” documents, project updates, incident reports, guidelines, policies, and more. However, the sheer volume makes it impossible for individuals to read and consume all the available information.

Generative AI can unlock the insights within these documents, allowing users to query them through a natural language interface. By providing quality-controlled responses, AI ensures that only valid and relevant lessons are shared, helping organisations to learn from past experiences and avoid repeating mistakes. Learn how we provided this for Network Rail.

It’s partly for this reason that, in IDC’s 2022 Knowledge Management Strategies Survey, improved business execution was the top benefit experienced by businesses that had implemented knowledge management systems, driving significant AI return on investment.

5. Health and Safety

Health and safety is a critical area in which businesses must navigate complex rules and policies. Front-line operatives and managers often need help accessing relevant information when they need it most. Generative AI can be used to create a virtual “Safety Assistant” that makes health and safety information more accessible and actionable.

Additionally, compliance monitoring is another area where AI can make a significant impact. By scanning project documents, job reports, incident logs, and other relevant materials, generative AI can identify early signs of non-compliance or potential risks, allowing H&S teams to address issues before they escalate into serious incidents.

There are countless use cases for generative AI, but it’s crucial you tailor yours to your challenges. Learn more about how to find your specific use cases.

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What is the Information Tsunami Knowledge Management Problem?

Many large businesses struggle with knowledge management, and it’s a significant challenge that can disrupt critical processes if not addressed effectively. Traditionally, when we think of knowledge management, we often focus on the volume of information – the Document Mountain detailed above. But the issue goes beyond just managing large volumes of data. The speed at which it comes in can create problems of its own: this is the Information Tsunami.

In a world where speed is essential, information doesn’t just trickle in slowly like pages in a library. It can come in fast and furious, and without the right tools, it becomes overwhelming. This is where many businesses get stuck. So, what is the Information Tsunami, and how do we solve it?

The Cause: Real-Time Monitoring Systems

Many businesses today have invested heavily in real-time monitoring systems to keep track of their critical processes and operations. These systems are made possible by more affordable software, sensors, and IoT technologies. Examples include:

• Telemetry systems for physical networks (pipes, vehicles, or assets)
• Environmental sensors (temperature, traffic, or footfall)
• Live customer chat and feedback monitoring
• Real-time supply chain and product management systems (inventory, product quality, manufacturing processes)
• Smart products using IoT technology that send real-time performance data and alerts.

These monitoring systems create high volumes of data at high velocity. This information needs a fast response if it’s to be used effectively. Unlike ‘Document Mountain’, this is about responding quickly and decisively. In front-line, fast-paced operational or commercial environments, simply gathering the information and reflecting on it later is not an option.

By collecting real-time data, businesses can prevent problems or address them quickly. This sounds great in theory, but in practice, it often leads to information overload.

The Problem: A Flood of Data

What usually happens is that control teams are quickly overwhelmed by a flood of incoming information from these monitoring systems. Imagine being bombarded by constant alerts – many of which are minor or irrelevant – while trying to spot the critical ones that need immediate attention. Necessary signals get lost in the noise.

The result is often a failure to act on vital information, leading to significant operational or financial costs. Businesses that have invested heavily in monitoring systems may not see the return on investment they were hoping for because they can’t effectively manage the sheer volume and speed of incoming data. It’s like trying to find a needle in a haystack while the hay keeps piling up around you.

Previous Solutions and Their Limitations

In response, many companies have tried to solve the Information Tsunami using various technologies. Some common approaches we come across include:

• Rules-based prioritisation of incoming alerts
• Automated event-handling systems
• Anomaly detection techniques
• Machine learning models designed to filter out the noise.

While these solutions work in simple scenarios, they often fall short in more complex environments. Here’s why:

1. Rules-based systems struggle with the complexity of handling real-world incidents, which often requires more nuance and flexibility than simple if-then rules can provide.
2. Event-handling policies are frequently stored as free text documents, making them difficult to interpret by statistical or machine learning models or translate into simple ‘if-then’ rules.
3. These systems often can’t dynamically respond to specific user queries or instructions or be tailored to specific users, limiting their usefulness in fast-paced control and customer service environments.
4. Effective alert management requires context from secondary data sources like free text maintenance reports and querying supporting data stores, many of which are unstructured. Existing systems can’t easily retrieve or analyse this data.

Because of these limitations, these solutions often generate too many exceptions or miss important alerts. They’re complicated and time-consuming to use. Or generate incorrect actions. As a result, control teams are still overwhelmed, and trust in these systems erodes.

Taming the Information Tsunami with Generative AI

Generative AI offers a new approach to solving the Information Tsunami. While generative AI is often associated with processing large amounts of static data, such as the Document Mountain, its ability to handle real-time high-velocity data makes it uniquely suited to managing the Information Tsunami.

The natural language processing capabilities of Large Language Models (LLMs) allow them to respond dynamically to complex, real-time data streams. When combined with machine learning and other analytical techniques, LLMs can interpret data in a more human-like way. This flexibility allows AI systems to interact with users through natural language, pulling in information from various structured or unstructured sources to provide richer, more relevant responses.

This makes generative AI a much more powerful tool for handling the Information Tsunami, allowing human teams to focus on critical issues that require immediate attention.

Use Cases for Generative AI in Managing the Information Tsunami

Let’s explore a few real-world examples of how generative AI can be used to manage the Information Tsunami:

1. Quality Management in Supply Chains

Even minor quality issues in a supply chain can cause significant disruptions if not addressed quickly. Generative AI can help by monitoring these issues in real time, assessing their downstream impacts, and proactively alerting the appropriate teams. The AI can analyse complex product documentation and organisational structures to ensure the right information reaches the right people before the problem escalates.

2. Operational Control

In operational environments, AI can act as an alert triage manager. It can filter incoming alerts based on protocols, automatically handling minor issues and escalating more critical ones to human operatives. This ensures that teams aren’t overwhelmed by unnecessary data and can focus on high-priority cases. Additionally, the AI can advise on proper response procedures by referencing relevant policies or regulations.

3. Data Governance

In modern enterprises, Agile and Dev Ops practices mean data and systems are constantly evolving. As a result, tracking changes and maintaining all of these is so time-consuming as to be nearly impossible. While automated data quality monitoring has existed for a while, generative AI takes it further by updating critical data governance documentation, such as glossaries, lineage maps, and catalogues, in real time. 

This automation augments human teams relieving them from time-consuming tasks. It ensures that documentation remains current, even as data systems evolve, helping flag non-compliance and errors – a truly powerful capability.

4. Technical Oversight

Maintaining compliance with security and architectural standards is a massive challenge in organisations with hundreds of software and data engineering teams. AI can assist by reviewing design submissions for compliance and flagging exceptions. Over time, this increases adherence to standards without requiring significant resources from central oversight teams.

See how we revolutionised knowledge management for Network Rail with generative AI.

Hone Your Knowledge Management Strategy with Intelligent Agents

Generative AI holds enormous potential to tackle both the Document Mountain and the Information Tsunami. When implemented effectively, it can transform knowledge management, streamline operations, and unlock the value hidden in your data.

While generative AI is a powerful tool, we believe that the best way to solve these complex use cases is through the deployment of intelligent agents. These AI-driven agents are designed to tackle specific challenges within the enterprise, combining the power of generative AI with other advanced technologies to deliver even greater value.

Check out our guide for more insights into why we recommend intelligent agents as a solution to these kinds of problems.

Make Your Data Pull Its Weight With Oakland’s Generative AI Experts

Document Mountain is a prime example of a problem that generative AI is uniquely suited to solve. By leveraging AI to unlock the insights trapped in vast repositories of unstructured data, businesses can drive operational efficiencies, enhance decision-making, and ultimately achieve better outcomes.

In future blogs, we’ll explore other common enterprise use cases for generative AI. But for now, remember: the key to success with AI lies in choosing the right problems to solve. And when it comes to scaling the Document Mountain, generative AI is the solution you’ve been waiting for.

Learn more about our approach to AI, then contact our experts to learn how their extensive experience working with enterprises can be leveraged to benefit your business.