Oakland

How to Improve Your Knowledge Management Strategy with Gen AI

Generative AI and large language models (LLMs) have captured the imagination of business. 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. 

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. It means our Data and AI consultants have developed a deep understanding of when generative AI should and shouldn’t be used.

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.

Below, we explore how generative AI can help enterprises scale this mountain and tap into its significant value.

What is the Document Mountain Knowledge Management Problem?

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:

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. 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 Knowledge Management

The powerful natural language processing capabilities of generative AI and Large Language Models let enterprises to unlock the insights hidden within their Document Mountains.

Generative AI excels at reading, interpreting, and summarising large volumes of free text, and it 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 be used to 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 are often accumulated over time. 

Effectively utilising this information remains a long-standing challenge. Best practices are not always 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 where businesses must navigate complex rules and policies. Front-line operatives and managers often struggle to access 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.

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

Hone Your Knowledge Management Strategy with Intelligent Agents

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. 

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