Organisations today generate more information than ever before, making proactive knowledge management and a clear knowledge management strategy an essential business requirement. But with knowledge sources spread across text-based information, data, tools, frameworks, processes and more, the scope of assets to manage, sort and maintain is neverending.
A knowledge hub or knowledge base – whatever you choose to call it – isn’t new but in today’s context, where organisations are overwhelmed with the amount of information they generate, they’ve become increasingly important.
In essence, a knowledge base acts as an organisation’s resource library where users can go to access the information they need to get a job done. That job might involve:
In this context, knowledge hubs are often internally or externally focused – serving employees, or an external audience. In some instances, a knowledge hub may need to serve both, making access levels and permissions a key consideration.
Most of us are familiar in our working life with knowledge hubs of some kind – from the simple Excel file directory someone took the initiative to create, to the more complex company intranet or wiki.
The problem is, from a user perspective, knowledge hubs frequently suck.
The need to store and deliver a huge amount of information makes knowledge bases inherently difficult for organisations to manage and maintain long term. This inevitably creates issues for users who need to find their way through ever more complex navigations, only to find outdated information. And the result? A company invests significantly in knowledge management, only to hear a regular lament from users – namely, that they can never find what they need.
One of the key challenges we see with the organisations we work with is that their underpinning knowledge management strategy often struggles to take into account the way artificial intelligence (AI) and machine learning (ML) currently is and will radically transform our concept of knowledge management in the near future.
Rather than static knowledge base software where managers upload content and periodically refresh it, and a user searches and browses for what they need, AI is flipping the old paradigms of information storage and retrieval on their head. Instead, AI can be used to deliver accurate knowledge in more intuitive ways. It can deliver up-to-date information where it’s needed, when it’s needed – based on interactions and data – and in turn create more personalised and helpful experiences for end users.
Today, for example, AI is already being used by organisations to deliver knowledge experiences that are:
One method for delivering more accurate and relevant information is using a decentralised approach to knowledge-sourcing – something that’s hard to do today when relying on humans, but which AI makes much more feasible in the future.
Instead of a centralised, monolithic repository of information, decentralisation uses AI to intelligently leverage up-to-date knowledge from multiple sources. These could be several repositories across one organisation, or multiple external sources. AI can then tap into this knowledge network as needed, and synthesise relevant pieces of information into accurate, context-specific content – delivered seamlessly to end users, shaped by their preferences and behaviours.
When it comes to AI, this is really just the beginning. Consider how Google today now provides an AI summary of your search – synthesising information and serving up a pithy, short overview of the subject. Imagine how an AI-enabled knowledge platform will learn over time, delivering ever more proactive knowledge support to users. And think of the possibilities AI presents in terms of spotting inaccuracies, managing information updates and creating smart review workflows for knowledge managers.
If you have a traditional digital knowledge hub today – or you’re still struggling in the realms of a cloud-based folder structure where knowledge frequently goes to die – it’s time to reimagine what a knowledge base can be. Stop thinking of them as a centralised location with static content that people navigate, and future-proof your knowledge management strategy by applying an AI lens.
Below, we share three straightforward considerations to apply when building a future-ready knowledge base.
It’s really common for knowledge management to prioritise, and get lost in, the weeds of auditing and organising the information you’ve got today. But while this is vital work, it’s equally vital to think beyond the knowledge itself to the value you’re trying to create with it. That means building a comprehensive understanding of your intended audience, so that you know what actions you’re trying to inspire and enable. With that insight, you can then design a knowledge service and experience that is genuinely useful and empowers people to get further, faster.
The first question to ask is always:
“Who is going to use this and what does knowledge need to help them accomplish?”
Answering this question thoroughly with user research will uncover the real challenge you face. It will help you replace assumptions about how your knowledge base should be organised and work, with validated insights about what your audience really needs.
Having invested time in understanding the value you want to create and the actions you want to inspire, it’s time to identify the right technology and methodologies to deliver your knowledge in faster, easier and better ways.
This is where we turn to machine learning and AI models for inspiration – and we can already see interesting new platforms taking shape:
Casetext in the legal industry assists attorneys by offering proactive insights into case law, statutes, and legal precedents. By processing thousands of legal documents and cases, Casetext can alert attorneys to relevant case law and even suggest arguments based on the specifics of their case.
Carnegie Learning is bridging the gap in classrooms, by offering personalised learning experiences based on a student’s learning process. It recommends specific models, exercises or even alternative explanations if a student is struggling with a particular concept.
These examples of AI-enabled knowledge delivery ultimately showcase the difference between a focus on static content in a traditional knowledge hub, and adaptable, dynamic knowledge delivery that responds to user needs.
With the help of AI, users no longer need to know exactly what they’re looking for and where to look. Instead, the technology does the heavy lifting and provides relevant information faster – and often of a higher quality due to AI’s ability to synthesise information from several decentralised sources.
All of this potential leads to the big question:
“How can you use technology to make your knowledge faster, easier, or better at helping people achieve their goals?”
Answering this question could produce an expansive symphony of ideas and possibilities – with no singular solution. It’s also where we would recommend running a series of short, cost-effective experiments to rapidly prioritise and validate a technology or idea to progress with.
Now that you understand your audience and have a plan for delivering your information, you need to figure out the best way to bring your knowledge base to life. We encourage every client to consider this step really carefully. As with every digital product or service, this means deciding whether to:
Building a bespoke product from scratch requires larger upfront investment and will take longer to create. This is not always the most commercially viable or practical option for an organisation, but it has a lot of benefits when it comes to the experience you offer and the control you have – and the possibilities can be as grand as your budget and development capacity allows.
Buying access to a pre-existing SaaS knowledge base can be a more cost-effective option, and allows you to launch your knowledge hub faster. But it often comes with more restrictions that are important to consider. There will be limitations on what features you'll have access to, and the experience you can provide to your target audience. This is particularly true when it comes to AI where you will be limited by the operating company’s roadmap and development capability. The platform may also become defunct, or be sold – making contingency planning important.
Modifying several existing technology solutions is a middle ground and an option worth considering. This will involve setting up an environment where you can customise your own modular building blocks of technologies and functionality. The costs may be less than a bespoke product and more than a third-party SaaS platform, but you’ll have a good balance of control and cost over the long-term.
The big question that should drive this decision:
“How do you create a knowledge base that will continue to serve you and your users 2, 5, 10 years down the track?”
Answering this will require reflecting on your resourcing, budget, project goals, and organisational objectives and is a crucial consideration in deciding whether to build, buy or modify your knowledge solution.
Ultimately, we believe AI should be at the heart of every knowledge management strategy. Leveraging AI to create intuitive knowledge experiences means organisations can transform knowledge bases into powerful tools that resolve existing user frustrations and empower users to more effectively self-serve.
To do this, you need to focus on real user-driven insights, incorporate AI and a decentralised approach in your product considerations, and choose a development option that has the flexibility and longevity for your unique circumstances.
Aligning on product strategy and technical implementation are key barriers that often stop organisations from reaching their innovation goals. If you need assistance shaping your knowledge base strategy or bringing your knowledge hub to life, take a look at our services or get in touch.