Institutional knowledge is one of the key pillars of any modern enterprise. A business must have a reliable, easy-to-access source of information to fuel processes ranging from product development to customer service to regulatory compliance. Knowledge management impacts virtually every aspect of a business.
However, as enterprises grow and their knowledge base grows alongside, it can become increasingly difficult for workers to find the information they need, when they need it. Or, worse, they may be sorting through outdated sources, or conflicting information, with no clear guidance.
Next-gen AI systems, powered by large language models and extensive training, present a way forward: “Smart” AI agents capable of processing, collating, and providing reliable context-aware information on demand.
Robust AI-focused knowledge management makes this possible.
Key Takeaways
- AI agents can provide huge productivity boosts – but only with reliable data to train on.
- A well-managed knowledge base is the foundation of reliable AI.
- Any data put into the KB must be vetted and verified for accuracy, because AI is poor at distinguishing bad data.
- Properly setting up an AI system will be time-consuming at first, but yields excellent results down the line.
How Are AI Agents Reshaping Enterprise Knowledge Management?
In the past, enterprise knowledge management was often scattershot. Key information could be scattered across an entire organization, buried in emails, PDFs, videos, department-specific databases, and more. If they were lucky, employees had some sort of manually-searchable knowledge base or wiki, but it rarely contained all the current information they needed.
The problems this could cause were numerous, including:
- Customer support staff giving incorrect information or requiring multiple calls to resolve issues – impacting customer satisfaction.
- Sales staff misunderstanding the product’s capabilities.
- Different departments or offices working off different SOPs.
- Difficulty ensuring regulatory compliance.
- Information which changed frequently often fell out-of-date without constant oversight.
- Reduced worker productivity due to lengthy processes to get necessary information.
- Institutional knowledge from experienced workers was easily lost if they left the operation.
- Training materials were often out-of-date, driving up the time and costs of onboarding.
- Impaired future forecasting due to a lack of up-to-date verified data.
New AI systems, backed up by enterprise knowledge management focused on creating a knowledge base for AI agents, offer solutions to these issues.
Well-trained AI agents are capable of taking in virtually all verifiable information from across an operation, including elements that were traditionally difficult to compile such as e-mails and PDFs. This information is vectorized (turned into mathematical data), analyzed, contextualized, and meta-tagged for easier search and retrieval. AI agents can even utilize Retrieval-Augmented Generation (RAG) processes to retrieve ‘new’ information from outside the central database, when necessary.
The result is an AI capable of behaving more like a research assistant than a simple web search. Workers can ask the AI agents questions in natural language, and receive natural responses. Once trained, the AI does all the hard work keeping up with informational changes, providing your workforce with accurate information and context-aware insights on demand.
The key to all of this an enterprise knowledge base focused on empowering those AI agents.
How Do Enterprise Knowledge Bases Power AI Agents?
A properly set-up knowledge base essentially becomes the ‘brain’ of your AI system. It’s where all the AI’s data and – for lack of a better term – “memories” reside. The KB helps guide the AI and ensure that it remains on-track whenever given a task.
This happens in a few different ways:
1. Centralized knowledge
AIs typically give the most reliable results when they have a single vetted repository of knowledge to pull from, or at most a few well-managed sources. A knowledge base for AI agents handles this, especially when it’s overseen by humans who periodically audit it for accuracy and standards compliance.
2. Creating a ‘memory bank’
One core issue with current LLM AI systems is that they have no memories to speak of. They can have a stack of tokens that give them limited recall and context in the moment, but as soon as an interaction ends or they’re told “disregard previous inputs” those tokens vanish. The knowledge base therefore becomes a sort of memory system for them, by preserving the things they need to remember long-term.
For example: an AI system assisting a customer with a difficult tech problem requiring multiple contacts will have no direct memory of previous interactions, from session to session. However, that customer’s call logs and tickets are preserved in the KB, allowing the AI agent to rebuild the context for each new interaction. So the AI can pick up where it left off.
3. Building in safety procedures
The other major function of a KB is to create boundaries on the AI’s behavior, preventing it from going rogue or hallucinating wildly. A well-managed AI agent KB includes security policies, standards for accuracy, and other guardrails that keep the AI on-task.
In addition, keep in mind that the KB is also accessible to human workers. So they benefit as well, even if they aren’t utilizing an AI agent. Everyone works more productively with a single trusted source for enterprise knowledge.
Why Do AI Agents Need a Trusted Knowledge Source?
One of the most basic facts of current Machine Learning (ML) systems is that an AI is only as smart and reliable as the information it’s fed. Typical complaints about AI such as ‘hallucinations’ and other incorrect responses ultimately come from the AI being trained on incorrect or contradictory data.
Or, as it’s sometimes shorthanded: “Garbage In, Garbage Out.” Give a Machine Learning AI bad data, and it will give bad answers.
So it’s absolutely vital that your KB be vetted for accuracy, along with being metatagged and cross-linked especially before beginning initial LLM training. Once the AI gets up to speed, it will be able to spot contradictions in the data and alert human workers, but at first it will have no such ability. Like a small child, it will take everything it learns at face value, and try to sort out contradictions on its own. This usually leads to poor results.
Aside from ‘hallucinations,’ the other challenge to overcome in AI responses is repeatability. If an AI is asked the same question five times, it should give the same basic answer five times. If your AI starts giving different answers to the same prompt, that’s usually a sign that it’s been given conflicting information.
In short, spending time at the beginning ensuring your knowledge sources are as accurate and consistent as possible, with good tagging and cross-referencing, will lead to much better long-term AI reliability.
What Are the Key Requirements for a Knowledge Base That Supports AI Agents?
As a checklist, these are the most important pre-requisites and features to look for in an AI knowledge base:
- A properly vetted, verified, and meta-tagged data source.
- Semantic search (“natural language”) abilities
- RAG – Retrieval-Augmented Generation, allowing the AI to autonomously seek information from trusted sources.
- A system for approving and updating KB information in realtime.
- Robust security and permissions controls.
- Contextual understanding and response personalization.
- Hardware or cloud systems capable of powering the software.
Frequently Asked Questions
Retrieval-Augmented Generation (RAG) is a way of going beyond the traditional KB. The AI is capable of autonomously seeking and collating knowledge from trusted sources. RAG can also allow an AI agent to access outside sources via interfaces such asMCP. This allows the AI to answer a broader range of questions, or seek information not currently in the KB.
For example, a tech support worker facing a difficult hardware/software conflict might have their AI search the manufacturer of a particular component for detailed technical documentation.
A well-organized and vetted knowledge base is the foundation of accurate AI agent behavior.
The AI needs to train on data which is as reliable as it can be, to produce reliable results.
In addition, this helps your human workers as well, since everyone should be able to rely on a robust KB.
AI is only as reliable as the knowledge it draws on. If an AI doesn’t train on well-structured or contradictory data, you can get:
– Hallucinations, or fabricated information.
– Inconsistent responses to repeated queries.
– Out-of-date information being cited as current.
– Improper or false context, such as incorrectly citing customer history.
– Poor standards or regulatory compliance, without proper alerts.
All these add up to lost productivity, poor customer experiences, and possibly even legal issues
Through a mixture of human verification and Automated Data Synchronization. ADS allows an AI to automatically spot and integrate new data added to trusted sources – but your knowledge management must be overseen by humans who ensure that new additions are reliable.
Conclusion: Modern Knowledge Management Calls For AI
Businesses run on data, and lots of it. However, as the amount of data and institutional knowledge within an operation increases, the ability of people to properly sort through it to find answers decreases.
AI agents can solve this problem by leveraging their ability to digest and analyze huge amounts of data, then repeat them for human users – but only if the knowledge base is structured to bring good results from AI.
Properly implemented, an AI backed by a well-managed KB will be a true teammate, assisting your human workers and boosting productivity across your operation.
KMS Lighthouse – an industry leader in AI-focused knowledge management – can help make this happen for your business. Just contact us to schedule a free demonstration.
