You're putting together a company presentation. You know the information exists somewhere, but you're not quite sure where. Is it tucked away in Confluence or SharePoint? Buried in an email from three months ago? Or does the answer simply live in a colleague's head?
You search, ask around and eventually uncover three different documents containing conflicting information.
Sound familiar?
Knowledge fragmentation is one of the most damaging yet overlooked challenges facing organisations today.
It rarely feels like a major crisis, but it quietly drains productivity every day. Teams waste hours reinventing the wheel, decisions are made using incomplete information, and valuable expertise walks out of the door whenever someone leaves the company.
This is where Context Engineering comes in. It helps organisations organise knowledge across multiple sources so that both people and AI can access accurate, reliable and up-to-date information when they need it most.
What is Context Engineering?
The instinctive response is often to create a single source of truth and move everything into one central repository. In reality, that's rarely practical and often unnecessary.
Context Engineering is not about storing everything neatly in one system. It’s about ensuring the right, current and trustworthy context is available exactly when a person or AI system needs it.
How to get there? The answer has far-reaching implications. In practice, Context Engineering typically evolves through four stages:
Answering: AI finds relevant information and provides targeted answers.
Learning: feedback and new insights continuously enrich the available context.
Advising: AI uses that context to generate recommendations and proposals.
Acting: with the right governance and guardrails in place, AI can carry out specific actions on your behalf.
Step 1: Answering
Many organisations face the same challenge as Wageningen University & Research: information spread across tens of thousands of pages and search tools that return overwhelming volumes of results, leaving users to work out what is actually relevant.
The solution lies in context.
To provide meaningful answers, AI first needs to understand which information matters. Rather than relying solely on keywords, content is analysed based on its meaning, purpose and context.
Think about someone searching for an open day. They're rarely asking a single question. They might want to know:
When is it?
Where is it?
How do I register?
What can I expect on the day?
By expanding and clarifying the user's intent in this way, AI can retrieve more relevant information and generate significantly better answers.
The end result is a search experience that prioritises relevance over volume, helping visitors find the information they need far more quickly.
Step 2: Learning
Every interaction with AI creates new context.
With a user’s quest for info on an open day, for example, their questions reveal intent. Over time, AI can identify patterns across thousands of interactions and use those insights to enrich the context layer behind the scenes.
As a result, the system becomes increasingly effective at understanding who is asking the question, where they are in their journey and what information is most likely to help them next.
The same principle can be applied internally through a company brain: a single access point to the collective knowledge already spread across your organisation. Project updates, meeting notes, decisions communicated by e-mail and other valuable resources become searchable and accessible without needing to be stored in one location.
This becomes particularly valuable when experienced employees move on. Years of hard-earned knowledge won't disappear overnight anymore. By capturing key decisions, project insights and lessons learned, organisations retain critical context long after individuals have left.
Step 3: Advising
Consider a childcare organisation responsible for monitoring changes in legislation and regulation.
Teams may spend hours tracking updates across multiple websites, gathering signals, reviewing documents and assessing whether existing policies need to be updated. It's a time-consuming process that can quickly become overwhelming.
With the right context layer in place, AI can take on much of this workload:
Gathering information from public sources on an ongoing basis.
Assessing whether changes are likely to impact existing policies.
Identifying the relevant internal documents.
Recommending updates in the organisation's preferred tone of voice.
The role of the employee shifts from carrying out the analysis to reviewing, refining and approving the outcome.
AI advises. People decide.
What does this mean for your organisation?
Look for processes where employees spend significant amounts of time gathering, comparing and evaluating information before they can make a decision.
These are often ideal opportunities for AI to provide an initial assessment or recommendation while keeping the final judgement firmly in human hands.
Step 4: Acting
The final stage is the most advanced: AI begins carrying out actions.
This doesn't mean handing over complete control. AI operates within clearly defined boundaries, supported by the right policies and governance framework. These safeguards are essential and should never be treated as optional.
Successful execution depends on three key foundations:
A permissions layer defining exactly what actions the AI agent can and cannot perform.
Human-in-the-loop oversight, ensuring someone remains responsible for reviewing, approving or intervening where necessary.
Trust, built gradually through demonstrated performance in answering, learning and advising.
Trust is not established overnight. It develops as the context layer becomes richer and the system consistently proves its value.
Where should you start?
Begin by identifying activities that consume disproportionate amounts of time.
Where does your team spend hours searching for information, compiling reports or performing repetitive analysis? Once you've identified those areas, focus on understanding and documenting the underlying process. AI can enhance a strong process, but it cannot fix a broken one.
Only then should you decide where AI fits in. Which stage best matches your needs and ambitions: answering, learning, advising or acting?
Your first use case doesn't need to be ambitious.
Choose a clearly defined process where reliable context can make a measurable difference and build from there. That might be AI-powered search, a company brain or another application designed to support answering, learning, advising or acting.
Most importantly, remember that simply making information available is not enough. Consider who owns the source, how information is kept up to date and which sources AI is permitted to use. Ultimately, the quality of the output depends on the quality of the context behind it.
Your foundations define your ceiling
If you're exploring what Context Engineering could mean for your organisation, start by assessing your existing information sources, business processes and AI ambitions.
That exercise quickly reveals which context is required and where an initial implementation is likely to deliver the greatest value.
For organisations looking to build this capability, we offer Bonzai, our unified AI platform for integrations, governance and AI orchestration. It provides the infrastructure needed to create a scalable, compliant and well-governed context layer.
