Context layer: when AI understands your business

The person who builds a report knows where the sales figures should come from. An experienced expert knows how to handle an exception. AI knows neither, unless someone has described it. Without that, it falls back on general meanings and whatever information it can find. The answer may sound right and still not reflect how your company defines things.

As AI agents begin to prepare actions, people move from doing the work to reviewing and approving it. If an agent misreads a concept, the mistake may not surface until it shows up as a wrong answer or a flawed action.

The need for shared context shows up in day-to-day business

People work around these situations with experience and by asking each other. For AI, anything that hasn’t been described is a gap.

Today	With shared context
Two reports give two different numbers for the same thing.	One definition that AI uses too, and the answer shows where the number comes from.
Only certain colleagues know the right answer.	Ways of working and exception handling are written down so AI can use them.
No one is sure what AI should be allowed to see.	Access rights apply when AI uses the information, too.
The definition was made once, and no one keeps it up to date.	The definition has an owner and is updated with AI’s help when the business changes.
Every AI project defines the same things from scratch.	The next use case builds on what has already been defined.

What does the information mean?

“Active customer”, “sales” or “margin” can mean different things, even within the same company. AI needs to know your company’s shared definitions, including the ones that haven’t been written down.

What is true right now?

Yesterday’s right number can be wrong today. That’s why AI needs to know where the process stands, what currently applies and from whose point of view the question is asked.

Can the information be trusted, and can it be used?

Not all the information AI finds is equally reliable, and not all of it may be used. Context shows where the information comes from and who has the right to use it.

Many companies have already defined their concepts, documented their information sources and agreed on access rights. But these are often spread across different places, and some of the knowledge is held only by experienced people.

Context layer brings concepts, information sources, access rights and ways of working together in a form that AI can use as well. AI can help even in building it, by identifying concepts and rules in documents for people to review. Once described, context can be reused in later AI solutions.

We believe competitive advantage comes from how well AI knows your company

Every company has access to the same AI models. What sets you apart is how well AI understands your company’s data, operations and rules. When your context is in order, information can be put to better use in decision-making. It also allows AI to be used for more than individual tasks, and increasingly to redesign processes and ways of working.

When building a context layer, Efima combines data, process and technology expertise. We help identify the information, definitions and ways of working that already exist in your company and turn them into context AI can use. We work together with the people who know the business, and build context step by step, starting from real use cases.

The better AI knows your company's context, the more you can trust its answers

Getting your context in order – where should you start?

AI is taking over some of the interpreting people used to do

The need for shared context shows up in day-to-day business

AI needs the same onboarding as a new employee

What does the information mean?

What is true right now?

Can the information be trusted, and can it be used?

Your company already has much of this

We believe competitive advantage comes from how well AI knows your company

Getting your context in order – where should you start?

Teemu Kuusisto

Hanne Kivimäki