Numerical scenarios are illustrative unless a source is linked. Validate operational, legal, financial, and regulatory decisions against current primary guidance and your own baseline.
Context is not just data
AI-readable context is the knowledge a workflow needs in order to produce useful, trustworthy work.
It includes:
SOPs and playbooks
policies and constraints
approved examples
customer notes
product documentation
decision history
source-of-truth records
quality standards
permissions and exclusions
The question is not "Can AI access our data?"
The better question is:
Can AI access the right context, at the right time, with the right permissions, and show where it came from?
The workflow context pack
For each AI-native workflow, create a context pack.
This pack can live in your internal documentation system, repository, knowledge base, or workflow tool. The format matters less than ownership, freshness, and retrievability.
Make context legible
Useful AI context has structure.
Use short files or pages with clear titles. Put one topic per document. Add summaries. Label owners. Link to source systems. Use consistent terms. Keep examples close to the workflow they support.
For high-risk workflows, require source citation. The model should not just produce an answer. It should show which approved source, policy, transcript, record, or example influenced the output.
This is especially important for agents. An agent with vague context and broad tools is risky. An agent with scoped context, narrow tools, and a clear quality bar is operable.
The context maintenance loop
Context decays.
Policies change. Product behaviour changes. Customers use new language. Sales objections shift. Support categories evolve. Compliance requirements update.
Assign each context pack:
an owner
a review cadence
a source freshness rule
a change log
an escalation path for disputed content
a way for users to flag missing or wrong context
AI-native companies treat context like product infrastructure. They do not let it become a dumping ground.
