By now, you're not really debating whether AI belongs in your business. It's already there, running in your tools and workflows, probably more widely than you've fully mapped out.

What's harder to know is whether you've given it enough information and business context to do what you're asking of it. In the 2026 Modern Data Report, 61% of data leaders said a reliable context layer is necessary for AI agents, but only 16% said their organizations deliberately engineer one.

Part of the challenge is that AI's role is changing. A few years ago, generative AI was largely used to create content, summarize information, and answer questions. Now, agentic AI can make recommendations, execute workflows, and act on the organization's behalf.

That raises the bar for readiness. Accurate data can tell an agent what happened, but it may not explain why a rule did not apply, which priority took precedence, or when a person should step in.

This is the AI Mandate Gap: the distance between what you ask AI to do and what your data and business context can support. Closing it means building readiness around the decisions AI will actually support, rather than waiting for the entire data foundation to reach an ideal state.

# “Data Readiness” Means Something Different in the AI Era

For years, data readiness largely meant ensuring your data was clean, accessible, and well organized. In the context of AI, that’s rarely enough.

Data gives you the facts. Policies and documented processes tell you how work is supposed to happen. But neither necessarily explains why someone made a particular decision when the situation didn’t fit the process exactly. That missing layer is context.

In most organizations, context sits between documented practices and business outcomes. It can include the judgment people apply to unusual situations, the reasons behind an exception, competing priorities, historical decisions, or information that changes how a standard rule should be interpreted.

And much of it may not live in a formal system at all. Usually, it can be found in meeting discussions, emails, Slack conversations, recorded calls, and the accumulated judgment of people who have worked through similar situations before.

Consider a client discount. A company may have a policy that limits discounts to 10%, while its CRM shows that a particular client received 15%. The policy and the outcome are both visible. What may be missing is everything that happened in between. Perhaps the client was strategically important, the company had consultants available to take on additional work, or the team expected the relationship to lead to a larger opportunity. Those factors may have been discussed across several conversations before someone decided that exceeding the standard discount made sense.

If an agent sees only the policy and the CRM outcome, it can identify the exception but not the reasoning behind it. That is why companies can improve data quality and still find that AI struggles with decisions people handle through experience and judgment.

Related Read: Tech Industry Signals: Five Operating Priorities for the AI-Enabled Enterprise
Explore why organizational knowledge and business context are becoming essential to AI readiness.

# The More Judgment Involved, the More Context AI Needs

Not every decision requires the same depth of context. The Cynefin framework, a sense-making model for choosing how to act in different types of situations, offers a useful lens. It distinguishes between environments where cause and effect are clear, and those where several reasonable paths exist, and expertise or experimentation is needed to decide what to do next.

Take invoice processing. If a contract specifies a discount, the workflow may simply involve finding the agreed rate, applying it, and issuing the invoice. Move upstream to the commercial negotiation, and the decision becomes harder to make. Should the business offer a larger discount to win this customer? The answer might depend on expected revenue, available capacity, margin, strategic value, future work, and current priorities.

That means there could be several equally valid answers, and getting it right can directly impact your broader business goals.

Alaska Airlines' Flyways AI demonstrates the upside of providing AI tools with enough context. The company uses AI to analyze weather, winds, turbulence, airspace constraints, and traffic volume together and recommend routes to dispatchers. With enough contextual data, AI processes many variables simultaneously and quickly, helping the company save both time and fuel.

This goes to show that the context an AI system needs should reflect the decision it is making and the cost of getting it wrong. A tightly rule-based task may need very little. A decision involving tradeoffs, exceptions, or significant risk needs a richer picture.

# How To Build Context Into an AI Workflow

You don’t need to resolve context across the whole organization to make progress. Build it around one useful workflow, learn what the AI actually needs, and widen the scope from there.

# 1. Choose one workflow with a clear business outcome

Pick a workflow where better or faster decisions would create visible value. Be specific about the outcome: reduce stockouts, shorten quote approval, improve case resolution, or reduce manual review. Starting with one workflow keeps the work bounded and gives you something concrete to test.

# 2. Map the decision before you automate it

Work backward from the outcome to the decision AI must support, the action it may take, and the information required to do both reliably:

Business outcome -> decision AI must support -> action AI may take -> context required -> data required

Then make the operating boundaries explicit.

  • Which priorities should win when objectives conflict?
  • Who owns an exception that crosses teams?
  • When can AI act, and when must it ask for approval, escalate, or hand the decision back to a person?

Employees often answer these questions informally, but an agent needs clear boundaries to operate efficiently and accurately.

# 3. Start capturing the context people already use

Many businesses have never treated day-to-day judgment as useful data. If the reasoning behind decisions has lived in conversation, start capturing it now. Record the meetings where important decisions are discussed. Preserve relevant call transcripts. Make the right email, Slack, Teams, and other internal communications available to the AI systems that need them, inside the organization's privacy, security, retention, and access controls.

Focus on the qualitative information that changes a decision:

  • Why did a team approve an exception?
  • Which trade-offs did it consider?
  • What changed since a policy was written?
  • Why did someone escalate the situation instead of following the standard process?

You don’t need to feed every conversation into a model, and you don’t need years of historical records for this to become useful. Start with the workflow in front of you and build the record from today.

# 4. Connect the context so AI can retrieve it

Capturing conversations creates raw material. The next challenge is turning that material into something the business, and eventually AI, can use.

One emerging approach is a context graph, a living record of how decisions are actually made across the organization. It connects the formal rule and the final outcome with the context in between: the people involved, the information they considered, the exceptions they made, the approvals they sought, and the reasoning behind the decision.

If we consider the customer discount from earlier, a system of record may show the final price. A context graph could also preserve why the standard policy did not apply, who approved the exception, what customer circumstances influenced the decision, and whether a similar case had set a precedent.

Over time, those decision traces build institutional memory. Instead of leaving business judgment scattered across conversations, documents, and people’s heads, the organization can make it available for people and AI to draw on when similar decisions arise.

# 5. Run AI alongside people before giving it more authority

Don’t wait until the context looks complete, and don’t hand over the decision on day one. Run the AI recommendation alongside the people already doing the work. Compare the two. Where do they agree? Where did the employee consider something the system missed? Add the missing context and test again.

This gives you evidence about how much context the workflow really needs. More information will not always improve the answer, and each source adds cost, complexity, and potentially sensitive data to manage.

Let risk set the threshold. A low-consequence recommendation may work with a narrower context set, while a financial, regulatory, safety, or customer-critical decision may justify deeper context and continued human approval.

Once AI can make useful recommendations consistently within those boundaries, expand its role or move to the next workflow. The context you capture along the way becomes part of the organization's operating memory, giving future systems more to learn from than a policy manual and a list of outcomes.

# Build Readiness Around Real Decisions

Agentic AI will make data quality more important, but clean data alone will not give an agent the judgment your people use every day.

Businesses need to capture more of the information between the written rule and the recorded outcome: the discussions, exceptions, tradeoffs, and decisions that explain how the organization actually operates.

Start with one valuable workflow. Identify the decision at its center. Capture the context around it, test AI alongside experienced people, and learn what the system needs before giving it more responsibility.

At the end, the real advantage may come not from giving AI more autonomy, but from giving it a better understanding of how your business actually makes good decisions.

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