The missing layer in AI-powered analytics

Teams experimenting with AI in analytics already understand one thing: context matters. Without it, AI struggles to deliver insights that are accurate, relevant, or useful.

It’s not exactly a revelation to say that AI needs context to work. The real challenge is that “providing context” is easier said than done. 

Without the right context, many AI analytics tools behave like surface-level pattern matchers. They generate answers based on the data immediately available to them, without a true understanding of how your product works, how your metrics are defined, or what outcomes your business is trying to achieve. When that underlying context is missing (or just poorly defined), AI defaults to hand-wavey explanations that may sound convincing but don’t reflect reality.

But those are worst-case scenarios. 

With the right context, AI helps us understand our data faster. It allows us to create personalized experiences for customers, increase efficiency, and reduce costs, becoming a genuine analytical partner.

So what separates high-context from low-context AI work? And how do you actually provide the kind of context that makes a difference?

In low-context situations, AI misinterprets intent, prioritizes the wrong signals, and will often make broad overgeneralizations. AI without context can’t connect the dots across systems, teams, and workflows or find the information it needs to see the complete picture. The result is an analysis that may look confident on the surface, but breaks down when applied to real decisions.

"Most teams think AI will fix their messy data. It won't. AI just amplifies whatever foundation you give it – garbage in, confident garbage out. Data governance isn't a nice-to-have anymore. It's the difference between useful AI and expensive noise."

Limited or inaccurate context can be just as bad as having no context at all. If your sources are wrong or the data you’re using to inform AI is impossible to parse, the AI is likely to draw wrong conclusions. This means AI outputs risk optimizing for what is easiest to summarize rather than what is most meaningful.

Context is what turns AI from a generic answer engine into a useful analytical tool. Without it, even the most advanced models will deliver insights that feel disconnected from reality.

Context comes in different forms. It should always be clear, precise, and well-structured, so that LLMs can parse and use the information provided. 

Here are a few key examples of high-quality context that can shape and train your AI to be useful: 

Data context gives you (and AI) a better understanding of your data, how it’s structured, and how different pieces connect to each other. Context like event structures, properties, metric definitions, and schemas that lay out how your data is organized will help LLMs answer your queries more accurately. 

User context includes any additional information you can provide AI about who your users are and how they interact with your product. This includes things like user segments and cohorts, user behavior, personas, and qualitative data like session replays and heatmaps. 

Providing context about your business goals and KPIs will help LLMs understand your constraints and guide you towards desired outcomes. Strategy frameworks like Metric Trees are especially useful context, since they tie all of your actions together into an easy-to-digest structure. 

Environmental context includes any external information about your industry and the market that AI should factor into its analysis. For example, trends, anomalies, seasonality data, competitive insights, or market signals are all valuable context for LLMs. 

We’ve established that providing the right context is important, so let’s look at how to make sure you have that context. 

Data governance is one of the most important factors for AI analytics. Confirming that your data is trustworthy, organized, accessible, and reliable at scale helps LLMs interpret that data and understand user behavior. Data governance helps you confirm that the results AI provides are grounded in truth, not guesswork. 

"It’s like a feedback loop that feeds into itself. Let's say… governance is a problem, you fix it and have better governance projects, and then you’re able to take advantage of more data governance tools in the project, and then it keeps looping. But if you’re off track, then all your data is off as well and the AI tools are no help to you."

Sometimes, even with our best efforts and intentions, context is still lacking. Minimum viable context (MVC) helps you determine what is the least amount of context that you need to get useful answers. 

The MVC for AI analytics usually consists of the following inputs: 

Example of an objective: “I’m trying to understand why week-1 retention dropped so I can decide whether to prioritize onboarding improvements.”

Without this information, AI will sometimes optimize for explanation instead of action. 

Example of a metric definition: Simply saying “analyze churn” can lead AI to make incorrect assumptions. But if you include “churn = users who fail to complete Event X within 30 days after signup” as a definition, you can ensure that you’re working from the same starting point. 

Event semantics example: “Completed_Project means the user created and published their first project, not just saved a draft.” 

Scope example 1: “This analysis applies to new SMB customers on self-serve plans, not enterprise accounts.”

Scope example 2: “This drop occurred after a pricing page redesign on March 12.”

To get the most out of AI analysis, ground your context in specifics whenever possible. That means providing both internal and external context. 

Internal context grounds AI insights in your company’s source of truth rather than generic product knowledge, like how to reference metric definitions, event names, and metric trees. Otherwise, AI can default to generic definitions, which might not align with your company’s.

Adding external context like competitive intelligence gives AI strategic and qualitative signals that aren’t visible in event data alone. This is what transforms AI from a data analyst into a product thinking partner.

Good prompting is the first step to providing AI with the context it needs. AI prompting is part art and part science, and the best way to improve is through practice. Here are a couple of tactics to improve your prompts: 

Context is cumulative, not a one-time input. Most AI failures happen when we try to consolidate context, analysis, and recommendations into a single prompt. For example: “Why did activation drop last month and what should we do about it?”

Instead, effective prompting breaks the task into layers and builds context progressively. AI performs best when treated like a reasoning system you guide step by step, not an answer generator you query once.

Prompt 1: "We're a B2B SaaS project management tool. Our core user action is creating a project, and we measure success by weekly active users."

Prompt 2: "Our main user segments are SMB teams (1-10 users) and mid-market teams (11-50 users). SMB makes up 70% of our user base but mid-market drives 60% of revenue."

Prompt 3: "Week-1 retention dropped from 35% to 28% over the past month, specifically among SMB users. We released a new onboarding flow on March 3rd. Analyze what might be driving this drop."

Ask the AI to list what context it needs, then supply it. Once you get to a final output you’re happy with, re-ask AI how you could’ve prompted it to arrive at this output. 

The next era of analytics must continue to build on context. AI is reshaping analytics, allowing us to go deeper faster, and without requiring large amounts of resources. But the key to successfully using AI as a tool for strategic growth is the ability to provide and use context to inform your analysis. Without it, the intelligence gathered will always risk being incomplete or inaccurate. 

Power your analysis with context-aware intelligence today. Try Learn more about Mixpanel’s MCP Server here.