Context is Everything

In a go-to-market organization, context matters. In fact, context is everything.

Out of the gate, even the smallest teams carry real complexity. Managing that complexity requires context. Context isn’t just the “what” of an organization, but also the “how.” 

We can separate context into two categories: human-derived context, and system-derived context. Human-derived context is the knowledge that teams define, document, and reinforce through experience: how processes work, how decisions get made, and how systems are expected to be used. System-derived context is context inferred automatically by technology, based on how the business actually operates across tools, data, and behaviors. 

Both of these matter. One often follows the other. But too often, organizations stop at human-derived context and declare sufficiency.

The problem with stopping there? If humans are always required to maintain context consistently, human capital becomes not only valuable, paradoxically it also becomes a blocker. Questions and answers must be routed through the same individuals who know how things work. Lose one of these key people, and you’ve lost the keystone of your organization’s context. That knowledge leaves with them, or worse, survives only as fragments in a deeply buried document or Confluence page, left to someone else to interpret and carry forward. 

The result isn’t just lost knowledge; it’s slower answers, delayed decisions, and increased risk in the moments when speed matters the most. The reality is that human-derived context just doesn’t scale consistently. Organizations that want to be able to react quickly to market shifts, grow efficiently, and make revenue teams more productive need to move beyond this model and toward system-derived context.

How does that happen? Let’s talk about it. 

We’re operating in an AI-forward environment. For better or worse, tools will now include AI features by default. But what does this mean for organizations when it comes to business context? The AI tools that will deliver tangible ROI for go-to-market teams won’t be the ones that summarize data in a vacuum, they’ll be the ones that can derive business context quickly, accurately and consistently. 

That means selecting AI tooling that’s capable of looking across systems, documents, conversations, and records to understand exactly how a complex revenue business truly runs. It means tools that inherently understand sales cycles, rules of engagement, sales plays, systems architecture, and even patterns in how the people in your organization run their businesses.

This is where traditional reporting and dashboarding tools struggle. They’re excellent at showing the what of your business, but far less effective at explaining why it happened, what changed, and what to do next.

Deriving this level of understanding requires a finely-tuned data layer with access to critical, often siloed data, and the ability to understand and infer relationships across systems (even when those relationships aren’t explicitly defined). That’s where Model Context Protocols (MCPs) come into play.

MCPs are purpose-built to read from source systems, contextualize raw data, and make it meaningful in real time. They don’t replace systems of record, they continuously interpret them. When deployed across a revenue and sales tech stack, an MCP-driven data layer becomes a living, operational source of business context. You get a fully-automated context engine that evolves as your organization, processes and market conditions change–without relying on human intervention to stay up-to-date. 

So now we’ve defined what a context engine is and introduced MCPs, how does it actually work in practice? 

Using Alysio’s GTM AI as an example, let’s walk through how a context engine derives business context and uses it to deliver relevant answers to business questions, while also allowing users to take action directly inside their systems. This is where the real impact occurs. Alysio’s platform is not merely a Q&A interface; MCPs also enable secure writeback functionality. 

Each MCP corresponds to a tool commonly used in a revenue organization. These include CRMs, conversational intelligence, data providers, email and calendar, marketing automation, etc. An MCP can be built for any platform with accessible API endpoints, using standard authentication or API keys scoped to the end user. Permissions are inherited from each tool, so GTM systems admins can feel safe knowing that their users are only accessing authorized data. 

How do these MCPs differ from simple API integrations? 

MCPs don’t simply pull data and display it. They’re designed with purpose-built tools that map to specific capabilities and API endpoints, and these tools are tuned to understand what the data represents, how it relates to other systems, and how it should be presented to different audiences. Without this layer of interpretation, data is just data, moving back and forth without meaning. 

Ask any RevOps or Sales leader about contextualizing data for executives vs. frontline managers, and they’ll tell you it’s no small task. Add in multiple systems with loosely defined or entirely inferred relationships, and the challenge compounds quickly. Traditional solutions require humans to predefine those relationships and interpretations. MCPs derive them dynamically, in real time. 

That’s why MCP architecture is so critical. It allows users to access contextualized insights across systems, compiling from multiple sources, regardless of how fragmented the underlying data may be, while leveraging real-time context to analyze, interpret, and recommend action. 

For example, when a CRO asks why pipeline coverage looks healthy but forecast confidence isn’t aligned, the answer rarely lives in a single system. The answer is based partially on CRM data but also on deal conversations, the behavior of sales reps, and organizationally-defined risk. Historically, answering that question requires meetings, follow-ups, manual analysis, and data presentation. But with a context engine in place, that insight can be delivered immediately, along with suggested actions to address the risk. 

In the pre-AI era, this process could take hours or days, and involve multiple human touchpoints and iterations. Now, with the Alysio context engine, those extra steps are removed. Data goes from source directly to decision-maker. Speed-to-insight, followed by speed-to-action.

Organizations move quickly, and the ability to understand not only what happened, but what’s likely to happen next, with suggested actions built in, allows leaders to make decisions sooner and to mitigate revenue risk, before it impacts the bottom line. 

The Final Word.

To recap, organizations of every size operate with increasing systems, data and business complexity. Managing that complexity has traditionally been time- intensive, human- dependent, and a largely retrospective effort. With the right AI tooling, like Alysio, organizations can leverage system- derived context engines that inherently understand business logic, processes, and flows.

The result is faster decisions, fewer handoffs, and less reliance on institutional knowledge locked inside individuals. When context is automated, organizations can move from reacting to business changes to anticipating them and reacting with knowledge, speed, and intent.