Unlock the Power of Context: The Future of Platforms in a Multi-System World

I can’t believe it’s been nearly a decade in my journey in platform engineering at ExactTarget and Salesforce. It started when I became one of the first engineers on Journey Builder at ExactTarget, a product that Salesforce acquired for billions of dollars.

This article will lay out the case for truly next-generation automation, which includes marketing use cases, CRM, and cross-industry.

The key to the next generation of platforms isn’t just scale or security. It’s context — across systems, across workflows, and across every touchpoint. And the more we build interconnected systems, the more context becomes critical. If you’re a leader in a growing organization, you likely already feel the pain of disjointed systems, fragmented data, and a lack of cohesion between tools like chat, CRM, and sales platforms. The ability to draw contextual insights from these silos and turn them into meaningful actions is where the future lies.

While the big players will say they have cross-system data, it’s not REALLY, and the amount of work it takes to integrate and provide a quality user experience on thes platforms that are decades old just isn’t living up to old promises. Folks are feeling nickel and dimed, upsold unnecessary products, and mired in technical debt.

Yes, this article is dense but it’s an important foundation. So, let’s begin.

Let’s start with a basic principle: A platform should be more than the sum of its parts. In the platforms I’ve built, extensibility has always been a core focus. It’s not enough for a platform to be secure and scalable; it must also be flexible. My rule is simple: Anything I can do through the UI, anyone should be able to do through APIs.

Why is that important? Because it’s not just about building a nice UI. It’s about creating a system where context flows freely across different tools and systems. If someone wanted to rebuild our UI from scratch using our APIs, they could. But more importantly, they would be able to access the same context and logic that the UI does. This is how we build platforms that don’t just serve single-purpose applications but can be extended, customized, and integrated into any workflow or system.

Take, for example, when we built Journey Builder at ExactTarget. Underneath the marketing workflows, what we were really building was a directed acyclic graph (DAG). Essentially, every workflow — whether it’s triggered by a chat message, a CRM entry, or a sales action — can be thought of as a sequence of steps in a graph. It sounds technical, but it’s quite simple: an event triggers a workflow, which leads to actions, decisions, and outcomes. Think of it as a series of tasks moving through a pipeline, like sending an email, updating a CRM record, or triggering a text message.

Now, consider the time it takes to map data from one system to another. In practice, mapping fields from one source to another is often a highly manual, tedious process. Different systems store data in varied formats, so aligning a single customer profile between CRM, marketing, and sales platforms can take weeks or even months to set up properly. Every integration has its own logic, and aligning these disparate data sets can be a massive time sink, especially when new data structures or tools are introduced. Without extensibility and a clear flow of context across systems, the time spent on mapping data alone can prevent platforms from delivering value quickly. This is why building flexible APIs and dynamic data models that allow seamless data exchange is critical — it accelerates the speed of deployment and ensures that systems remain adaptable as business needs evolve.

One of the biggest challenges we face today is connecting the dots across the many systems that organizations use. Think about your own company: you likely have tools for CRM (like Salesforce), chat systems (Slack or Microsoft Teams), and sales enablement. But often, they don’t talk to each other as seamlessly as they should.

Context across systems is the missing link. For example, if a sales rep logs a lead in Salesforce, that data could trigger an action in a chat system to alert a team to follow up. But that’s just the beginning. Imagine if the chat system could not only trigger an action but understand the full context of that lead, pulling in past interactions, emails, and even related sales calls. Suddenly, your workflows aren’t just reactions to isolated events; they are context-driven actions across multiple systems. That’s the future we’re building toward.

At ExactTarget, we took the first steps toward this when building Journey Builder. It allowed marketing teams to automate communication workflows in real-time, reacting to user behavior. But the future takes this concept much further: every system — whether it’s CRM, chat, sales, or even customer service — should be interconnected, drawing on the same contextual information to make smarter decisions.

Of course, the challenge of pulling context from multiple systems isn’t new. The more systems you have, the more disjointed your data becomes. Every time a company acquires another, adopts a new tool, or grows beyond its original systems, it faces the same problem: data silos.

How many times have you heard the term “single source of truth”? It’s something everyone strives for but few achieve. Why? Because each system has its own version of the truth. Your CRM might have one set of customer data, while your marketing tools have another. Your chat logs in Slack might hold critical context, but that data isn’t always connected to the sales conversation happening in your CRM.

At ExactTarget, we went through the wringer on this when building Contact Studio. We had to create a meta-layer of information to try and map identities across multiple systems, unifying customer data into a single profile. This is typically referred to as Identity Resolution — but back then, it was a grueling process involving relational databases and manual mapping.

Today, with the rise of generative AI, this process has the potential to become much more intelligent and seamless. Generative AI can ingest and synthesize vast amounts of data from different systems, creating a cohesive context for each entity — be it a customer, a lead, or a product. Suddenly, context isn’t limited to a single system or workflow; it’s cross-system and always-on.

Now let’s take this a step further. What if the concept of a traditional CRM could be replaced entirely?

The problem with CRM systems is that they often act as the central hub for customer data, but they don’t naturally interact well with all the other systems where data lives: chat logs, marketing platforms, email systems, and even social media interactions. The CRM becomes a bottleneck rather than a liberator of data.

The future lies in creating a fluid, cross-referenced data context that isn’t tied to any single system but integrates seamlessly with every tool you use. Imagine having a dynamic, AI-powered data layer that connects people, companies, interactions, and channels — and this data context can be accessed and updated in real-time by generative AI-powered automations. This would fundamentally change how businesses interact with their customers and data.

Picture a sales rep working on a lead: instead of going into a CRM, pulling a report, and manually updating a sales opportunity, the system could automatically pull in every conversation that lead has had across Slack, email, and social media, cross-referenced with their past interactions in your marketing systems. And instead of the sales rep having to manually log activities, the AI can suggest actions, send updates, and even initiate conversations on their behalf.

With contextual data automation, you no longer need to manually manage different systems — the AI-powered layer handles this for you, connecting data from any source and delivering real-time insights across every channel. CRM, as we know it, becomes unnecessary. Instead, we move toward context-aware, real-time automation systems that are always listening, always learning, and always connecting data in ways that drive action.

With generative AI at the core, platforms can go beyond static workflows and deterministic logic. They can now create dynamic, contextually aware automations that pull in data from chat systems, CRM, sales tools, and more, creating a full picture of the customer or entity in real-time.

This isn’t just an evolution of CRM — it’s the replacement of it. The AI doesn’t just provide data; it understands the full context of every interaction and uses that knowledge to automate actions, decisions, and next steps. The sales conversation, the marketing journey, and the support ticket resolution are no longer siloed activities. Instead, they are fully integrated into a continuous, cross-referenced experience powered by generative AI.

Imagine the competitive edge when your sales team no longer needs to toggle between systems to understand their leads. Instead, the system proactively delivers the context they need, in real-time, through the channels they’re already using — whether it’s Slack, email, or a custom dashboard.

This brings us to the importance of real-time systems. In today’s world, the expectation for real-time results isn’t just a “nice-to-have.” It’s a requirement. Whether it’s a sales query, a marketing workflow, or a customer service interaction, users expect instant responses and actions.

But real-time systems are hard. They’re expensive to build and maintain, and they require constant monitoring. The complexity of real-time architecture is compounded when you start pulling data from multiple systems, each with its own timing and data structure.

That’s where generative AI shines. By leveraging AI to process data in real-time, you can enable your systems to understand context and take action instantly. For example, if a customer mentions a billing issue in a chat with your support team, your system can pull in their CRM history, sales interactions, and even billing data to resolve the issue without needing to escalate. The entire process happens in seconds, powered by context and real-time data from across your ecosystem.

Ultimately, the future of platforms lies in contextual automation — where actions across all systems, CRM, chat, call transcripts, documents, slide decks, sales data, and other systems aren’t just reactive but proactive, driven by real-time context. Platforms that can integrate seamlessly across systems, drawing on data lakes and unifying identities, will create the kind of personalized, intelligent workflows that drive real business results.

The companies that can harness this cross-system context will be the ones to lead the next wave of digital transformation. And as we continue to unlock the power of generative AI, the barriers that once made cross-system context difficult — like data silos and fragmented workflows — are rapidly disappearing.

If this vision of the future resonates with you, or if you’re struggling to connect the dots across your own systems, let’s talk. I’ve spent my career building platforms that solve these problems, and I’d love to help your organization unlock the power of context, automation, and generative AI.

We’ve reached an exciting inflection point. The technology is here, the data is ready, and the opportunity to transform how we work is just beginning.

Are you ready? Let’s chat about our Roxie AI platform. You can learn more and book a demo here.