What changes when marketing moves from a system of records to a system of action?

The center of gravity shifts from tagged events in CRM or CDP to unstructured signals that conversations produce. An agentic layer engages, understands, remembers, and acts across channels so context drives the next best action, not static fields. The stack is built to interpret language, not forms.

How does the conversation graph improve orchestration and context continuity?

Every exchange across WhatsApp, Instagram, email, and webchat lands on a unified timeline that stores sentiment, intent, emotion, and actions. Engagement and memory live together, so there is no lag between insight and response, and workflows progress with context carryover.

How are unstructured messages turned into qualified intent and actions?

Large language models interpret mood, buying stage, and prior intent from free text and voice. The platform then triggers the next best step automatically, closing the gap between understanding and execution inside ongoing conversations.

Why are structured only stacks now insufficient for modern journeys?

Most enterprise data is unstructured and grows faster than structured data, while customers expect brands to understand their unique context across messaging channels used at massive scale. Retrofitted assistants on top of forms can not match unstructured native systems that listen and act in real time.

What integration pattern keeps systems of record useful without slowing orchestration?

Use the agentic layer for front line engagement and decisioning, and keep CRM and CDP as archival and reporting systems. Sync outcomes and identifiers through APIs or event streams so profiles stay consistent while the conversation graph powers action. This avoids middleware bloat and preserves a clean handoff.

How should security, compliance, and data governance be approached?

Require encryption in transit and at rest, strict access controls, audit trails, and clear retention policies. Align data residency and consent handling with internal governance, and evaluate how transcripts, messages, and derived attributes are stored in the conversation graph. Treat unstructured data with the same rigor as structured records.

What ROI and time to value can leaders expect from acting on unstructured data?

Gains come from capturing intent, emotion, and urgency that forms miss, then responding in seconds across the channels customers already use. With most data unstructured and expectations for contextual understanding high, shifting orchestration to the agentic layer improves conversion efficiency without reworking every downstream system.

Will this approach scale across high volume messaging without losing performance?

A single agentic layer engages across WhatsApp, Instagram, email, and webchat while the conversation graph maintains continuity. Autonomous classification and action reduce queue time, and a unified timeline prevents duplicated outreach, enabling consistent performance as conversation volume grows.

How does an unstructured native, agentic approach compare to retrofitted AI and siloed drips?

Drips and add on assistants treat language as an afterthought and keep memory separate from action. An agentic system makes conversation the primary data, keeps context persistent, and turns understanding into immediate orchestration across channels, reducing lag and fragmentation.

From System of Records to System of Action

Dikshant Dave

May 16, 2025

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5 min read

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general

Over the past two decades, modern business has been built on structured data. Rows and columns in spreadsheets. IDs and timestamps in CRMs. Event logs and drop-down fields in marketing automation tools. Structured data was the bedrock of scale—it enabled systems to communicate, analytics to be run, and funnels to be tracked. But we’re now reaching the edge of what structured systems can comprehend.

The explosion of digital channels, conversational interfaces, customer behavior across touchpoints, and artificial intelligence has made one thing clear: the future will belong to systems that natively understand, store, and act on unstructured data.

The Legacy of Structured Systems: CRM, CDP, and Automation

To understand this shift, it’s worth tracing the lineage of the platforms we rely on today—and why their foundational assumptions are starting to crack.

In the early 2000s, CRM systems like Salesforce emerged as the central hub of customer relationships. They were built to store well-defined objects: Leads, Contacts, Accounts, Deals. Each had fields like name, status, lifecycle stage, and owner. Marketing platforms such as HubSpot and Marketo added engagement tracking—clicks, email opens, form submissions—and layered automation logic on top. Everything had to be event-driven, tagged, or scored to be usable.

When Structured Worked: Simpler Journeys, Simpler Tools

This approach worked remarkably well when customer journeys were linear, digital signals were narrow, and interaction modes were few. The marketing automation stack was effectively a high-functioning calculator—if a lead clicks an email, assign 10 points; if they download a whitepaper, assign 20 more. It was deterministic, clean, and structured.

CDPs and the Illusion of Comprehensiveness

The rise of Customer Data Platforms (CDPs) in the 2010s, like Segment, mParticle, and Tealium, attempted to unify structured data from websites, mobile apps, and product telemetry. These platforms became the system of record for customer behavior—storing everything from last purchase date to preferred language to campaign UTM source.

For a while, this worked. CDPs powered segmentation. CRMs handled pipeline. Marketing automation platforms like Braze and WebEngage handled workflows. This triumvirate—CDP + CRM + Marketing Automation—formed the backbone of modern martech.

Why the Future Will Be Built on Systems Natively Designed for Unstructured Data

But all of these systems were built on a shared assumption: that customer data is structured, tagged, and originates from systems—not humans.

That’s no longer true.

Today, the majority of customer interaction is unstructured—from messages sent over WhatsApp and Instagram DMs, to support tickets, call transcripts, live chat, product reviews, and social comments. According to IDC, over 80% of enterprise data is unstructured, and growing at nearly twice the rate of structured data (IDC, 2022).

In marketing and sales, this shift is profound. A prospect may send a message that reads: “I’ve been looking at your pricing—seems a bit much for our stage. Can you help?” Traditional systems can’t interpret that. There's no checkbox for “price sensitivity” or a dropdown for “tone: hesitant.” Yet within that one message lies rich signals: interest, hesitation, budget concern, urgency.

Legacy systems flatten this information—if it’s stored at all. CRMs log it as “activity.” CDPs ignore it. Automation platforms can’t trigger on it. And marketing teams are left blind to the most human parts of the customer journey: intent, emotion, mood, resistance, curiosity.

This is why unstructured data—voice, text, chat, intent, memory—must become the new foundation.

Forces Powering the Shift to Unstructured-Native Systems

What’s driving this transformation?

First, the channels themselves have changed. Messaging platforms like WhatsApp, Instagram, Telegram, and WeChat dominate customer interactions. According to Meta, over 1 billion users message businesses every week on WhatsApp alone. These aren’t form fills or checkboxes—they’re fluid, contextual conversations.

Second, AI has caught up. With large language models (LLMs) like GPT-4, Claude, and open-source alternatives, machines can now interpret unstructured data with remarkable accuracy. This means systems can infer intent, detect emotion, classify sentiment, and even generate relevant responses in real time.

Third, customer expectations have shifted. According to Salesforce’s 2023 State of the Connected Customer report, 73% of customers expect brands to understand their unique needs and context, not just send blast messages based on past clicks. Unstructured data is where that context lives.

Despite this, most current systems are being retrofitted to work with LLMs. You’ll see CRMs adding “AI assistants,” or CDPs offering “text field parsing.” These are add-ons, not foundations. It’s like bolting an engine onto a bicycle and calling it a car.

That’s why a new category of systems is emerging—those that are natively built on unstructured data.

These platforms don’t force customers into forms. They listen. They don’t require manual tagging. They understand. They don’t separate engagement from memory. They unify it.

Zigment: A Natively Agentic Approach to Customer Journeys

Take Zigment, a new breed of Agentic AI platform designed specifically for customer journeys. Rather than integrating a CDP, chatbot, and automation system, Zigment operates as a single agentic layer that engages, understands, remembers, and acts—across all channels. When a customer writes a message, Zigment doesn’t just respond—it classifies mood, identifies buying stage, tracks past intent, and triggers the next best action automatically.

Conversation Graphs: Where Memory Meets Action

This isn’t just an improvement in interface—it’s a paradigm shift in architecture. Every conversation, across WhatsApp, Instagram, email, or webchat, feeds into a unified Conversation Graph—a timeline that stores sentiment, intent, emotion, and action. No separate tools. No middleware. No lag between insight and action.

While startups like Lindy.ai or Inflection’s Pi are experimenting with intelligent assistants and AI agents for general productivity, most of these are still task-specific or user-bound. Zigment represents a broader shift: enterprise-grade agentic systems that handle end-to-end customer journeys across marketing, sales, and support—natively on unstructured data.

The Gradual Decline of Structured-Only Systems

So what happens to existing systems?

They won’t disappear overnight. CRM giants will continue to operate. CDPs will serve data infrastructure needs. But over time, their relevance will shift from front-line orchestration to back-end archival. The systems of action—the ones that actually talk to customers, understand them, and drive outcomes—will move upstream to agentic platforms.

It’s a replay of past transitions. The mainframe gave way to desktop software. Desktop apps gave way to cloud SaaS. SaaS is now giving way to autonomous, real-time, context-aware systems built on language, not forms.

Replatforming for Relevance: Toward Systems That Understand

Businesses that continue to operate on structured-only systems will soon find themselves unable to detect key buying signals, slow to respond across modern channels, and blind to what customers are actually saying.

The winners will be those who replatform—not just to AI, but to unstructured-native, agentic architectures. Systems that don’t just record data, but make meaning from it. That don’t just automate, but understand.

The future isn’t made of forms. It’s made of conversations. And only systems that were born for that world will thrive in it.