The rapid surge of unstructured data is challenging marketing technology and operations
We’re seeing a clear shift in how data is created, and how it needs to be managed. As AI continues to scale, it’s generating massive volumes of unstructured content. Tweets, customer reviews, podcast transcripts, chat logs, video calls, none of this fits neatly into rows and columns. Traditional systems like CRMs and marketing platforms can’t process it cleanly because it doesn’t follow predictable formats. That’s a growing problem for anyone serious about making decisions based on data.
Generative AI is fundamentally altering the volume, structure, and nature of marketing content. The challenge is, unstructured data holds real signals, customer opinions, buyer intent, patterns in behavior, but most companies aren’t prepared to use it. The average tech stack still favors structured inputs because they’re easier to validate, report on, and act on quickly. That puts unstructured inputs at a disadvantage, and the insights inside them stay locked up.
Executives need to see this for what it is: a change in the raw materials. The reality is that the structure of valuable data no longer matches the capabilities of the systems built to manage it. If 90% of your raw data is unstructured, as reported in a 2022 study by IDC and Box, that’s a fundamental mismatch. And the percentage is only rising. With every AI-generated sales conversation, support ticket transcript, or voice note, that challenge compounds.
The takeaway here is straightforward. The future is increasingly made of data that doesn’t follow the old rules. If your systems, governance models, and teams are built for structure-only input, then you’re already falling behind. Leaders who solve the unstructured data pipeline now will gain an edge others will have to catch up to later.
Unstructured data requires a fundamentally different management approach compared to structured data
Unstructured data isn’t just a bigger version of what you already manage. It’s categorically different. Structured data is expected, clean, and formatted. You can plug it into your CRM or dashboard with limited friction. It plays by established rules. Unstructured data doesn’t. It’s variable, non-uniform, and context-heavy. That makes it hard to track, evaluate, or sort using conventional systems.
Trying to manage both types with the same frameworks is a flawed assumption. The idea that governance, quality control, and validation layers should be identical across the board misses the point. You can’t expect unstructured text from a customer complaint or internal meeting transcript to be processed the same way as a dropdown menu entry in Salesforce. Throwing both into the same system without acknowledging their differences causes blind spots. Poor targeting. Misclassification. Wasted workflows.
This is an operational risk. But it’s also a major strategic gap. If unstructured data isn’t being governed differently, it falls below the threshold of what teams consider usable. That’s how high-value signals get ignored, and how AI and automation systems get biased by incomplete or misrepresented inputs. At scale, that affects product feedback loops, customer journeys, and decision-making accuracy.
Executives need to stop thinking of unstructured data as just “messy.” It’s highly valuable, but only if it’s treated with infrastructure designed for its characteristics. It needs its own data validation rules, metadata handling, processing pipelines, and oversight.
Ignoring this is not an option. The growth isn’t slowing. The systems we rely on for customer data, campaign orchestration, and attribution are already falling behind if they’re optimized only for structured input. The companies that get this right will discover a deeper layer of intelligence in their data. The ones that don’t will make bigger decisions with less insight.
Challenges inherent in data management are amplified by the growth of unstructured data
If you’re running marketing operations or managing data workflows, you already know the baseline problems, poor governance clarity, variable data quality, pressure to deliver quickly. These issues are persistent in structured environments. Now, multiply that complexity with inputs that don’t follow consistent patterns, don’t map cleanly to your systems, and change daily. That’s unstructured data.
The volume of data generated by AI tools, voice, text, video, hybrid formats, isn’t just growing. It’s creating entirely new processing demands in real time. When markets move this quickly and expectations for personalization are this high, waiting to clean and format unstructured inputs later isn’t practical. Campaigns still launch. Dashboards still pull insights. So teams make trade-offs that introduce future cleanup debt and risk faulty data-driven decisions.
Fast-paced execution environments don’t leave time for full-system overhauls. Teams are under pressure to keep moving without the luxury of perfection. That’s dangerous when most tools in your stack aren’t equipped to handle or validate the edge cases that unstructured inputs create. It creates a cycle, quick fixes, more clutter, more risk.
This compounds further when new talent enters the system. Junior hires tasked with pulling insights or building automations may not recognize the risks in how certain data was sourced or labeled. Platform features driven by AI may mask imperfections through sleek interfaces that give executives confidence, but under the surface, the integrity of the data is shaky.
Foundational issues don’t go away because you layer AI on top of them, they’re amplified. Fixing this starts with refining how your organization defines “good enough.” Define new standards for quality, metadata, and auditability that actually match how unstructured data flows through your systems. Then educate your teams on how these new formats alter the way campaigns, reporting, and automation should work.
The sooner you build responsibilities around unstructured data governance, the sooner you reduce the impact these risks have on scale. That’s the gap most organizations haven’t closed yet.
Tackle the unstructured data issue by acquiring technologies aimed at resolving its challenges
In 2024, Salesforce and HubSpot both announced acquisitions targeting unstructured data processing. These weren’t experimental plays. They were strategic decisions to fill a real capability gap in their platforms. The message is clear: handling unstructured content at scale has moved beyond tech conversation and into enterprise execution. Customers are demanding it, and leading platforms are reacting fast.
The platforms that dominated with structured CRM-based logic now need to evolve. Structured records, contact fields, drop-down menus, form submissions, aren’t enough to keep up with how real conversations and contexts unfold. Buyers interact across formats. They produce reviews, social posts, and call transcripts that contain valuable insights long before they ever fill out a form. Standard systems simply aren’t built to extract and use that context.
This is where generative AI comes in. As noted in MarTech Tribe’s MarTech for 2025 report, compiled by HubSpot’s Chief Strategy Officer Scott Brinker, “the ability to handle unstructured data in the cloud is crucial for unlocking the value of generative AI.” Without systems capable of ingesting and interpreting these formats, the power of AI remains underleveraged. AI needs context, signal strength, and relevance, everything that unstructured inputs contain.
If you’re leading a growth business, this matters now. It means your current martech stack may already be behind. It means your AI initiatives will miss their full potential if they aren’t grounded in clean, interpretable, unstructured inputs. And it means your competitors, especially those investing in AI-native tools, will close the insight gap faster than you.
Upgrading your systems won’t just be about features, it’ll be about how well they embed AI to actually understand human context. The winners in martech for the next 12–24 months will be those who don’t treat unstructured data support as a bonus, but as a core function. The vendors already moving in this direction are the ones to watch. Anything less will lead to bandwidth waste, false signals, and weaker customer insights.
Unstructured data has a profound impact on core marketing operations
Targeting the right audience starts with defining accurate personas. Most marketing and sales teams begin with structured fields, job titles, company size, industry, seniority. These live in your CRM and are easy to use for segmentation. The issue is that these fields are usually broad, static, and often misleading. They don’t reflect intent, function, or context. And more often than not, they’re driven by self-reported or third-party data that’s either vague or outdated.
Unstructured data offers a deeper lens. It includes the language people use in emails, transcripts of meetings they join, or public profile content that speaks to real responsibilities. For example, someone with “contract” in their title could be aligned with legal, procurement, or outsourced project roles. Grouping them based on keywords alone leads to misclassification. Most systems would slot that person into a legal persona, incorrectly, resulting in misaligned messaging, reduced engagement, and lost deal momentum.
With proper unstructured data processing, that contact’s behavior and context can be reevaluated. If they’re consistently present in sourcing discussions or mention RFP processes in conversation, AI tools can flag this and suggest a reclassification to a procurement profile. This changes everything, from email targeting to messaging tone to how sales follows up.
Most marketing systems today are built around predefined metadata. They organize based on drop-downs, not nuance. That’s a limitation. The better approach is to let unstructured data inform who someone truly is within the buying process. Your systems need to evolve from static profile-based logic to flexible, input-aware targeting that updates as more data is captured.
Executives need to understand the operational value here. Better persona precision reduces funnel leakage, improves campaign performance, and shortens sales cycles. But that only happens if teams can extract meaning from unstructured signals, at speed, and at scale. It means integrating natural language processing and behavior analysis directly into your CRM or MAP. It also means verifying that the insights are driving action, not simply being stored.
Treating unstructured data as core, not supplemental, shifts persona development from assumption-based to evidence-driven. That’s where competitive advantage happens. It’s also where data governance and campaign orchestration become aligned, because decisions aren’t being made on weak assumptions. They’re based on how people behave, not just how they define themselves.
AI capabilities must focus on understanding and analyzing unstructured data
A large portion of AI tools integrated into marketing platforms today are engineered to produce more, more copy, more campaigns, more interactions. That speed can help. But generating content without first understanding the full context behind customer interactions is a short-term fix. It adds surface-level output without addressing the core issue: unstructured data needs to be processed, not bypassed.
Take HubSpot’s recent AI features as an example. One option allows users to check a box labeled “Customer Conversations Data.” It seems simple, but what’s actually happening when that data gets selected? Is it being categorized? Analyzed for sentiment? Used to inform tone or relevance in future outreach? Or is it just being swept into a model that assumes it will figure out the rest? That difference matters. Automation in this context should begin with interpretation, not acceleration.
AI that doesn’t properly process inputs will compound existing problems. You might create more emails, more workflows, more content, none of it meaningfully personalized, none of it aligned with what customers actually said or did. At best, that results in mediocre engagement. At worst, it signals to customers that you’re not listening.
Executives leading digital transformation efforts need to demand transparency from their platforms. You should know what’s being extracted from unstructured input, how it’s being analyzed, and what the system is doing with that detail before anything gets published or triggered downstream.
There’s no shortage of AI tools offering to scale output. The real differentiator will be platforms that prioritize analysis first, tools that parse signals from transcripts, emails, chats, and reviews in a way that builds intelligence layer by layer. That kind of structure lifts everything, persona accuracy, messaging tone, segmentation logic, even attribution.
If that level of data intelligence isn’t part of your system’s embedded AI roadmap, your content won’t get smarter over time, it will just get noisier. And the opportunity AI brings to create truly adaptive, context-aware customer experiences will be missed.
Organizations must evolve their governance frameworks and infrastructure to effectively harness unstructured data
Most enterprise data governance models were built around structured inputs, form fills, contact records, transactional logs. These come from predictable sources and follow known standards. Unstructured data doesn’t. It arrives from open-ended channels: emails, support tickets, social posts, live chats, transcripts. It’s inconsistent and dynamic. And because it doesn’t fit within predefined categories, it often passes through systems without context, without classification, and without accountability.
That’s a liability. When data enters a platform without a clear origin or transformation trail, interpretation can go wrong. Context gets stripped. Decisions are made based on flattening snapshots instead of dynamic narratives. Once that happens at scale, the problem grows silently but quickly. And as generative AI gets more deeply embedded into business systems, unstructured content becomes a primary feed into decision-making. If you’re treating that data the same way you would structured input, you’re introducing gaps into every output it touches.
This is why governance needs to mature. One starting point is metadata. Organizations must track when a piece of data came into the system, how it was originally sourced (text, audio, video), and whether it was transformed into structured form later. You need proof of that entire process, essentially a chain of custody for data. Without it, your team can’t audit decisions or retrace how a customer profile or campaign signal was constructed, especially when AI models are involved in transforming and scoring content.
Many modern AI systems offer auto-structuring capabilities, grouping phrases, tagging entities, scoring sentiment. That’s useful, but not sufficient. These models are predictive, not deterministic. Meaning, they make probabilistic calls. If the original data context is erased or not recorded, it becomes impossible to validate why certain automation triggered, or why a customer was misrouted.
Executives must make sure their platforms, processes, and people treat unstructured data sourcing and usage with the same rigor as attribution or lead scoring. That means designing infrastructure that preserves the full data journey, from intake to transformation to activation. It’s about giving teams transparency over how signals evolve, how automation decisions are made, and how data flows across systems.
When done right, this isn’t a compliance process, it becomes competitive infrastructure. Companies that master this will outlearn their markets. They’ll structure context faster, personalize better, and ship smarter strategies because they’ll know exactly where their insights come from, and how to trust them.
New performance metrics and cross-functional AI literacy training are key
The way most organizations measure data readiness hasn’t kept pace with the way unstructured data now enters and moves through systems. Traditional KPIs, completion rates, field match accuracy, or database size, only give you visibility into structured layers. They don’t measure how effectively your AI and operations teams are interpreting freeform content, identifying sentiment, or tagging behavior from unstructured sources.
You need better metrics. That includes evaluating how AI models are transforming raw, unstructured data into actionable segments, and how accurately those outputs mirror reality. It also means designing new sampling protocols to routinely test whether automated classification systems are working. Without this layer of assessment, teams operate under the illusion of confidence, running outreach and campaigns based on AI-generated profiles that haven’t been validated.
And validation requires people who actually understand how AI models function, where they’re strong, where they’re brittle, and how they handle ambiguity. This is where most organizations fall short. AI literacy training is rarely prioritized, especially for non-technical teams. That’s a mistake. Your marketers, analysts, and operations leaders need to understand how large language models make predictions, why hallucinations occur, what data quality signals they consider, and how to spot flawed outputs before they trigger workflows.
Without this base knowledge, your organization creates a fragile dependency on systems it can’t fully audit. That risk compounds with scale. But with training, cross-functional collaboration becomes more grounded. Marketing and data teams speak the same operational language. Customer experience leaders can weigh in on how AI outcomes align with real user feedback. Product teams can flag inconsistent inputs that need adjusted pipelines. Everyone works from a shared understanding of how AI is shaping strategic operations.
Executives should push AI literacy as a near-term operational priority, taught, reinforced, and tracked across departments. Not as a technical deep dive, but as applied business knowledge. It’s the only way to ensure that AI becomes an accelerated decision enabler instead of an unchecked output engine.
Precision starts with teams who know what to look for, and what questions to ask when AI handles the inputs.
Addressing the challenges of unstructured data
Most operational roadmaps in marketing and data teams were built to manage structured data. That’s no longer enough. Generative AI tools are injecting vast amounts of unstructured content into ecosystems, conversations, transcripts, sentiment fragments, none of it captured in dropdown fields or binary categories. To respond to that shift, teams will need to step outside traditional scope and redirect both capital and focus.
That includes more than retooling systems. It means reallocating resources, personnel, budget, and project capacity, to prioritize unstructured data initiatives. Teams currently focused on structured data hygiene must broaden their scope. They now need to evaluate how to extract, verify, and activate unstructured signals, especially in coordination with systems that weren’t designed for this type of input.
Martech teams can’t do this in isolation. Data and insights about customer sentiment or product usage typically sit in customer service, sales enablement, research, and product departments. These are sources of unstructured value—support transcripts, public-facing feedback, call notes, UX recordings, that marketing needs access to. Bridging those datasets requires cross-functional alignment. Otherwise, insights remain locked in silos, and AI models trained on incomplete inputs generate irrelevant or misleading outputs.
These realignments won’t happen organically. Executives need to lead coordination efforts by setting shared goals, defining unified data governance standards, and making time for collaborative operational planning. Senior leaders across departments must understand that accurate personalization and predictive modeling are no longer just technology discussions, they’re business enablers that rely on cohesive input strategies.
Equally important is investing in AI literacy across functions. Teams handling unstructured data need to understand how AI parses input, how that input can become decision logic, and where bias or mismatch can occur. Without that foundation, collaboration between data, marketing, and product will break under the pressure of scale.
The move to manage unstructured data demands an organizational shift. Teams must operate with shared context and updated priorities. That alignment will determine whether AI investments deliver exponential learning and output—or create more noise, confusion, and inefficiency.
Executives should act now to centralize ownership, coordinate data-sharing across functions, and make sure all teams have the tools, and understanding, needed to operate in this new input environment. This is the next layer of scalable infrastructure.
Recap
Most systems in place today were designed for a version of data that no longer defines reality. Structured inputs made sense when customer behaviors followed predictable patterns, when data came in clean rows, and when AI wasn’t generating thousands of context-rich signals every day. That playbook is outdated.
Unstructured data is the new core input. Emails, chats, voice notes, reviews, auto-generated content, this is now what drives customer understanding, product feedback, lead qualification, and campaign logic. And if your stack isn’t built to process and act on it, your outcomes will suffer.
This shift won’t be solved through dashboards or surface-level automation. It demands foundational change, more adaptive systems, evolved governance, new KPIs, and teams trained to understand how AI shapes these inputs. Cross-functional collaboration becomes mandatory. So does transparency across tooling.
For executives looking to scale personalization, increase data confidence, and future-proof decision-making, this is the moment to move. Don’t wait for clean handoffs or polished standards. The companies that hardwire unstructured processing into their operations today will outperform on speed, accuracy, and adaptability tomorrow.
Lead the transition before you’re forced to follow it.
