The Hidden Achilles’ Heel of AI Implementations
Why data—not models—is what ultimately determines whether AI succeeds or stalls
The secret to successful AI
Everywhere you look, companies are racing to implement AI—smarter marketing, predictive sales, self-service support, and products that adapt in real time. The models are impressive, the promises are bold, and the pressure to act is real. But beneath nearly every AI initiative sits the same hidden Achilles’ heel: the data it depends on. When that data is fragmented, inconsistent, or poorly understood, even the best AI implementations stall—or fail outright.
The fix isn’t a rip-and-replace of your tech stack. It’s cheaper and more controllable: get explicit about how you describe your business, your customers, and their journey—then add lightweight “scaffolding” so that context gets captured consistently as work happens.
That’s the step most AI programs skip. Teams rush to models, integrations, and use cases before putting scaffolding in place across marketing, sales, service, and product. Without that foundation, AI only amplifies confusion. With it, AI investments finally have a fair shot at producing measurable ROI.
The four categories of customer-engagement AI use cases
Once that scaffolding is in place, the question stops being “What can AI do?” and becomes “Where does AI actually create value for our business?”
Across industries, AI use cases tied to customer engagement consistently fall into four categories. Each serves a different purpose—and each depends on business-specific context being captured in a clean, consistent way. Without that foundation, these use cases stay small, siloed, or experimental. With it, they become scalable and measurable.
Co-Creation & Design Acceleration
Description: Tools that assist with day-to-day work—drafting copy, building slide decks, summarizing research, suggesting experiments or product ideas.
Data Needed: General-purpose AI models further trained on business-specific content and metadata.
Workflow Automation & Orchestration
Description: Business processes, applications, and models stitched together with rules and statistical analysis to increase efficiency.
Data Needed: Business-specific project, content, and workflow metadata combined with general-purpose models.
Audience Insight & Segmentation
Description: Analyzing and targeting audiences to identify conversion, retention, and upsell opportunities.
Data Needed: Historic customer engagement data, lifecycle performance, and business-specific objective metadata.
Adaptive Experiences & Personalization
Description: Dynamically changing products, content, or offers based on behavior and business priorities.
Data Needed: Historic engagement data, lifecycle performance, and business-specific objective metadata.
What “bad data” really means
While these use cases address different business needs, they all rely on some combination of:
Business-specific customer engagement data
Business-specific customer lifecycle performance data
Business-specific objective, project, content, and audience metadata
When companies say AI initiatives are hard to implement—or fail altogether—they usually blame “bad data.” That term hides several distinct and very fixable problems:
Poor quality
Duplicate records, missing fields, stale information, inconsistent tagging, broken tracking, or bot-polluted behavior data all degrade model performance.
Incomplete signals
Many organizations log activity but fail to associate it with meaningful outcomes. Models trained on data without clear success signals can’t learn what “good” looks like.
Inconsistent labeling
Humans tag leads, tickets, opportunities, and content inconsistently—often because forms are confusing or time-pressured. Models trained on this data spend their energy compensating for noise.
Inadvertent or intentional bias
Different teams optimize for different metrics. Some metrics are overlooked; others are gamed. AI trained on biased data reflects—and reinforces—that bias.
Inadequate knowledge bases
Documentation goes stale. Policies conflict. Critical knowledge lives on laptops or in people’s heads. Models can’t reason over requirements they can’t access.
Data fragmentation
Data generated across CRMs, CDWs, DAMs, MAPs, and service platforms is collected, tagged, and stored differently. Fragmented data dramatically increases the cost, effort, and oversight required to run AI effectively.
Can’t we just live with bad data?
In the short term, yes—and many companies do.
You can still get value from AI that doesn’t rely heavily on clean internal data:
· Copilots for writing, summarization, and brainstorming
· Narrow, local models embedded inside individual tools
Useful? Absolutely. Transformative? No.
If your goal is AI that materially changes how your business operates, you eventually have to face the data problem head-on.
Can’t we make this someone else’s problem?
Can’t AI fix this?
No. AI outputs are only as good as the data they’re trained on. If you don’t understand or trust the data, AI won’t magically improve it.
Don’t third-party platforms solve this?
No. Salesforce, Adobe, HubSpot, and others provide powerful capabilities—but they all assume clean, well-understood data as a starting point.
Can’t we assign a central AI, data, or operations team to fix it?
Not effectively. Centralized control creates bottlenecks, and fixing data after the fact strips it of operational value while placing unrealistic interpretive burdens on one team.
How to fix bad data without boiling the ocean
There’s no silver bullet for data quality. But you don’t need to rebuild your business from scratch either. Companies that successfully address data problems focus on a few simple scaffolding principles:
Outline a shared model
Establish a simple, common story for who your customers are, what you sell, and how customers move through your business—not a 300-page blueprint.
Fix data at the source
Improve the forms and workflows where data is created. Make them easier to complete and harder to get wrong.
Clean and monitor pipelines
Standardize, deduplicate, and validate data as it flows into analytics and AI environments. Set alerts when schemas or key tables change.
Instrument outcomes and processes
Define clear truth events—revenue, renewal, churn, satisfaction—and clean logs of how work moves through the system.
Assign ownership
Make people accountable for segments, product hierarchies, content catalogs, and playbook libraries. Governance is part of the job.
This level of alignment is harder than it sounds. Execution teams focus on delivery. Leadership focuses on outcomes. Outside partners only see what they’re shown. Platforms deliver capabilities—but not operating models.
What Schema6 brings to the table
Schema6 delivers three overlapping solutions that embed data best practices directly into how your business operates—so clean, well-understood data becomes the default.
These include:
An off-the-shelf customer lifecycle framework and metadata ontology that is simple to understand, easy to apply, and fully customizable.
Targeted implementation solutions that introduce this framework into a small number of high-leverage business processes.
Organizational design services that ensure the right roles and responsibilities exist to operate the model at scale.
At the core of Schema6 is a simple principle: every customer engagement can be described using six facets—the 6Qs (WHO, WHAT, WHERE, WHEN, WHY, HOW). By categorizing engagements the same way, using governed classifications, organizations capture standardized context that can be reused across analytics, automation, and AI.
Schema6 focuses on where data originates—planning, audience definition, behavioral capture, work intake, and prioritization—refining each step to make correct execution easier and immediately more valuable.
Isn’t this only for big companies?
No. The Schema6 approach applies to organizations of any size or maturity.
For small companies
Get more value from off-the-shelf AI tools
Simplify day-to-day customer engagement work
Measure cumulative results over time
For mid-sized companies
Control costs by reducing tool sprawl
Track activity across a growing business using shared IDs and metadata standards
Lower the risk and upfront cost of introducing DAMs, CDPs, and CDMs
For large enterprises
Connect marketing, sales, CX, and service with a shared operating model
Integrate Salesforce, Adobe, Workfront, Seismic, MAPs, CRM, service platforms, and product analytics through a shared semantic layer
Enable cross-department AI initiatives
You don’t need massive investments in technology, headcount, or change programs—just lightweight scaffolding around what you already do.
Start leveraging clean, AI-ready data
If you’re a CMO, CRO, CCO, or COO, Schema6 offers a practical way to reduce the cost and risk of AI implementation investments.
If you lead marketing, sales, CX, or service operations, it replaces one-off definitions and mappings with shared scaffolding that scales.
If you’re responsible for data, analytics, or AI, it turns operational exhaust into consistent, model-ready inputs you can trust.
If you work in product or digital, it connects in-product behavior to campaigns, journeys, and service in a way AI can actually learn from.
If this sounds like the missing foundation in your AI efforts, DM me to start a focused conversation.
We’ll identify where your data breaks down today and what a practical, low-disruption path to measurable ROI looks like.
Because AI will only ever be as effective as the data beneath it—
and Schema6 is a disciplined way to finally make that data work.
