Editorial
eBook
Designing an AI-Native Contact Center
Here's an uncomfortable question for every executive with a GenAI budget: Who's going to maintain the content your AI depends on?
The question is not who built it or who launched it, which may have been associated with some fanfare, but how can content be kept fresh and relevant over time.
Who's going to keep it accurate, current and trustworthy six months from now? A year from now? When product features change, or corporate policies are updated or the organization restructures, who will track and document these changes in your organizational content?
If you don't have a clear answer, you don't have a scalable AI initiative. You have a static pilot project waiting to decay.
Table of Contents
- The Key to Long-Term GenAI Success
- The 5 Capability Dimensions
- The EIS Content Operations Maturity Model
- Industry Benchmark: Content Operations Maturity
- The 5 Roles You Need at Scale
- Assessing Your Current Maturity State
- How to Jump Maturity Levels
- The Bottom Line
The Key to Long-Term GenAI Success
The dirty secret of enterprise GenAI is that content operations — not model sophistication — determines long-term success.
Organizations obsess over which LLM to use while ignoring the operational reality that, over time, content goes stale and metadata drifts. Data quality erodes the moment you stop actively managing it. Pilot projects succeed because companies invest in data and content curation.
Recent research from PWC confirms this: organizations with data and content operations are more likely to report successful AI implementations compared to those with ad-hoc content practices. The correlation isn't coincidental — it's causal.
However, enterprises need scalable content operations that do not rely on such labor-intensive maintenance. The gap between those two states is where most GenAI initiatives go to die.
After 25 years of helping Fortune 1000 organizations manage enterprise content, we've developed a five-level maturity model that assesses operational readiness across multiple capability dimensions. Each level corresponds to a specific scale ceiling that represents the point at which your current operations can no longer support growth. In addition, each level is supported by five capability dimensions. The way in which these capabilities are manifested affects the maturity level of the organization.
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The 5 Capability Dimensions
Each maturity level should be assessed across the five distinct capability dimensions described below. Organizations rarely advance uniformly; they might be competent in governance but still aware in information architecture.
- Content Operations: How content gets created, maintained and retired. The lifecycle from ideation through archival.
- Information Architecture: How content is structured, modeled and organized. The taxonomy, metadata and content schemas that enable retrieval.
- Technology Integration: How systems work together. The degree of automation, data flow and cross-platform coordination.
- User Proficiency & Content Practices: How effectively people use the systems and follow the practices. The human side of content operations.
- Governance: How decisions get made, quality gets assured and standards get enforced. The organizational framework that keeps everything working.
Understanding your organization’s maturity level across all five dimensions reveals where investment will have the greatest impact. Although maturity can vary across dimensions, there are some interdependencies to keep in mind. Advancing governance without advancing information architecture creates compliance theater. Advancing technology without advancing user proficiency creates expensive shelfware.
The EIS Content Operations Maturity Model
The five maturity levels progress from Unpredictable through Aware, Competent and Synchronized to Choreographed. Understanding where you are across each dimension tells you exactly what you need to build next.
| Maturity Capability | 1-Unpredictable | 2-Aware | 3-Competent | 4-Synchronized | 5-Choreographed |
|---|---|---|---|---|---|
| Content operations | Information sprawl: chaotic, inconsistent and unguided | Stable and labor-intensive rudimentary lifecycle; siloed activities with department-level incentives; one-off experiments; low reuse | Comprehensive content lifecycle; rudimentary sharing; broad audience segmentation | Adaptive content repurposed across applications and channels; content effectiveness reporting | Automation-supported production enables meaningful personalization; accountability for content effectiveness |
| Information Architecture | Static, monolithic documents; tool- and device-specific content structures | templates and copies with burdensome tagging requirements; | case-driven component reuse, rules-based tagging, limited customization | multi-dimensional modeling of content in selected channels for few defined customer groups | high-fidelity multi-dimensional content model across multiple content asset types and channels |
| Technology Integration | Multiple tools with inconsistent architecture | Partially harmonized infrastructure, manual reuse, significant overlapping functionality | Semi-automated content reuse across systems with harmonized metadata throughout customer lifecycle | Programmatic content reuse and component assembly using signals from upstream and downstream applications | Omni channel, integrated, harmonized, personalized content from headless CMS feeding all downstream systems |
| User Proficiency & Content Practices | Poor or minimal usage, lack of awareness of capabilities or content practices | Early adopters and power users using out-of-box features, little control of content | Departmental collaboration with basic content control | Cross-team project-oriented collaboration with information lifecycle management | Automated workflows and reporting for compliance with enterprise content standards |
| Governance | Information sprawl, lack of vision, no intentional decision making | Awareness of challenges, activity is monitored but not constrained | Assigned responsibilities, oversight and communications infrastructure in place | Intentional decision making, resource allocation, change controls in effect | Agenda-driven business leadership and stakeholder engagement to effect continuous process improvement |
Level 1: Unpredictable
The Scale Ceiling: ~100 documents, ~10 users
At Level 1, content operations is characterized by chaos. There's no intentional system — just individuals doing what seems right in the moment.
Content Operations: Information sprawl is the norm. Content creation is chaotic, inconsistent and unguided. Different people create similar content in different ways with no awareness of what already exists.
Information Architecture: Documents are static and monolithic. Content structures are tool-specific and device-specific. There's no taxonomy beyond basic folder organization, if that.
Technology Integration: Multiple tools operate with inconsistent architecture. Systems don't talk to each other. Data is siloed, duplicated and often contradictory.
User Proficiency & Content Practices: Poor or minimal usage of available capabilities is the norm. Most employees don't know what's available or how to contribute effectively. Content practices vary wildly by individual and department.
Governance: Non-existent, unfortunately. There's no vision, no intentional decision-making, no oversight. Problems get fixed only when they cause significant pain.
Why Organizations Stay Here: Level 1 works for pilot projects because one person can hold everything in their head. The data science team manually curates 100 documents. The project lead personally reviews every output. Quality is maintained through heroic individual effort.
Why It Fails at Scale: That hero becomes a bottleneck because everything gets funneled through them. They get overwhelmed and quality silently degrades. You can't scale acts of heroics. Or they go on vacation and nothing gets updated. Ultimately, they leave, and institutional knowledge walks out the door.
Level 2: Aware
The Scale Ceiling: ~1,000 documents, ~100 users
At Level 2, organizations have recognized the problem and begun responding — but efforts remain fragmented and labor-intensive.
Content Operations: Stable but still labor-intensive. There's a rudimentary content lifecycle, but activities remain siloed within departments. Each team has their own incentives, their own approaches. Content reuse is low, and duplication is high.
Information Architecture: Templates and copies proliferate. Tagging requirements exist but feel burdensome. There's some component reuse driven by specific cases, with rules-based tagging and limited customization.
Technology Integration: Infrastructure is partially harmonized. Manual reuse across systems is possible but tedious. Significant overlapping functionality creates confusion about which system to use.
User Proficiency & Content Practices: Early adopters and power users leverage out-of-box features, but most employees still have little control of content and minimal awareness of best practices.
Governance: Departmental expectations exist, but territorialism dominates. Compliance happens through passionate individual SMEs, not systematic processes. Content-related activities are monitored but not constrained.
Why Organizations Plateau Here: Level 2 feels like progress. The organization has documentation and templates. People have been trained on standards. But documentation isn't execution. The processes exist on paper; following them is optional. Without enforcement, the gap between policy and practice grows wider over time.
The Telltale Sign: Quality varies significantly depending on who created the content and when it was created. Reviews only happen when someone remembers to schedule them. Your AI works up to a point but then falters, and you can't figure out why.
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Level 3: Competent
The Scale Ceiling: ~10,000 documents, ~1,000 users
At Level 3, content operations becomes a genuine organizational capability rather than a set of good intentions. This is the minimum level required for enterprise GenAI that works reliably.
Content Operations: Comprehensive content lifecycle with defined stages and clear ownership has been established. Rudimentary sharing takes place across departments. Broad audience segmentation informs what content gets created and for whom.
Information Architecture: Multi-dimensional modeling of content has been implemented, at least in selected channels for defined customer groups. Content isn't just stored; it's structured to enable retrieval and reuse.
Technology Integration: Semi-automated content reuse across systems has begun to emerge. Harmonized metadata is present throughout the customer lifecycle. Systems begin working together rather than in isolation.
User Proficiency & Content Practices: Departmental collaboration with basic content control can now take place. Teams work together on shared content. Basic lifecycle management is in place so that people know when content needs review.
Governance: Funded, centralized oversight with defined policy with repeatable processes that don't depend on individual memory. Assigned responsibilities lets everyone know who owns what. Analytics reporting provides visibility into content health.
What Changes at Level 3: Workflows are enforced, not suggested. Content can't be published without required metadata. Reviews happen on schedule because the system triggers them. Audits occur because they're calendared, not because someone got around to it.
The Business Impact: Below Level 3, content operations relies on luck and individual effort. At Level 3, systems maintain quality even when individuals are busy, distracted or gone. AI becomes trustworthy because the content it retrieves is trustworthy.
Level 4: Synchronized
The Scale Ceiling: ~50,000 documents, ~5,000 users
Level 4 is where operations become adaptive. Content doesn't just get managed — it gets optimized based on how it performs.
Content Operations: Adaptive content is repurposed across applications and channels. Content effectiveness is actually measured and reported. You know what's working, not just what exists.
Information Architecture: High-fidelity multi-dimensional content models span multiple content asset types and channels. The same taxonomy, metadata and content structures work across the enterprise.
Technology Integration: Programmatic content reuse and component assembly has been enabled. Upstream and downstream applications send signals that inform content delivery. Systems aren't just integrated, they're intelligent.
User Proficiency & Content Practices: Cross-team, project-oriented collaboration has become established. Information lifecycle management is mature — creation, review, update and retirement happen systematically. Content practices are consistent across the organization.
Governance: Specialized stewardship service teams maintain quality. A culture of personal responsibility emerges. Cross-functional decision-making replaces departmental silos. Governance enables agility rather than blocking it.
The Feedback Loop Activates: Monitor doesn't just mean "check that it's still there." It means tracking how content performs; for example, is it being retrieved? Is it answering user questions? Are users giving positive or negative feedback? Is it still accurate given business changes?
The Business Impact: When users can't find answers, that gap gets identified and prioritized. When AI retrieves wrong content, that error triggers a correction workflow. The system gets better because it's designed to learn from its failures.
Level 5: Choreographed
The Scale Ceiling: 100,000+ documents, 10,000+ users
Level 5 is where content operations becomes a sustainable competitive advantage. Few organizations reach this level. Those that do find that every subsequent AI initiative becomes easier, because the operational infrastructure already exists.
Content Operations: Automation-supported production enables meaningful personalization at scale. There's accountability for content effectiveness — not just content creation. Content operations is a recognized business function, not a support activity.
Information Architecture: High-fidelity, multi-dimensional content models span all content asset types and channels. The architecture adapts to new use cases without requiring redesign.
Technology Integration: Omnichannel, integrated, harmonized, personalized content flows from a headless CMS architecture feeding all downstream systems. Content is created once and published everywhere, automatically assembled for each context.
User Proficiency & Content Practices: Automated workflows and reporting ensure compliance with enterprise content standards. Contribution is frictionless. Best practices aren't trained — they're embedded in the tools.
Governance: Integrated operations are driven by business value. There's an unconscious competency — quality happens automatically. Proactive, agenda-driven leadership continuously improves the system rather than just maintaining it.
The "Create Once, Publish Everywhere" Reality: A troubleshooting procedure exists as a component. It gets published to the self-service portal, surfaced by the AI assistant, displayed to call center agents and embedded in field service applications. One source of truth, and multiple points of delivery characterize this level.
One large technology firm we work with handles over one million knowledge transactions per day using this approach. The same components serve channel partners, marketing campaigns, customer self-service, contact center agents, field support, embedded product knowledge, bot functionality and personalization at scale.
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Industry Benchmark: Content Operations Maturity
A 2024 survey of Fortune 1000 companies by AIIM found that only 12% of organizations rate their content operations maturity at Level 4 (Synchronized) or above. The majority (58%) operate at Level 2 (Aware), while 23% remain at Level 1 (Unpredictable). The remaining 7% have achieved Level 3 (Competent) — the minimum threshold for reliable enterprise GenAI.
Organizations that advanced from Level 2 to Level 3 reported:
• 45% reduction in content duplication
• 67% improvement in content findability
• 38% faster onboarding for new employees
• 52% higher user satisfaction with internal search
The 5 Roles You Need at Scale
Regardless of maturity level, sustainable content operations require five distinct functions. In early stages, one person might wear multiple hats. At scale, these become dedicated roles or teams.
1. Content Strategy
Defines what content is needed, for whom and why. Aligns content investments with business priorities. Without this role, you create content nobody needs while leaving critical gaps unfilled.
2. Content Creation
Produces the actual content — whether that's SMEs writing documentation, technical writers crafting procedures or marketing developing collateral. The people who know the subject matter and can articulate it clearly.
3. Content Enrichment
Adds the metadata, tags and links that make content findable and useful. This is increasingly AI-assisted, but requires human judgment to validate and refine. Without enrichment, content exists but can't be retrieved accurately.
4. Content Governance
Ensures quality, accuracy and compliance. Reviews content before publication, enforces standards, manages the approval process. The organizational immune system that catches problems before users do.
5. Content Operations
Manages the workflow, tools and processes that keep everything moving. The operational backbone that turns good intentions into consistent execution.
Missing any of these functions creates a failure mode. Miss content strategy and you build the wrong things. Miss enrichment and your AI can't find what you've built. Miss governance and quality erodes. Miss operations and nothing happens consistently.
Assessing Your Current Maturity State
Where does your organization fall on the maturity model? Here's a diagnostic across each dimension:
You're at Level 1 (Unpredictable) if:
- Content creation is chaotic — no one knows what exists or who owns it
- There's no taxonomy beyond folder names
- Systems don't share data; everything is siloed
- Most people don't know how to find or contribute content
- There's no governance — problems get fixed only when they cause visible pain
You're at Level 2 (Aware) if:
- You have templates and guidelines, but they're inconsistently followed
- Tagging exists but feels burdensome; compliance is spotty
- Some systems are connected, but there's lots of manual work
- Power users know the system; everyone else struggles
- Departments have their own standards; there's no enterprise view
You're at Level 3 (Competent) if:
- Content lifecycle stages are defined and generally followed
- Multi-dimensional content models exist for key use cases
- Systems share data through semi-automated processes
- Teams collaborate on shared content with basic controls
- Governance is funded with assigned responsibilities and regular reporting
You're at Level 4 (Synchronized) if:
- Content effectiveness is measured and drives decisions
- Enterprise-wide content models span multiple channels
- Systems exchange signals and assemble content programmatically
- Cross-functional collaboration with mature lifecycle management
- Stewardship teams maintain quality; governance enables agility
You're at Level 5 (Choreographed) if:
- Automation enables personalization at scale
- Architecture adapts to new use cases without redesign
- Omni-channel content flows automatically to all touchpoints
- Contribution is frictionless; best practices are embedded
- Governance drives continuous improvement proactively
Most organizations overestimate their maturity level. They confuse having documentation (Level 2) while having operational discipline (Level 3). They mistake occasional cross-team projects (Level 3) for systematic optimization (Level 4).
Be honest with yourself, because the success of your AI initiative depends on it.
How to Jump Maturity Levels
You can't jump from the Unpredictable to Choreographed maturity level. Each level builds capabilities required for the next. The path forward is therefore sequential, Here are some suggestions to ease the transitions:
From Unpredictable to Aware: Acknowledge the chaos. Document what exists. Create templates and guidelines. Identify owners for key content areas. Establish basic monitoring.
From Aware to Competent: Enforce what you've documented. Fund central oversight. Define clear processes with assigned responsibilities. Implement systems that support (and require) compliance. Start measuring.
From Competent to Synchronized: Connect effectiveness to decisions. Build enterprise-wide models. Enable cross-functional collaboration. Create feedback loops from usage data. Invest in stewardship.
From Synchronized to Choreographed: Automate what works. Build adaptive architectures embed best practices in tools. Drive operations from business value, not just compliance. Make continuous improvement the default.
The investment required increases at each level. So does the return. Organizations at Level 5 don't just have better AI — they have a sustainable competitive advantage in how they manage and leverage organizational knowledge.
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The Bottom Line
GenAI can accelerate content operations. It can suggest metadata, identify gaps, flag inconsistencies and automate routine tasks. But it cannot replace the operational discipline that keeps content accurate, current and trustworthy.
Humans must remain in the loop for quality control. The question is whether those humans are operating in an ad hoc scramble or a choreographed operational system.
Content operations maturity determines your AI ceiling. Build the operations, and technology will take you as far as you want to go. Skip the operations, and no amount of technology investment will save you.
The organizations that understand this are building operational capabilities while their competitors chase the latest model release. When the dust settles, operational maturity — not technical sophistication — will separate the winners from the also-rans.
Where are you on the maturity model? More importantly, what are you doing about it?
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