This, of course, is an ongoing challenge, rather than a moment in time initiative. To ensure the important work you’ve done to get your content, data, and other assets AI-ready is not lost, you need governance as well as an operating model to guide it. Indeed, well before any AI readiness initiative, governance and the organization must be top of mind. 

Governance is not a new term within the field. Historically, we’ve identified four core components to governance in the context of content or data:

• Business Case and Measurable Success Criteria: Defining the value of the solution and the governance model itself, as well as what success looks like for both.
• Roles and Responsibilities: Defining the individuals and groups necessary for governance, as well as the specific authorities and expectations of their roles.
• Policies and Procedures: Detailing the timelines, steps, definitions, and actions for the associated roles to play.
• Communications and Training: Laying out the approach to two-way communications between the associated governance roles/groups and the various stakeholders.

These traditional components of governance all have held up, tried and true, over the quarter-century since we first defined them. In the context of AI, however, it is important to go deeper and consider the unique aspects that artificial intelligence brings into the conversation. Virtually every expert in the field agrees that AI governance should be a priority for any organization, but that must be detailed further in order to be meaningful.

In the context of AI readiness for knowledge assets, we focus AI governance, and more broadly its supporting operating model, on five key elements for success:

• Coordination and Enablement Over Execution
• Connection Instead of Migration
• Filling Gaps to Address the Unanswerable Questions
• Acting on “Hallucinations”
• Embedding Automation (Where It Makes Sense)

There is, of course, more to AI governance than these five elements, but in the context of AI readiness for knowledge assets, our experience shows that these are the areas where organizations should be focusing and shifting away from traditional models. 

1) Coordination and Enablement Over Execution

In traditional governance models (i.e. content governance, data governance, etc.), most of the work was done in the context of a single system. Content would be in a content management system and have a content governance model. Data would be in a data management solution and have a data governance model. The shift is that today’s AI governance solutions shouldn’t care what types of assets you have or where they are housed. This presents an amazing opportunity to remove artificial silos within an organization, but brings a marked challenge. 

If you were previously defining a content governance model, you most likely possessed some level of control or ownership over your content and document management systems. Likewise, if you were in charge of data governance, you likely “own” some or all of the major data solutions like master data management or a data warehouse within your organization. With AI, however, an enormous benefit of a correctly architected enterprise AI solution that leverages a semantic layer is that you likely don’t own these source systems. The system housing the content, data, and other knowledge assets is likely, at least partly, managed by other parts of your organization. In other words, in an AI world, you have less control over the sources of the knowledge assets, and thereby over the knowledge assets themselves. This may well change as organizations evolve in the “Age of AI,” but for now, the role and responsibility for AI governance becomes more about coordination and less about execution or enforcement.

In practice, this means an AI Governance for Knowledge Asset Readiness group must coordinate with the owners of the various source systems for knowledge assets, providing additive guidance to define what it means to have AI-ready assets as well as training and communications to enable and engage system and asset owners to understand what they must do to have their content, data, and other assets included within the AI models. The word “must” in the previous sentence is purposeful. You alone may not possess the authority of an information system owner to define standards for their assets, but you should have the authority to choose not to include those assets within the enterprise AI solution set.

How do you apply that authority? As the lines continue to blur between the purview of KM, Data, and AI teams, this AI Governance for Knowledge Asset Readiness group should comprise representatives from each of these once siloed teams to co-own outcomes as new AI use cases, features, and capabilities are developed. The AI governance group should be responsible for delineating key interaction points and expected outcomes across teams and business functions to build alignment, facilitate planning and coordination, and establish expectations for business and technical stakeholders alike as AI solutions evolve. Further, this group should define what it means (and what is required) for an asset to be AI-ready. We cover this in detail in previous articles, but in short, this boils down to semantic structure, quality, and entitlements as the three core pillars to AI readiness for knowledge assets. 

2) Connection Instead of Migration

The idea of connections over migration aligns with the previous point. Past monolithic efforts in your organization would commonly have included massive migrations and consolidations of systems and solutions. The roadmaps of past MDMs, data warehouses, and enterprise content management initiatives are littered with failed migrations. Again, part of the value of an enterprise AI initiative that leverages a semantic layer, or at least a knowledge graph, is that you don’t need to absorb the cost, complexity, and probable failure of a massive migration. 

Instead, the role of the AI Governance for Knowledge Asset Readiness group is one of connections. Once the group has set the expectation for AI-ready knowledge assets, the next step is to ensure the systems that house those assets are connected and available, ready for the enterprise AI solutions to be ingested and understood. This can be a highly iterative process, not to be rushed, as the sanctity of the assets ingested by AI is more important than their depth. Said differently, you have few chances to deliver wrong answers—your end users will lose trust quickly in a solution that delivers inaccurate information that they know is unmistakably incorrect; but if they receive an incomplete answer instead, they will be more likely to raise this and continue to engage. The role of this AI governance group is to ensure the right systems and their assets are reliably available for the AI solution(s) at the right time, after your knowledge assets have passed through the appropriate requirements.

3) Filling Gaps to Address the Unanswerable Questions

As the AI solutions are deployed, the shift for AI governance moves from being proactive to reactive. There is a great opportunity associated with this that bears a particular focus. In the history of knowledge management, and more broadly the fields of content management, data management, and information management, there’s always been a creeping concern that an organization “doesn’t know what it doesn’t know.” What are the gaps in knowledge? What are the organizational blind spots? These questions have been nearly impossible to answer at the enterprise level. However, with enterprise-level AI solutions implemented, the ability to have this awareness is suddenly a possibility.

Even before deploying AI solutions, a well-designed semantic layer can help pinpoint organizational gaps in knowledge by finding taxonomy elements lacking in applied knowledge assets. However, this potential is magnified once the AI solution is fully defined. Today’s mature AI solutions are “smart” enough to know when they can’t answer a question and highlight that unanswerable question to the AI governance group. Imagine possessing the organizational intelligence to know what your colleagues are seeking to understand, having insights into that which they are trying to learn or answer, but are currently unable to. 

In this way, once an AI solution is deployed, the primary role of the AI governance group should be to diagnose and then respond to these automatically identified knowledge gaps, using their standards to fill them. It may be that the information does, in fact, exist within the enterprise, but that the AI solution wasn’t connected to those knowledge assets. Alternatively, it may be that the right semantic structure wasn’t placed on the assets, resulting in a missed connection and a false gap from the AI. However, it may also be that the answer to the “unanswerable” question only exists as tacit knowledge in the heads of the organization’s experts, or doesn’t exist at all. This is the most core and true value of the field of knowledge management, and has never been so possible.

4) Acting on “Hallucinations”

Aligned with the idea of filling gaps, a similar role for the AI governance group should be to address hallucinations or failures for AI to deliver an accurate, consistent, and complete “answer.” For organizations attempting to implement enterprise AI, a hallucination is little more than a cute word for an error, and should be treated as such by the AI governance group. There are many reasons for these errors, ranging from poor quality (i.e., wrong, outdated, near-duplicate, or conflicting) knowledge assets, insufficient semantic structure (e.g., taxonomy, ontology, or a business glossary), or poor logic built into the model itself. Any of these issues should be treated with immediate action. Your organization’s end users will quickly lose trust in an AI solution that delivers inaccurate results. Your governance model and associated organizational structure must be equipped to act quickly, first to leverage communications and feedback channels to ensure your end users are telling you when they believe something is inaccurate or incomplete, and moreover, to diagnose and address it.

As a note, for the most mature organizations, this action won’t be entirely reactive. For the most mature, organizational subject matter experts will be involved in perpetuity, especially right before and after enterprise AI deployment, to hunt for errors in these systems. Commonly, you can consider this governance function as the “Hallucination Killers” within your organization, likely to be one of the most critical actions as AI continues to expand.

5) Embedding Automation (Where It Makes Sense)

Finally, one of the most important roles of an AI governance group will be to use AI to make AI better. Almost everything we’ve described above can be automated. AI can and should be used to automate identification of knowledge gaps as well as solve the issue of those knowledge gaps by pinpointing organizational subject matter experts and targeting them to deliver their learning and experience at the right moments. It can also play a major role in helping to apply the appropriate semantic structure to knowledge, through tagging of taxonomy terms as metadata or identification of potential terms for inclusion in a business glossary. Central to all of this automation, however, is to ensure the ‘human is in the loop’, or rather, the AI governance group plays an advisory and oversight role throughout these automations, to ensure the design doesn’t fall out of alignment. This element further facilitates AI governance coordination across the organization by supporting stakeholders and knowledge asset stewards through technical enablement.

All of this presents a world of possibility. Governance was historically one of the drier and more esoteric concepts within the field, often where good projects went bad. We have the opportunity to do governance better by leveraging AI in the areas where humans historically fell short, while maintaining the important role of human experts with the right authority to ensure organizational alignment and value.

If your AI efforts aren’t yet yielding the results you expected, or you’re seeking to get things started right from the beginning, contact EK to help you.

The post Defining Governance and Operating Models for AI Readiness of Knowledge Assets appeared first on Enterprise Knowledge.

The potential of AI agents for productivity gains within organizations is undeniable—and it’s no longer a distant future. However, the key question today is: when is the right time to build and deploy an AI agent, and when is simpler automation the more effective choice?

While the idea of a fully autonomous assistant handling routine tasks is appealing, AI agents require a complex framework to succeed. This includes breaking down silos, ensuring knowledge assets are AI-ready, and implementing guardrails to meet enterprise standards for accuracy, trust, performance, ethics, and security.

Over the past couple of years, we’ve worked closely with executives who are navigating what it truly means for their organizations to be “AI-ready” or “AI-powered”, and as AI technologies evolve, this challenge has only become more complex and urgent for all of us.

To move forward effectively, it’s crucial to understand the role of AI agents compared to traditional or narrow AI, automation, or augmentation solutions. Specifically, it is important to recognize the unique advantages of agent-based AI solutions, identify the right use cases, and ensure organizations have the best foundation to scale effectively.

In the first part of this two-part series, I’ll outline the core building blocks for organizations looking to integrate AI agents. The goal of this series is to provide insights that help set realistic expectations and contribute to informed decisions around AI agent integration—moving beyond technical experiments—to deliver meaningful outcomes and value to the organization.

Understanding AI Agents

Components of Agentic AI Framework

1. Relevant Language and AI Models

Language models are the agent’s cognitive core, essentially its “brain”, responsible for reasoning, planning, and decision-making. While not every AI agent requires a Large Language Model (LLM), most modern and effective agents rely on LLMs and reinforcement learning to evaluate strategies and select the best course of action. LLM-powered agents are especially adept at handling complex, dynamic, and ambiguous tasks that demand interpretation and autonomous decision-making.

Choosing the right language model also depends on the use case, task complexity, desired level of autonomy, and the organization’s technical environment. Some tasks are better served to remain simple, with more deterministic workflows or specialized algorithms. For example, an expertise-focused agent (e.g., a financial fraud detection agent) is more effective when developed with purpose-built algorithms than with a general-purpose LLM because the subject area requires hyper-specific, non-generalizable knowledge. On the other hand, well-defined, repetitive tasks, such as data sorting, form validation, or compliance checks, can be handled by rule-based agents or classical machine learning models, which are cheaper, faster, and more predictable. LLMs, meanwhile, add the most value in tasks that require flexible reasoning and adaptation, such as orchestrating integration with multiple tools, APIs, and databases to perform real-world actions like dynamic customer service process, placing trades or interpreting incomplete and ambiguous information. In practice, we are finding that a hybrid approach works best.

2. Semantic Layer and Unified Business Logic

AI agents need access to a shared, consistent view of enterprise data to avoid conflicting actions, poor decision-making, or the reinforcement of data silos. Increasingly, agents will also need to interact with external data and coordinate with other agents, which compounds the risk of misalignment, duplication, or even contradictory outcomes. This is where a semantic layer becomes critical. By standardizing definitions, relationships, and business context across knowledge and data sources, the semantic layer provides agents with a common language for interpreting and acting on information, connecting agents to a unified business logic. Across several recent projects, implementing a semantic layer has improved the accuracy and precision of initial AI results from around 50% to between 80% and 95%, depending on the use case.

The semantic layer includes metadata management, business glossaries, and taxonomy/ontology/graph data schemas that work together to provide a unified and contextualized view of data across typically siloed systems and business units, enabling agents to understand and reason about information within the enterprise context. These semantic models define the relationships between data entities and concepts, creating a structured representation of the business domain the agent is operating in. Semantic models form the foundation for understanding data and how it relates to the business. By incorporating two or more of these semantic model components, the semantic layer provides the foundation for building robust and effective agentic perception, cognition, action, and learning that can understand, reason, and act on org-specific business data. For any AI, but specifically for AI agents, a semantic layer is critical in providing access to:

• Organizational context and meaning to raw data to serve as a grounding ‘map’ for accurate interpretation and agent action;
• Standardized business terms that establish a consistent vocabulary for business metrics (e.g., defining “revenue” or “store performance” ), preventing confusion and ensuring the AI uses the same definitions as the business; and
• Explainability and trust through metadata and lineage to validate and track why agent recommendations are compliant and safe to adopt.

Overall, the semantic layer ensures that all agents are working from the same trusted source of truth, and enables them to exchange information coherently, align with organizational policies, and deliver reliable, explainable results at scale. Specifically, in a multi-agent system with multiple domain-specific agents, all agents may not work off the same semantic layer, but each will have the organizational business context to interpret messages from each other as courtesy of the domain-specific semantic layers.

The bottom line is that, without this reasoning layer, the “black box” nature of agents’ decision-making processes erodes trust, making it difficult for organizations to adopt and rely on these source systems.

3. Access to AI-Ready Knowledge Assets and Sources

Agents require accurate, comprehensive, and context-rich organizational knowledge assets to make sound decisions. Without access to high-quality, well-structured data, agents, especially those powered by LLMs, struggle to understand complex tasks or reason effectively, often leading to unreliable or “hallucinated” outputs. In practice, this means organizations making strides with effective AI agents need to:

• Capture and codify expert knowledge in a machine-readable form that is readily interpretable by AI models so that tacit know-how, policies, and best practices are accessible to agents, not just locked in human workflows or static documents;
• Connect structured and unstructured data sources, from databases and transactional systems to documents, emails, and wikis, into a connected, searchable layer that agents can query and act upon; 
• Provide semantically enriched assets with well-managed metadata, consistent labels, and standardized formats to make them interoperable with common AI platforms; 
• Align and organize internal and external data so agents can seamlessly draw on employee-facing knowledge (policies, procedures, internal systems) as well as customer-facing assets (product documentation, FAQs, regulatory updates) while maintaining consistency, compliance, and brand integrity; and
• Enable access to AI assets and systems while maintaining strict controls over who can use it, how it is used, and where it flows.

This also means, beyond static access to knowledge, agents must also query and interact dynamically with various sources of data and content. Doing this includes connecting to applications, websites, content repositories, and data management systems, and taking direct actions, such as reading/writing into enterprise applications, updating records, or initiating workflows.

Enabling this capability requires a strong design and engineering foundation, allowing agents to integrate with external systems and services through standard APIs, operate within existing security protocols, and respect enterprise governance and record compliance requirements. A unified approach, bringing together disparate data sources into a connected layer (see semantic layer component above), helps break down silos and ensures agents can operate with a holistic, enterprise-wide view of knowledge.

4. Instructions, Guardrails, and Observability

Organizations are largely unprepared for agentic AI due to several factors: the steep leap from traditional, predictable AI to complex multi-agent orchestration, persistent governance gaps, a shortage of specialized expertise, integration challenges, and inconsistent data quality, to name a few. Most critically, the ability to effectively control and monitor agent autonomy remains a fundamental barrier—posing significant security, compliance, and privacy risks. Recent real-world cases highlight how quickly things can go wrong, including tales of agents deleting valuable data, offering illegal or unethical advice, and amplifying bias in hiring decisions or in public-sector deployments. These failures underscore the risks of granting autonomous AI agents high-level permissions over live production systems without robust oversight, guardrails, and fail-safes. Until these gaps are addressed, autonomy without accountability will remain one of the greatest barriers to enterprise readiness in the agentic AI era.

As such, for AI agents to operate effectively within the enterprise, they must be guided by clear instructions, protected by guardrails, and monitored through dedicated evaluation and observability frameworks.

• Instructions: Instructions define an AI agent’s purpose, goals, and persona. Agents don’t inherently understand how a specific business or organization operates. Instead, that knowledge comes from existing enterprise standards, such as process documentation, compliance policies, and operating models, which provide the foundational inputs for guiding agent behavior. LLMs can interpret these high-level standards and convert them into clear, step-by-step instructions, ensuring agents act in ways that align with organizational expectations. For example, in a marketing context, an LLM can take a general directive like, “All published content must reflect the brand voice and comply with regulatory guidelines”, and turn it into actionable instructions for a marketing agent. The agent can then assist the marketing team by reviewing a draft email campaign, identifying tone or compliance issues, and suggesting revisions to ensure the content meets both brand and regulatory standards.
• Guardrails: Guardrails are safety measures that act as the protective boundaries within which agents operate. Agents need guardrails across different functions to prevent them from producing harmful, biased, or inappropriate content and to enforce security and ethical standards. These include relevance and output validation guardrails, personally identifiable information (PII) filters that detect unsafe inputs or prevent leakage of PII, reputation and brand alignment checks, privacy and security guardrails that enforce authentication, authorization, and access controls to prevent unauthorized data exposure, and guardrails against prompt attacks and content filters for harmful topics. 
• Observability: Even with strong instructions and guardrails, agents must be monitored in real time to ensure they behave as expected. Observability includes logging actions, tracking decision paths, monitoring model outputs, cost monitoring and performance optimization, and surfacing anomalies for human review. A good starting point for managing agent access is mapping operational and security risks for specific use cases and leveraging unified entitlements (identity and access control across systems) to apply strict role-based permissions and extend existing data security measures to cover agent workflows.

Together, instructions, guardrails, and observability form a governance layer that ensures agents operate not only autonomously, but also responsibly and in alignment with organizational goals. To achieve this, it is critical to plan for and invest in AI management platforms and services that define agent workflows, orchestrate these interactions, and supervise AI agents. Key capabilities to look for in an AI management platform include: 

• Prompt chaining where the output of one LLM call feeds the next, enabling multi-step reasoning; 
• Instruction pipelines to standardize and manage how agents are guided;
• Agent orchestration frameworks for coordinating multiple agents across complex tasks; and 
• Evaluation and observability (E&O) monitoring solutions that offer features like content and topic moderation, PII detection and redaction, and protection against prompt injection or “jailbreaking” attacks. Furthermore, because training models involve iterative experimentation, tuning, and distributed computation, it is paramount to have benchmarks and business objectives defined from the onset in order to optimize model performance through evaluation and validation.

In contrast to the predictable expenses of standard software, AI project costs are highly dynamic and often underestimated during initial planning. Many organizations are grappling with unexpected AI cost overruns due to hidden expenses in data management, infrastructure, and maintenance for AI. This can severely impact budgets, especially for agentic environments. Tracking system utilization, scaling resources dynamically, and implementing automated provisioning allows organizations to maintain consistent performance and optimization for agent workloads, even under variable demand, while managing cost spikes and avoiding any surprises.

Many traditional enterprise observability tools are now extending their capabilities to support AI-specific monitoring. Lifecycle management tools such as MLflow, Azure ML, Vertex AI, or Databricks help with the management of this process at enterprise scale by tracking model versions, automating retraining schedules, and managing deployments across environments. As with any new technology, the effective practice is to start with these existing solutions where possible, then close the gaps with agent-specific, fit-for-purpose tools to build a comprehensive oversight and governance framework.

5. Humans and Organizational Operating Models

There is no denying it—the integration of AI agents will transform ways of working worldwide. However, a significant gap still exists between the rapid adoption plans for AI agents and the reality on the ground. Why? Because too often, AI implementations are treated as technological experiments, with a focus on performance metrics or captivating demos. This approach frequently overlooks the critical human element needed for AI’s long-term success. Without a human-centered operating model, AI deployments continue to run the risk of being technologically impressive but practically unfit for organizational use.

Human Intervention and Human-In-the-Loop Validation: One of the most pressing considerations in integrating AI into business operations is the role of humans in overseeing, validating, and intervening in AI decisions. Agentic AI has the power to automate many tasks, but it still requires human oversight, particularly in high-risk or high-impact decisions. A transparent framework for when and how humans intervene is essential for mitigating these risks and ensuring AI complies with regulatory and organizational standards. Emerging practices we are seeing are showing early success when combining agent autonomy with human checkpoints, wherein subject matter experts (SMEs) are identified and designated as part of the “AI product team” from the onset to define the requirements for and ensure that AI agents consistently focus on and meet the right organizational use cases throughout development. 

Shift in Roles and Reskilling: For AI to truly integrate into an organization’s workflow, a fundamental shift in the fabric of an organization’s roles and operating model is becoming necessary. Many roles as we know them today are shifting—even for the most seasoned software and ML engineers. Organizations are starting to rethink their structure to blend human expertise with agentic autonomy. This involves redesigning workflows to allow AI agents to automate routine tasks while humans focus on strategic, creative, and problem-solving roles. 

Implementing and managing agentic AI requires specialized knowledge in areas such as AI model orchestration, agent–human interaction design, and AI operations. These skill sets are often underdeveloped in many organizations and, as a result, AI projects are failing to scale effectively. The gap isn’t just technical; it also includes a cultural shift toward understanding how AI agents generate results and the responsibility associated with their outputs. To bridge this gap, we are seeing organizations start to invest in restructuring data, AI, content, and knowledge operations/teams and reskilling their workforce in roles like AI product management, knowledge and semantic modeling, and AI policy and governance.

Ways of Working: To support agentic AI delivery at scale, it is becoming evident that agile methodologies must also evolve beyond their traditional scope of software engineering and adapt to the unique challenges posed by AI development lifecycles. Agentic AI, requires an agile framework that is flexible, experimental, and capable of iterative improvements. This further requires deep interdisciplinary collaboration across data scientists, AI engineers, software engineers, domain experts, and business stakeholders to navigate complex business and data environments.

Furthermore, traditional CI/CD pipelines, which focus on code deployment, need to be expanded to support continuous model training, testing, human intervention, and deployment. Integrating ML/AI Ops is critical for managing agent model drift and enabling autonomous updates. The successful development and large-scale adoption of agentic AI hinges on these evolving workflows that empower organizations to experiment, iterate, and adapt safely as both AI behaviors and business needs evolve.

Conclusion 

Agentic AI will not succeed through technology advancements alone. Given the inherent complexity and autonomy of AI agents, it is essential to evaluate organizational readiness and conduct a thorough cost-benefit analysis when determining whether an agentic capability is essential or merely a nice-to-have.

Success will ultimately depend on more than just cutting-edge models and algorithms. It also requires dismantling artificial, system-imposed silos between business and technical teams, while treating organizational knowledge and people as critical assets in AI design. Therefore, a thoughtful evolution of the organizational operating model and the seamless integration of AI into the business’s core is critical. This involves selecting the right project management and delivery frameworks, acquiring the most suitable solutions, implementing foundational knowledge and data management and governance practices, and reskilling, attracting, hiring, and retaining individuals with the necessary skill sets. These considerations make up the core building blocks for organizations to begin integrating AI agents.

The good news is that when built on the right foundations, AI solutions can be reused across multiple use cases, bridge diverse data sources, transcend organizational silos, and continue delivering value beyond the initial hype. 

Is your organization looking to evaluate AI readiness? How well does it measure up against these readiness factors? Explore our case studies and knowledge base on how other organizations are tackling this or get in touch to learn more about our approaches to content and data readiness for AI.

The post When Should You Use An AI Agent? Part One: Understanding the Components and Organizational Foundations for AI Readiness appeared first on Enterprise Knowledge.

Semantic Layer Operating Model – The Components

Establishing a clear operating model is essential for translating the vision of a semantic layer into a practical, scalable reality. It enables teams to deliver value incrementally, define release scopes that are both feasible and impactful, and build repeatable frameworks that support consistent expansion over time. This also ensures the implementation work connects to the broader product vision and semantic layer strategy for an organization so that each decision directly contributes to long-term goals. 

At EK, we often structure our semantic layer operating model around two primary components that form the foundation of an MVP release: design releases and development releases. EK is currently putting these components into practice with a government agency in the intelligence analysis space as we transition from the definition of a semantic layer strategy into an MVP implementation phase for a user-facing semantic solution. Delivery is broken into iterative cycles that combine use case and data model expansion with user feature and service development. To ensure alignment across teams, it is critical to not only define the scope and content of these releases, but also establish a shared language for describing them and expected timeframes of completion. In collaboration with the agency, EK defined “use cases” as 1-month units of design releases, “pilots” as staggered 1-month units of development releases, and “MVP” as the culminating technical release that integrates multiple use cases and pilots into a cohesive solution over the course of 6 months.

Sample Structure for Semantic Layer Operating Model

It is important to note to that these are not hard and fast rules for what should constitute your semantic layer operating model, as components and timeframes are shaped by team structures and capacity, existing tooling and/or procurement timelines, business expectations for solution releases, and other organization or solution-specific requirements. The following definitions can serve as a guide to tailor an operating model that best fits your needs and constraints.

Design Release

Design releases focus on the strategic and conceptual definition of a semantic layer use case. They are the foundation for implementation, ensuring that each increment is grounded in clear user needs, meaningful data connections, and well-scoped schema design. A design release captures a focused slice of the broader vision, allowing for thoughtful expansion while maintaining alignment with the overall product vision and semantic layer strategy.

For example, a semantic layer design release or “use case” for a knowledge graph-based solution should:

Design releases help teams align on scope and semantic modeling needs, surface technical dependencies early, and create a shared understanding of the solution design priorities to enable technical development.

Development Release

Development releases translate design outputs into working technical components. These releases prioritize feasibility, rapid iteration, and incremental value, while maintaining a clear path toward scalability. Development releases often begin with limited-scope pilots that validate capability approaches and inform future automation and scale.

A semantic layer development release or “pilot” for a graph-based solution should:

Development releases help demonstrate tangible progress and mitigate risks for enterprise-level implementation to build towards the encompassing product vision in alignment with an organization’s semantic layer strategy.

MVP Release

The below image illustrates how design and development releases work together, with a generalized example from EK’s semantic layer implementation strategy with the government agency focused on intelligence analysis.

Sample Design and Development Release Scopes

Ultimately, these iterative design and development releases culminate in an MVP: a fully integrated release of the semantic layer-based solution that brings together multiple completed use cases and pilots into a unified, usable platform. It includes the implemented semantic models, integrated data sources, and functional technical components necessary to support a robust set of targeted user capabilities, and is ready for use in a broader context within the organization. The MVP manifests core elements of the product vision, demonstrating the full value of incremental delivery and providing a clear path for business adoption, continuous expansion, and long-term scalability of the solution as part of the semantic layer.

Conclusion

Establishing a well-defined operating model is critical for successfully developing and scaling a semantic layer solution. By structuring work around design and development releases, organizations can maintain clear alignment between technical implementation, business needs, and product strategy. This model enables teams to deliver incremental value, iterate based on lessons learned, and lay the groundwork for scalable, long-term solutions that drive more connected decision-making across the enterprise. Contact EK to learn more about defining a solution strategy and structuring an operating model for implementing a semantic layer at your organization.

The post How to Implement a Semantic Layer: A Proven Operating Model appeared first on Enterprise Knowledge.

If you are exploring ways your organization can benefit from implementing a data governance program, we can help! EK has deep experience in designing and implementing solutions that optimize the way you use your knowledge, data, and information, and we can produce actionable and personalized recommendations for you. Please contact us for more information.

Special thank you to Nina Spoelker for her contributions to this infographic! 

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