AI Stack

The AI-related topics of conversation across most leadership boardroom meetings seem to center on models and their capabilities. AI architects are excited to share the latest iterations, and C-suite executives like talking about them. But what’s often not discussed is the high-level view of the entire AI stack.

No doubt, the smartest LLMs and latest agent capabilities make for more compelling discussion, but the models alone rarely make or break a company’s AI initiatives — the key is the surrounding stack as a whole. An AI stack is the full set of technologies and frameworks that operate collectively to enable productive AI. Consider it the AI infrastructure beneath the intelligence.

AI models are capable of reasoning, but they may lack the ability to access governed data or explain answers on their own. Those capabilities operate within the stack around it and determine whether AI performs and scales across the business.

What Is an AI Stack?

An AI stack is a multi-layered collection of technologies, data systems, governance frameworks, and operational tooling (each in corresponding configurations) that an organization requires to create and scale its AI applications. A stack’s components are interconnected. For example, cloud-based platforms serve as the storage layer for all data while semantic layers help define and explain what the data means. Governance layers determine who or what has access, and orchestration layers facilitate the delivery of intelligence to end-users.

A model alone represents just one layer, not the whole system. And the most adept models lack essential capabilities, such as the ability to define meaning and access to data. The value and intention of an AI technology stack reside within the interactions of the layers, as a well-architected stack converts raw data into trustworthy, scalable outcomes for the business.

Enterprise investments are a testament to that reality. A report from CloudZero found an average enterprise spend of $85,500 per month on AI, a 36% jump in a single year. Most of that expense goes toward the supporting layers rather than the model itself. Data preparation alone routinely consumes 30% to 50% of an enterprise AI budget. Companies are investing in an entire AI stack, not just a standalone model. How well a stack’s pieces work together is what graduates a pilot to production.

What Are the Core Layers of an AI Stack?

While every organization has unique models to streamline operations and maximize efficiencies, most enterprise AI stacks share the same core layers. Each one carries out a distinct process, but the overall output depends on how well they work together.

1. Data Layer

The first step in building an AI stack begins with the layer that holds raw data. Raw data may originate from enterprise systems, warehouses, lakehouses, and other data sources. The quality and reliability of raw data directly affect the dependability of the entire AI stack. As such, CDOs and data architects are typically at the forefront of any viable AI strategy to ensure data integrity and accessibility throughout the entire process.

2. Context and Semantic Layer 

Although raw data provides valuable input for AI applications, raw data alone does not provide sufficient context or actionable insights. This layer is responsible for contextualizing raw data by providing shared definitions and metrics so that common terms, such as “Active Customer,” mean the same thing to both the marketing and finance departments. As a result, semantic layers have become the most commonly used framework for delivering consistent definitions to AI applications and enabling the trustworthiness of answers generated by multiple tools.

3. Model Layer 

This is likely the layer that’s the closest thing to an actual AI application. It encompasses machine learning models, foundation models, and LLMs that perform reasoning, summarization, prediction, and other tasks to produce AI-generated responses. However, even these models rely heavily on the stack’s supporting layers, including AI context, semantics, data, orchestration, and agent layers, to validate assumptions and provide a basis of truth.

4. Orchestration and Agent Layer 

AI-generated response values are meaningless unless acted upon. Workflows, retrieval systems, and AI agents act as coordinators — automating processes, pulling information from other tools, and triggering follow-up actions based on earlier results. Automation teams and AI architects use this layer to transform an AI-generated answer into meaningful business action.

5. Governance and Security Layer 

While creating value is important, enforcing trust rather than assuming it is equally important. To prevent unauthorized activity and maintain accountability, permissions, compliance rules, observability, and guardrails are implemented within this layer. As AI agents begin performing activities autonomously, it will increasingly fall to risk and compliance leaders to assertively confirm that a proposed solution meets security and regulatory requirements before promoting it to production.

6. Application Layer

Ultimately, once the stack is created, it’s delivered to end-users. AI copilots, conversational analytics, enterprise search, and AI assistants are examples of how AI applications can be used in everyday business interactions. This final layer is where all the lower-layer components come together to produce tangible business value.

How the AI Stack Supports Enterprise AI

No single layer produces consistently reliable AI outcomes. The value of using multiple layers is in how each layer connects with others. For example, if a business user types into a copilot “how are Q3 renewals trending,” the inquiry moves downward through the layers (orchestration) and then back upward as an output to the user.

Orchestration understands the request. Then the semantic layer determines what “renewals” refers to. Next, the data layer returns the appropriate numbers that have been vetted for compliance, and afterward, the model will phrase the response. The governance layer ensures that the end user can see only the data to which they have access. This process is what powers AI agents for data analysis, advanced conversational analytics systems, decision intelligence, and enterprise search.

That’s why enterprise AI is an architectural issue rather than a question of the best model. One standout session at AtScale’s 2026 Semantic Layer Summit covered how Carrefour France migrated approximately 3,000 KPIs onto a universal semantic layer to govern their agentic analytics — giving their AI agents and dashboards a single, consistent definition of the business to work from. The agents work because every layer beneath them understands what the data means. Strong stacks make AI trustworthy. Weak ones leave it guessing.

AI Stack vs. Modern Data Stack

The modern data stack and the AI stack should be viewed as complementary rather than competitive. In essence, one is built on top of the other: the modern data stack provides a means for an organization to begin to understand its own data, while the AI stack enables it to operationalize the intelligence it gathers through machine learning.

Over the last decade, managing data has become problematic for many companies until the modern data stack emerged. Its primary function is to collect company data from source systems, load it into a cloud-based data warehouse (or similar), transform the raw data into clean tables, and serve them to company dashboards. The end result is a consistent, reliable view of what happened across the business.

All of this exists as a base layer for the AI stack, which adds several additional layers to the original data processing. These include semantic context, foundation models, orchestration, agents, and governance to ensure that automated processes do not pose safety or security risks. A key takeaway for those who lead companies’ data initiatives is that the previous investment in warehouses and pipelines has not been lost. Instead, they have formed the basis of a company’s AI stack.

What Are the Common Challenges in Building an AI Stack?

The underlying reason most AI initiatives stall is that the supporting stack isn’t ready. Gartner predicts that 60% of AI projects lacking AI-ready data will be abandoned through 2026, and the obstacles behind these projections tend to repeat across industries and organizations investing in AI.

Fragmented Data

Information is typically siloed in multiple databases, lakehouses, SaaS applications, and operational systems that do not talk to each other. A Cloudera report found that approximately 80% of organizations report that limited data availability across environments prevents them from using AI.

Inconsistent Business Definitions

Each department defines terms like “revenue” or “active customer” in its own way, creating inconsistencies when using AI. Strategy’s latest AI trends survey revealed that 99% of data leaders struggle with defining consistent business metrics across tools and departments. This semantic drift creates doubt and diminishes confidence in AI output.

Governance Gaps

Most policies are documented but lack proper integration into the data flow. Cloudera reports that fewer than one in five organizations report that their data is fully governed. This creates vulnerabilities in permission levels, data lineage, and audibility for the very areas that require them most.

Infrastructure Complexity

Building a scalable platform that combines cloud infrastructure providers, pipelines, vector stores, and service layers is complex. Cloudera’s survey results found that nearly three-quarters of organizations report performance constraints that inhibit their AI initiatives.

Model Sprawl

Organizations are notorious for developing three or more foundational AI models in isolation — each one using a different stack. This results in duplicate costs, inconsistent governance, and significant technical debt.

Management of Autonomous AI Agents

As the number of autonomous agents increases, managing those agents becomes increasingly burdensome. According to Deloitte, only about one in five companies has a mature model for governing autonomous AI agents, even as those agents start taking real actions on enterprise systems.

Together, these gaps in AI architecture provide insight into why most pilots never achieve large-scale enterprise success. Companies that overcome the same gaps generally address foundational issues before implementing additional AI on top.

Why Context and Governance Are Becoming Critical Layers

In the early stages, the overwhelming assumption was that giving AI access to data was enough, which produced countless inaccurate outputs and failed AI pilots. Models are proficient at reading data tables, but don’t know that “active customer” excludes free trials. Without a governed context, AI models fill in the gap with their best guesses, and those guesses can’t scale with confidence.

This has turned the semantic layer from a BI convenience to an AI necessity. It supplies the missing meaning, defining metrics once, enforcing them consistently, and giving every model and agent the same governed view of the business. And the gains in accuracy are significant. In AtScale’s NLQ benchmark testing, Snowflake Cortex Analyst reached 100% accuracy when grounded in the semantic layer, up from a 54% industry average and just 16% with raw SQL access.

Context also makes AI explainable. When answers trace back to defined metrics and clear lineage, leaders can audit how a recommendation was reached, which matters enormously in regulated industries. As Dael Williamson, EMEA Field CTO at Databricks, told AtScale, “We discovered that semantic data dramatically improves model accuracy. Good governance and structure are key to scaling AI.”

Trusted context is what turns a plausible answer into a reliable decision.

The Future of the AI Stack

A future generation of AI Stack is likely going to have a new dimension. Rather than measuring success by model performance, the most important aspect will be how well organizations govern their contextual management and provide trusted business knowledge. As models become commoditized, differentiation will shift to the layers of an organization’s stack that inform the models about what the actual business means.

We are already seeing this trend. Agents are transitioning from simply answering questions to taking action; Gartner estimates agentic AI spending will exceed $2.59 trillion in 2026. By 2027, we estimate agentic AI spending will surpass chatbot spending. All autonomous workflows, conversational analytics, and decision intelligence will operate from a common governed layer. That’s why we are starting to see the development of standards, such as the Model Context Protocol, to allow agents direct access to governed semantic metadata.

As we move toward an agentic-based world, managing context and oversight will become increasingly critical. According to Gartner research, more than 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, lack of clarity around ROI, and poor risk management. Organizations that win in the future will not be the ones with the most impressive model. Rather, they will be the organizations whose stack enables every agent to reason from a single trusted version of their business. In an agentic world, a managed and governed context represents one of the strongest competitive moats a business can build.

Why Strong AI Foundations Matter

AI success depends on far more than the model. As enterprises race to adopt AI into their workflows, the need for trusted data, consistent definitions, governance, and shared business meaning only grows. This is why semantic layers have become a core part of a mature AI stack, providing organizations with a single, governed source of truth that improves consistency, strengthens governance, and builds the AI readiness needed to scale reliably. 

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