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From Capability to Execution: Is a New Category Taking Shape in the Enterprise AI Stack?
A new model is defining how AI systems are assembled, deployed, and governed in production environments, as the market packages capabilities into reusable approaches across workflows, retrieval, and orchestration.
03/30/2026
Key Highlights
- The enterprise AI stack is evolving with a new set of capabilities that establish how systems are assembled and perform in production.
- Vendors are packaging execution through blueprints, libraries, retrieval, and orchestration frameworks.
- HyperFRAME Research Lens data confirms only 22.8% of AI projects meet ROI objectives, indicating a gap in execution rather than capability.
- Variability in retrieval, workflow design, and orchestration limits repeatability across deployments.
- This category may emerge as a distinct layer or consolidate into adjacent platforms over time.
The News
HyperFRAME Research is introducing the concept of an emerging layer within the enterprise AI stack, focused on how models, data, and infrastructure are assembled into deployable systems. The analysis reflects ongoing market activity as vendors package capabilities into blueprints, libraries, retrieval frameworks, and orchestration patterns. Our research frames execution as a distinct architectural role that governs how AI systems operate in production and how outcomes are delivered across enterprise workflows.
Analyst Take
An emerging category is coalescing within the enterprise AI stack. This layer sits between the data platform and infrastructure below and the application and agent tiers above, and it sets how technical capabilities are assembled into production-ready systems. It brings together blueprints that define common use cases, libraries that encapsulate reusable functionality, workflow frameworks that structure multi-step execution, retrieval and context assembly mechanisms that deliver relevant data to models, and orchestration patterns that coordinate tasks and tools. These components are often described separately, and we believe they are coming together into a cohesive function that governs how AI systems run.
Leading vendors are approaching this area from different directions. Their approaches vary in packaging and reflect a shared requirement: enterprises need structured ways to translate capability into execution.
Enterprise teams operate with capable models, scalable infrastructure, and maturing data platforms, yet business outcomes remain uneven. Many organizations demonstrate value in controlled implementations, while broader rollout across workflows introduces variability in performance, cost, and reliability. Highlighting this gap, HyperFRAME Research Lens (1H 2026) data shows that only 22.8% of AI and machine learning projects meet their original ROI objectives.
Only 22.8% of AI and machine learning projects meet their original ROI objectives. This indicates a gap in how AI systems are assembled, deployed, and scaled across enterprise environments.
Deployment requires decisions about retrieval, context assembly, workflow structure, and integration. These decisions affect latency, accuracy, and cost per task. They are often implemented differently across teams, which creates inconsistency in behavior and slows expansion beyond initial use cases. HyperFRAME Research Lens data further indicates that only 37% of organizations have a structured process for evaluating and implementing AI systems. Execution remains dependent on specialized expertise, which limits repeatability.
Where This Category Appears in the Enterprise AI Stack
A common failure point shows up in retrieval-augmented generation (RAG) pipelines used in enterprise workflows. A system performs well in testing with access to enterprise data and adequate infrastructure. As usage expands, performance becomes inconsistent. Responses vary based on context assembly, latency increases under load, and costs rise as retrieval expands. Each new use case introduces adjustments to indexing, ranking, and workflow logic, often within the same organization, leading to inconsistent behavior over time.
A similar pattern appears in agent-driven workflows. An agent can complete multi-step tasks with acceptable results in early deployments. As scope expands, variability increases. Tool selection differs for similar tasks, execution paths diverge, and latency compounds across steps. Costs increase with additional calls and retries, while guardrails and routing logic are applied inconsistently. System behavior becomes difficult to predict and harder to manage.
These examples demonstrate how challenges appear in practice. The individual components perform as expected, but variability emerges in how they are connected and applied. Retrieval logic differs across implementations, workflow design evolves independently across teams, and orchestration between models, tools, and data sources lacks consistency. These differences affect system behavior in ways that are difficult to predict and scale.
The enterprise AI stack can be understood as a set of interdependent layers.
Infrastructure provides compute, storage, and networking that determine performance, scalability, and cost, while data platforms provide persistence, metadata, and governed access to enterprise data. This determines how data is retrieved, how workflows are structured, and how tasks are coordinated, and applications and agents consume these outputs to deliver business outcomes. In both RAG and agent-based systems, variability emerges in how retrieval, ranking, workflow logic, and orchestration are implemented, which affects latency, accuracy, cost, and consistency under different conditions. This set of capabilities standardizes these functions by defining context assembly, multi-step workflows, and coordination across models, tools, and services. This introduces structure that enables reuse and more predictable system behavior across use cases.
This set of capabilities operates within discrete boundaries. Data quality, completeness, and accessibility remain responsibilities of the data platform. According to HyperFRAME Research Lens data, only about 14% of enterprises report a fully AI-ready data architecture, which continues to influence system reliability. Governance, security, and policy enforcement remain separate functions. Integration across enterprise systems requires coordination across applications and services. Infrastructure performance and cost depend on underlying compute and storage. This depends on these capabilities and organizes how they are applied.
What Was Announced
This emerging category is not represented by a single product or platform but by a set of capabilities packaged into production-ready systems. They appear across multiple vendors, each addressing different aspects of how AI systems are assembled, operated, and governed:
- NVIDIA’s approach centers on CUDA-X libraries, NIM microservices, and AI Blueprints, which package model inference, tool integration, and deployment patterns into reusable components. These capabilities determine how models are operationalized within pipelines and integrated with enterprise data and services.
- Microsoft and AWS are advancing this layer through copilots, solution accelerators, and industry-specific templates. These offerings encode workflow logic and domain-specific patterns, enabling organizations to apply AI within business processes without rebuilding the logic for each use case.
- Elastic is positioning retrieval, vector search, and semantic ranking as core system functions. Its approach focuses on how data is discovered, filtered, and delivered as context for inference, which directly influences system accuracy, latency, and cost.
- IBM is delivering these capabilities through services, frameworks, and repeatable implementation models. The company is focused on structuring enterprise adoption into repeatable methods that define how AI systems are integrated, governed, and operated across environments.
Across these approaches, a consistent picture is becoming clearer. Vendors are packaging retrieval, workflow design, orchestration, and integration into reusable constructs that shape how AI systems are executed in practice. These capabilities do not replace infrastructure or data platforms but sit above them, organizing how systems operate and how outcomes are delivered.
Looking Ahead
Infrastructure and platform leaders evaluate AI systems based on performance, efficiency, and reliability. This introduces a new dimension of evaluation that centers on how effectively systems translate technical capability into repeatable workflows.
Leaders should examine how retrieval and context assembly affect accuracy and latency. They should evaluate how workflow frameworks structure multi-step execution and how system components are orchestrated. They should assess whether blueprints, libraries, and templates reduce time to value while improving consistency across use cases. These factors influence efficiency, cost per task, and the ability to scale AI across the enterprise.
Execution becomes a measurable aspect of system design. It affects resource utilization, operational overhead, and consistency of outcomes. Organizations that standardize can reduce variability and improve performance across environments.
In our view, the market is coalescing around this area, and its long-term structure remains open. One path is a distinct and recognized layer within the enterprise AI stack, reflecting the role of execution in system behavior and outcomes. A second path is consolidation. Retrieval may integrate further into data platforms. Workflow and orchestration capabilities may integrate into application frameworks or control planes. Blueprints and libraries may become embedded within vendor ecosystems as standard constructs.
Current vendor approaches suggest that both dynamics are underway. Capabilities are being packaged into reusable forms while also integrating into broader architectures. This indicates that the function will persist even as its boundaries evolve.
With either path, this category is likely to become one of the primary competitive boundaries in the enterprise AI stack. Infrastructure scale alone is no longer sufficient to differentiate vendors, and model performance continues to commoditize. The emerging advantage will come from how effectively platforms operationalize context, workflow logic, and policy enforcement into repeatable, measurable models. Vendors that treat execution as a first-class architectural function will be better positioned to support large-scale agent ecosystems and realize greater value from AI systems.
Don Gentile | Analyst-in-Residence -- Storage & Data Resiliency
Don Gentile brings three decades of experience turning complex enterprise technologies into clear, differentiated narratives that drive competitive relevance and market leadership. He has helped shape iconic infrastructure platforms including IBM z16 and z17 mainframes, HPE ProLiant servers, and HPE GreenLake — guiding strategies that connect technology innovation with customer needs and fast-moving market dynamics.
His current focus spans flash storage, storage area networking, hyperconverged infrastructure (HCI), software-defined storage (SDS), hybrid cloud storage, Ceph/open source, cyber resiliency, and emerging models for integrating AI workloads across storage and compute. By applying deep knowledge of infrastructure technologies with proven skills in positioning, content strategy, and thought leadership, Don helps vendors sharpen their story, differentiate their offerings, and achieve stronger competitive standing across business, media, and technical audiences.
Stephanie Walter | Practice Leader - AI Stack
Stephanie Walter is a results-driven technology executive and analyst in residence with over 20 years leading innovation in Cloud, SaaS, Middleware, Data, and AI. She has guided product life cycles from concept to go-to-market in both senior roles at IBM and fractional executive capacities, blending engineering expertise with business strategy and market insights. From software engineering and architecture to executive product management, Stephanie has driven large-scale transformations, developed technical talent, and solved complex challenges across startup, growth-stage, and enterprise environments.