- Agentic AI may change how organizations realize value from enterprise software as adoption expands.
- Inference, the process of running an AI model to generate an output, is becoming an increasingly important driver of ongoing compute costs.
- Model selection, infrastructure and agent design are three levers through which disciplined execution may help companies improve performance while managing costs at scale.
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AI is becoming embedded across more business functions, shifting attention from technical capability alone to the operational decisions that may shape long-term business value. That includes not only what AI systems can do, but also how efficiently they can be utilized at scale.
The emergence of Agentic AI has played a major role in that shift. Unlike traditional software designed to automate fixed processes, Agentic AI systems can adapt to new information, coordinate multiple steps and pursue defined objectives with greater autonomy. We believe this may expand how enterprise software delivers value while introducing new operational considerations as adoption grows.
How companies balance the opportunities created by Agentic AI with the cost and complexity of operating it may play an important role in realizing long-term value.
Why Enterprise AI Is Moving Into a New Phase
As AI becomes integrated into day-to-day business operations, organizations are paying greater attention to the economics of running these systems over time. Agentic AI may create new opportunities for enterprise software, but scaled adoption also introduces operating costs that companies must manage while maintaining performance.
AI requires compute throughout its lifecycle, from model development and refinement to the inference required once models are deployed. Because every action an AI agent takes relies on inference to generate an output, inference has become an ongoing operating expense rather than a one-time investment. As organizations embed AI across more products and workflows, inference is becoming an increasingly important component of the cost structure for enterprise AI and an emerging line item in the P&L for software companies delivering Agentic AI at scale.
Why Inference Economics Matters at Scale
Each time a human or an agent uses an AI model, it incurs a variable cost known as inference. As organizations deploy Agentic AI at scale, inference is becoming a central component of the cost structure for these systems. Organizations that manage how models are selected and deployed may be better positioned to improve efficiency while maintaining the performance required for enterprise applications.
For organizations transitioning from SaaS to Agentic AI, inference management may become an important operational capability and a meaningful factor in long-term value creation.
These challenges were discussed during CNBC’s coverage of Vista’s launch of Vector Core Compute, an enterprise AI inference cloud developed to help software companies manage inference costs at scale. Vector Core Compute is one example of Vista’s broader approach to helping portfolio companies address the technical and operational challenges of deploying Agentic AI at scale.
“When you agentify enterprise software, the agents that are then deployed need inference, which is a compute capacity. As these agents work 24/7, 365, they start to use a fair amount of inference. And so the cost of inference is going up, and we needed a solution that was more sustainable for our software companies. And that’s what the platform is designed to do; it’s designed to help deliver one of the lowest possible costs per token, which we believe can make enterprise AI more viable at scale.”
Where Operating Discipline Can Create an Advantage
In our view, organizations may increasingly differentiate themselves not only through the AI they adopt, but through the operational decisions that determine how efficiently those systems run over time. In “Understanding Inference and the Economics of Enterprise AI,” Vista outlines several areas that we believe may have a meaningful impact on the economics of Agentic AI as organizations scale adoption.
Rather than relying on a single technical solution, Vista believes leaders should consider how multiple operational levers work together to influence efficiency and long-term cost.
- Aligning models with the task at hand. Different AI models offer different tradeoffs in capability and cost. Matching the model to the specific use case may help organizations achieve the performance they need without unnecessarily increasing inference costs.
- Building infrastructure that supports efficient execution. The underlying infrastructure used to run AI workloads can influence both cost and responsiveness. As AI adoption expands, infrastructure choices may become an increasingly important part of managing enterprise-scale deployments.
- Designing agents with efficiency in mind. The way AI agents are structured, including how they gather context, plan tasks and interact with models, can influence the amount of inference required to complete a workflow. Careful design may help reduce unnecessary computation while preserving consistent performance.
Together, these levers illustrate why companies may be differentiated by how effectively they operationalize AI.
The Next Phase of Enterprise AI Value Creation
The next phase of enterprise AI may require leaders to look beyond adoption alone and assess how AI affects both the value a company can deliver and the cost of delivering it. As usage scales, managing inference economics may increasingly become a business and operating consideration rather than a purely technical one.
- For business leaders and investors, the balance between model performance, operating cost, infrastructure and agent design may provide important insight into how organizations create long-term value from AI.
- Companies that approach these decisions strategically may be better able to scale Agentic AI while maintaining the economics of their products and workflows.
- Ultimately, we believe the organizations that create lasting value may be those that treat AI not simply as a technology investment, but as an operating discipline that can be scaled efficiently over time.
This perspective reflects a broader shift in enterprise AI: we believe as organizations move beyond experimentation, disciplined execution at scale is becoming central to long-term value creation.
In a February 2026 video about enterprise software’s next chapter, the opportunity presented by Agentic AI was summarized this way:
“The Agentic AI era is here. We believe it could be one of the most valuable chapters in software’s history.”
Vista’s 2026 Mid-Year Report on AI Impact Across the Portfolio (July 2026) illustrates the early proof points that reinforce the opportunity outlined in February, highlighting how portfolio companies are putting Agentic AI into practice and beginning to demonstrate measurable results.
For more insights on enterprise AI, follow Robert F. Smith on LinkedIn and subscribe to his YouTube channel.