Transform your AI vision into reality: How to scale AI value and control costs

For most enterprises, the AI challenge isn’t vision — it’s value. For IT leaders, that means delivering performance, security, and scalability without runaway cost. Use cases are clear, pilot projects abound, and proofs of concept deliver promise. Yet across industries, the returns remain elusive. In our 2025 REVelate survey, 88% of organizations reported an improved ability to move AI from experimentation to production, but nearly 60% of IT and AI leaders expect at most 50% ROI.

The problem isn’t failure. Rather, it’s the gap between working AI and valuable AI. Models function as intended, but the systems around them fail to translate that success into enterprise-wide impact. At Domino, we describe this as the Enterprise AI Equation:

AI value = (Individual value per agent/application × Users) − (Infrastructure and maintenance costs)

Every enterprise is solving for this equation, consciously or not. The latest Domino release is designed to help — combining elasticity, visibility, and operational efficiency so organizations can scale AI value without scaling cost.

Elasticity without waste

AI app usage is highly variable, demanding bursts of compute followed by idle time. Static infrastructure can’t match that rhythm and it leaves teams over-provisioned, underutilized, and over budget. Domino’s new autoscaling capability addresses this directly. Compute footprints expand or contract automatically to ensure the best user experience for thousands of users while minimizing cost. When paired with Spot Instance optimization — which can lower compute costs by 90%, according to Amazon — elasticity becomes a form of cost control rather than a budgeting risk.

For IT, that means dynamic optimization instead of manual workload scheduling. For data scientists, it’s compute that works when needed and disappears when it’s not, freeing teams from infrastructure tuning to focus on model iteration and delivery. For the business, it’s the value of ensuring higher quality at a lower cost.

Connecting data science to the business

Scaling AI isn’t just a compute problem. Many organizations struggle to get their best work into the hands of end users. The MIT State of AI in Business 2025 report found that 70% of enterprises cite “bridging data science and the business” as their top barrier to realizing AI value.

Domino’s new app discovery feature helps close that gap. It provides a searchable experience for business users to find, launch, and use validated AI applications built in Domino. By surfacing the right apps for the right roles, organizations can reuse proven models, increase adoption, and extend the reach of data-science work beyond technical teams.

In practice, this shifts AI from siloed experimentation to shared capability. When a fraud-detection model built for retail banking becomes visible to commercial lending and audit teams, its value multiplies without additional development. Each reuse compounds ROI and shortens the feedback loop between experimentation and outcomes.

These are the first terms in the Enterprise AI Equation — scaled applications multiplied by user adoption. When organizations operationalize those multipliers, AI becomes a reusable business system, not a series of isolated projects.

Managed flexibility for enterprise realities

Hybrid and multicloud architectures are now the rule, not the exception. Enterprises want to run workloads close to their data — for cost, performance, or regulatory reasons — without splintering infrastructure or governance. Domino’s new Managed Data Planes enable precisely that.

Managed Data Planes let IT organizations run compute in specific regions while maintaining centralized orchestration and governance through Domino Cloud. This architecture combines SaaS simplicity with full operational control. Teams can deploy workloads where their data resides to protect data privacy while minimizing data transfer costs. Environments can be isolated for security or production purposes, all within a fully managed service.

Turning complexity into composability

The AI landscape continues to evolve at unprecedented speed. New frameworks, foundation models, and hardware arrive every month. Chasing each one is unsustainable; building a composable foundation that can adapt to them is not.

Behind every improvement in this Domino release is a larger architectural principle: SaaS is becoming the standard delivery model for enterprise AI. Managed platforms allow organizations to modernize in place — to scale what works and pay only for what’s in use. They deliver predictable cost, faster deployment, continuous updates, and easier governance across distributed environments.

The combination of autoscaling, Spot Instances, app discovery, and Managed Data Planes turns infrastructure from a constraint into a catalyst. These capabilities work together to shrink the cost and complexity terms in the Enterprise AI Equation while amplifying the multipliers of adoption and scale.

As more enterprises adopt GenAI and advanced machine learning, success will depend less on model performance and more on operational maturity — the ability to reuse, govern, and deliver AI as a standard capability. For IT, this means consistent configuration, security, and monitoring across every environment, without the overhead of maintaining infrastructure. The organizations that master this equation will turn AI into a self-reinforcing system of value creation.

Closing the gap

The data is clear: enterprises know how to make AI work; now they must make it pay. The difference lies in systems and processes, not algorithms.

Give AI an operating system that serves and augments the business. Establish ownership, measure outcomes at the application level, and reuse patterns that connect models to users. Talent, infrastructure, and governance only create value when they reduce time to adoption and simplify operations instead of adding steps.

Closing the gap requires the right architecture and intent. Domino’s latest release provides the foundation for elasticity, visibility, and cost control, but value also requires a mindset shift from scaling models to scaling outcomes. AI at scale is not about more power; it is about less friction. With the right systems, AI becomes a living portfolio of business assets that are measurable, governed, and continuously improving.

The new autoscaling, app discovery, Spot Instances, and Managed Data Planes features are available today in Domino Cloud on AWS, with support for additional cloud providers coming soon. For further details, check out the what’s new page or the latest press release.