How to scale AI in 2026: 5 moves for efficiency and governance

Enterprise AI is entering a more disciplined phase. Organizations are confronting the realities of growing model complexity, a widening range of AI use cases and increasing pressure around cost, security and governance.

In collaboration with IBM, Enterprise Strategy Group (ESG) surveyed 400 technical and business stakeholders. The research shows that while AI investment is increasing, the constraints shaping adoption have changed.

As enterprises explore AI use cases, they often build a portfolio of models rather than choosing just one. Governance, security and cost efficiency have become decisive factors in whether initiatives can scale beyond pilots.

Because of these shifts, AI success in 2026 will depend less on individual models and more on the systems, controls and foundations that surround them.

Based on the findings, IBM has identified 5 moves to make in 2026 for scalable AI:

1.        Set a strong foundation with centralized solutions

2.        Adopt a multi-model strategy

3.        Make governance and security prerequisites for scale

4.        Prioritize optimization early to make AI sustainable

5.        Treat generative AI as a top-tier investment priority

Survey respondents rated the importance of 16 common enterprise AI use cases, such as AI agents, customer support automation and code development. Most respondents deemed all 16 use cases presented to be important to their overall AI strategies.

When many use cases are simultaneously important, scaling depends less on optimizing for a single application. It depends more on building shared capabilities that make new use cases faster, cheaper and safer to deliver. That level of broad adoption favors reusable models, shared data foundations and centralized governance over siloed, one-off solutions.

The practical move in 2026 is to invest in inconsistent data preparation and access patterns, repeatable evaluation and deployment approaches and governance that can be applied across models, agents and workflows. Organizations that build this reusable foundation are better positioned to expand use cases without duplicating effort, fragmenting architecture or creating governance gaps.

Most enterprises do not rely on a single AI model or provider. Instead, they are building portfolios of models to support different tasks, data types and performance requirements.

The ESG research shows that 81% of organizations are using three or more gen AI models. This finding reflects a clear shift toward fit-for-purpose deployment, where models are selected based on workload needs rather than generalized capability.

A multi-model approach allows organizations to manage the tradeoffs that increasingly determine wither AI can scale, such as performance, cost and latency. Different use cases place different demands on models, so strategic model selection helps ensure that they can be deployed, tuned and reused efficiently.

According to the ESG research, 60% of respondents ranked security, compliance and regulatory requirements as the top factors influencing decision-making for AI models, tools and platforms. Data privacy, security vulnerabilities and compliance and regulatory risks ranked as the leading concerns for organizations deploying AI models.

As AI use expands across teams and workflows, consistent controls and traceability become necessary to sustain deployment in production environments and to maintain confidence with stakeholders.

AI-experienced organizations are prioritizing governance capabilities such as access and usage controls, centralized model registries for traceability and audits and continuous monitoring to detect drift or anomalous behavior.

Embedding governance into the AI lifecycle across data, models, agents and usage, enables organizations to move faster with clearer guardrails.

The ESG research shows that model efficiency techniques, particularly model compression, are viewed as the most valuable optimization approaches.

As organizations move from pilots to production, cost can become a limiting factor even when use cases deliver value. Cost pressures extend beyond compute, with enterprises also navigating expenses related to talent, tools, platforms and ongoing model management. As a result, there is no single dominant cost-mitigation strategy; instead, organizations are actively experimenting to find the right balance between performance and efficiency.

This environment increasingly favors smaller, fit-for-purpose models designed specifically for enterprise workloads, along with repeatable practices to measure and manage efficiency over time. The scalable move in 2026 is to build operational discipline to optimize continuously. Then, AI can expand across more use cases and teams without cost and infrastructure requirements growing disproportionately.

For organizations with more mature AI programs, gen AI models have shot to the top of their technology agendas. 68% of surveyed organizations now rank gen AI models among their three highest strategic technology priorities, with a quarter identifying gen AI is their single highest priority.

Early success appears to drive this focus. Most respondents report the ROI on their AI investments to be very good to extraordinary, challenging the notion that AI initiatives struggle to deliver value. Not only are the returns strong, but they are also being realized quickly, with nearly all respondents seeing value within the first year.

This investment and reported ROI signals that gen AI is emerging as a durable source of productivity and efficiency gains for organizations that have scaled their AI programs beyond pilots.

As gen AI becomes a core investment priority, expectations will continue to rise. Scaling beyond pilots requires enterprise-grade integration, lifecycle management and governance.

As organizations adopt multi-model strategies and pursue various use cases, leaders must treat AI as a form of enterprise infrastructure that can scale reliably across teams, use cases and environments.

As teams are moving toward portfolios of models aligned to different tasks and constraints, model design choices become increasingly important.

IBM® Granite® is a family of open, performant and trusted small language models (SLMs) designed to support enterprise AI at scale. Open source under Apache 2.0, Granite provides transparency into model architecture and control over customization, deployment and integration with existing tools and data.

Granite models are intentionally smaller and more efficient than frontier models, making them well suited for common enterprise workloads where massive models can be overkill. The family is built with enterprise-grade security and governance, featuring digital signatures for authenticity, rigorous safety and bias testing, built-in guardrails.

IBM watsonx® is designed to work across any cloud, application, model, agent or data type. The portfolio of AI products enables organizations to build, deploy and manage AI with their existing technology investments instead of replacing them. It helps organizations prepare and connect data for AI, customize models such as Granite for specific tasks and operationalize those models into agents that execute work across the enterprise.

As security, privacy and compliance increasingly shape how quickly AI can scale, enterprises need consistent oversight across models, data and usage. IBM watsonx provides governance capabilities that help to manage risk, enforce policies and maintain visibility as AI moves into production.

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