Smaller, Smarter, Faster: AI Will Scale Differently in 2026

by Yamini Kalra

For the last three years, the enterprise conversation about artificial intelligence has followed a predictable arc: excitement, experimentation and headlines. If you’re lucky, a pilot or two may even take off and scale. But the enterprise technology agenda for 2026 will no longer be defined by the hype, but by the utility of AI, as the mood among CIOs shift from “what can AI do” to “how to get the maximum returns.”

Gartner’s Top Strategic Technology Trends for 2026 report signals that for CIOs preparing the budget for 2026, the technological heavy lifting has begun: moving AI out of the sandbox and into the production environment, complete with the security, compliance and sovereignty guardrails necessary to prove ROI.

“Technology leaders face a pivotal year in 2026, where disruption, innovation and risk are expanding at unprecedented speed,” said Gene Alvarez, distinguished vice president analyst at Gartner. “The top strategic technology trends identified for 2026 are tightly interwoven and reflect the realities of an AI-powered, hyperconnected world where organizations must drive responsible innovation, operational excellence and digital trust.”

The centerpiece of that thesis is the pivot from large, general-purpose LLMs to domain-specific language models, or DSLMs, and modular multiagent systems, MAS, designed to execute and audit business workflows. DSLMs promise higher accuracy, lower downstream compliance risk and cheaper inference costs; MAS promise orchestration and scale.

By 2028, Gartner predicts that over half of the generative AI models used by enterprises will be domain-specific. Enterprises have started catching up.

“There will always be nuances that general-purpose models can’t address efficiently,” said Hitesh Ganjoo, CEO of Iksha Labs, in an interview with Information Security Media Group. Ganjoo, who has been actively experimenting with specialized AI agents across verticals including healthcare, education, finance and hiring, said, “The future isn’t about one big model. It’s about a thousand smaller ones each tuned to do one thing very well.”

This specificity extends to MAS, which Gartner identifies as the new engine for complex automation. Think “swarms” of specialized AI agents that can handle an entire business process – from a sales agent logging a deal to a finance agent issuing an invoice and a support agent creating a ticket.

But both DSLMs and MAS demand heavy lifts in data, MLOps and governance – areas where many organizations are still underprepared. Spending is soaring, but returns are clustered among a small set of “future-built” companies. Boston Consulting Group’s 2025 study finds only about 5% of firms are achieving AI value at scale, while a larger slice – roughly a third – are beginning to scale with mixed results. That concentration of value means that implementation discipline – not vendor selection alone – is the decisive variable.

This was echoed at the CIO.inc Business Transformation Summit this year. Nishant Pradhan, chief AI officer at Mirae Asset Investment Managers, said that 65% of projects fail due to the strategy-execution gap, which “is not something new, and has been talked about for decades.”

“There needs to be a comprehensive tracking of those projects with respect to the goals and metrics [and it needs to be done] regularly,” Pradhan said. “Some organizations aren’t able to do this and the project, or the value it adds to the top or bottom line, is somewhat lost.”

The New Factory: AI-Native Development and Physical Bots

Gartner’s AI-Native Development Platform trend forecasts an ecosystem where small, nimble teams, and even non-technical domain experts, can create applications.

But market data shows a massive bottleneck. The 2025 State of the API Report from Postman reveals 89% of developers use AI, but only 24% are designing their APIs to be used by AI agents.

The leaders will be those who adopt an “API-first” strategy, creating the building blocks that AI agents can easily consume.

“APIs have evolved from technical enablers to strategic revenue generators with businesses using diverse monetization models based on usage, access and value,” John Musser, senior director of engineering at Ford Pro, told ISMG. According to the Postman report, the ROI is already compelling: 65% of organizations now generate revenue from their APIs, and nearly half are increasing investment.

This trend pairs directly with physical AI. As AI becomes the developer, its creations are moving into the real world as robots, drones and smart equipment that can “sense, decide and act.”

The New Risk: Sovereignty, Security and Supply Chains

The back half of Gartner’s report is a sober reminder of the price of admission.

First is geopatriation. This is the C-suite-level trend of yanking critical data and apps out of global public clouds and moving them to local or “sovereign” clouds. Driven by regulations like Europe’s GDPR and fears over the US CLOUD Act, this market is exploding. Second, the security model is flipping. Gartner’s Preemptive Cybersecurity trend predicts a massive shift, forecasting that 50% of IT security spending will move from “detection and response” to “proactive protection” by 2030, up from less than 5% in 2024. Real-world use cases are backing this up, with one Telefónica Tech implementation showing a 72% reduction in phishing incidents over six months by neutralizing threats before they execute.

At the same time, the Digital Provenance trend – verifying the origin and integrity of data, code and media – will emerge as a compliance essential, not a nice-to-have. Gartner warns that by 2029, firms that neglect provenance could face “sanction risks potentially running into billions.”

What Enterprises Can Learn From Gartner’s Playbook

• AI without ROI is just R&D. Enterprises must measure outcomes, not enthusiasm.

• Governance is strategy. From multiagent systems to digital provenance, control and trust are as vital as innovation.

• Data is king. Hyperconnected architectures demand clean, shared, compliant data foundations.

• Speed ≠ readiness. The fastest adopters may not be the most successful if the organization isn’t culturally or operationally aligned.