This year has seen a variety of changes in the AI landscape, from the proliferation of agents to major questions around AI’s true return on investment. In the end, it’s all about the data.
Below, IBM executives offer their 2026 predictions on topics ranging from shadow agents and AI investing trends to the ROI of AI, how AI is changing the consulting space, and monumental shifts in AI infrastructure and data:
Shadow agents will accelerate data exposure faster than we can detect it: As autonomous AI agents begin to operate independently across enterprise environments, often outside sanctioned workflows, they access sensitive data with minimal human oversight. These agents replicate and evolve without leaving clear audit trails or conforming to legacy security frameworks. They move faster than conventional monitoring can follow. This creates a new exposure problem: businesses will know data was exposed but won't know which agents moved it, where it went, or why. Systems that can trace agent data access across machine-to-machine interactions will become essential.—Suja Viswesan, IBM security software leader
Startups don’t want cash cows; they want catalysts. In 2026, AI startups will realize capital alone isn’t enough—Corporate Venture Capital will become a must-have in their cap tables to scale: Partners who provide new opportunities, including access to valuable networks, customer ecosystems, and clear pathways to scale will offer the most value in the long term. Because of this, startups will seek catalysts who can support them through an incredibly competitive landscape, integrate into enterprise workflows, and unlock market potential.—Emily Fontaine, IBM global head of venture capital
We'll see governments and regulated industries in particular move data to adopt a strategic mix of on-prem and cloud solutions—the days of a one-size-fits-all approach will soon be over and hybrid will be key: Although these organizations face the same rising demand for advanced compute workloads as any other, they have had to balance this demand with increasing concerns about cost predictability, sovereignty and operational control, all while managing security and compliance requirements. And while risk management remains paramount—organizations still navigate the need to have full control over where data is stored and processed, as well as maintain compliance with local data protection laws—regulated industries will start to take a workload-by-workload approach, deciding where to host data and applications. They can now choose what's best for them, and they will. - Alan Peacock, GM, IBM Cloud
2026 will see enterprises standardize how they orchestrate AI agents, turning what is now fragmented AI into a unified layer of business automation: The shift will move from simply embedding AI features into existing tools toward building ecosystems where agents interact fluidly across platforms. Partnerships between AI model providers, cloud infrastructure companies, and enterprise software vendors will deepen. The industry will start to favor orchestrated AI architectures—systems designed to connect rather than conflict. Model developers will collaborate on hybrid frameworks that balance large frontier models with smaller, domain-specific models, helping businesses tailor AI performance to specific needs while maintaining cost efficiency. The alignment around open, interoperable standards will lay the groundwork for agentic AI to truly scale in the enterprise. Open projects such as Model Context Protocol (MCP), LlamaStack, and the AI Alliance Open Trusted Data Initiative (OTDI) will continue to play a crucial role in establishing the trust and governance frameworks needed for agents to hand off tasks, negotiate, and chain capabilities autonomously, creating a more transparent and connected AI ecosystem. This new level of orchestration will reshape how businesses deploy AI and will define the next stage of enterprise AI maturity.—Kareem Yusuf Ph.D, senior vice president, ecosystem, strategic partners and initiatives, IBM
2026 will be the year of operating AI agents at scale: Enterprises will run dozens or even hundreds of agents in production, built by multiple teams across multiple platforms and executing in diverse environments. As this happens, focus will shift heavily toward observability, evaluation, and optimization of agentic workflows, along with strong policy enforcement to manage and govern increasingly autonomous agent behavior.—Maryam Ashoori, VP of product and engineering, watsonx.gov