2026 is shaping up to be a pivotal year for enterprise AI adoption.
Enthusiasm remains high: 65% of organizations have already deployed GenAI, according to the recent “Building a high-performance data and AI organization” report from MIT Technology Review Insights. Now, organizations are hyper-focused on harnessing the power of AI to deliver tangible results for their businesses.
When speaking to customers and business leaders across industries, the priority remains building unified, governed data estates that can power high-quality AI agents and applications. And as companies look to scale their use of these specialized agents and apps that can reason within their unique environments, customized evaluations are proving critical.
So what’s next? Here are the trends we predict will shape data and AI efforts in 2026.
Model choice is a non-negotiable
The current battle for supremacy among frontier LLMs has been a boom for enterprises.
The AI labs continue to push each other to make underlying models more powerful, and organizations don’t want to commit to one provider out of fear of missing out on the latest and greatest. Instead, they want the ability to choose LLMs based on their performance and cost for specific tasks.
“When innovation is this fluid, IT flexibility and the ability to switch between underlying models become major competitive advantages. Open technologies give companies the control they need to thrive in the new era of constant AI-driven disruption.” – Dael Williamson, Field CTO
Unified AI governance is critical for enterprise AI agents
Once considered just access controls, governance is a critical layer in agentic AI systems.
Governance now extends to AI workloads, dashboards, and more – covering semantics and lineage. In essence, governance is how organizations control their AI agents. It serves as the contextual layer guiding AI agents to the right data and controlling the systems from acting inappropriately.
“Any successful AI strategy has to answer three questions: Can the business identify the data used? Do they understand which LLMs are being called? And can they explain what happened across the entire agentic AI chain? A strong and unified governance is the key to addressing each of these challenges.” – Robin Sutara, Field CDO
AI development consolidates to where all the data resides
In many organizations, AI development is often split across potentially dozens of different tools and domains. This impacts overall performance, slows down the path to value, and makes it harder for organizations to track and govern their AI workloads.
Instead, when companies build AI agents and applications that connect all their data in open and interoperable formats, they eliminate much of this operational complexity, as well as accelerate the pace of AI adoption. Unified, multi-modal data — spanning structured and unstructured — is key to success. And with core requirements like unified governance and end-to-end lineage built into the foundation, enterprises can more confidently extend access across their organization.
“The best, most adaptable businesses are using data to guide them in a fast-changing global marketplace. Simplifying the AI architecture and building new agents and applications where core, multi-modal business data already resides helps a wider number of users get to this important, business-critical intelligence faster.” – Dael Williamson
A focus on “boring AI” paired with human expertise
While some continue their quest for AI superintelligence, enterprises will focus on applying AI to their most repetitive and routine tasks. And they’ll increasingly aim to arm their domain experts with highly specialized AI agents to maximize the use of their decades of industry experience. Ultimately, the power of AI is about unlocking the potential for people to innovate.
“A people-first approach to AI deployment is key. Organizations can maximize on institutional knowledge by arming veterans and newcomers alike with specialized tools that keep them focused on high-value tasks.” – Robin Sutara
To get more insights into how leaders are accelerating AI initiatives with confidence, read the new MIT Technology Review report: Building a High-Performance Data and AI Organization.
