It was another big year for AI adoption and use around the world in 2025, as AI agents, AI-enabled applications, and deeper AI capabilities continued to emerge and find new use cases inside a broad range of enterprises.
And that will continue in 2026 as the new year arrives, bringing new waves of sweeping AI innovations, progress, and problems, according to a sampling of channel executives who shared their predictions with ChannelE2E.
Andrew Hillier, the CTO and co-founder of infrastructure resource optimization vendor
Densify, said he sees 2026 as the year when businesses shift their focus from AI training to AI inferencing.
“In 2026, we can expect a significant shift in how companies approach AI infrastructure,” he said. “The models have become smart enough that most organizations will not need to train their own—they can leverage off-the-shelf models and point them at their data. This means concentrations of workloads will be more inference-focused, which changes dynamics around GPU availability, efficiency, and performance.”
These changes are part of a coming shift in the GPU adoption lifecycle, said Hillier. “Companies have deployed hundreds of thousands of GPUs to get AI services running and will soon face the scrutiny that comes with large cost line items and questions around utilization and yield. The focus will begin to shift from not just ‘do we have enough GPUs?’ to ‘are we using them properly?’”
Hillier said he also expects that
Multi-Instance GPUs (MIGs), which allow GPUs to be “sliced” and isolated into up to seven instances each, will see significant adoption in 2026. “The ability to slice newer GPUs into smaller units gives organizations the flexibility to match infrastructure to workload requirements, rather than running oversized resources for simple tasks, getting more yield from these expensive assets,” said Hillier.
These changes will come, he said, because GPU costs have become such a massive part of overall AI expenses that optimization is no longer optional.
“No matter how you measure ROI, it improves dramatically when you are not wasting money on idle infrastructure,” said Hillier. “In 2026, we will see CFOs demanding the same level of scrutiny for GPU spend that they have always applied to other infrastructure.”
AI Has No ‘Easy Button’
To
Jim Johnson, the AI solutions and consulting managing partner at AI analytics vendor
AnswerRocket, 2026 will be the year when businesses turn to agentic engineering and AgentOps to truly begin to harness the power of AI.
In 2025, “enterprises and consumers learned what many AI experts have tried to explain: this technology does not have an ‘easy button,’” he said. “AI pilots are not succeeding and scaling because organizations have treated them like traditional software deployments. You cannot build it, ship it, and move on to the next project.”
Instead, enterprises in 2026 will continue to learn that the gap between a demo and a production-grade agent requires continuous operational discipline, said Johnson. “Agents require constant care and feeding, including monitoring, feedback integration, model maintenance, and performance optimization.”
Progress will come as organizations invest more deeply in the steps needed to effectively develop, deploy, and maintain functional agents, said Johnson.
“Agentic engineering requires bridging business domain expertise, enterprise IT capabilities, and AI technical knowledge,” he said. “Instead of starting with the technology, you start with the business problem—or opportunity—and design work-specialized agents iteratively through multiple cycles. Post-deployment, agent ops will gain acceptance as a necessary new discipline required to keep agents effective based on user feedback, business needs, model changes, etc. The companies that get this will start realizing business value from AI, while the ones that do not will keep struggling at the pilot stage.”
More AI Automation Will Be Needed in 2026
David Schwartz, the CEO of help desk automation vendor
Pia, said that as AI use continues to grow, spread, and mature inside businesses in the new year, additional automation will be a required enabler of the technology.
“As we look toward 2026, we are seeing a shift in how organizations think about AI and automation,” said Schwartz. “The experimentation phase is giving way to more practical conversations around what is realistic to roll out and what actually works in day-to-day operations. We are also seeing early interest from MSP partners seeking help with their own automation efforts, which points to a future where automation becomes part of their services, not just something used internally.”
Context Engineering to Become Critical for Successful AI in 2026
Sean Falconer, the head of AI at data streaming platform vendor
Confluent, told ChannelE2E that he believes the next phase for enterprise AI in 2026 will be about context, semantics, and standards.
“As enterprises scale beyond simple chatbots to deploy sophisticated multi-agent systems, the engineering focus will shift from crafting better prompts to architecting better context,” said Falconer. “Multi-agent workflows rapidly expand context requirements with tool definitions, conversation history, instructions, and data from multiple sources.”
This will come as companies discover that managing context is highly domain-specific work that cannot be solved with off-the-shelf tools, he said. “By mid-2026, context engineering will emerge as a distinct discipline, with dedicated teams and specialized infrastructure built to dynamically serve the minimal but complete information AI agents need to function reliably.”
As this occurs, businesses will find that they need a “
semantic layer” to help process and extract critical business insights from their raw data, he said.
“In 2026, enterprises will realize that AI agents do not just need data—they need meaning,” said Falconer. “As models evolve from passive assistants to autonomous actors, the absence of a semantic layer will become the chief blocker to trustworthy AI. Companies that have spent years perfecting data lakes and warehouses will discover those assets are insufficient: AI can retrieve data, but without semantic context, it cannot interpret action, implication, or intent.”
These needs will bring about shifts for enterprises—from owning raw data to owning the interpretation of that data—said Falconer. “Off-the-shelf agents will struggle in complex domains because semantics are deeply domain-specific, forcing companies to invest in bespoke semantic infrastructure. By late 2026, the semantic layer will be recognized as the new cornerstone of AI reliability, as fundamental as the database was to analytics.”
