A year in tech can feel like a decade anywhere else. Think about it: a year ago, we were discussing how ChatGPT wasn’t able to count the number of “r”s in “strawberry.” Reasoning models from Chinese frontier labs (like DeepSeek-R1) hadn’t taken the world by storm, and neither had open-source reasoning agents. Claude’s dedicated coding agent didn’t exist yet. IBM’s Granite 3.0 had only just arrived. And the agent conversation was only beginning: MCP had just gained traction in the spring, with a notable endorsement from Sam Altman. Meanwhile, in the world of infrastructure, chips and compute resources were becoming scarce, giving new territories a competitive advantage.
Over the last few weeks, IBM Think spoke with a dozen experts in tech—researchers, founders and leaders from IBM and beyond—to get their insights on what to expect in the year ahead. Each one shared a common belief for the year ahead: the pace of innovation won’t slow down in 2026. “It’s such a crazy time,” Peter Staar, a Principal Research Staff Member at the IBM Research Zurich Laboratory, told IBM Think in an interview. “And it’s only accelerating.” New agentic capabilities will give way to new possibilities for businesses and individuals alike. “I really see the parallels of music production à la Rick Rubin style with AI creation,” IBM’s Distinguished Engineer Chris Hay told IBM Think. “I don’t limit it to coding. I think we [will] all become AI composers, whether you’re a marketer, programmer or PM.” Many believe efficiency will be the new frontier. “GPUs will remain king, but ASIC-based accelerators, chiplet designs, analog inference and even quantum-assisted optimizers will mature,” Kaoutar El Maghraoui, a Principal Research Scientist at IBM, said during this week’s Mixture of Experts.
“Maybe a new class of chips for agentic workloads will emerge.” After much skepticism around AI’s ROI, AI capabilities will pave new ways to do business in the enterprise. And open-source reasoning models and agents will keep pushing boundaries to conquer enterprise AI. At the same time, trust and security will become key priorities as many enterprises sharpen their focus on AI sovereignty. That’s just the opening act for what’s to come in enterprise tech in the days ahead. Read on for 18 expert predictions to watch out for in 2026.
Quantum will outperform classical computers
IBM has publicly stated that 2026 will mark the first time a quantum computer will be able to outperform a classical computer— the point at which a quantum computer can solve a problem better than all classical-only methods. According to IBM, this milestone will unlock breakthroughs in drug development, materials science, financial optimization and more industries facing incredibly complex challenges. “We’ve moved past theory,” Jamie Garcia, Director, Strategic Growth and Quantum Partnerships at IBM, told IBM Think. “Today, we’re using the industry’s best-available quantum computers for real use cases. While these aren’t productionscale problems, they’re signals where we expect value to increase as quantum continues maturing.
And we are seeing incredible progress in research across drug development, materials discovery and optimization for finance and logistics.” Garcia also highlights the convergence with AI: tools like Qiskit Code Assistant are already helping developers generate quantum code automatically. IBM is building a quantum-centric supercomputing architecture that combines quantum computing with powerful high-performance computing and AI infrastructure, supported by CPUs, GPUs and other compute engines, she explained. To push this goal into the future, AMD and IBM are exploring how to integrate AMD CPUs, GPUs and FPGAs with IBM quantum computers to efficiently accelerate a new class of emerging algorithms, which are outside the current reach of either paradigm working independently.
Hardware efficiency will become the new scaling strategy
“2026 will be the year of frontier versus efficient model classes,” Kaoutar El Maghraoui, a Principal Research Scientist at IBM, said during a recent episode of Mixture of Experts. Next to huge models with billions of parameters, efficient, hardware-aware models running on modest accelerators will appear. “We can’t keep scaling compute, so the industry must scale efficiency instead.”
In 2025, demand outran the supply chain, forcing companies to optimize around compute availability. That pressure split hardware strategies: scale-up with superchips like H200, B200, GB200— or scale-out with edge optimizations, quantization breakthroughs and small LLMs, she said. This will also mean that edge AI will move from hype to reality. And the hardware race won’t only be about GPUs anymore. “GPUs will remain king, but ASIC-based accelerators, chiplet designs, analog inference and even quantum-assisted optimizers will mature,” El Maghraoui said.
“Maybe a new class of chips for agentic workloads will emerge.” Systems, not models, will define AI leadership In 2026, the competition won’t be on the AI models, but on the systems. “We’re going to hit a bit of a commodity point,” Gabe Goodhart, Chief Architect, AI Open Innovation at IBM, said in an interview with IBM Think. “It’s a buyer’s market. You can pick the model that fits your use case just right and be off to the races. The model itself is not going to be the main differentiator.”
What matters now is orchestration: combining models, tools and workflows. “If you go to ChatGPT, you are not talking to an AI model,” he explained. “You are talking to a software system that includes tools for searching the web, doing all sorts of different individual scripted programmatic tasks, and most likely an agentic loop.” “In 2026, I think we’ll see more sort of cooperative model routing,” Goodhart said. “You’ll have smaller models that can do lots of things and delegate to the bigger model when needed. Whoever nails that system-level integration will shape the market.”
Agentic parsing will replace monolithic document processing
In 2026, document processing will stop being a one-model job. Instead of forcing a single system to interpret an entire file, synthetic parsing pipelines break documents into their parts (titles, paragraphs, tables, images) and route each to the model that understands it best. “This allows us to reduce computational cost while improving fidelity because each element is interpreted by the model class that understands it best,” Brian Raymond, Founder and CEO of Unstructured, told IBM Think. Unstructured transforms unstructured data into clean data ready for AI.
“The result is a flexible reconstruction layer that synthesizes a precise representation of the original source while maintaining strong guarantees about structure, lineage and meaning,” Raymond said. Unstructured recently integrated the object detection capabilities of IBM Research’s Docling in order to accomplish this objective, increasing overall accuracy. Next comes agentic parsing. Think of it as a team of domain experts— only they’re AI agents—continuously scanning your corpus, building deep semantic profiles and indexing everything across a multidimensional graph.
“This provides search that can operate across intent, structure, content and metadata simultaneously and makes previously inaccessible internal knowledge available in real time,” Raymond said. Together, these advances point to self-aware enterprise data systems, a foundation for faster decisions and smarter workflows in 2026.
