The trends that will shape AI and tech in 2026

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.

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 production-scale 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.

“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.”

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.”

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.

“We’ve moved past the era of single-purpose agents,” Chris Hay, Distinguished Engineer at IBM, said during a recent Mixture of Experts episode. In 2024, agents were small and specialized: the email writer, the research helper. But now, with reasoning capabilities, agents can plan, call tools and complete complex tasks.

“We’re seeing the rise of what I call the ‘super agent,’” Hay said.

“In 2026, I see agent control planes and multi-agent dashboards becoming real. You’ll kick off tasks from one place, and those agents will operate across environments—your browser, your editor, your inbox—without you having to manage a dozen separate tools,” Hay said. Forget the static software in the user experience and user interface. Expect interfaces and apps that can adapt to any scenario, Hay predicts, making every user an AI composer.

“Whoever owns that front door to the super agent will shape the market.”

2026 will be defined by three trends that move AI beyond personal productivity, says Kevin Chung, Chief Strategy Officer at Writer, an enterprise AI platform for agentic work.

“First, AI is shifting from individual usage to team and workflow orchestration,” Chung told IBM Think. That means coordinating entire workflows, connecting data across departments and moving projects from idea to completion.

Second, as reasoning capabilities improve, systems won’t just follow instructions: they’ll anticipate needs. “This evolution transforms AI from a passive assistant into an active collaborator capable of meaningful problem-solving and decision-making,” he said.

Finally, Chung sees the most exciting shift: the democratization of AI agent creation. “The ability to design and deploy intelligent agents is moving beyond developers into the hands of everyday business users,” he explained. “By lowering the technical barriers, organizations will see a wave of innovation driven by people closest to real problems.”

Agentic systems turned LLMs and coding assistants into something more dynamic in 2025. And this is just the beginning, according to Ismael Faro, VP of Quantum and AI at IBM Research. He sees software moving from informal interactions to a structured approach where users set goals and validate progress while autonomous agents execute tasks and request human approval.

“Software practice will evolve from vibe coding to Objective-Validation Protocol,” said Faro in an interview with IBM Think. “The users are going to define goals and validate while collections of agents autonomously execute, extending the idea of human-in-the-loop, requesting human approval at critical checkpoints.”

This shift will enable the emergence of agentic runtimes to run complex workflows with a control mechanism, and move agent behavior from static, code-bound outputs to dynamic adaptation, enabled by policy-driven schemas that balance flexibility and control.

This will be the foundation for an “Agentic Operating System (AOS),” Faro explained, which will standardize orchestration, safety, compliance and resource governance across agent swarms.

“With disciplined attention to security, resource management, compliance and operational excellence, enterprises can leverage expert system agents to reclaim leadership in mission-critical computing,” he said.

Generative models need to be multisensory so they can interpret the world like humans and even detect signals we might miss, said Aaron Baughman, IBM Fellow and Master Inventor, in a recent episode of Mixture of Experts.

Baughman has worked with multimodal AI in sports and leads some of IBM’s work with the US Open, ESPN Fantasy Football and the Masters, notably. For him, multimodal AI is a trend he expects to see more of in 2026.

“These models will be able to perceive and act in a world much more like a human. They’ll be able to bridge language, vision and action, all together,” he said. “In the near future, we’re to start seeing these multimodal digital workers that can autonomously complete these different tasks to interpret things, maybe even like complex healthcare cases.”

But autonomy won’t mean removing human oversight. “It’s also important in the future to have this human-in-the-loop AI,” Baughman said, “so that the human can fine tune and change the skill.”

It’s been just a year since Anthropic launched MCP, alongside IBM’s ACP and Google’s A2A. If 2025 was the year of the agent, 2026 should be the year where all multi-agent systems move into production, IBM’s Kate Blair told IBM Think in an interview. That shift depends on protocol maturity and convergence.

“2026 is when these patterns are going to come out of the lab and into real life,” said Blair, who leads IBM’s BeeAI and Agent Stack initiatives. Both projects were contributed to the Linux Foundation.

The Linux Foundation recently announced the formation of the Agentic AI Foundation and Anthropic’s contribution of MCP. “We’re excited that MCP has come under open governance,” Blair said. “Openly governed, community standards are what is going to unlock more creativity, more innovation and more solutions.”

The A2A project is about to hit its first major release. “We’re already seeing collaboration between A2A and MCP to standardize on a single card to describe an entity, whether it’s a tool or resource in MCP or an agent in A2A,” she said.

Blair sees this unified card as a catalyst for interoperability and the opportunity to share registries, discovery and utilization across agents and agentic systems.

“I’m excited to get to the next level where we’re really speaking about widespread production use cases, about agents talking to other agents.”

Atolio provides a secure and private AI platform for enterprises. The challenge they observe among their clients is the need to be fast and experiment with new tech while also mitigating the risk of losing control of their AI data.

“The most significant trend we see emerging next year is the shift from AI experimentation and excitement to private and secure deployments with real ROI expectations within enterprises,” David Lanstein, Cofounder and CEO of Atolio, told IBM Think.

“Data leaks continue to erode enterprise trust,” he said. “The unsolved challenge of prompt injection attacks in production environments makes data sovereignty and first-class permissioning non-negotiable requirements.”

The solution isn’t bigger models, but smarter data. As Lanstein puts it: “True value will come from feeding models high-quality, permission-aware structured data to generate intelligent, relevant and trustworthy answers.”

“What excites me most is the convergence necessary to make this happen,” he said, pointing to “a renewed commitment to security, advancements and solutions that understand context and user needs more deeply, and the continued evolution of the MCP ecosystem.”

Founded in 2020, AuthMind is tackling one of the toughest problems in cybersecurity: giving companies a clear, near real-time view of every identity’s access and activity so they can stop attacks before they start.

“In the coming years, agentic AI and other non-human identities will outnumber human users in the organization significantly,” Shlomi Yanai, CEO and Cofounder at AuthMind, told IBM Think.

The shift will redefine enterprise security and governance. “This is now a board-level concern to ensure each agent is accounted for and acting the way it was intended to, increasing both productivity and security,” Yanai said. As organizations scale AI adoption, the challenge is no longer just deploying models; it’s managing identity with new users: autonomous agents operating across systems.

For enterprises, getting the advantage in this context means answering three critical questions: Do we know every AI agent that exists? Do we understand what it is accessing? And are we confident in what it’s doing when it does access a system?

Discovering, observing and protecting not just every human but also every AI agent is becoming essential to responsible and secure AI adoption. “I am super excited to follow the companies that master this visibility, accountability and trust across all AI agent identities,” Yanai said.

“The most powerful trend I see for next year is AI tackling complex enterprise workflows,” said Steven Aberle, Founder of Rohirrim, an AI-native startup focused on complete procurement ecosystems, in an interview with IBM Think. “Not as a proof of concept, but as a dependable system that can execute deep tasks, end to end.”

Generative and agentic systems will interpret intent, search across vast networks, choose the right tools and keep going until outcomes are achieved. “That shift creates entirely new categories of platforms, and even new markets, because we’re no longer limited by what a single human or a single application can hold in mind,” he explained. “It is true machine automation.”

Transformers made this possible. “They gave us systems that can absorb enormous bodies of text, code and history, then respond with nuance and precision with effective guardrails in place,” Aberle said. “We’re entering an era where AI moves from simply answering questions to directly influencing outcomes.”

In procurement, that means tracking requirements, spotting gaps early and suggesting fixes, giving professionals clarity and speed for fairer, faster decisions.

A year ago, Matt White, Executive Director of the PyTorch Foundation, predicted that smaller models will push AI to the edge.

“The industry validated the thesis that smaller, domain-optimized models would become central,” White recently told IBM Think. “Advances in distillation, quantization and memory-efficient runtimes pushed inference to edge clusters and embedded devices, driven by cost, latency and data-sovereignty needs.”

According to White, three forces will be defining open-source AI in 2026: global model diversification, led by Chinese multilingual and reasoning-tuned releases; interoperability as a competitive axis, as frameworks and runtimes align around shared standards; and hardened governance, with security-audited releases and transparent data pipelines.

“As agentic systems emerge, PyTorch’s role as a common substrate for training, simulation and orchestration will only deepen,” White said. “Developers need flexible tooling for multimodal reasoning, memory components and safety-aligned evaluation, and that’s where open source thrives.”

IBM’s Peter Staar predicts 2026 will mark a shift in AI research priorities that favor the palpable. “Robotics and physical AI are definitely going to pick up,” he said. While large language models remain dominant, Staar notes that the industry is hitting diminishing returns from scaling. “People are getting tired of scaling and are looking for new ideas,” he explained.

Staar sees a lot of interest for AI that can sense, act and learn in real environments; this is where the technical challenge will lie: this could be the next frontier for innovation.

At the same time, Staar believes that open-source AI will continue to shape the competitive landscape. “The ones in the lead want to keep it closed, and the ones catching up go open,” he said.

With NVIDIA emerging as a major driver of open ecosystems —largely because its business depends on widespread GPU adoption rather than proprietary models—Staar predicts collaboration will accelerate as AI moves beyond screens and into the physical world.

2024 ended on a high note for open-source AI with Meta’s Llama models gaining traction. Since then, the open-source AI ecosystem has grown a lot, with smaller, domain-specific models achieving impressive results—it’s the case for IBM’s Granite, Ai2’s Olmo 3 and, of course, DeepSeek’s models. Anthony Annunziata, Director of Open Source AI at IBM and the AI Alliance, sees this trend accelerating in 2026.

“We’re going to see smaller reasoning models that are multimodal and easier to tune for specific domains,” he said during an interview with IBM Think.

Advances in fine‑tuning and reinforcement learning also mean that enterprises can adopt open-source AI, feeding the appetite for smaller and efficient models. “Instead of one giant model for everything, you’ll have smaller, more efficient models that are just as accurate—maybe more so—when tuned for the right use case,” he said.

Open source, agentic AI will accelerate this trend. “General‑purpose agents aren’t enough for legal, health or manufacturing,” Annunziata said. “You need domain‑enriched models and architectures that reflect expert workflows.”

Open-source AI is a necessity. “If you believe we’re heading toward an economy where automated AI capabilities do a lot of work, then the standards of interaction must be open, he said.” “Otherwise, you end up with fragmented silos, or a winner-take-all platform.”

“The massive middle of the enterprise bell curve begins to move from experimentation to production-grade systems,” said Tomás Hernando Kofman, Cofounder of Not Diamond, a multi-model AI infrastructure platform, in an interview with IBM Think.

That transition won’t be easy: “AI teams will have to invest heavily in evaluation, reliability, optimization, efficiency, scalability and maintainability,” he said.

This will take serious coordination and resources. If companies don’t make the investment, they’ll end up stuck: not having the right capabilities means the systems aren’t useful, and that lack of utility just reinforces the problem.

On the frontier, the challenges look different. “I believe the field will grapple with three major hurdles: continuous learning, memory and scalability,” Kofman said.

Work will happen at both the model architecture level and in agentic systems.

“We’ll begin to see decentralized networks of agents that can learn from each other, share information and retain important knowledge over long horizons—weeks, months, even years,” he said. “These systems will improve dynamism and continuous improvement while allowing agents and models to specialize into efficient, focused capabilities.”

No single entity can solve the deepfake and weaponized AI crisis, especially as new threat vectors—like weaponized AI agents—emerged at the tail end of this year, said Ben Colman, CEO & Cofounder of Reality Defender, a cybersecurity company offering deepfake detection tools. The rapid evolution of generative AI demands a collaborative ecosystem.

“Strategic partnerships are essential, not just for bolstering defense, but for anticipating the next wave of sophisticated models and industry-specific vulnerabilities,” he told IBM Think.

“I see these partnerships in our industry between ourselves, others working on different aspects of a similar problem and so on—happening as fast as advancements in AI, if not faster.”

Colman observes a shift toward a layered security model. “By stacking different defenses, gaps in one layer are covered by another to create an impenetrable shield,” he said.

Integration will define the next phase. “When these emerging technologies couple with detection platforms like ours, the result is a comprehensive ‘defense in depth’ strategy,” he said. “This ensures organizations are protected across all media formats and entry points, for all use cases, and in all toolsets—rather than relying on a single point of failure.”

Organizations can’t afford to have their AI projects interrupted—but there’s only so much that business leaders can control. AI sovereignty—the ability to govern AI systems, data and infrastructure without relying on external entities—has become mission-critical, said Anthony Marshall, Senior Director and Vice President, IBM Institute for Business Value (IBV).

For 93% of executives surveyed by the IBV, factoring AI sovereignty into business strategy will be a must in 2026. “This isn’t just box-checking,” said Marshall.

Half of executives worry about over-dependence on compute resources in certain regions (a concern especially high among business leaders in the Middle East and APAC), and many believe that leaning on those resources can introduce a wide range of risks. Think data breaches, loss of access to data and intellectual property theft.

Transparency and trust will also remain priorities. “Both regulators and consumers ask organizations to explain how AI agents come to specific decisions. Organizations must design agents that can show their work, for even the most complex outputs,” said Marshall.

That means building sovereignty through modularity—architecting AI environments so workloads, data and agents can shift among trusted regions and providers.

“Continuous monitoring is essential to detect and address model drift before it compromises performance or introduces bias,” said Marshall.