From Agentic AI to Quantum: The Trends Shaping AI and Tech in 2026
Daniel Levi
Posted On January 1, 2026
Happy New Year, and welcome to 2026.
Every year, we cover the trends and technologies shaping what comes next. Last year, we examined the top 15 AI trends for 2025, drawing on expert predictions about where the technology was headed. This year is no different — but the context has changed.
A single year in technology can feel like a decade elsewhere. Over the past 12 months, AI has moved from novelty to infrastructure, from single-purpose tools to coordinated systems, and from open experimentation to serious conversations about cost, control, and trust. What once felt speculative is now operational. What once felt limitless is now running into real-world constraints.
That shift is why this piece avoids predictions.
Forecasts tend to age poorly in a field moving this fast. A more useful exercise is to study signals — the changes already underway that quietly shape what becomes possible next. Recently, IBM published a collection of perspectives from its researchers, engineers, and industry leaders on how AI and enterprise technology may evolve in 2026. Read individually, these insights resemble expert commentary. Taken together, they point to something more consequential: a structural change in how AI is being built, deployed, and governed.
This article reads between the lines of that work to surface the forces that matter most for founders, builders, operators, and policymakers alike. From agentic systems and compute constraints to trust, security, and open standards, the forces shaping 2026 are less about ever-larger models and more about systems, efficiency, and control.
What follows is not a roadmap. It is a clearer picture of the terrain ahead — and where real leverage is beginning to emerge.
Trend #1: Agentic AI Moves From Tools to Coordinated Systems
For much of the past year, AI progress was measured by better models and smarter assistants. In 2026, that focus shifts decisively toward agentic systems — AI that can plan, reason, delegate tasks, and operate across tools and environments with limited human intervention.
IBM’s experts consistently point to the same inflection point: individual AI tools are giving way to coordinated agents that behave less like helpers and more like teammates.
Rather than repeatedly prompting a model, users will increasingly define goals, constraints, and checkpoints, while collections of agents execute tasks in the background. These agents can search, write code, analyze data, call APIs, and coordinate with other agents — all while escalating decisions to humans only when needed.
“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’s notable is that IBM doesn’t frame this as a consumer novelty. The emphasis is firmly on enterprise workflows: engineering, IT operations, data processing, compliance, procurement, and knowledge management. In these environments, the value of agentic AI isn’t convenience — it’s continuity, speed, and reliability at scale.
Several contributors describe the rise of what they call “super agents”: systems that operate across applications, channels, and contexts from a single control plane. Instead of juggling separate AI tools for email, research, coding, and documentation, users initiate work once and let agents coordinate the rest. Whoever controls that orchestration layer — the “front door” to agent activity — is positioned to shape entire categories.
Another critical shift is architectural. The competitive advantage is no longer the intelligence of a single model, but how multiple models and tools are routed, governed, and combined. Smaller models handle routine tasks, while more powerful models are invoked only when complexity demands it. This cooperative routing reduces cost, improves responsiveness, and makes agentic systems viable in real production environments.
Goodhart explained:
“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.”
IBM’s perspective also suggests something deeper: agentic AI is pushing software away from static interfaces toward adaptive systems. Applications no longer wait passively for input. They observe context, anticipate needs, and take initiative within defined boundaries. That change quietly redefines what “software” means in day-to-day work.
“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.”
Taken together, this shift points to the rise of agentic runtimes capable of executing complex, multi-step workflows under explicit control mechanisms. Rather than producing static, code-bound outputs, agents would adapt in real time, guided by policy-driven schemas designed to balance autonomy with oversight.
That evolution, Faro said, lays the groundwork for an “Agentic Operating System (AOS)”—a standardized layer for orchestration, safety, compliance, and resource governance across large-scale agent swarms.
If you want it slightly more technical, more executive-friendly, or more forward-looking, I can adjust the framing without touching the quotes.
For founders and builders, the takeaway is subtle but essential. The opportunity in agentic AI is not just to build more agents — it’s to build the infrastructure around them: orchestration layers, control planes, approval workflows, observability tools, and user experiences that make autonomous systems trustworthy and manageable.
By 2026, agentic AI won’t be judged by how impressive it looks in a demo, but by how safely and consistently it operates inside real organizations. That shift — from clever outputs to dependable execution — is what turns agents from experiments into systems.
Trend #2: Compute and Efficiency Become the Real AI Battleground
As agentic AI systems grow more capable, a quieter constraint is coming into focus: compute is no longer abundant. By 2026, progress in AI won’t be limited by ideas or algorithms as much as by how efficiently intelligence can be produced, routed, and sustained.
IBM’s experts repeatedly return to the same conclusion: the era of simply scaling compute upward is ending. Demand has already begun to outpace supply, forcing companies to rethink how models are trained, deployed, and invoked. The result is a clear pivot away from brute-force scaling and toward hardware-aware efficiency.
This shift shows up across the stack. Large, frontier models still matter, but they are increasingly complemented by smaller, specialized models designed to run closer to the edge, consume less power, and respond faster. Instead of a single model handling everything, systems increasingly rely on tiered intelligence: lightweight models handle routine tasks, while more powerful models are invoked only when complexity requires it.
Hardware diversification reinforces this trend. GPUs remain central, but they are no longer the only focus. Application-specific accelerators, chiplet-based designs, analog inference, and even early forms of quantum-assisted optimization are maturing in parallel. Rather than betting on a single compute architecture, organizations are assembling heterogeneous compute environments tuned to specific workloads.
This is where quantum enters the picture — not as a replacement for classical computing, but as a complement. IBM has been explicit that 2026 marks a milestone where quantum systems begin to outperform classical methods on specific problems. While these use cases won’t immediately reshape everyday software, they matter because they target domains where classical compute struggles most: optimization, materials science, drug discovery, and complex financial modeling.
The more critical signal is convergence. Quantum systems are being designed to work alongside CPUs, GPUs, and AI accelerators within unified architectures. Tools that help developers generate quantum code and integrate it into hybrid workflows hint at a future where quantum compute becomes another specialized resource — invoked selectively, not universally.
“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.”
That shift began to take shape in 2025, when demand outpaced supply and forced companies to optimize around limited compute capacity. The pressure split hardware strategies in two directions: scaling up with superchips like H200, B200, and GB200, or scaling out through edge optimization, advances in quantization, and smaller language models, she said.
The result is that edge AI is poised to move from hype to deployment. At the same time, the hardware race is widening beyond GPUs alone. “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.”
For builders and operators, the takeaway is straightforward: efficiency is now a first-class design constraint. AI systems that assume unlimited compute will struggle. Systems that intelligently route tasks, minimize inference costs, and adapt to hardware realities will scale further, faster, and more sustainably.
By 2026, competitive advantage won’t come from who can access the most compute, but from who can do the most with what they have. In a world of constrained resources, efficiency isn’t just optimization — it’s strategy.
Trend #3: Systems, Not Models, Define AI Leadership
As AI capabilities mature, the center of gravity is shifting from individual models to the systems that orchestrate them. By 2026, IBM’s experts argue, model quality alone will no longer be a meaningful differentiator. The advantage will lie in how intelligence is assembled, routed, governed, and integrated into real workflows.
The reason is simple: models are becoming easier to access. Organizations can now choose from a growing menu of proprietary and open models, each optimized for different tasks. In this environment, selecting a model is increasingly a configuration decision rather than a strategic one. What separates leaders from laggards is how those models are combined with tools, data, and business logic.
IBM describes this as a move toward system-level competition. Modern AI applications are no longer a single model responding to a prompt. They are software systems that include retrieval pipelines, tool execution, memory layers, decision logic, monitoring, and fallback mechanisms. When users interact with an AI product, they are engaging with an orchestrated environment, not a standalone model.
This orchestration layer is where differentiation now lives. Systems that can dynamically route tasks between smaller and larger models, invoke external tools when needed, and adapt to context in real time are more responsive, more cost-efficient, and more reliable. Cooperative model routing allows organizations to balance performance and efficiency without sacrificing capability.
According to Kevin Chung, Chief Strategy Officer at Writer, an enterprise AI platform for agentic work, 2026 will be defined by three emerging trends that move AI beyond personal productivity.
“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 improves, systems will move beyond following instructions to anticipating 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 points to what he sees as 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.”
The shift also changes how value is created. In earlier phases of AI adoption, progress was measured by raw capability: better answers, more fluent language, stronger reasoning. In 2026, progress is measured by outcomes: how quickly work moves, how often systems fail gracefully, and how easily AI fits into existing operations.
This is especially evident in enterprise environments, where AI must coexist with legacy systems, compliance requirements, and human oversight. A robust model that cannot be reliably integrated is far less valuable than a well-orchestrated system that delivers consistent results. Stability, observability, and control matter as much as intelligence.
For founders and builders, this trend has a clear implication. Building on top of the “best model” is no longer enough. Durable products are emerging at the system layer: orchestration platforms, routing engines, workflow engines, integration frameworks, and tooling that make AI usable at scale.
By 2026, AI leadership will belong to companies that treat intelligence as infrastructure, not magic—designed, monitored, and improved like any other critical system. Models may power the engine, but systems determine where the vehicle can actually go.
Trend #4: Trust, Security, and Governance Move From Checklists to Strategy
As AI systems become more autonomous and deeply embedded in enterprise workflows, trust becomes an operational requirement rather than an abstract concern. By 2026, IBM’s experts suggest that security, governance, and sovereignty will no longer sit at the edges of AI strategy. They will shape how systems are designed from the start.
One reason is scale. As organizations deploy agentic systems, the number of non-human actors inside enterprise environments grows rapidly. AI agents access data, call tools, trigger workflows, and interact with other systems, often without direct human supervision. Each agent introduces a new identity, a new permission set, and a new potential failure point.
This changes the security model. Traditional identity and access management were built around human users. In an agent-driven environment, enterprises must account for machine identities that outnumber people and operate continuously. Visibility into what agents exist, what they can access, and how they behave becomes essential, not optional.
Governance concerns extend beyond security. Regulators, customers, and internal stakeholders increasingly expect AI systems to explain how decisions are made. As AI agents influence outcomes across finance, healthcare, procurement, and compliance, organizations need systems that can demonstrate their work. Explainability, audit trails, and continuous monitoring move from best practices to baseline expectations.
AI sovereignty adds another layer. Many enterprises are becoming wary of over-dependence on specific regions, providers, or infrastructure stacks. Concerns about concentration, data residency requirements, and geopolitical risk are forcing leaders to carefully consider where AI workloads run and who ultimately controls them. Modular architectures that allow workloads, data, and agents to shift across trusted environments are becoming a strategic advantage.
Notably, IBM’s reporting does not frame trust as a brake on innovation. It’s framed as an enabler. Secure, observable, and governable systems can scale safely into mission-critical roles. Without that foundation, AI remains stuck in pilots and proofs of concept.
For founders and builders, the message is clear. Trust, security, and governance are no longer “enterprise add-ons” to be bolted on later. They are product features. Companies that treat these concerns as first-class design constraints will move faster in regulated environments and earn deeper customer confidence.
By 2026, the most valuable AI systems won’t just be capable. They’ll be trusted enough to run the business.
Trend #5: Open Source and Domain-Specific Models Reshape the AI Stack
As AI systems mature, a quiet but decisive shift is underway: the industry is moving away from one-size-fits-all models toward smaller, domain-specific systems, many of them built in the open. IBM’s experts see this trend accelerating through 2026, driven by practical constraints around cost, control, and real-world performance.
The logic is straightforward. Large general-purpose models remain powerful, but they are expensive to run, difficult to govern, and often unnecessary for narrowly defined tasks. In contrast, domain-specific models — tuned for legal reasoning, healthcare workflows, manufacturing processes, or financial analysis — can deliver results that are at least as good, and often better, with far less compute.
Open-source ecosystems are central to this shift. Leaders from the PyTorch Foundation point to advances in distillation, quantization, and memory-efficient runtimes that have made smaller models viable across edge devices, private clusters, and regulated environments. These techniques are not just optimizations; they are enablers for broader adoption.
“The industry validated the thesis that smaller, domain-optimized models would become central,” Matt White, Executive Director of the PyTorch Foundation 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.”
IBM also highlights its progress in this direction with domain-oriented models such as IBM Granite, alongside momentum from other open-source releases, including DeepSeek and Llama, and newer reasoning-focused systems. The common thread is specialization: models designed to reflect expert workflows rather than generic language ability.
Interoperability is another driver. As agentic systems become more prevalent, AI applications increasingly rely on multiple models working together. Open standards and shared tooling make it easier to route tasks across models, integrate with orchestration layers, and maintain visibility into decision-making. Closed systems, by contrast, risk becoming bottlenecks in increasingly modular architectures.
Governance also plays a role. Enterprises deploying AI at scale need transparency into training data, evaluation methods, and update cycles. Open-source models offer clearer inspection paths and more flexible control, which becomes especially important in regulated industries or regions with strict data sovereignty requirements.
“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.”
For founders and builders, the opportunity is not simply to release open models, but to build around them: domain-specific fine-tuning, evaluation frameworks, safety tooling, orchestration layers, and vertical applications that translate raw capability into business outcomes.
By 2026, AI progress will look less like a race to build the biggest model and more like an ecosystem of specialized components working together. Open source doesn’t replace proprietary innovation — but it increasingly defines the foundation on which scalable, trustworthy AI systems are built.
Trend #6: Enterprise AI Shifts From Experimentation to Real ROI
After years of pilots, proofs of concept, and internal demos, enterprise AI is entering a more demanding phase. By 2026, IBM’s experts suggest that excitement alone will no longer justify deployment. AI systems will increasingly be judged on measurable return, operational reliability, and risk control.
This shift reflects a simple reality: enterprises are no longer asking whether AI works. They’re asking whether it works consistently, securely, and at scale. Budgets are tightening, scrutiny is rising, and leadership teams want to see tangible outcomes tied to productivity, cost reduction, or revenue impact.
One of the clearest signals in IBM’s reporting is that value no longer comes from bigger models. It comes from better data, tighter integration, and greater transparency and accountability. Enterprises are learning that feeding AI systems high-quality, permission-aware, structured data produces far more reliable results than increasing model size or complexity.
Security directly impacts ROI. Data leaks, prompt injection attacks, and unclear access controls can quickly erase any gains AI delivers. As a result, private and secure deployments are becoming the default for enterprise use cases, especially in regulated industries. AI systems that cannot guarantee data sovereignty and fine-grained permissions struggle to move beyond experimentation.
There is also a cultural shift underway. Enterprises are increasingly treating AI systems as mission-critical infrastructure. That means defining performance benchmarks, monitoring drift, auditing behavior, and continuously evaluating outcomes. AI that cannot be observed, explained, or corrected introduces too much operational risk.
IBM’s contributors frame this moment as a convergence. Advances in agentic systems, improved orchestration, and maturing governance frameworks are finally enabling AI to deliver on long-standing promises. But the bar is higher. Enterprises expect AI to reduce friction, not introduce new uncertainty.
For founders and builders, the implication is clear. Products that promise transformation without accountability will face resistance. Solutions that clearly demonstrate value — faster workflows, lower costs, improved accuracy, or better decisions — will gain traction. In this environment, ROI is not a metric to add later; it’s the starting point.
By 2026, enterprise AI success won’t be defined by how impressive a system looks in isolation, but by how reliably it delivers value in the real world — day after day, under real constraints.
Final Trend: What This Signals Heading Into 2026
Taken together, IBM’s outlook points to a clear transition underway as 2026 approaches. AI is moving out of its exploratory phase and into a period defined by structure, constraint, and accountability. The breakthroughs ahead are less about sudden leaps in intelligence and more about how effectively AI systems are designed to operate in the real world.
Across agentic AI, compute efficiency, system-level orchestration, governance, open-source specialization, and enterprise ROI, a consistent pattern emerges: AI is becoming infrastructure. It is no longer something organizations experiment with at the edges. It is something they must integrate, manage, and trust at the core of their operations.
That shift brings trade-offs. Autonomy increases, but so does responsibility. As capabilities expand, so do concerns about cost, security, and control. The organizations best positioned for 2026 are not those chasing novelty, but those investing in systems that are resilient, observable, and aligned with how work actually gets done.
For founders and builders, the message is sobering but encouraging. The opportunity is no longer to make AI smarter, but to make it usable, governable, and economically sound. Products that help organizations coordinate agents, optimize compute, specialize intelligence, and prove value will matter more than products that showcase raw capability.
For enterprises, the path forward is becoming clearer. AI strategies that prioritize modularity, efficiency, and trust are more likely to scale. Those that rely on opaque systems or unchecked complexity risk stalling under their own weight.
As 2026 comes into view, the defining question is no longer what AI can do. It’s how responsibly and reliably it can be deployed. The trends outlined here suggest that the next chapter of AI will be written not by spectacle, but by execution — quietly, systematically, and at scale.
In closing, this piece draws on IBM’s 2026 outlook not to echo predictions, but to interpret what these trends mean for founders, builders, and operators working through real constraints. As AI becomes infrastructure rather than experiment, we’ll continue tracking the shifts that matter most — where execution, trust, and long-term value intersect.
