Why 2026 Will Be Even Bigger for AI and Automation
Anil Yarimca
TL;DR
2026 will be bigger because AI is moving from experimentation to operations, and operations force structure. Infrastructure capacity is still ramping, but the real differentiator will be orchestration, governance, and measurable outcomes. The teams that win will treat AI and automation as an operating system, not a collection of demos.
What is changing, and why it matters
In 2024 and 2025, many organizations proved that AI can produce useful outputs. The next step is harder. You need repeatable execution across systems, clear ownership, and failure recovery that does not depend on heroics.
That pressure pushes adoption toward workflows, orchestration, monitoring, and governance. It also pushes teams away from isolated copilots and toward tool-connected, workflow-embedded systems that can actually do work.
McKinsey’s 2025 State of AI survey is consistent with this direction: usage is broad, but scaled impact depends heavily on rewiring workflows, not just deploying models.
1) Infrastructure keeps expanding, so more automation becomes feasible
A simple driver is capacity. The AI infrastructure buildout is still underway, and 2026 forecasts point to continued growth in hyperscaler capex and semiconductor investment. When capacity rises, always-on AI workflows become more realistic, especially for high-volume operations that cannot tolerate latency spikes.
TSMC’s 2026 capex guidance is one visible signal of sustained demand for AI chips and related supply chain investment.
Forecasts for hyperscaler infrastructure spending in 2026 also show continued acceleration tied to AI-first buildouts.
Why this matters for automation: capacity expansion lowers the cost of running more agents, more tool calls, more retrieval, and more monitoring. That unlocks automation patterns that were too expensive, too slow, or too unreliable before.
2) Copilots will not be the center, workflows will be
Copilots are a great adoption wedge, but they are often hard to operationalize. They live in chat surfaces, rely on humans to move outcomes into systems, and rarely provide item-level traceability. In 2026, more teams will try to turn assistant behavior into actual workflows.
A practical way to think about it is this: copilots help humans do tasks. Workflows help organizations run processes. Most enterprise value lives in the second category.
This is also why the “agent workflow” narrative is growing. It forces teams to define triggers, state, tool permissions, error handling, and human approvals. Without that structure, agentic efforts stall after early success.
3) Agents will become more actionable because tool calling becomes normal
In 2026, more AI systems will move beyond text and into action, mostly through tool calling. Tool calling is the mechanism that lets an agent invoke approved functions such as APIs, database queries, RPA actions, queue operations, and workflow steps.
The key shift is that tool calling forces you to be explicit. What tools exist, what parameters are allowed, what outputs are valid, what happens on failure, who can approve high-risk actions. That explicitness is why tool calling is a production enabler, not just a feature.
OpenAI’s practical agent guidance is helpful here because it frames agents as a system of primitives like models, tools, state, and orchestration, rather than “just prompting.”
4) Governance becomes the main work, not the final step
As soon as automations touch customer operations, financial transactions, regulated workflows, or security-sensitive systems, governance is unavoidable. In 2026, more organizations will standardize policies for:
Access control for tools and data, including least privilege
Audit trails for decisions and actions, including who approved what
Evaluation and monitoring, including what “good” looks like
Versioning and change control, including rollback paths
Gartner’s 2026 strategic predictions emphasize governance and organizational shifts tied to AI and productivity tools. This is the kind of reference that helps align business and risk stakeholders around why governance must be planned early.
5) The new bottleneck is operating discipline
In 2026, the difference between teams that “use AI” and teams that “get value from AI” will be operating discipline. That includes:
Clear owners for each workflow
Defined SLAs for response and throughput
Retry, escalation, and exception handling policies
Monitoring that covers outcomes, not just uptime
A feedback loop that improves prompts, tools, and rules over time
This sounds unglamorous, but it is the reality of production. It is also why workflow-first platforms and orchestration layers matter more as adoption scales.
A useful phrase for internal alignment is: reliability is a feature. If a workflow cannot be monitored, it cannot be trusted.
6) Consolidation pressure will push automation into shared platforms
As organizations build more AI and automation, they run into a tooling problem. Too many point solutions create fragmented logs, unclear ownership, and duplicated integrations. In 2026, more teams will consolidate into shared layers for orchestration, context, and monitoring.
This does not mean one vendor wins everything. It means organizations will choose a small set of platforms where workflows live, bots run, agents call tools, and governance is enforced.
What changes inside organizations in 2026
The biggest shifts will be organizational, not technical.
First, IT, security, and operations teams will become more involved earlier, because “production AI” forces controls. Second, business teams will keep pushing for speed, but speed will be measured by time-to-operate, not time-to-demo. Third, leadership will ask harder questions: where is the value, what is automated end to end, what risk is introduced, and who owns failures.
FAQs
Will 2026 be bigger because models get smarter?
Model capability will keep improving, but the bigger effect is operationalization. The teams that win will build better workflows, better tool contracts, and better monitoring, not just better prompts.
Are AI agents replacing RPA and workflow automation?
Agents do not replace execution layers. They make decisions and choose actions, but execution still needs workflows, integrations, and often RPA for UI-bound systems. In production, agents usually sit inside workflows, not instead of them.
Why do so many AI automation pilots fail to scale?
Most pilots skip governance, error handling, and ownership. They prove usefulness but do not prove operability. Scaling requires reliable triggers, state, monitoring, and escalation paths.
What is the biggest risk when agents start acting through tools?
Uncontrolled tool access. If agents can call powerful tools without constraints, you get unpredictable behavior and hard-to-audit outcomes. The fix is permissioning, validation, and workflow-based approvals.
What should teams build first if they want to win in 2026?
A workflow that ships end to end, with monitoring and clear ownership. One reliable production workflow beats ten impressive demos.
What does “AI plus automation” look like in a mature setup?
AI handles interpretation and decision support, automation handles execution and orchestration. Humans stay in the loop for low confidence or high risk steps. Monitoring tracks outcomes and drives continuous improvement.
Conclusion
2026 will be bigger for AI and automation because the focus is shifting from capability to control. Infrastructure growth will expand what is feasible, but workflows, orchestration, and governance will decide what is successful. The organizations that treat AI as an operational system, with measurable outcomes and reliable execution, will see real scale while others remain stuck at the demo stage.