Agentic artificial intelligence (AI) will reshape process excellence in 2026 in ways that go well beyond traditional task automation.
Instead of simply executing predefined workflows, agentic systems will autonomously diagnose, optimize, and orchestrate business processes end-to-end.
Agentic AI will continue to make operations self-optimizing, predictive, and radically more autonomous. This will shift process excellence from a manual discipline into an AI-driven strategic engine, leaving many of the factors that have traditionally limited concepts such as robotic process automation (RPA) in the rearview mirror.
The PEX Report 2025/26 found that 40 percent of surveyed organizations currently use AI agents/agentic AI to support business transformation, with more than half (59 percent) planning to invest in agentic AI/AI agents in the next 12 months.
Agentic AI investment could reach a mammoth US$155 billion by 2030, according to Bank of America analysis.
8 ways agentic AI will reshape process excellence in 2026
1. Continuous, autonomous process optimization
Agentic AI tools will constantly monitor process performance such as cycle times, defects, and bottlenecks. They will utonomously:
- Detect anomalies before humans notice.
- Propose and test improvements in sandboxes.
- Deploy validated optimizations automatically.
- Adapt to mutations in process behavior.
This changes process excellence from being project-driven to always-on. “Agentic AI is a step in the right direction. Companies are rightly drawn to structured systems that enable, empower, and embolden their workforce,” says Doug Shannon, AI and intelligent automation thought leader.
2. AI-facilitated process discovery and mapping
As process mining evolves from static models to living models, agentic systems will observe real-time activities across systems, infer process variants and exceptions, update documentation automatically, and simulate impacts of proposed changes.
This will enable organizations to maintain real-time process twins, not outdated diagrams. “While agentic AI is still an emerging area, emphasize interoperability to set yourself up for future growth and workflows that span multiple departments and business applications,” says Andreas Welsch, AI expert and author of the AI Leadership Handbook.
Watch: The future of agentic AI in process excellence!
3. End-to-end workflow orchestration
Instead of isolated bots, agentic AI will coordinate tasks across ERP/CRM systems, service desks, emails and communication channels, and human approvals. These agents will manage multi-step processes like:
- Procure-to-pay.
- Incident management.
- Customer onboarding.
- Quality assurance loops.
This means more than just ‘doing steps’ – it’s about understanding the purpose of the process. Go after focused, high-impact use cases; places where agents can deliver real value without huge risks, says Sana Zia Hassan, senior manager – AI at EY.
“Don’t just deploy AI; embed it into your core workflows,” adds Lee Bogner, global chief generative AI and AI strategic enterprise architect at Mars. “Design for human-AI collaboration, because people need to adopt the system for value to show up. “Keep evolving; AI performance grows with iteration and real-world feedback.”
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4. Human-in-the-loop becomes human-as-the-director
With increasing agentic AI adoption, humans will continue to shift from micro-managing workflows to defining goals, setting constraints (compliance, risk, quality), reviewing agent decisions, and governing AI behavior.
The workforce becomes process governors, not process operators. “It’s also important to set up the right foundations early: think agent orchestration, clear handoff points with humans, and strong monitoring tools so you always know what your AI is doing and why,” says Hassan.
As maturity grows, businesses can aim to shift from human-in-the-loop to human-on-the-loop oversight, delivering greater autonomy while preserving accountability, says Shannon. “Value emerges when employees see that AI is there to help them succeed rather than replace them.”
5. Sharp rise in operational autonomy
By 2026, many mature enterprises will allow agentic AI to autonomously handle routine decision-making, exception handling, routing, prioritization, and workload balancing. This reduces manual triage work and creates highly fluid operations.
Start by identifying the key performance indicators and process performance indicators of the process you’re looking to improve, says Welsch. “Get alignment across stakeholders that this problem is worth solving and check in regularly to see you’re on track and share your progress.” Unfortunately, far too many AI projects start as technology explorations or solutions looking for a problem, an approach that quickly lands businesses in the territory of failed AI projects, he adds.
6. Predictive “next-best-action” guidance across departments
Agentic AI will integrate process data with external signals to recommend and execute next steps. This spans:
- Rerouting work to avoid SLA breaches.
- Changing resource allocations.
- Flagging compliance risks before violations.
- Optimizing supply chain decisions dynamically.
These factors will help process excellence evolve into predictive excellence.
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7. AI-native process governance and compliance
Agentic AI has the potential to radically transform process governance and compliance, automatically:
- Enforcing policy rules.
- Detecting deviation patterns.
- Generating audit trails.
- Assessing process risk levels.
- Maintaining compliance posture reports.
This is particularly significant in highly regulated industries such as finance, healthcare, and insurance.
However, the vast growth of agentic AI makes AI governance an essential component of successful adoption. “Without it, AI can drift, harm the brand, or create hidden risks,” warns Shannon. The PEX Report 2025/26 found that less than half of businesses (43 percent) currently have an AI governance policy, with a quarter (25 percent) in the process of implementing one.
Watch: Agentic AI governance: Balancing innovation with responsibility!
8. Proliferation of specialized AI agents
Finally, organizations will increasingly host ecosystems of AI agents specifically designed for process excellence. These include:
- Process observers (mine and detect issues).
- Process designers (propose new workflows).
- Automation builders (generate automations or scripts).
- Process owner assistants (governance plus reporting).
- Process auditors (compliance, exception logic).
This will enable process excellence disciplines to shift from manual Lean Six Sigma expertise to AI-enhanced operational design.