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Raising the Bar: Why AI Makes BPM More Essential, and More Demanding, Than Ever

Written by Nathaniel Palmer, CEO | Feb 02, 2026

2026 is shaping up to be a defining year for BPM, as AI shifts from isolated pilots to always-on agents that orchestrate how work actually gets done. In the latest “BPM Skills in2026 – Hot or Not” edition of the BPM Skills series, BPM Tips again turns to global experts to explore the trends, skills needed (and not needed), and resources that matter most as processes become more autonomous, data-driven, and AI-augmented.

Infocap CEO Nathaniel Palmer, author of the Amazon #1 bestseller “Gigatrends,”contributes his perspective, offering a pragmatic roadmap for navigating thenext wave of intelligent automation.

 

How do AI and other trends impact the way organizations manage and run their processes?

Finally (!!) we are witnessing the inescapable yet fundamental shift from process as a static artifact to living, adaptive system.

For decades BPM was defined by documenting workflows, standardizing execution, and incrementally improving efficiency. The notion of adaptable, dynamic defined processed emerged as a first-class citizen within the BPM discipline in the late-2000s with Adaptable and Dynamic Case Management. Yet until now it was cast within the false dichotomy of Adaptability versus Automation, rather than embracing and enabling Adaptable Automation.

Today, AI (notably Agentic AI) turns that notion on its head. Unlike Generative AI tools that provide answers or generate content, the newest wave of AI can act by executing tasks, collaborating with humans, and dynamically adapting to new challenges. “Agentic” or “Agent AI” moves beyond providing information to taking action, enabling processes which are no longer simply executed, but interpreted, optimized, and acted upon dynamically by digital workers operating, either with agency (autonomously) or working in concert with humans co-workers.

This present three significant changes in perspective on how changing how organizations manage and run processes.

First, work is moving from information → action. Generative AI was interesting when it produced answers. It becomes transformational when it executes multi-step workflows autonomously. That turns processes into decision-driven systems, not flowcharts.

Second, organizations are shifting from task automation to end-to-end orchestration. Intelligent automation now spans documents, decisions, integrations, compliance, and human collaboration collapsing silos that BPM unintentionally reinforced for decades.

Third, trust becomes the limiting factor. Black-box AI fails in regulated, mission-critical environments. The future belongs to glass-box automation: observable, explainable, auditable systems grounded in operational excellence disciplines, not statistical mysticism.

In short, AI doesn’t replace or obviate process management, but rather hastens its need for successful business transformations, especially where AI adoption is deemed a key success factor.

What are the skills, techniques, behaviors, and attitudes that can help BPM practitioners create value for their organizations in 2026?

Most of all (and building on the points above) the BPM practitioner of 2026 is no longer a process modeler but rather the designer of human-machine collaboration. This true not just for human facing processes, but in understanding and leading the holistic orchestration of processes (or more apropos, attempting to holistically understand the process and moments of automation within your enterprise).

The new mission of BPM practitioners is make palpable and comprehendible to business stakeholders the re-envisioning the structure of the task to be not a single, discrete unit of work, but business outcomes, and to remove the distinction between what supports a task and the task itself, as well as who performs the work.

This is framed by making the work done by humans more consistent, predictable, and less reliant upon subjective interpretation of policies and rules, while simultaneously expanding the aperture for what is automatable, where digital workers and human workers use the same systems, follow the same rules, as well as are equally observable and accountable. Success requires a new set of critical skills and techniques than previously defined BPM as a discipline. These include:

    • Decision Intelligence & Rule Design: the ability to externalize decisions from code and models into explicit, governed logic is foundational. If you can’t explain why a system acted, you don’t control it.
    • Agent Orchestration & Digital Workforce Design: practitioners must design how AI agents, humans, and systems collaborate—who decides, who executes, who escalates.
    • Operational Data Literacy: not data science, but knowing which data matters operationally, how it flows, and how it creates accountability.
    • Process Observability & Metrics: AI without measurement is theater, not transformation.
    • BPMN as an AI Orchestration Language: there are very individuals sufficiently knowledgeable of BPMN, DMN, and CMMN to use create useful models of agentic workflows which stand on their own, yet BPMN remains the closest thing to a true lingua franca for AI Orchestration.

Behaviors and attitudes that create value:

    • Skeptical optimism: excited about AI, intolerant of hype.
    • Human-centric mindset: automation exists to amplify human capability, not obscure responsibility.
    • Systems thinking: understanding second- and third-order impacts of automation across people, compliance, and culture.
    • Governance-first thinking: designing control, transparency, and auditability from day one.

The practitioners who thrive will be those who can translate ambition into execution, rather than evangelizing a particular methodology or technology. Be a change agent and transformer, not an ideologue.

What are the best resources to learn those skills? (e.g., books, articles, courses)

Books

    • Gigatrends (Koulopoulos/Palmer, 2024): a foundational primer for understanding where work, identity, AI and automation are heading over the next decade and beyond.
    • Decision Management Systems (Taylor/Raden) still one of the clearest foundations for understanding decision intelligence.
    • Business Process Management: A Rigorous Approach (Martyn A. Ould): still the single best source for understanding BPM as a discipline and as a learning foundation to build upon with contemporary concepts such as agentic AI.

Technical Learning Paths

    • Python (if not already conversant, start your own learning path and explore frameworks such as Django, Flask, FastAPI, et al.)
    • Decision intelligence and rules-based automation platforms
    • Low-code / no-code workflow orchestration tools
    • AI governance and compliance training (especially for regulated sectors)

The driving the learn path behind the modern BPM Practitioner should be learning how to operationalize AI, not how to demo it.

Which skills are no longer relevant or not practically applicable yet (hype)?

Some hard truths about skills that are no longer relevant or mostly hype:

    • Pure process modeling without execution context: BPMN diagrams that never touch production systems are mostly irrelevant, and this is most of them (i.e., out of the sum total of process modeling artifacts only a small percent make it execution). Process modeling is not dwindling in value as much as it is becoming a lost art, but what will sustain it is the ability to create models as living artifacts, able to be linked to execution context.
    • “Prompt engineering” as a standalone skill: useful tactically, but not a profession. Prompts don’t scale, but the key to success for a BPM Practitioner has always come down to the ability to ask the right questions. In the GenAI era this will often mean framing the right questions as prompts, but prompts are only as effective the questions they represent (however they are expressed).
    • Black-box machine learning for core operations: if you can’t explain or audit it, you can’t deploy it responsibly at scale. All decisions and actions made through automation must be transparent, observable, and appealable.
    • AI “ethics” without operational accountability: Ethical AI discussions disconnected from real workflows, controls, and metrics are well-intentioned but insufficient. Focusing on automated outcomes is more important than chasing model training bias.
    • AI-powered automation without modeling: The biggest hype of all is the belief that AI strategy can exist without operational excellence. It cannot. That gap is where most failures occur. Automating poorly designed processes is faster than process improvement, and can also be more effective when transparent and aligned to outcomes. The critical difference is not upfront re-engineering but continuous measurement and optimization.

AI doesn’t diminish the role of BPM; it raises the bar and hastens the need for skill BPM professionals able to apply traditional methods to contemporary system design. The future belongs to practitioners who can design clarity in a world of increasing autonomy.