The AI-BPM Reality Check: Why 99% of Organizations Aren't Ready…Yet (Part 2)

Here are the capabilities you need to develop  

(Part 2 of 3)     Reading Time: 4-5 min

EXECUTIVE BRIEFING: In Part 1, we established that AI's primary impact on Business Process Management (BPM) extends beyond technology to organizational transformation. Now, we explore the direct implications for our people and teams: the traditional BPM skillset needs to evolve. For decades, we built BPM teams skilled in process mapping, statistical analysis, and project management. We now face a Capability Evolution: the recognition that these existing competencies, while valuable, must expand to manage a world where processes increasingly run themselves.

While AI fluency is important, a team of data scientists and process modelers cannot, on their own, navigate the complexities of algorithmic accountability or orchestrate effective human-machine collaboration. Relying solely on yesterday's BPM skills is like asking an expert cartographer to navigate a spacecraft; their core discipline remains relevant, but the fundamental principles of the environment have transformed.

This article explores three new core capabilities that must be integrated into any modern BPM function, evolving it from a center for efficiency into a hub for intelligent process stewardship.

[This is Part 2 of a 3-part series. Part 1 explored the readiness gap that explains why only 1% of organizations achieve AI maturity.]

1. From Process Manager to Process Steward: Leading When Control is Shared

The traditional role of a Process Owner or Manager centered on designing, enforcing, and optimizing predictable processes. This paradigm evolves when the "employee" executing the process is a self-learning AI. The BPM leader's role must expand from Manager to Steward. A manager controls a static process. A steward guides and bears ultimate responsibility for a dynamic, autonomous system they don't fully control.

Developing the BPM Steward:

• Evolved Competency: Probabilistic Process Design. Traditional BPMN diagrams are deterministic. The new BPM requires leaders who can design and manage processes based on probabilities. A steward must be comfortable approving a process that follows a prescribed path 80% of the time, while understanding, modeling, and preparing for the other 20%. This represents a profound shift from seeking Six Sigma certainty to intelligently managing operational opportunity.

• Evolved Structure: The Emergence of the Chief Process Risk Officer. As BPM becomes the engine for autonomous operations, its oversight must be elevated. Organizations need senior BPM leaders—perhaps Chief Process Risk Officers—accountable not just for process efficiency, but for the business outcomes and risks generated by autonomous processes. Their role is to answer to the board when an AI-driven process produces unexpected results.

• BPM Scenario: A supply chain AI autonomously re-routes shipments based on predicted port delays. A traditional Process Manager would focus on whether the AI followed its programmed rules. A Process Steward asks different questions: "What is the financial impact if the AI's prediction proves incorrect? What is the confidence level of the model? Have we designed appropriate human intervention points for shipments above certain thresholds?"

2. From Process Execution to Human-AI Collaboration: Elevating the Human Role

In traditional BPM, employees were "users" who executed steps within defined processes. In the new model, where AI executes many steps, the human role is elevated and transformed. The goal transcends mere "AI adoption" by process participants; it becomes the formal design of Human-AI Process Collaboration. This requires a new model for how work gets done.

Architecting Collaborative Processes:

• Evolved Competency: Strategic AI Partnership. The most valuable process participants will be those with deep domain expertise who can effectively partner with AI recommendations. For a claims adjuster, this means not just accepting an AI's fraud assessment, but having the skill to inquire: "What factors influenced this flag? What are the key data points driving this conclusion? Show me comparable cases." This skill of "structured inquiry" becomes a core process competency.

• Evolved Design: The "Human-in-the-Loop" as a Formal BPMN Element. In your process maps, "human intervention" cannot remain abstract; it must be a formal, designed step with its own SLAs, inputs, and outputs. This role serves as the process's ethical and logical safeguard. Its performance is measured not on transaction speed, but on judgment quality and decision accuracy. 

To enable this, team members must develop new, specific skills beyond simple exception handling. These core competencies include: Adversarial Testing, where employees actively try to find the AI’s blind spots; Ethical Boundary Setting, where they define and enforce the operational guardrails for autonomous decisions; and Explanatory Bridging, the crucial skill of translating the AI's complex reasoning for broader business stakeholders to ensure transparent and trusted operations.

• BPM Scenario: In a customer onboarding process, an AI approves 95% of applications automatically. The remaining 5% route to a "Human-in-the-Loop" specialist. Their job isn't to duplicate the AI's work, but to handle the nuanced exceptions the AI flags for review, with their decisions feeding back to enhance the model. The process is designed around this human-AI partnership.

3. From Process Data to Process Fuel: The New Engine of BPM

As established in Part 1, data serves as the foundation for your autonomous processes. For BPM professionals, this means data transforms from a passive byproduct used for monitoring dashboards into the active fuel that drives the process itself. Traditional data governance becomes insufficient. The new BPM standard is Process Data Excellence. This expands the BPM team's responsibility to include the quality of data within their processes.

Building Data Excellence into the BPM Function:

• Evolved Competency: Process-Specific Data Analysis. BPM professionals must now be able to diagnose a process by examining its data. This means tracing the provenance of data used in specific automated decisions. If an AI makes suboptimal credit decisions, the BPM analyst's first task isn't to remap the process, but to investigate: "Has the data changed? Is a third-party API providing inconsistent information?"

• Evolved Investment: Funding Data as a Process Prerequisite. Budget for data quality can no longer be generic IT overhead. It must be treated as a direct cost of running specific processes. Treating data quality as a generic IT overhead is like a Formula 1 team viewing high-octane fuel as an administrative expense. The quality of the fuel directly determines performance on the track. A BPM business case for a new AI initiative must now include a line item for "data readiness" for that process, linking the investment directly to operational outcomes.

• BPM Scenario: An insurance company's automated claims process begins experiencing issues. The BPM team, using their evolved skills, discovers the root cause isn't a broken process step, but a change in a data feed that altered a key variable. By prioritizing the integrity of the process's fuel, they resolve the problem far faster than traditional analysis would have allowed.

Conclusion: From Capability to Action

These three evolved capabilities—Process Stewardship, Human-AI Collaboration, and Process Data Excellence—represent a fundamental shift in how we approach business process management. They transform BPM from a function focused on efficiency to one centered on intelligent oversight and continuous adaptation.

Yet understanding these new capabilities is only valuable if we act on them. The window for building these competencies while maintaining competitive advantage is narrowing. In the final part of this series, we'll provide a concrete 90-day roadmap that moves from insight to implementation. You'll learn how to assess your true readiness, make the hard choices about where to invest, and create irreversible momentum toward AI-powered transformation. The time for preparation is ending; the time for action is now.

Further Reading 

For leaders wishing to explore the new capability models discussed in this article, the following resources offer valuable perspectives.

• "Competing in the Age of AI" by Marco Iansiti and Karim R. Lakhani (Harvard Business Review)

• Why it Matters: This essential article on how AI transforms management work provides a blueprint for redesigning the firm's operating model around AI, directly supporting the shift from traditional BPM function to intelligent process stewardship.

• "How AI Can Be the New All-Star on Your Team" (BCG)

• "Superagency in the workplace: Empowering people to unlock AI's full potential at work" (McKinsey)

• Why it Matters: This report provides a data-driven framework for Human-Agent Teaming. It moves beyond abstract concepts to offer concrete examples of how human roles can be redesigned to complement AI, maximizing the value of both.