BPM-AI Transformation: The Role of Change Management and Centers of Excellence
If AI is the engine of BPM transformation, change management is the transmission.
This is part 2 of a 3-part blog series examining the BPM-AI transformation journey in detail. (Part 1: Key Drivers and Challenges & Part 3: The Evolution of Transaction Processing and Strategic Frameworks.)
What is the framework for AI transformation?
McKinsey describes successful AI transformation as a capability-building effort, not a technology deployment. Drawn from McKinsey’s work on hundreds of large-scale technology and AI transformations, their framework emphasizes six enduring capabilities:
- Strategic road mapping.
- Talent.
- Operating model.
- Technology.
- Data.
- Adoption & scaling.
What is change management's role?
Traditional change management—communication plans, training programs, and resistance management—remains necessary, but it is not enough for AI-driven BPM transformation. McKinsey’s guidance places greater emphasis on rewiring the organization around high-value business problems, redesigning workflows, building the right capabilities, and scaling adoption rather than treating AI as a series of disconnected pilots. (McKinsey & Company)
The 5 requirements for AI-Driven BPM
- Clarifying 3–5 end-to-end workflows most impacted by AI (e.g., procure-to-pay, hire-to-retire, idea-to-product.)
- Redesigning work at the process level, not just the task level, to exploit AI capabilities fully.
- Formal, workflow-specific AI training that reduces anxiety and builds measurable skill confidence.
- CEO-level sponsorship that signals AI as strategic, not experimental.
- Feedback mechanisms that surface adoption barriers in real time.
How do organizations move from ambition to activation?
Deloitte’s 2026 State of AI in the Enterprise report frames the next phase of enterprise AI as a move from ambition to activation. A survey of 3,235 leaders across 24 countries found that AI access and production expectations are rising. However, only 34% of organizations are using AI to deeply transform their business. Most organizations are still redesigning selected processes or using AI at a surface level rather than reimagining business models end-to-end. (Deloitte)
Key statistic:
Deloitte reports that 34% of surveyed organizations are “deeply transforming” with AI, while 30% are redesigning key processes and 37% are using AI at a surface level. (Deloitte)
How do organizations support business transformation?
The PEX Report 2025/26, based on a survey of more than 200 professionals, identifies BPM as the most common technology used to support business transformation. ARIS’ summary of the PEX findings reports that 53% of organizations cite BPM as their top transformation tool and positions BPM as a governance foundation for responsible AI adoption, transparency, compliance, and knowledge retention. (Process Excellence Network)(ARIS)
What role do Centers of Excellence (CoE) play?
Gartner’s 2025 AI Hype Cycle indicates that organizations are shifting from generative AI hype toward scalable foundations such as AI-ready data, AI agents, governance, and operational readiness. (Gartner) This shift strengthens the case for an AI Center of Excellence. A CoE provides a cross-functional mechanism that aligns business use cases, governance, data readiness, technology choices, and adoption practices to scale AI responsibly across the BPM lifecycle.
An effective BPM/AI CoE in 2026 operates across five functional pillars:
- Governance & Standards: Defines AI usage policies, process model standards, data quality mandates, and ethical guardrails.
- Technology Stewardship: Evaluates, pilots, and industrializes iBPMS platforms, process mining tools, RPA suites, and agentic AI frameworks.
- Knowledge Management: Builds a reusable library of process patterns, AI prompt libraries, and accelerators across Bus.
- Talent Development: Runs reskilling academies, AI literacy programs, and champions networks for process owners.
- Value Realization: Tracks ROI, operational KPIs, and process health metrics with transparent dashboards.
How can a CoE and Hyperautomation coordinate?
In hyperautomation programs, the CoE should coordinate initiatives that span BPM, RPA, AI/ML, integration platforms, and low-code/no-code tools. Without that coordination, organizations can fall into fragmented point solutions that create new silos. A CoE helps reduce automation sprawl by enforcing shared standards, an integrated technology stack, and a process architecture that connects automation choices back to business outcomes. (Gartner, McKinsey & Company).
A CoE is the backbone of change management.
The CoE does not merely govern technology; it provides the operating muscle for change. McKinsey’s transformation research shows that comprehensive, action-oriented transformation practices materially improve the odds of success, and that value capture depends on sustained execution across the transformation lifecycle. In practical terms, the CoE institutionalizes the routines, standards, ownership, and feedback loops needed to keep AI-enabled BPM transformation moving beyond individual project cycles. (McKinsey & Company).
For BPM leaders, the imperative is clear.
Success requires disciplined change management that converts the workforce from process executors to AI-augmented process stewards, anchored by a CoE that provides the governance, standards, and talent engine to sustain transformation beyond individual program cycles. (Deloitte, McKinsey & Company)
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