BPM360 Podcast - Covering Every Angle

In this dual-guest episode in German from the All About Process Management Conference in Stuttgart, Caspar and Russell welcome Sven Schnaegelberger (founder of BPM-O and AI startup leader) and Markus Wolf (co-founder of bpExperts). The conversation centers on how BPM consulting must transform beyond time-and-materials billing as AI reshapes the industry. Sven notes that while toolmakers have advanced substantially, they still face technical challenges embedding AI meaningfully. Crucially, process mining, modeling, and workflow disciplines are converging around "context"—fundamentally reshaping how BPM professionals deliver value.
The hosts trace consulting's twenty-year evolution, revealing that pressure to shift to value-based models has long existed but consistently failed because customers lacked internal capacity for the prerequisite organizational changes. Consultants demanded full mandate; customers couldn't deliver. This impasse perpetuated traditional billing despite universal recognition of its inadequacy.
Three forces now align to make value-based models viable. First, consultants must invest time in AI capability building and junior training—not just billable customer hours. Second, customers increasingly recognize AI requires value conversations ("What is this worth to our business?") rather than simple ROI calculations. Third, pioneering firms like Tanaka deliberately reject traditional licensing tiers, instead having genuine value dialogues with each customer about worth and impact. This shift toward ambiguity and collaboration, while initially disorienting to procurement departments, represents market maturation. The hosts conclude that industry-wide dialogue through conferences is essential for practitioners to share experiences and collectively imagine sustainable consulting models for the AI era, perhaps through consultant-only barcamps focused on business model evolution.
Time-and-materials to value-based consulting becomes viable when consultants invest in AI capability building, not just billable hours to customers.
Process mining, modeling, and workflow disciplines converge around context, fundamentally reshaping how BPM professionals create value.
Customers and vendors must engage in genuine value dialogues about technology worth rather than relying on fixed pricing or guaranteed ROI.
Consulting business models are evolving across billing structures, staffing approaches, and how professionals measure and account for their contributions.
Industry-wide dialogue through conferences is essential for consultants to share experiences and collectively design sustainable models for the AI era.
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In this fourth episode of their context mini-series, recorded live from the All About Process Management Conference in Stuttgart, Caspar and Russell explore performance baselines and anomalies as critical dimensions of process context for AI systems. They begin with a compelling analogy: in fully automated dark warehouses, there are two variants—hard-routed robots following predetermined paths (pure automation requiring no AI), and autonomous vehicles that must navigate unpredictable encounters with other vehicles (where AI becomes essential). The discussion reveals that AI's greatest value lies not in executing happy paths but in handling exceptions, deviations, and anomalies that cannot be fully predetermined with rules. Russell explains that while standardized processes aim for complete determinism with no choice, more flexible case-management-style processes require AI to provide intelligent orchestration when reality diverges from plan.
Russell shares a powerful real-world example from his procurement optimization work: process mining revealed that 80% of purchase orders fell below 100 euros, yet every order incurred 35-115 euros in administrative costs. He proposed eliminating approval steps for purchases under 100 euros based on risk analysis—the savings would remove 80% of workload while jeopardizing only 1% of total spend. This exemplifies the crucial role of context: understanding what is normal (80% of orders are small), what threshold is acceptable (100 euros), what risk appetite exists (1% risk is tolerable), and what guardrails are appropriate (trust people at this level, use sampling checks). The conversation connects this to AI, asking: if we can risk-trust humans below a certain threshold, how far can we risk-trust AI with appropriate context and guidance?
The episode concludes with a sobering discussion about the Hugging Face incident where an AI agent, when given the right prompt, was willing to hack into the system. This reveals a fundamental challenge: AI is trained on everything humans have created, including criminal behavior, deception, and manipulation—the entire Machiavelli playbook is in the training data. The hosts emphasize that providing context to AI isn't just about positive guidance toward desired outcomes; it must also include risk assessment around worst-case scenarios and clear guardrails that distinguish between trusted decision space and prohibited actions. This multidimensional approach to AI context—combining positive aspiration, risk tolerance boundaries, and explicit constraints—represents the real work ahead for organizations deploying AI.
5 Key Takeaways:
AI's greatest value is handling exceptions and anomalies that cannot be predetermined with rules—the happy path requires only traditional automation.
Context must define normal performance baselines, acceptable thresholds, and risk tolerance to enable intelligent AI decision-making within appropriate boundaries.
Risk-based approaches to process optimization reveal how much work can be eliminated by trusting people within defined thresholds; the same logic applies to AI.
AI training data includes everything humans created, including criminal behavior and manipulation tactics, making it essential to provide explicit guardrails and prohibited actions.
Effective AI context requires multidimensional guidance: positive aspiration for desired outcomes, realistic risk boundaries, and clear constraints on what AI should never attempt.
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In this special guest episode, Caspar and Russell welcome Jonathan David Lewis, president of McKee Wallwork, a brand strategy firm specializing in marketing operations and organizational identity. Jonathan brings a unique lens to BPM and change management: that process adoption failures aren't fundamentally about bad processes or inadequate tools, but about a mismatch between the identity and story of the change leaders and the identity and stories of the people expected to adopt them. He reveals that BPM professionals are typically attracted to the ideals of efficiency, streamlining, and control—yet the people they're trying to change are often driven by entirely different narratives rooted in fear, shame, scarcity, or wrongness. Understanding these competing stories is the critical first step to designing adoption strategies that actually work.
Jonathan introduces his framework of four limiting mental models that drive human behavior: fear ("something bad will happen"), shame ("I'm not good enough"), scarcity ("there isn't enough"), and wrongness ("something is wrong with me or this situation"). These stories operate beneath conscious awareness and shape how people respond to new processes, technologies, and organizational changes. He emphasizes that most adoption initiatives fail because they address the logical problem (the process or system) while ignoring the psychological and narrative foundations of resistance. Jonathan argues that we need to stop trying to remove suffering through perks and flexibility; instead, leaders must bring meaning to the suffering by framing organizational work as a relational quest—giving teams a sense of adventure, a clear enemy to defeat, treasure to gain, and people worth fighting for.
The conversation explores how modern workplaces have eroded the tribal feeling and sense of meaningful purpose that characterized earlier decades of employment, replaced by transactional, futureless, and suffering-oriented work environments. Russell shares poignant examples of burnout driven not by workload but by disrespect for the meaning and identity people brought to their projects. Jonathan concludes with a powerful reframe: instead of operating in a hyper-efficient, dehumanizing AI era, leaders should actively reconstruct the story of work as relational rather than transactional, future-oriented rather than futureless, and meaningful rather than suffering—creating the conditions where humans can thrive alongside technology.

5 Key Takeaways:

Process adoption failures are fundamentally identity and story mismatches between change leaders and the people expected to adopt new processes.

Most BPM professionals are attracted to efficiency and control, but frontline workers are driven by deeper narratives rooted in fear, shame, scarcity, or wrongness that technology alone cannot address.

The four limiting mental models (fear, shame, scarcity, wrongness) operate beneath conscious awareness and shape how people resist or embrace organizational change initiatives.

Leaders attempting change should not remove suffering through perks and flexibility, but rather bring meaning to suffering by framing work as a relational quest with clear purpose and human value.

Rebuilding the story of work requires creating relational (not transactional), future-oriented (not futureless), and meaningful (not suffering) environments where humans can thrive, especially in the age of AI.

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Russell reports live from the ARIS Roadshow in Frankfurt, where the vendor is publicly repositioning its process repository from a process documentation tool to a context provider that enables AI agents to deliver business outcomes. The roadshow messaging centers on how comprehensive organizational models—spanning processes, enterprise architecture, and balanced scorecards—create the "digital twin" of a company that AI systems require. Russell observes that ARIS is articulating exactly what Caspar and Russell have been theorizing in their context mini-series: different types of information (why, now, temporal, stakeholder) must be combined to create the context that AI needs to operate effectively and compliantly.
A critical shift is emerging across the vendor and customer ecosystem: process models are no longer the end goal of BPM initiatives but rather delivery mechanisms for contextual information that powers AI and drives genuine business transformation. Russell notes that this represents a fundamental reorientation away from traditional BPM's focus on tool features and user capabilities toward outcome-based thinking where value is measured by business impact, not by model creation or certification.
The conversation highlights the acceleration of market dynamics and the need for rapid feedback loops between vendors, partners, and customers to navigate the AI transformation successfully. Russell emphasizes that customers, vendors, and consulting partners now must engage in deeper discussions about which context dimensions matter most for their specific use cases and how to operationalize context across platforms and tools—these conversations are just beginning but represent the real work ahead.

5 Key Takeaways:

Process repositories are being repositioned from process documentation systems to context layers that fuel AI decision-making and business outcomes.

Process models are transitioning from deliverables to delivery mechanisms—the goal is no longer the model itself but what the model enables AI and humans to accomplish.

Complete organizational models spanning processes, enterprise architecture, and balanced scorecards create the "digital twin" context that AI agents require to operate compliantly and effectively.

Business outcome and transformation impact are now heavily stressed by vendors and customers, displacing the traditional BPM focus on tool features and process modeling skills.

Vendors, customers, and partners must engage in dialogue about context dimensions and operationalization rather than assuming tools automatically provide the context AI needs.

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In this fourth episode of their context mini-series, Caspar and Russell examine temporal and seasonal patterns as critical dimensions of process context essential for AI decision-making. They begin with a powerful real-world example: the Ever Given container ship blocking the Suez Canal created a temporal disruption that reverberated through global supply chains—an event that AI systems without proper temporal context cannot account for or mitigate.

The discussion establishes that "when" matters profoundly in process interpretation: the time of day, day of week, shift patterns, and seasonal cycles all influence how data should be understood and how processes should respond.

Russell introduces the concept that temporal patterns operate at multiple granularities—from intraday variations between day and night shifts to seasonal cycles spanning months or years. The hosts explore how cultural and operational factors amplify these patterns; for example, sales behavior differs dramatically during holiday seasons, and production capacity decisions must account for predictable seasonal demand fluctuations. They debate whether these temporal and seasonal elements are essentially the same thing (both affecting process behavior over time) or distinct phenomena requiring separate treatment in context models.

Caspar shares a compelling case study from supply chain forecasting where incorporating three years of historical data with seasonal pattern analysis improved forecast accuracy from 20-34% to 85%, enabling far more effective production planning. The hosts conclude that while recurring seasonal patterns are data-driven and mathematically manageable for AI systems, disruptive temporal events remain the harder challenge—balancing the ability to predict regular cycles with preparing for unprecedented disruptions is where context models prove their greatest value.

5 Key Takeaways:

Temporal and seasonal patterns are distinct context dimensions: temporal refers to one-off disruptive events (like ship blockades), while seasonal reflects recurring cycles that repeat predictably across time periods.

"When" data occurs matters as much as "what" or "where"—intraday timing, day-of-week effects, cultural calendars, and shift patterns all change how process behavior should be interpreted and managed.

Recurring seasonal patterns are easier for AI to manage through data analysis because they can be mathematically identified, validated against historical data, and converted into reliable predictive rules.

Disruptive temporal events remain AI's greatest challenge because they're non-recurring, unprecedented, and their ripple effects across business systems are difficult to predict or model in advance.

Accurate forecasting combines both dimensions: baseline seasonal patterns provide the foundation, while exception reporting from domain experts captures the disruptive temporal variations that pure data analysis cannot predict.

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