2024-09-25-Skan_SSON-未来的流程智能市场报告_21页_4mb

报告摘要

Process Intelligence (PI) is a framework that captures both structured and unstructured data using AI to provide real-time insights into business processes, identify bottlenecks, and enable improvements. Unlike process mining, which focuses on system-centric data, PI offers end-to-end visibility, including human activities, and helps organizations optimize efficiency, compliance, and productivity.
In 2024, PI is vital due to intense market pressures from rising customer expectations, rapid technological change, and talent shortages, making it essential for competitive advantage and operational excellence.

Most organizations are still relying on process mining tools, with 52% prioritizing investment. However, challenges include resistance to change, inadequate data quality, lack of clear vision, and insufficient resources.
Key strategic targets for SSO/GBS organizations include cost efficiency (73%), service excellence (35%), and compliance, with quality improvement and compliance being low drivers of PI implementation despite their importance.

PI addresses data quality issues through AI-powered data collection without integration, accelerating Time to Value and reducing costs.
Benefits include operational excellence (OPEX) via improved visibility, strategic automation enhanced by RPA and AI, regulatory compliance through gap identification and monitoring, increased strategic alignment by providing a single source of truth, and end-to-end visibility that fosters collaboration and accountability.
Key advantages are data-driven decision-making, cost reduction, and streamlined operations.

AI is integrated at the foundation level to synthesize data and identify opportunities. Generative AI enhances PI by enabling conversational interactions, predictive analytics, automated hypothesis generation, and agentic AI functions for goal-driven tasks.
AI is expected to redefine human-technology collaboration, allowing organizations to monitor workflows, optimize performance, and focus on strategic oversight. Gaps in data privacy, sustainability, and ethical AI use are noted challenges.

Essential steps include stakeholder buy-in through clear communication of ROI, selecting the right tool aligned with business strategy, and identifying a strategic use case to demonstrate value.
Best practices involve change management, upskilling staff, and adopting a graded approach to minimize risks. Pitfalls to avoid include choosing unsuitable tools, lack of ownership, unclear accountability, misconceptions about ROI, and poor data readiness.

Sanofi views PI as crucial for survival, driving efficiency and data-based decisions. Nokia emphasizes PI lenses (operational, risk, decision-making, customer-centric) for transformative power.
Skan's case study with a US healthcare payer achieved significant savings, reduced cycle times, and identified automation opportunities, highlighting PI's role in optimizing end-to-end processes and enhancing visibility.
Vinay Mummigatti from Skan discusses Generative AI's three levels (data enhancement, interpretability, strategic optimization) to improve PI value, along with workforce concerns addressed through privacy measures and ethical AI practices.

PI will evolve with emerging technologies like the convergence of process mining, digital twins, multimodal AI, and edge-AI, enabling smarter automation, operational agility, and digital transformation.
Trends include agentic AI, enhanced human-AI collaboration, and a focus on data privacy and ethical considerations, positioning PI as a key enabler for future-proofing organizations.

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