AI Projects Stalling? Fix Fragmented Workflows to Unlock ROI

AI adoption is accelerating, yet many enterprises report stalled initiatives and limited ROI. The issue is not model performance. It is outdated, fragmented workflows. This AI project stalled scenario often stems from layering intelligent systems onto operating models designed for slower, manual coordination. 

For decades, enterprises relied on “systems of record” supported by human intervention. Employees reconciled spreadsheets, chased approvals by email, and manually connected disconnected platforms. That structure worked in predictable environments. Today’s markets demand real-time decisions across volatile supply chains, pricing, and customer expectations. McKinsey research shows 82% of supply chain leaders report tariff-related disruption affecting 20–40% of operations. Decision tempo has increased, but workflows have not evolved. 

AI exposes these structural gaps. Agentic systems assume clean data flows, visible commitments, and automated propagation of changes across functions. When those assumptions fail, pilots succeed in isolation but collapse at scale. Boston Consulting Group identifies this as an “AI Adoption Puzzle”: companies move beyond pilots, yet few see bottom-line impact because fragmented processes remain untouched. 

The shift underway moves enterprises from “systems of record” to “systems of agency.” Instead of documenting history, systems must orchestrate future actions. That transition requires three foundational steps: 

• System-level visibility: Shared data definitions and cross-functional transparency. 

• Contextual intelligence: Agents that summarize, route, and prepare decisions within live workflows. 

• Event-driven orchestration: Autonomous action triggered by verified events, with human escalation where judgment is required. 

Autonomy is not a software feature. It is the outcome of clarity. Organizations that redesign workflows, align governance, and embed trusted data foundations will unlock AI leverage. Those that simply accelerate fragmented processes will amplify inefficiencies. 

Enterprise AI success depends less on selecting better models and more on rebuilding the operating model that intelligence runs on. 

 

Source: 

https://www.zdnet.com/article/ai-project-stalled-outdated-workflows-cognitive-industrial-revolution/