👀 The Blind Spots of AI in Business Processes
AI has become a powerful tool for identifying inefficiencies, automating workflows, and predicting outcomes.
It can process large amounts of structured data faster than any analyst ever could.
But AI doesn’t see everything.
Behind the data are human behaviours, cultural nuances, and subtle decisions that can’t be captured in numbers alone.
This is where process analysts step in — adding context and interpretation AI simply can’t provide.
🛠️ Case Example 1: Approvals That Aren’t Just Signatures
AI may see a bottleneck where approvals take too long.
The data might suggest automating or removing the step entirely.
But a process analyst knows that approval may not just be a formality.
It could be about building trust between teams, managing risk, or fulfilling compliance obligations.
Without context, AI could recommend shortcuts that put the business at risk.
Analysts explain the “why” behind the bottleneck so automation doesn’t undermine accountability.
📊 Case Example 2: Data Gaps vs. Human Workarounds
AI assumes that missing data means missing work.
But in reality, employees often develop informal workarounds.
For instance, staff might share information over email or messaging apps instead of updating the official system.
AI can’t see these shadow processes, but analysts can uncover them through interviews and observation.
By recognising these hidden practices, analysts ensure workflows are documented and aligned before automation is introduced.
🤝 Case Example 3: Human Relationships in Processes
AI sees transactions, but not relationships.
For example, a sales process may look inefficient on paper with multiple touchpoints.
AI might suggest reducing them.
Yet, those touchpoints could be building trust with customers and leading to long-term contracts.
A process analyst recognises the relational value behind “inefficient” steps.
They balance efficiency with the human factors that drive loyalty and business growth.
⚖️ Case Example 4: Ethics and Cultural Nuances
AI doesn’t account for ethical or cultural considerations.
An algorithm may recommend scheduling more shifts late at night to maximise efficiency.
But a process analyst understands the impact on employee wellbeing and retention.
Or in a global company, AI may not recognise cultural holidays or local regulations that change workflows.
Analysts bring this awareness into recommendations, ensuring processes are sustainable and people-focused.
💡 Why Process Analysts Remain Essential
AI highlights trends and anomalies, but analysts give them meaning.
They uncover gaps that machines miss — from informal workarounds to the human reasons behind inefficiency.
By combining AI’s speed with human context, businesses create processes that are not only efficient but also resilient and adaptable.
This partnership ensures AI adds value instead of amplifying risks or creating frustration.
🚀 The Future: Humans and AI Together
The future of business processes isn’t AI replacing analysts.
It’s AI amplifying what analysts can do.
Machines surface the data, while analysts interpret, contextualise, and guide decisions.
Together, they build workflows that are efficient, ethical, and ready for change.
What AI can’t see, process analysts can.
And that human insight will always be the missing piece that makes automation truly effective.
