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In most cases, it isn’t.

The bottleneck is rarely the capability of the IT team. It’s the way work flows through the system, fragmented, manual, and heavily dependent on handoffs.

If you step back and look closely, the delay is already built into the workflow.

Where the Delay Actually Happens

Let’s break a typical enterprise scenario.

An employee raises a request. It gets logged in a system like ServiceNow. From there:

• The ticket is categorised (sometimes incorrectly)
• It moves to a queue
• It waits for assignment
• It may require approval
• It gets reassigned if misrouted
• Context is re-explained at each step

None of these steps are individually slow. But together, they create cumulative delay.

A study by McKinsey & Company has consistently highlighted that a significant portion of operational inefficiency in enterprises comes from handoffs, rework, and lack of process clarity, not individual productivity. In other words, people are working. The system isn’t.

The Hidden Cost of “Normal” Workflows

Most organisations accept this friction as normal.

But the cost is real:

• Increased resolution time
• Lower employee satisfaction
• Higher operational overhead
• Escalations that could have been avoided

For example, if a simple access request takes 48 hours instead of 4, the issue isn’t technical complexity. It’s process friction like approvals, routing, and visibility gaps.

Multiply this across hundreds or thousands of requests, and the impact becomes significant.

Why Traditional Automation Didn’t Fully Solve It

Many organisations have already invested in automation. Basic workflows are automated. Tickets are routed. Notifications are triggered. But most of this automation is rule-based.

If X happens → do Y.

This works in structured environments. But enterprise workflows are rarely that predictable. Requests vary. Context changes. Exceptions are common. That’s where traditional automation hits its limit.

What Changes with AI-Enabled Workflows

The next shift is not more automation. It’s smarter automation.

With AI layered into platforms like ServiceNow, workflows can move from reactive to adaptive.

For example:

• Incoming tickets can be auto-classified with higher accuracy
• Priority can be assigned based on business impact, not just keywords
• Resolution suggestions can be generated using historical data
• Repetitive queries can be handled through virtual agents

Instead of just moving tickets faster, the system starts thinking with context.

According to Gartner, organisations that combine automation with AI-driven decisioning can significantly reduce service resolution times while improving user experience.

The Real Problem: Workflow Design, Not Team Performance

Blaming IT teams is easy because they are the visible layer.

But the real issue sits beneath:

• Poorly defined processes
• Too many approval layers
• Lack of ownership clarity
• No visibility into workflow bottlenecks
• Over-reliance on manual intervention

Even the best teams struggle inside inefficient systems. Fixing performance without fixing workflows is like pushing harder on a system that’s already constrained.

What Better Workflows Actually Look Like

Efficient organisations don’t just automate tasks. They redesign workflows.

That includes:

• Reducing unnecessary handoffs
• Automating decision points where possible
• Introducing AI for classification and prioritisation
• Creating clear ownership at each stage
• Building visibility across the lifecycle of a request

The goal is not just speed. It’s flow. When workflows are designed well, teams don’t need to “work faster.” The system naturally moves faster.

The Takeaway

If your IT function feels slow, the first instinct shouldn’t be to question the team. It should be to question the workflow. Because in most cases, the delay is not happening at the point of execution. It’s happening in the structure of how work moves.

AI and automation can help. But only if the underlying process is clear, efficient, and built for scale.

Fix the workflow, and the performance follows.

FAQs

1. How do I know if my workflows are the problem?
If tickets frequently get reassigned, approvals take longer than execution, or there is a lack of visibility on status, your workflow is likely the bottleneck.

2. Can automation alone fix slow workflows?
Not entirely. Rule-based automation improves efficiency, but without context and adaptability, it cannot handle complex or dynamic scenarios effectively.

3. Where does AI add the most value in workflows?
AI is most effective in classification, prioritisation, recommendation, and handling repetitive queries, areas where context and pattern recognition matter.

4. Do organisations need to rebuild everything to improve workflows?
No. Most improvements come from refining existing processes, reducing unnecessary steps, and layering intelligence gradually.