Don't Automate Bad Workflows: AI Should Begin with Redesign

Artificial intelligence has become one of the biggest priorities in executive suites, with organizations investing heavily in new capabilities and employees experimenting with AI daily. Technology leaders are under pressure to identify opportunities that improve productivity and reduce costs. However, before asking how AI can speed up processes, it’s essential to question whether the workflow still makes sense.

In many organizations, the first question is ‘What can we automate?’ This might seem like a logical starting point, but I believe it’s the wrong approach. Too often, companies use AI to automate workflows designed years ago for a different business environment. These workflows have accumulated unnecessary approvals, duplicate activities, manual handoffs, and outdated policies over time.

AI may execute those processes faster, but it does nothing to address underlying complexity. This challenge is not unique; Harvard Business Review’s article ‘The secret to successful AI-driven process redesign’ highlights that organizations create the greatest value when they rethink business processes before applying AI, rather than simply layering technology onto existing ways of working.

MIT Sloan’s article ‘How AI is reshaping workflows and redefining jobs’ also emphasizes that AI delivers its biggest impact when organizations redesign how work flows across the enterprise instead of focusing only on automating individual tasks. These findings reinforce an important lesson for leaders: before asking where AI belongs, ask whether the workflow itself still makes sense.

Every business process reflects yesterday’s decisions. Most processes were never designed from beginning to end; they evolved over many years as organizations expanded into new markets, acquired businesses, introduced new systems, responded to audits or adapted to changing regulations. Each change made sense at the time but collectively creates unnecessary complexity.

For example, consider a purchasing process that requires six approvals before an order can be placed. One approval may have been added after an audit; another resulted from an acquisition; and a third was introduced because one business unit wanted additional oversight. Eventually, those approvals simply become ‘the way we do things.’ AI can summarize purchase requests, route approvals automatically, notify managers, and even recommend decisions.

What it cannot determine on its own is whether six approvals are still necessary. That requires leadership. The same pattern exists throughout finance, manufacturing, supply chain, human resources, customer service, and countless other business functions. Organizations often focus on making individual activities faster while overlooking opportunities to eliminate activities altogether.

This is where workflow redesign becomes essential. Instead of asking how AI can automate each step, leaders should ask which steps continue to create value and which exist simply because they have always been part of the process. Sometimes the greatest improvement comes from eliminating work rather than automating it.

The organizations creating the most business value from AI tend to approach the problem differently. Rather than starting with technology, they begin with the business outcome they want to achieve. This might be reducing order cycle time, improving forecast accuracy, increasing manufacturing throughput, accelerating product development, or improving customer responsiveness.

A clearly defined objective creates a much stronger foundation than simply looking for places to use AI. Once the outcome is clear, the next step is understanding the entire workflow. Many delays occur not because individual tasks are inefficient but because work passes through too many people, systems, or approval points.

Mapping the complete process often reveals unnecessary handoffs and redundant activities that can be removed before automation is introduced. Deloitte’s research on enterprise AI adoption highlights that organizations generating the greatest business value are redesigning how work is performed rather than simply automating existing tasks.

In other words, they view AI as an opportunity to change how work gets done instead of accelerating yesterday’s approach. Leaders should also distinguish between administrative work and human judgment. AI excels at gathering information, organizing data, preparing summaries, and performing repetitive tasks but struggles with decisions requiring experience, context, creativity, negotiation, or ethical judgment.

The objective is not to replace people; it’s to remove low-value administrative work so employees can spend more time applying their expertise where it matters most. Standardization is equally important when every business unit performs the same work differently, AI solutions become more difficult to implement, maintain, and scale.

Simplifying and standardizing workflows before introducing AI creates a stronger foundation for enterprise adoption while producing more consistent business results. Finally, organizations should measure business outcomes instead of technology activity. The number of AI assistants deployed or prompts submitted may indicate adoption but does not demonstrate business value.

Leaders should instead measure improvements in cycle time, quality, customer satisfaction, operating cost, revenue growth, and employee productivity – those are the outcomes executives ultimately care about. A simple framework for AI-enabled workflow redesign involves four steps: simplify, standardize, redesign, and automate.