{"slug": "dont-automate-bad-workflows-why-ai-should-begin-with-redesign", "title": "Don’t automate bad workflows: Why AI should begin with redesign", "summary": "Harvard Business Review and MIT Sloan research indicate that organizations achieve the greatest value from AI when they redesign business processes before applying the technology, rather than automating existing workflows. Leaders are urged to ask whether workflows still make sense and to focus on eliminating unnecessary steps, such as redundant approvals, to improve outcomes like cycle time and forecast accuracy.", "body_md": "Artificial intelligence has quickly become one of the biggest priorities in the executive suite. Organizations are investing heavily in new capabilities, employees are experimenting with AI every day, and technology leaders are under pressure to identify opportunities that improve productivity and reduce costs.\n\nIn many organizations, the first question is, “What can we automate?”\n\nIt sounds like the right place to start, but I believe it is the wrong question.\n\nToo often, organizations use AI to automate workflows that were designed years ago for a very different business environment. Those workflows have accumulated unnecessary approvals, duplicate activities, manual handoffs and outdated policies over time.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 the underlying complexity.\n\nThis challenge is not unique to my experience. In its article, [The secret to successful AI-driven process redesign,](https://hbr.org/2025/01/the-secret-to-successful-ai-driven-process-redesign) Harvard Business Review explains that organizations create the greatest value when they rethink business processes before applying AI, rather than simply layering technology onto existing ways of working. Likewise, MIT Sloan’s article, [How AI is reshaping workflows and redefining jobs](https://mitsloan.mit.edu/ideas-made-to-matter/how-ai-reshaping-workflows-and-redefining-jobs), argues that AI delivers its biggest impact when organizations redesign how work flows across the enterprise instead of focusing only on automating individual tasks.\n\nThose findings reinforce an important lesson for leaders. Before asking where AI belongs, ask whether the workflow itself still makes sense.\n\nMost business 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.\n\nEach change made sense at the time. Collectively, they often create unnecessary complexity.\n\nConsider a purchasing process that requires six approvals before an order can be placed. One approval may have been added after an audit. Another may have resulted from an acquisition. A third may have been introduced because one business unit wanted additional oversight. Eventually, those approvals simply become “the way we do things.”\n\nArtificial intelligence 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.\n\nThat requires leadership.\n\nThe 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.\n\nThis 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.\n\nSometimes the greatest improvement comes from eliminating work rather than automating it.\n\nThe 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.\n\nThat outcome 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.\n\nOnce 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, too many systems or too many approval points. Mapping the complete process often reveals unnecessary handoffs and redundant activities that can be removed before automation is introduced.\n\nDeloitte has reached a similar conclusion in its ongoing research on enterprise AI adoption. Its latest [State of Generative AI in the Enterprise](https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html) report 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.\n\nLeaders should also distinguish between administrative work and human judgment.\n\nAI is exceptionally good at gathering information, organizing data, preparing summaries and performing repetitive tasks. People continue to provide the greatest value when decisions require experience, context, creativity, negotiation or ethical judgment.\n\nThe objective should not be to replace people. It should be to remove low-value administrative work so employees can spend more time applying their expertise where it matters most.\n\nStandardization 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.\n\nFinally, organizations should measure business outcomes instead of technology activity.\n\nThe number of AI assistants deployed or prompts submitted may indicate adoption, but they do 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.\n\nOver the past several years, I have found it helpful to think about workflow redesign as a simple four-step sequence.\n\nOrganizations often reverse these steps. They automate first and hope efficiency follows. In reality, automation should be the final step, not the first.\n\nFollowing this sequence helps ensure AI is solving the right problem rather than making an outdated process run faster.\n\nOne of the most valuable questions leaders can ask is surprisingly simple.\n\nIf we were designing this process today, would we build it the same way?\n\nThat question changes the conversation. It encourages people to challenge assumptions, eliminate unnecessary complexity and rethink how work should flow before technology enters the discussion.\n\nIt is also remarkably consistent with what leading researchers are finding. Harvard Business Review emphasizes that successful AI initiatives begin by improving the underlying process. MIT Sloan concludes that organizations achieve the greatest impact when they redesign workflows instead of automating isolated tasks. Deloitte’s research points to the same pattern, showing that the strongest business results come from treating AI as an opportunity to rethink operations rather than simply increase efficiency.\n\nWhen independent research consistently reaches the same conclusion, it is worth paying attention.\n\nArtificial intelligence is one of the most significant technologies organizations have adopted in decades. Its greatest value will not come from helping us execute yesterday’s workflows more quickly. It will come from allowing us to rethink how work should be done in the first place.\n\nLeaders who redesign workflows before automating them will create simpler processes, better employee experiences and stronger business outcomes. Those who automate first may improve efficiency for a while, but they also risk embedding yesterday’s assumptions into tomorrow’s technology.", "url": "https://wpnews.pro/news/dont-automate-bad-workflows-why-ai-should-begin-with-redesign", "canonical_source": "https://www.cio.com/article/4207454/dont-automate-bad-workflows-why-ai-should-begin-with-redesign.html", "published_at": "2026-08-11 11:00:00+00:00", "updated_at": "2026-08-11 11:22:36.557782+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-policy"], "entities": ["Harvard Business Review", "MIT Sloan"], "alternates": {"html": "https://wpnews.pro/news/dont-automate-bad-workflows-why-ai-should-begin-with-redesign", "markdown": "https://wpnews.pro/news/dont-automate-bad-workflows-why-ai-should-begin-with-redesign.md", "text": "https://wpnews.pro/news/dont-automate-bad-workflows-why-ai-should-begin-with-redesign.txt", "jsonld": "https://wpnews.pro/news/dont-automate-bad-workflows-why-ai-should-begin-with-redesign.jsonld"}}