How to Redesign a Workflow for AI Without Automating the MessUntitled project

AI can speed up individual tasks, but faster activity does not always create better business performance. This episode examines why meaningful AI value comes from redesigning workflows around outcomes, constraints, decisions, and accountability.

It explores how leaders can avoid automating inefficient processes.

TLDR/ At a Glance

• Workflow redesign over task acceleration
• Cycle time, handoffs, and bottlenecks
• Human judgement and AI role clarity
• Autonomy matched to risk
• Metrics beyond adoption and usage
• Governance embedded in operations

The key takeaway is that AI creates durable value when organisations improve the flow of work, not simply the speed of isolated tasks.

Read the written version on my LinkedIn page 

Transcript

Why Faster Tasks Are Not Transformation

SPEAKER_00 0:00

How to redesign a workflow for AI without automating the mess. Most organizations are still treating AI as a task accelerator rather than a workflow redesign challenge. They use it to draft faster, summarize faster, search faster, respond faster, and produce more output. That is useful, but it is not the same as transformation. In this article, we explore why AI value depends on redesigning workflows, not simply speeding up individual tasks. The argument is simple. AI does not create durable enterprise value by making broken work faster. It creates value when leaders redesign the flow of work around outcomes, constraints, decisions, handoffs, human judgment, AI roles, and measurable performance. Why faster tasks do not automatically shorten cycle time? The problem with many AI initiatives is that they start at the task level. A team asks where AI can help and quickly finds useful opportunities. Draft the report, summarize the meeting, classify the ticket, generate the response, search the knowledge base, or prepare the analysis. Those use cases can produce real gains. OECD's review of experimental studies found task level productivity improvements across areas such as customer support, software development, consulting, writing, and summarization. The issue is not that AI fails at tasks, the issue is that task speed is not the same as workflow speed. In most knowledge work, cycle time is not dominated by the time it takes to complete one task. It is dominated by waiting, handoffs, approvals, missing information, rework, exception handling, and coordination. A draft may take five minutes instead of 50, but if it still waits two days for review, the workflow has not meaningfully improved. That is why organizations can report high adoption and still struggle to show enterprise impact. McKinsey's 2025 global survey found that 88% of organizations were using AI in at least one business function, yet only about one-third had started scaling. Only 39% reported enterprise eBIT impact, and just 6% met the threshold for AI high performers. The difference was not model access, it was operational redesign.

Where Knowledge Work Really Gets Stuck

SPEAKER_00 2:26

Where knowledge work actually gets stuck. Knowledge work often looks slow because people are slow. In reality, the system around them is often the constraint. Work gets stuck between teams, inside inboxes, across fragmented systems, in unclear approvals, and in the search for the right information. Microsoft's 2025 workplace research reported that employees are interrupted every two minutes during the workday, with 275 interruptions a day, while 48% of employees said work felt chaotic and fragmented. That is not just a productivity problem, it is a workflow design problem. The same pattern has been visible for years. McKinsey's earlier research found that interaction workers spent large portions of their week on email and searching for information. Harvard Business Review has also documented the growth of collaboration overload, where employees spend more time coordinating work than doing the highest value work itself. AI can help with some of this, but only if leaders redesign the flow. If AI simply generates more drafts, more messages, and more analysis inside the same fragmented environment, it may increase output while leaving the real constraint untouched.

The Risk Of Automating The Mess

SPEAKER_00 3:46

The danger of automating the mess. Automating the mess happens when AI is added to a broken workflow without changing the workflow itself. The result is usually more activity, not better performance. A customer service team may generate responses faster, but escalation rules remain unclear. A finance team may produce analysis faster, but decision ownership remains fragmented. A legal team may draft faster, but review and sign-off still sit in the same bottleneck. A software team may write code faster, but pull request review, testing, and deployment remain constrained. In each case, AI improves a local activity while the system constraint remains. Sometimes the workflow even gets worse because faster upstream output creates more downstream review, more rework, and more exception handling. This is why leaders need to be careful with claims about time saved. A task may be faster, but the organization only benefits when the end-to-end workflow improves. The question is not, did AI speed up the task? The question is did AI reduce cycle time, improve quality, lower rework, or increase throughput in the actual workflow. Start

Start With Outcomes And Map Flow

SPEAKER_00 5:06

with the outcome, not the tool. A better redesign process starts with the outcome. What business or customer result is the workflow meant to produce? Faster claims resolution, shorter time to quote, better forecasting, faster onboarding, reduced clinical documentation burden, higher first contact resolution. The wrong starting question is where can we use AI? The right starting question is what outcome are we trying to improve and where does the workflow currently break down? Once the outcome is clear, leaders should map the workflow from trigger to completion. What starts the work? What information is needed, which decisions occur, where does the work wait? Who approves it? Where does rework happen? What exceptions break the normal flow? This is where the real AI opportunity appears. AI may be useful for drafting, but the bigger opportunity may be earlier in the workflow, where information is gathered. It may be useful for summarization, but the real constraint may be decision latency. It may be useful for automation, but the real value may come from better triage and routing. The point is to redesign around the constraint. AI should be placed where it removes waiting, improves first pass quality, reduces coordination load, or shortens the path to decision. It should not be placed where it simply makes a visible task cheaper. Redesign the workflow before adding autonomy.

Designing Autonomy Around Real Risk

SPEAKER_00 6:41

AI autonomy should not be treated as a default setting. It should be designed around risk, reversibility, and consequence. In many workflows, AI should begin by assisting. It can retrieve information, summarize context, draft responses, classify requests, or prepare evidence for a human. This is useful where interpretation still matters and the cost of a wrong action is meaningful. In other workflows, AI can recommend. It can suggest a next best action, prioritize cases, flag risk, or propose a decision. The human still approves, but the workflow becomes faster because the decision arrives with better context and structure. Only in bounded lower risk, repeatable workflows should AI act with limited autonomy. Even then, it needs clear thresholds, permissions, monitoring, exception routing, and rollback. The goal is not to maximize autonomy. The goal is to match autonomy to the workflow. That distinction matters because more autonomous AI is not automatically more valuable. Deloitte's research suggests measurable ROI remains harder for agentic AI than for more established generative AI use cases. Complexity, integration, and control design all matter. What humans should keep and what AI should do. The most effective workflow designs do not ask whether humans or AI should do the work. They ask which parts of the work require judgment and which parts require speed, scale, memory, or structured execution. The goal is not to divide work equally between humans and AI. It is to allocate work according to where judgment, accountability, speed, and scale create the most value. AI is well suited to retrieving context, summarizing information, classifying requests, drafting routine outputs, reconciling structured data, routing work, monitoring exceptions, and executing bounded actions where success criteria are clear. Humans should retain responsibility for goals, judgment under ambiguity, risk appetite, exception resolution, trade-offs between customer and commercial outcomes, and approval of high consequence decisions. Humans should also remain accountable for the design and improvement of the workflow itself. This division of labor prevents two common mistakes. The first is underuse, where AI is limited to low-value drafting, even though it could remove real workflow friction. The second is overuse, where AI is allowed to act in areas where the organization has not defined risk, escalation, or accountability.

Metrics That Prove Workflow Improvement

SPEAKER_00 9:29

The metrics that prove real improvement. Usage metrics are not enough. Prompt volume, chatbot activity, license activation, and user counts can show adoption, but they do not prove workflow value. Leaders should measure the redesigned workflow through a small set of practical metrics. Cycle time means how long the workflow takes from trigger to completed outcome. Throughput means how many completed cases, tasks, or outputs the workflow handles. Quality means first pass accuracy, defect rate, or customer accepted output. Rework means how often work loops back for correction or missing information. Exception rate means how many cases leave the normal path and how long they take to resolve. Cost per case means the full cost of completion, including review, correction, and platform cost. Customer or stakeholder outcome means resolution, conversion, satisfaction, time back, or decision quality. Human review effort means how much oversight is still required to make the workflow safe and reliable. These metrics make it harder to confuse activity with performance. A workflow that starts more work but finishes the same amount has not improved. A workflow that drafts faster but increases rework has not improved. A workflow that reduces labor time but increases risk has not yet proved value. How to pilot one workflow properly. The best way to start is not with a broad AI program. It is with one workflow that has clear volume, visible friction, measurable outcomes, and a real business owner. First, select the workflow. Choose an area where delay, rework, or coordination pain is obvious. Then measure the baseline. Cycle time, throughput, quality, cost, rework, exceptions, and customer outcome. Without a baseline, the organization will end up relying on anecdotes. Next, identify the true bottleneck. Is the constraint intake quality, missing data, expert review, approval, handoff, customer response, system update, or exception handling. The answer determines where AI should sit. Then redesign the flow. Remove unnecessary approvals, standardize intake, clarify decision rights, define exception paths, assign human and AI roles, and build the controls before scaling. Only after that should the team pilot the AI enabled workflow. The pilot should measure the workflow, not just the model. The test is not whether the AI produces a good answer in isolation. The test is whether the redesigned workflow improves speed, quality, cost, risk, and experience compared with the baseline.

Real-World Patterns And Governance Built In

SPEAKER_00 12:19

What leaders can learn from real examples. Morgan Stanley shows the value of workflow fit. Its open AI supported tools were embedded into advisor knowledge retrieval and meeting follow-up, helping advisors find internal information, generate notes, prepare follow-ups, and update client workflows. The value came from placing AI inside the flow of advisory work, not from offering a generic chatbot. Intermountain Health shows the same pattern in clinical documentation. Dragon Copilot was embedded directly into the clinical workflow, reducing time spent on notes and helping clinicians spend more attention on the patient encounter. This is a strong example because the workflow problem was clear, documentation burden. Verizon's use of AI and customer service also points to the importance of workflow redesign. AI was used to support service agents with knowledge retrieval and live assistance, helping shift the role from issue handling toward resolution and selling. The value came from changing the role economics, not simply reducing call handling effort. Klarna is the useful cautionary case. Its AI assistant delivered impressive customer service automation results, but later public reporting showed a shift in emphasis away from pure cost reduction and toward growth and quality. That matters because workflow automation can deliver speed while still requiring judgment about customer experience, trust, and human support. Governance must be designed into the workflow. AI governance fails when it lives in a policy document but not in the work itself. A policy can define intent, but the workflow is where risk appears. A model retrieves the wrong information, a recommendation is accepted too quickly, an exception is missed, or a decision moves faster than accountability. That is why governance has to be designed at the same time as the workflow. Every AI-enabled workflow needs a clear owner, defined decision rights, risk thresholds, approval rules, permission boundaries, audit trails, exception routes, and rollback mechanisms. As AI moves from assistance to recommendation and then into bounded action, governance becomes part of operational design. Leaders need to decide where human judgment is required, where AI can act within limits, when escalation is triggered, and who owns the outcome if something goes wrong. The practical test is whether the organization can explain how the workflow works under normal conditions, how it behaves when something unusual happens, and how accountability is preserved when AI has influenced the outcome. Without that clarity, AI does not remove risk from the workflow. It simply makes the risk harder to see. Conclusion

Final Takeaway And Where To Read More

SPEAKER_00 15:15

AI does not fix broken workflows, it exposes them. Faster drafting, faster summarization, and faster analysis are useful, but they do not automatically shorten cycle time or create enterprise value. The real unit of AI transformation is the workflow, the outcome, the constraint, the handoffs, the decisions, the human roles, the AI roles, the controls, and the metrics. The organizations that outperform in the AI era will not be those that automate the most tasks. They will be the ones that redesign workflows faster than competitors can adapt their operating model. This concludes the article. You can also read this article on my LinkedIn page where I share regular insights on AI, strategy, and emerging technologies.