AI doesn’t eliminate inefficiency. It amplifies it

Over the past two years, I have spent a significant amount of time discussing artificial intelligence with technology leaders, business executives and teams across my own organization. Most conversations begin with questions about the use cases, tools, governance and return on investment. Leaders want to know which technologies are creating the most value, where to invest next and how quickly they should scale adoption.

Those are important questions, but I have noticed another pattern emerging as organizations move beyond experimentation and begin embedding AI into everyday work. In many cases, the technology itself is not the primary obstacle to success. Instead, AI is exposing organizational challenges that have existed for years. Processes that were already inefficient become more visible. Ambiguous decision-making structures become harder to ignore. Accountability gaps that once slowed projects quietly now become more apparent as work accelerates.

This has led me to a simple conclusion: AI does not eliminate inefficiency. It amplifies it.

That observation should not be interpreted as a criticism of AI. In fact, it highlights just how powerful the technology can be. AI accelerates workflows, shortens analysis cycles, improves access to information and increases employee productivity. However, because it accelerates the way work gets done, it also magnifies the strengths and weaknesses of the operating environment in which it is deployed. Organizations with strong processes and clear accountability often realize value quickly. Organizations with operational complexity frequently discover that technology alone cannot overcome management challenges.

AI accelerates existing operating models

Many organizations approach AI as a technology initiative. They evaluate platforms, launch pilots and identify tasks that can be automated. While those activities are important, they can also create a false impression that AI itself is the primary driver of transformation.

In my experience, the greatest value comes not from the technology alone but from the willingness to rethink how work gets done. AI can automate tasks, but it cannot redesign a broken workflow. If a process contains unnecessary approvals, duplicate activities, conflicting priorities or poorly defined handoffs, those issues remain regardless of how sophisticated the technology becomes.

This idea is consistent with a broader lesson I explore in my latest book, Digital Inside Out: digital transformation succeeds when organizations focus first on how work gets done, how decisions are made and how accountability is established. Technology can accelerate performance, but it rarely compensates for weaknesses in the underlying operating model. In many cases, new technologies simply make those weaknesses more visible.

Researchers at the MIT Sloan School of Management have reached a similar conclusion. Their work suggests that organizations generate the greatest value from AI when they redesign workflows rather than simply automate individual tasks. In other words, the most significant gains come from rethinking how work flows through the organization rather than accelerating isolated activities.

I have seen this pattern repeatedly throughout my career. Enterprise systems did not fix poor business processes. Collaboration platforms did not automatically improve communication. Analytics tools did not create accountability. Each technology delivered substantial benefits, but only when accompanied by process redesign, governance improvements and leadership commitment. AI follows the same pattern.

Organizations that simply layer AI on top of existing complexity often find themselves completing inefficient work faster. Employees may generate reports in minutes instead of hours, produce presentations more quickly and analyze larger volumes of information. Yet the underlying process may still contain the same bottlenecks that limited performance before AI was introduced. The technology increases speed, but it does not automatically improve effectiveness.

Why decision-making becomes the new bottleneck

One of the most interesting effects of AI is how it changes the nature of organizational constraints. Historically, many companies struggled because information was difficult to access. Data was fragmented across systems, reporting cycles were slow and analysis required significant manual effort. Leaders frequently spent considerable time gathering information before they could make decisions.

AI is rapidly reducing those barriers. Teams can now summarize large volumes of information, identify patterns, generate recommendations and produce insights in a fraction of the time previously required. Access to information is becoming less of a competitive differentiator because the effort required to generate it continues to decline.

As this happens, another challenge becomes more visible. Many organizations discover that their greatest constraint is no longer information. It is decision-making.

When ownership is unclear, faster insights do not necessarily produce faster outcomes. Teams may have access to excellent recommendations yet still struggle to determine who is responsible for acting on them. Multiple stakeholders may believe they have authority over a decision. Escalations become more common. Consensus-driven cultures can become overwhelmed by the volume of information being generated.

Some of the most difficult conversations I have encountered in AI initiatives have had little to do with models, prompts or technical architecture. Instead, they involve governance, ownership, accountability and decision rights. These challenges existed before AI, but the technology makes them more visible because it removes many of the delays previously associated with gathering and analyzing information.

This trend is likely to become even more pronounced as organizations adopt AI agents capable of executing tasks and workflows. While technology can automate actions, accountability remains a leadership responsibility. Leaders must still determine who owns outcomes, who approves actions and who is responsible when decisions create unintended consequences.

What leaders should fix before scaling AI

Deloitte’s annual State of AI in the Enterprise research highlights the challenges organizations face when attempting to scale AI beyond pilots and isolated use cases. This finding reinforces a lesson many leaders are learning firsthand: realizing value from AI requires organizational change, process redesign and strong leadership, not just new technology

For CIOs and business leaders, one of the most important priorities should be simplifying processes before automating them. AI can reduce manual effort, but it rarely eliminates complexity that has been embedded into a process over many years. Organizations often achieve greater value by removing unnecessary steps before introducing automation.  As Jon McNeill writes in his book, The Algorithm, “No need to waste time speeding up the old process. Instead, design, simplify, optimize and begin to work your new process. Then speed it up.”

Leaders should also establish clear decision rights before scaling AI-enabled workflows. As information becomes easier to generate, organizations need clarity regarding who is accountable for making decisions and driving action. Without that clarity, AI can create more recommendations than the organization is capable of acting upon.

Another important consideration is measurement. Many organizations continue to evaluate AI success through adoption rates, license utilization or employee engagement metrics. While these measures provide useful signals, they do not necessarily reflect business value. Leaders should focus on outcomes such as productivity improvements, revenue growth, cost reduction, customer experience enhancements and risk mitigation.

Most importantly, leaders should recognize that AI adoption is fundamentally a leadership challenge. Technology can accelerate work, but leaders determine how work is organized, governed, measured and improved. Organizations that treat AI solely as a technology initiative often struggle to move beyond experimentation. Organizations that use AI as an opportunity to improve processes, clarify accountability and modernize operating models are more likely to achieve sustainable results.

As AI adoption continues to accelerate, I believe the organizations that realize the greatest value will not necessarily be those with the largest investments or the most advanced models. They will be the organizations willing to address the management and operational issues that AI brings into focus. In many cases, AI is not creating new problems. It is revealing existing ones with greater speed and clarity.

That may be one of the most valuable contributions AI can make. By exposing inefficiencies that organizations have learned to tolerate, it creates an opportunity for leaders to address them directly. The companies that seize that opportunity will be better positioned not only to benefit from AI, but also to improve the way their organizations operate long after the current wave of innovation has passed.

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