Across industries, executives are pouring unprecedented capital into data platforms, analytics, and artificial intelligence. The promise is compelling. Better insight. Faster decisions. Measurable growth. Yet the outcome is often familiar and frustrating. Major AI programs underperform. Productivity gains stall. Decision quality improves on paper but not in practice.
The issue is rarely the technology itself. More often, it is the system into which that technology is introduced.
AI does not repair execution gaps. It magnifies them. When culture, decision rights, and everyday workflows are misaligned, advanced technology exposes weaknesses that were previously hidden or manageable. In many organizations, the faster the insights arrive, the more clearly the organization’s constraints are revealed.
Most operating models still reflect an earlier era. Information moved slowly. Authority was centralized. Decisions were escalated upward, often by default. Those structures once offered stability. Today, they quietly undermine speed and accountability.
AI thrives on clarity. It demands timely decisions, clear ownership, and trust in data. When those conditions are absent, performance deteriorates quickly.
The cost of standing still
An operating model determines how work gets done. It governs who decides, how information flows, how teams coordinate, and how success is measured. While strategies evolve and technologies advance, operating models often change the least. Over time, layers accumulate. Exceptions multiply. Accountability blurs.
The friction is subtle at first. Then it compounds.
AI tools surface insights in real time, but decision authority remains ambiguous. Analytics highlight opportunities, yet incentives still reward risk avoidance. Collaboration is encouraged rhetorically, while processes reinforce functional silos. Instead of accelerating execution, technology adds strain.
In these environments, AI becomes a stress test. It does not create dysfunction, but it brings existing dysfunction into sharper focus. Where trust is weak, data is questioned. Where accountability is unclear, insights stall. Where leaders hesitate to shift authority, decisions bottleneck.
Why execution breaks down
Execution failures are rarely caused by a lack of ambition or investment. They occur because the operating model was never designed to support the behaviors required for sustained performance.
Three breakdowns appear repeatedly.
The first involves decision rights. AI enables faster, more distributed decision-making. Many organizations, however, continue to rely on centralized approvals. Insights move faster than leaders can process them, creating delays that negate the value of speed.
The second breakdown is procedural. New tools are layered onto legacy workflows. Employees adapt by working around systems rather than through them. Complexity increases. Friction becomes normalized.
The third breakdown is cultural. Data challenges intuition. Automation disrupts established roles. Without norms that support learning, accountability, and adaptation, insight is treated as advisory rather than actionable.
Under stable conditions, these gaps are survivable. Under the pressure created by advanced analytics and automation, they become structural liabilities.
Growth Is structural, not technical
Sustained growth does not come from technology alone. It comes from alignment. Structure, behavior, and accountability must reinforce one another.
Organizations that extract real value from AI approach the challenge differently. They do not focus exclusively on tools. They examine how decisions are made and where they stall. They clarify ownership of outcomes. They redesign workflows so insight leads directly to action. Cultural expectations are reinforced alongside procedural change.
This is not about replacing judgment with algorithms. It is about ensuring judgment is exercised at the right level, at the right time, with the right information.
When operating models are aligned, AI sharpens focus and accelerates learning. When they are not, AI increases noise and amplifies risk.
The strategic blind spot
Operating models are often treated as internal mechanics. Strategy and technology take priority. Structure is adjusted later, if at all. That sequence is costly.
Operating models shape what strategies can be executed and what technologies can realistically deliver. They are not passive infrastructure. They actively influence performance.
In an environment where advantage depends on speed and follow-through, the question is no longer whether to invest in AI. The more relevant question is whether the organization is built to act on what AI reveals.
For many enterprises, the answer is uncomfortable.
Rethinking how work gets done
Revisiting an operating model does not require dismantling the organization. It requires confronting reality. Where do decisions slow down. Where does accountability dissolve. Where do incentives conflict with stated priorities.
It means examining decision bottlenecks rather than reporting lines. It means aligning rewards to outcomes rather than activity. It means designing workflows around value creation instead of functional convenience. It also means addressing cultural norms that quietly undermine ownership.
Technology will continue to advance. AI will become faster, more accessible, and more deeply embedded in daily work. Organizations that leave their operating models untouched will move faster without moving forward.
Those that do the harder work of alignment will experience something different. AI will not feel like a gamble. It will feel like leverage.
Not because the technology changed, but because the organization did.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
This story was originally featured on Fortune.com
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