The Alternative Perspective: AI Is Overrated and Mostly Incremental
A prevalent alternative stance is that while AI is noteworthy, it does not fundamentally alter the competitive landscape for businesses. From this angle, AI is just another productivity enhancement tool, akin to spreadsheets, ERP solutions, or cloud technology. Beneficial, indeed, but not revolutionary.
Proponents of this perspective contend that the majority of AI advancements will quickly diminish as competition rises. If every company has access to similar models, agents, and tools, then AI becomes a baseline requirement rather than a sustainable edge. Profit margins stabilize, unique selling propositions fade, and the core factors for success remain brand reputation, operational execution, and distribution channels.
They further argue that numerous AI implementations often fall short of expectations. Models generate inaccuracies, agents need oversight, and issues with data quality diminish anticipated results. In this context, AI mainly alleviates staffing pressures or accelerates current operations without altering the foundational business model.
This standpoint is appealing due to its realistic and historically informed nature. Many former technologies heralded revolutionary change but yielded optimization instead. The flaw in this argument is not that it is invariably incorrect, but that it presumes organizations do not evolve structurally. AI appears incremental when compelled to function within outdated workflows, incentives, and organizational hierarchies.
Challenging Perspectives on AI in 2026
The More Bold Perspective: AI Will Disrupt Traditional Organizations
A more bold and disconcerting position is that AI will not merely improve businesses. It will reveal how much of contemporary corporate structure is designed mainly to manage human coordination rather than generate value.
From this viewpoint, numerous middle management layers, coordination roles, and even entire divisions serve primarily as optimization remnants of a pre-AI era. AI agents capable of planning, executing, and supervising work eliminate the necessity for these layers altogether. What persists are small, high-impact teams guiding strategy while AI technologies manage the bulk of operational tasks.
In this scenario, organizations that resist modernizing towards leaner, AI-integrated models are systematically outperformed by agile, AI-centric companies with significantly reduced operational expenditures and swifter decision-making processes. The disruption is both technological and organizational. The firm itself transforms to be smaller, more streamlined, and more dynamic.
This perspective suggests that AI advantages are less about boosting productivity and more about which organizations are ready to restructure components that have outlived their usefulness, even when such changes are culturally and politically challenging.
The More Defeatist View: AI Will Have Far Less Impact Than Promised
At the far end of the spectrum lies a defeatist view that AI will likely fail to provide significant competitive advantages for the majority of businesses. This argument posits that AI capabilities will quickly become commoditized, regulatory frameworks will impede rollout, and risk aversion will temper real-world impacts.
In this scenario, AI becomes something that every company possesses but few completely trust. Human decision-makers remain central because accountability is not automatable. Mistakes, concerns over bias, and regulatory examination push AI into advisory capacities instead of independent roles. Productivity improvements may occur, but they tend to be minimal and unevenly shared.
In this future, AI does not fundamentally transform industries but rather integrates seamlessly into existing software systems. The success does not go to those with superior AI systems, but rather to those with stronger strategic approaches, pricing authority, and customer engagement. AI becomes a background system rather than a catalyst for change.
The risk of this perspective is not its implausibility. It stems from the possibility that businesses adopting it prematurely might overlook the brief opportunity for structural transformation. If AI eventually proves to be transformative, late adopters will not catch up merely by acquiring the same technologies.
