Business Failure Risks Rising as Companies Optimize Outdated Processes

Modern businesses face a growing risk of failure by prioritizing efficiency over innovation, according to insights across multiple sectors. While many organizations focus on optimizing existing workflows, experts warn that this approach often masks a fundamental misalignment with market needs.

The Trap of Incremental Optimization

The distinction between incremental improvement and disruptive innovation is a recurring theme in business strategy. Management author Oren Harari famously illustrated this by noting that the electric light bulb did not arise from the continuous improvement of candles. While candles can be made brighter or better-scented, they remain candles; they cannot provide the city-wide illumination achieved by shifting to entirely new physics. History is replete with industry leaders that failed because they focused on polishing existing products rather than reimagining their purpose. Nokia, BlackBerry, Blockbuster, and Kodak all perfected their respective fields—keyboards, screens, store layouts, and film—only to be displaced by competitors who changed the rules entirely. While iteration is essential for retention once a new model is established, it cannot replace the initial leap required to meet a fundamentally different demand.

The Risks of “Rented” Artificial Intelligence

The current rush to adopt off-the-shelf artificial intelligence highlights a similar reliance on efficiency at the expense of long-term capability. According to McKinsey’s 2025 Global Survey on AI, 78% of organizations now use AI in at least one business function, primarily to reduce costs. However, relying on vendor-managed AI tools creates a “rented-ground” problem. Companies that outsource their AI infrastructure forfeit their reasoning to the vendor, which controls the training data, model logic, and update cycles. This dependency leads to a loss of institutional knowledge. Gartner’s 2025 research projects that by 2027, 40% of enterprise AI projects will be abandoned due to issues with data quality, integration costs, and vendor dependency. Experts suggest that true competitive advantage comes from “empowerment-oriented” deployments—building organizational intelligence that compounds with use and remains under company control. By treating AI as an infrastructure decision rather than a procurement one, firms can ensure their systems evolve with their own unique context and reasoning.

Attribution Blind Spots in Marketing

Strategic missteps are also occurring due to incomplete data in marketing, particularly for high-value purchases. In sectors such as legal services, healthcare, and financial services, the phone call is a primary conversion point. Yet, standard measurement stacks—including Google Analytics 4 (GA4) and pay-per-click (PPC) reporting—often fail to capture these offline interactions. Because marketers optimize against the data they possess, channels that drive inbound calls often appear to have a high cost-per-acquisition (CPA) simply because the phone calls remain unrecorded. This leads to the defunding of effective campaigns and the reallocation of budgets toward channels that are easier to track, even if they are less effective. This “insight problem” creates a cycle where companies make strategic decisions based on precise but incomplete pictures, causing unmeasured channels to atrophy.

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Lessons from Dynamic Catalysis

The tension between static optimization and dynamic adaptation is also being observed in chemical research. A study from the University of Houston suggests that chemists may have been optimizing the wrong variables in catalysis for years. Traditionally, catalysts are viewed as “static,” working at a fixed speed. Researchers Omar Abdelrahman and Atharva Burte found that “dynamic” or “programmable” catalysts, which are actively oscillated, achieve superior performance not based on the time spent in each state, but on the progress of the reaction itself. This framework suggests that by focusing on how much reactant has been converted rather than the ticking of a clock, scientists can design next-generation catalyst systems that are significantly more efficient. As Paul Dauenhauer of the University of Minnesota notes, these findings emphasize that kinetic observables are better explained by the progression of the reaction. The potential shift here mirrors the broader business lesson: understanding the fundamental principles of a system—rather than just the time-averaged metrics—is the key to breaking traditional performance limits.