Organizations Struggle with AI Adoption amid Resistance to Change

Change is inherently difficult, particularly for large organizations. Over the past 15 years, many companies have struggled to implement new technologies such as mobile, Big Data, and cloud computing. Today, AI presents a new challenge, forcing companies and their employees to adapt, whether they want to or not.

One significant obstacle is technical debt. As organizations attempt to leverage new technologies, they often find their existing tech stack—designed for a prior era—ill-equipped to fully utilize AI. Changing these foundational systems carries substantial risk, making many managers hesitant to embrace such a shift, despite its potential rewards.

Institutional inertia is another barrier. Changing established workflows is difficult, as illustrated by the story of a small-town register of deeds office that struggled to adopt a new computer system. Despite the system's clear advantages, long-serving clerks were resistant to giving up their traditional methods, such as using a rubber stamp to validate documents. The system architect eventually allowed the clerks to keep their stamp, facilitating their acceptance of the new system.

This example underscores the importance of effective change management. Implementing new technology is not just about purchasing and deploying software; it’s about ensuring that employees are willing and able to use it. If their concerns and attachments to old methods are not addressed, they may resist or even sabotage the adoption of new technology.

AI represents a particularly radical shift in how work is conducted, raising concerns among employees who may feel their roles are threatened. Companies must navigate these concerns carefully to avoid alienating staff and squandering their investment in AI.

AI is not the first technological shift to challenge organizations. The advent of personal computers in the 1980s and the rise of the internet were similar moments of transformation. However, AI may be even more disruptive. Karim Lakhani, faculty chair at Harvard's Digital Data Design Institute, explains that while the internet lowered the cost of information transmission, AI is reducing the cost of expertise. Box CEO Aaron Levie adds that, unlike previous technologies, AI is capable of performing tasks that previously required human judgment, fundamentally altering the relationship between people and machines.

Levie argues that companies must rethink the role of computing within their organizations. This includes addressing concerns such as data accuracy, privacy, and the impact of AI on existing workflows. While some companies, like Box, believe their platforms are well-positioned to handle these challenges, many organizations are bombarded with competing claims from vendors, making it difficult to identify solutions that truly add value.

Another challenge is measuring the impact of generative AI on productivity. Without clear metrics to demonstrate its benefits, it can be difficult to convince skeptical employees of AI's value, leading to organizational tension.

Some experts, like Jamin Ball, partner at Altimeter Capital, believe AI is so transformative that companies must adopt it regardless of immediate benefits. Failure to do so, he warns, could result in lost market share and eventual irrelevance.

Gartner analyst Rita Sallam compares the AI revolution to the adoption of word processors, which transformed how ideas were developed and shared within organizations, even though their immediate cost savings were not apparent. AI, she suggests, could unlock similarly profound benefits, even if they are difficult to quantify.

Getting executive buy-in is crucial. Lakhani told Tech Crunch that AI differs from previous technological shifts because its benefits are more readily apparent to CEOs, who can directly experience its capabilities. This could accelerate AI adoption across organizations.

However, selling AI solutions requires more than just demonstrating their potential. Vendors must also address the "people problem"—the resistance and concerns of employees who will be using the technology. Lakhani identifies three key principles for successful AI adoption: machines will not replace humans, but humans with machines will replace those without; AI implementation must be driven from the top down with clear incentives; and coercive approaches will fail. Instead, companies must clearly define the purpose and benefits of AI adoption to gain employee support.

The road ahead is not easy. Organizations vary in their readiness for AI, and substantive change is always challenging. AI will test the adaptability of companies more than any previous technology, and their success or failure in implementing it could determine their future viability.