If your AI adoption is hitting a wall, stop and examine your own biases

Fast Company

7 days ago

FAST COMPANY EXECUTIVE BOARD
While buzz over AI continues to dominate technology conversations, a distinct gap exists between the stated intent to embrace AI and actual on-ground adoption.
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The Fast Company Executive Board is a private, fee-based network of influential leaders, experts, executives, and entrepreneurs who share their insights with our audience.
BY
While buzz over AI continues to dominate technology conversations, a distinct gap exists between the stated intent to embrace AI and actual on-ground adoption. While there is the apparent reason of unrealistic expectation from a technology that is actively evolving, there's also a greater challenge at play.
A recent Accenture survey revealed that while 98% of business owners want to adopt AI, only 10% of companies have generative AI models in production. The reason is not just about integrating the technology into workflows. Instead, the real barrier is re-examining how businesses view AI and other emerging technologies.
As with any technology, there will always be early adopters and late adopters for AI. Those who are inherently wary of trying new technology will find themselves at a disadvantage as AI changes not only how people work, but what work they do.
The challenge, therefore, shifts from how AI can be used for business value to how knowledge workers adapt to working with AI.
GROUND REALITY
What is the leadership's perception of employees who rely on AI tools? According to a recent survey by Slack, 48% or nearly half of the desk workers across companies find it uncomfortable to admit to using AI for various reasons.
For instance, employees find themselves out of their comfort zones while using these new-fangled tools. They may even fear that the new technology will render their role redundant. Additionally, in some high-pressure work environments, people hesitate to admit that they use AI since they fear being perceived as inauthentic or lazy. To be fair, these fears are not unfounded. They are rooted in real concerns resulting from systemic gaps in the understanding of AI.
That's why technology education and employee upskilling should become a priority to build confidence and trust in these technologies. Businesses building their own AI models must evaluate AI training modules closely as they directly affect the output. If employees aren't effectively using AI, it creates a roadblock in receiving the necessary feedback to adjust and change the way the AI itself is trained. In turn, this further impedes the use of AI. Thus, creating a circle of stagnation that stalls most AI integration projects.
BEHAVIOR CHANGE DESIGN
A conscious change in perspective is much needed to bring a fundamental shift in the psychology for those working with AI. This is critical to sustain the progress that AI promises. In an interesting research, Wharton Business School applied the Fogg Behavior Model to AI adoption and its three elements that fuel a change in behavior: motivation, ability, and a prompt.
Catalysts for motivation, such as clear rewards and open-mindedness, and the ability enhancers of training and real-world use cases, are essential to pushing the needle forward. Prompts to accelerate this progress include executive-level sponsorship and funding for AI initiatives across the business, enabling sustainable AI transformation.
Detailed training and skilling opportunities guide employees toward being more tech-forward. These must move past mere theory and include real-life situations, where they can implement their learnings and see the results for themselves.
Consider offering rewards, recognition, and incentives to those who are open to using AI in their work creatively and efficiently. This will not only give employees the necessary push to incorporate AI into their workflow, but also encourage them to experiment with it further. For these two aspects to yield results, leadership must cultivate an environment conducive to open experimentation and failure. Moreover, ensuring that AI initiatives and training receive the appropriate funding and resources also becomes a priority.
Integrating new technologies into established systems is never easy. However, to make something new work for us, the first step is often to acknowledge the need for a new approach.
PERCEPTION OF AI-DRIVEN SUCCESS
It can sometimes become difficult to understand what successful AI transformation looks like. As CEOs, we are more biased toward return on investment. Business metrics follow when most employees identify their own success metrics for using AI and follow through to achieve targeted benefits.
Voluntary usage, time saved, creative solutions, and improved work quality are better indicators of success than training completion, having SMEs optimize AI, or the number of pilot projects.
As AI agents continue to roll out into business applications, use cases will transform, and success will look different. From model efficiency, we are moving toward output efficiency. However, human involvement in the loop will continue in the foreseeable future, which means that human biases will continue to impact output.
In such a scenario, it is essential that organizations focus and invest in addressing behavior shifts that are conducive to AI adoption. Simply investing in AI solutions is not going to be enough.
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ABOUT THE AUTHOR
Vineet Jain is the CEO and Co-founder of Egnyte, the leading multi-cloud platform for content security and governance. Read Vineet's Executive Profile here. More