Why User Education Is Necessary To Avoid AI Failure – Forbes

AI failure

The more a technology or concept permeates and gets normalized in our day-to-day lives, the more we grow to expect from it. About two decades ago, a sub-56kpbs dial-up internet connection seemed miraculous. Today, with internet speeds as high as 2000Mbps becoming normal, the 56Kbps connection would be considered a failure of sortsin the developed world, at least. This shift in expectation also applies to AI. Having seen numerous practical AI applications aid human convenience and progress, both the general population and the AI research community now expects every new breakthrough in the field to be more earth-shattering than the previous one. Similarly, what qualifies as AI failure has also seen a massive shift in recent years, especially from a problem owners perspective.

Just the fact that an AI model performs a specific function with expected levels of efficacy is no longer the only requisite for its applications to be considered successful. These systems must also be able to provide significant real-world gains in the form of time saved or revenue earned. For instance, a smart parking system that can predict parking availability with 99.7% accuracyalthough undoubtedly efficaciouscannot be considered successful if its real-world adoption does not lead to tangible gains. Even with such a system installed, parking lot managers or smart city administrators may not be able to make optimal use of their parking spaces due to a number of reasons. These could vary from simple causeslike parking lot operators not being able to use the software interface optimallyto complicated oneslike patrons and drivers struggling or hesitating to adapt to the new system. Due to such reasons and many others, only a fraction of AI projects are ever successful. The estimates for the total percentage of AI projects that fail to deliver real value range from 85% to 90%.

In most of these cases, the lack of tangible results achieved by AI systems has much less to do with the technological aspect and more to do with the human aspect of these systems. The success and failure of these projects depend on how the people interact with the technologies to achieve intended objectives.

As researchers continue to work and add to the body of AI research, the effectiveness of AI and AI-driven systems is constantly increasing. However, as powerful as it may be, any AI-driven tool is just thata tool. The success and failure of AI initiatives, more often than not, are determined by how the usersboth primary and secondaryperceive, receive and operate these AI systems.

Business leaders such as owners, directors and C-suite executivesoften end up being only secondary users of AI, or any other technological application for that matter. However, they are among the bigger beneficiaries as well as the biggest enablers of such initiatives. After all, it is often their will and wherewithal which matters while driving AI initiatives. So, the most common reasons for AI initiatives not delivering real value often involve a lack of buy-in from business leaders. Buy-in does not necessarily mean just a willingness to dispense funds for AI initiatives. An increasing number of businesses are investing in AI initiatives anyway, which means that AI failure does not necessarily stem from an absence of investment.

Today, buy-in is represented by a total conviction in a technology or investments ability to make an impact. This conviction results in a commitment to making these technological endeavors successful through means that involve more than just the technology itself. For instance, a business truly committed to the success of its AI initiatives will also invest in the non-core aspects of the initiatives, such as safety and privacy, among others. Ultimately, it is this commitment that ensures that they take all necessary steps to ensure AI success.

More often than not, AI-based applications do not entirely automate manual processes. They only automate the most analysis-intensive tasks. This means that human operators are necessary to leverage and augment the data processing capabilities of AI. This makes the role of human users extremely important for these AI applications.

Even the best AI-enabled business intelligence tools will prove useless if the executives using them arent trained to navigate the dashboards or to understand the data. This problem becomes even more pronounced where AI tools are involved at an operations level, such as computer vision-based handheld vehicle inspection tools or a mobile parking app that users can use to find and book parking spots. When the users are not trained enough to be able to navigate and use technological interfaces, the applications may not deliver the expected outcomes. Although a well-designed User Experience (UX) can go a long way in these circumstances, it is equally crucial for users to be educated about these applications.

Before practical training on how to use new AI applications, users should be given awareness training on how the new technology will add value to their work. More importantly, they should be convinced that the objective of technology is not to replace them but to augment their efforts. Thats because the fear of obsolescence is among the biggest underlying reasons for low user adoption.

Be it consciously or subconsciously, many workersmost of whom are potential AI usersfear becoming obsolete as AI becomes more commonplace. This perception of threat often manifests itself as an unwillingness to adopt the technology. The lack of enthusiasm then leads to a lack of involvement in training, which ultimately hampers the results of AI initiatives.

AI initiatives will only become successful and deliver significant ROI when all the usersfrom top executives to blue-collar workersare educated not about the technology but also their roles in .

Most AI applications are bespoke solutions to problems that are specific to the companies and customers using them. This means that there isnt a fixed playbook on coexisting with and using AI tools. Hence, it is unreasonable to expect the users of AI solutions to educate themselves on their organizations AI initiatives. Businessesalong with the AI implementation partnersmust come together to create case-specific user education strategies for the entire lifecycle of the AI solutions. By creating and executing these user education strategies, businesses can ensure that their people facilitate AI initiatives in more ways than one.

Why User Education Is Necessary To Avoid AI Failure

Before even an AI project starts, it is imperative to ensure that the top leadership of the organization is on board with the project. And that is exactly what top-level user education achieves. When business leaders and leading investors are aware of what results to expect from proposed AI initiatives, they are more comfortable investing in the same. However, it is equally important to establish expectations in terms of the input and support that will be required from the leadership in making an AI initiative a success. By creating awareness regarding the potential outcomes and expected support will ensure that AI projects have the structural support to be sustainable and successful. Making top-level decision makers aware of challenges will also minimize the chances of them withdrawing support when projects run into obstacles.

In addition to making the secondary users aware of the potential benefits of AI initiatives, it is crucial to make sure that the primary users are not just accepting but enthusiastic about the adoption of AI. At the end of the day, if the end users do not use the technology the way it is supposed to be used, the technology will never be able to deliver on expectations. So, part of the expectations from top-level leadership should be convincing the lower-level managers and employees of the value of proposed AI initiatives. The leaders can do this by first establishingthrough open and clear communicationthat the AI applications will not replace the human workforce but will augment it. Another way the leadership can accelerate user adoption is by providing adequate reskilling opportunities to employees so that they can be better operators of AI tools. Moreover, translating the broader advantages of the new AI solutions into individual benefits for workers in different roles will ensure that workers welcome the infusion of AI into daily operations.

Practical training on how to use technology should constitute the final leg of the user education strategy. Once the leadership and the end users are motivated enough to use the new AI solutions, they will be more receptive to instructions of use. As a result, they will be better able to contribute to AI initiatives and participate in their success.

This process of user education should not be viewed as a linear, one-time activity aimed at mitigating AI failure. It should be considered as a cycle that begins with the discovery of new applications and ends with these applications becoming an integral, value-adding part of regular business operations. Businesses aiming to implement AI in the near future can get started right now by educating their people on why they shouldnt view the AI-driven future with fear but with hope.

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Why User Education Is Necessary To Avoid AI Failure - Forbes