5 Reasons Why Enterprise Generative AI Projects Fail

Every enterprise today wants to get on the road of AI-enabled operations or some form of automation. A recent study by IBM shows that 42% of large-scale organizations have already implemented AI capabilities while an additional 40% are quite active in considering AI implementation. These factors have made AI highly relevant and 59% of the companies have reported increasing their AI investment and deployment.

However, this enthusiasm for AI is often met with limitations. One of the finest real-life examples is LEVIDOW, LEVIDOW & OBERMAN, P.C., a law firm in the US that experienced dire consequences as the lawyers there provided legal opinions that contained fake citations and quotes sourced through ChatGPT. The firm and its lawyers were also ordered to pay $5,000 for rejecting their duty and relying on bogus judicial precedents.

One typical problem observed in enterprise generative AI projects is the difference between the set objectives and the achieved results. This blog will intend to look at the main causes of failure for such projects and some of the things that one should avoid to prevent the projects from failing.

So without further ado, let’s get started.

Let’s explore the key reasons.

Lack of purpose and direction is one of the significant causes of generative AI project failure. A lot of organizations jump into AI without knowing what they want to get out of it. This leads to situations where projects are not aligned with the business objectives and do not serve the intended purpose.

Lack of adequate AI skills and knowledge is cited as a major challenge by 33% of firms, demonstrating the importance of having a clear plan on how to use AI appropriately.

For instance, an enterprise may adopt AI just because the technology is popular in the market without the need to find a proper use for it. This lack of direction can lead to the project getting off track and ultimately failing.

All AI initiatives should have clear, measurable goals set before the project begins. Create a well-coordinated and realistic approach that supports your organizational objectives. This helps make sure everyone knows why the project is being done, and what will be considered as success.

However, one of the largest challenges to successful enterprise generative AI projects has been that there are not enough AI talents and knowledge. Nearly a third of firms claim that a lack of sufficient AI expertise hinders them from realizing the full potential of deploying AI. It is often difficult to design, deploy, and sustain AI systems because of the lack of the right type of knowledge.

Accuracy and handling of data in generative AI solutions define its efficiency and effectiveness. One of the main problems affecting many enterprises’ generative AI initiatives is data complexity which has been cited as a major challenge by 25% of respondents. Lack of data quality and failure to adhere to proper data management procedures compromise the result of the AI algorithm and decision-making outcomes.

For example, if the training datasets used in developing an AI model are either prejudiced or contain some errors, then the model outcomes will also be prejudiced or erroneous. Additionally, data management is challenging when dealing with large volumes of information that most organizations fail to address adequately.

It also has been identified that the quality of data and its handling directly affect AI models to provide better outcomes.

Ethical issues are another reason against AI usage: about 23% of companies mention it. Bias in implementing AI may also cause the software to be unfair or discriminatory to the users of the system and this is unforgivable as the reputation of the company will be at stake.

For example, if an AI system that selects employees for hire was trained on datasets that contained biases such that it would favor some groups over others, this would create biased hiring. This may lead to substantial ethical and legal issues that affect the business operations of an organization.

IT systems currently in place may not support some of the new AI technologies hence leading to compatibility challenges. Such challenges include data silos, organizational and system legacy, as well as incompatible software. For instance, an enterprise that seeks to implement AI-based customer support services may discover that the chosen solution cannot easily interface with an old CRM system, leading to development challenges and higher costs.

Despite enormous opportunities for generative AI for enterprises, several issues can lead to project failures. These are followed by the absence of goal and application definitions, inadequate AI personnel and knowledge, data quality and control problems, moral questions and prejudice, and compatibility problems with existing frameworks.

That, however, is something that companies have to address as they move forward and as AI starts becoming mainstream. To ensure that the power of AI can be fully realized, enterprises should consult with AI experts who have built the systems they require. Companies like us help enterprises identify AI opportunities and implement generative AI solutions the right way.