Factors Responsible for Failing the AI Strategy

 As enterprises move forward with integrating AI technology into their infrastructure, many of them are struggling to reap its intended benefits. Hence, they must be aware of the factors that are hampering their AI strategy.

Since enterprises sped up their digital transformation journey, they have been looking towards Artificial Intelligence as a beacon of hope that will help them to boost their productivity, business agility, and customer satisfaction while reducing their time for introducing products and services to the market. However, the reality shows a completely different scenario.

As many enterprise leaders took steps toward implementing AI into their existing technology stack or using it for the next promising project, they are constantly finding themselves disappointed instead of achieving their intended goals. In fact, in a study conducted by 2020 IDC, nearly 28% of AI and machine learning (ML) projects fail to move forward.

Experts believe that the reason enterprises are failing with AI is not having an effective AI strategy in place. Developing a successful AI strategy requires careful planning, creating well-defined goals and developing a strong management team.

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If an enterprise’s AI strategy is not living up to its expectations, then below are four factors that may be responsible for its failure:

• Not providing sufficient training

Not addressing the user needs is one of the biggest obstacles for a successful AI deployment. This involves not only preparing the staff to effectively use AI solutions but also not taking steps to update them about policies and putting the business support in place.

To avoid the above scenario, IT leaders can make sure that their workforce is adequately trained to work with new technologies.

• Inadequate governance

Without a fully deployed, enterprise-wide governance standard model, AI strategies cannot work or scale. Also, basing governance models on only a couple of factors is only a recipe for a not working AI.

Thus moving forward, enterprises must incorporate the concepts of responsible AI, which is robust, ethical, explainable and efficient. Not only that, but the model should also center on standard technology deployment practices that point out which AI methods can and cannot be used.

• Not Understanding AI’s value

Many new adopters of AI are failing to recognize the technology’s ROI benefits. They may not be clear on the depths to which it needs to penetrate the organization in order to derive value for it. These enterprises consider AI as just an add-on instead of integrating it into the core value chain of industry applications.

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Enterprises must embed AI into their technology stack. This makes the value tracking effortless, habitual and addictive. Thus, enterprises must embrace AI while with complete understanding and clarity for the big picture.

• Not monitoring efficiently

CIOs put a lot of effort into ensuring that their systems and applications are up and running and are not disrupting the business process. But when it comes to putting the same efforts into monitoring and ensuring the accuracy of AI models, many IT leaders don’t put in the needed effort. And the results are as bad as model bias or the AI is pivoting towards an unintended task.

Hence, to avoid this, CIOs and IT leaders shouldn’t overlook the management and monitoring of AI models and put the efforts as much as they must as the core model’s development.

Having the latest AI tools and technologies is great, but if it is used without proper strategies in place, it won’t deliver the intended results. By having the knowledge of the above factors, enterprises can sign up their AI strategy game and achieve their intended business goals.