The road to integrating AI is perilous, as it is with any disruptive technology, with the majority of initiatives falling by the wayside during the digital transformation process. This is why it’s critical for businesses to learn from previous mistakes, identify the hazards of implementing AI in production, and educate themselves with the technology to the point where they can confidently predict how certain AI solutions will meet their needs.
AI has acquired significant market traction in recent years. Businesses across industries have started finding ways in which AI solutions can be deployed on top of legacy IT infrastructure, to address a variety of business needs and pain points.
Despite the enormous potential of AI to alter any business, it is frequently underutilized. The reality is that the vast majority of AI efforts fail. According to Gartner, just 15% of AI solutions deployed by 2022 will be profitable, let alone generate a good return on investment.
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Any company embarking on a digital transformation journey should be concerned about the discrepancy between the big promise of transforming enterprises and the high failure rate in reality. Two crucial questions should be asked by businesses: “What causes the majority of AI projects to fail?” and, “Does there exist any methodology for overcoming this failure rate and clearing the way for successful AI deployments in production?”
The answer to the first question begins with data. AI models are trained with the company’s past data in order to generate accurate results. A well-trained model should be able to handle data that is comparable to the samples with which it was trained. In a controlled lab environment, this model may continue to function smoothly. It will, however, inevitably fail if it is fed data that comes from beyond the area of the training data distribution. Unfortunately, this is a common occurrence in real-world production settings.
Most AI projects fail in production because the data used to train the model in sterile lab environments is static and completely regulated, but data in real-world environments is significantly noisier and more complex.
The second stumbling block in AI implementation is keeping the solution on track once it has been implemented. Continuous data and model version control, constant monitoring of the model’s resilience and generalization, optimization of a human-machine feedback loop, and constant noise detection and correlation checks are all required. The continuing maintenance of an AI solution in production can be a difficult and costly part of the AI deployment process.
The dual challenge of deploying an AI solution in a loud, dynamic production environment while also keeping it on track to generate accurate forecasts is what makes AI implementation so difficult. Companies struggle with AI implementation, whether they try to do it in-house or hire an external provider.
So, here are a few strategies companies can adopt in order to overcome these challenges.
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Every AI application is distinct. Even within the same vertical or operating area, each company uses different data to achieve its own objectives, all based on its own business logic. All of these specificities must be translated and put into an AI solution in order for it to deliver the best fit for the data, environment, and business goals. Off-the-shelf AI solutions, on the other hand, are not tailored to the business’s specific demands and limits, and will be less effective at producing correct outputs and value.
The capability of AI solutions to cope under severe data settings, such as unlabeled, unstructured, and constantly changing data, can be used to assess their resilience. Instead of relying solely on performance evaluations in lab conditions when evaluating AI technologies, businesses should ensure that the expected outputs will endure their real-world production environments.
A solution based on artificial intelligence is always a means to an end, not a goal in itself. As a result, the value of AI solutions to the business should be assessed in the context of the company’s overall business goals, needs, and digital strategy. One-point solutions may address a specific use case, but they may result in a laborious patchwork whenever more AI solutions are implemented to address more use cases.
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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.
