Use cases in IBM study show how to focus AI projects for maximum value

Use cases in IBM study show how to focus AI projects for maximum value

A new report on opportunities to create business value through artificial intelligence (AI) contains a data point that demonstrates the long way to go: by the end of 2022, one in four large companies, or 25% , should be past the pilot phase to operationalize its work on AI. The remaining 75% are piloting or considering AI projects.

While this 25% figure is modest, it represents a significant increase from the 9% who had operationalized their AI work in 2020 and just 5% in 2018.

The data, contained in a report titled “How to Create Business Value with AI” from the IBV Institute for Business Value (IBV), is based on discussions with more than 35 organizations with AI implementations – and include a dozen case studies of the use of AI across as many industries. The report aims to debunk common myths surrounding AI and, in doing so, creates a guide to using it effectively, especially on business processes where it can have a tangible impact.

One of the report’s key recommendations is that “the C-suite and other leaders don’t buy into some of the myths surrounding it, such as ‘AI shortcuts don’t work’…Instead of that, they have to make decisions based on the reality of AI.”

Build on a proven foundation.

The IBV report cites the benefits of leveraging ready-to-use basic and pre-trained models that can provide a cost-effective and fast starting point with AI projects. One important factor that plays into this recommendation: Companies have struggled to leverage the work previously done by data scientists to train datasets, as each business problem was tackled with a new AI model.

Now, shortcuts are appearing; they are pre-trained models analogous to off-the-shelf software that can be installed and used quickly. The approach is designed to help organizations speed up their work without having to generate entirely new datasets for each application; instead, they can leverage knowledge gained while solving one problem to help solve related problems.

Examples of these pre-trained models include Google’s BERT and OpenAI’s GPT-3. These types of models have three key benefits: they improve AI economics by amortizing costs across multiple use cases, they improve outcomes by providing greater accuracy from larger datasets, and they bring new capabilities.

Shortcuts have also gained traction in commercial products, such as RapidMiner (recently acquired by Altair), whose latest product version includes a “fully automated AI” capability that generates models based solely on business expertise, targeting non-coders and non-data scientists. RapidMiner described this as part of its goal to democratize AI.

Illustrating the benefits of off-the-shelf models, Boston Scientific spent $50,000 while leveraging open source AI models to achieve its goal of automating the inspection of stents in the field of medical products, according to the IBM report. Through this and other measures, Boston Scientific was able to achieve direct savings of $5 million while achieving greater inspection accuracy than ever before.

Focus beyond cost savings.

Examples abound of companies looking to apply AI to automate or perform functions that become costly when performed repetitively by humans and there is little or no human added value. In this light, AI can be accurately seen as having the potential to reduce costs and that is of course a good outcome.

But the report notes that cutting costs is not the sweet spot for AI applications. Indeed, large organizations are focused on growing their business and competitive differentiation through the use of AI. As the authors expressed, these AI-leading companies are focused on customer-centric revenue growth.

For example, IFFCO-Tokio, an insurance joint venture based in India, deployed a form of AI to assess images of damaged cars and classify models, damaged parts and type of damage after an accident.

AI was able to determine if the parts could be repaired or needed to be replaced. Plus, it provides a cost estimate while keeping a human assessor involved. While settlement costs dropped by 40%, the customer “acceptance” rate increased from 30% to 65%, resulting in increased customer satisfaction, retention and acquisition .

Recognize that one size does not fit all.

How to create business value also includes a caveat of sorts, debunking the myth that AI is a one-size-fits-all proposition, or that AI can and should be considered in virtually any application or use case to drive business results. This is not the case, say the authors of the report.

Before embarking on an AI initiative, the first thing to consider is whether enabling AI can serve a larger strategic initiative or solve a critical business problem. In short, there must be a fit for the purposes of the AI ​​initiative.

Tailored uses of AI can solve distinct business problems, across geographies and industries. But the case studies in the report show that the right approach often becomes clearer after choosing the right data set to solve the problem. Laying the right foundation is an essential part of success – Boston Scientific and IFFCO-Tokio help illustrate this point clearly.

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