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Artificial Intelligence Needs To Speak The Language Of Business, Not The Other Way Around


Almost every business leader on the planet, 94%, believe AI will be critical to success over the next five years. Still, as Deloitte’s latest research on the state of AI finds, many companies still aren’t achieving the value they anticipated — there has been a 29% increase in the share of respondents who identify as AI “underachievers” this year as compared to the last year.

Issues diminishing the impact of AI include challenges improving its business value and a lack of full executive commitment, the Deloitte survey shows. Industry leaders and observers in the trenches agree that it is these organizational issues, rather than technical issues, that are holding back progress.

An important point is that AI needs to serve the customer, and help the business put the customer front and center.

Most AI projects fail to deliver value “because they don’t start from business realities, like the benefit of a correct prediction, the cost of an incorrect prediction, or constraints such as the size of the marketing budget,” says Arijit Sengupta, CEO and founder of Aible. “If your AI project looks and feels like a lab experiment, and your experts talk about things like log loss instead of revenue, profit and costs, your AI is almost guaranteed to fail to deliver results.”

The key is to make decision-makers more comfortable and knowledgeable about AI, build organizational support for AI, and keep the focus directly on how it can help the customer. “Organizations are more likely to buy into AI-based approaches when it directly ties to demonstrable customer value,” says Rajesh Raheja, chief engineering officer at Boomi. “For example, a recommendation engine that can show you what’s the next step to implement in a business process based on proven best practices it has learned will be far more useful to a business than the same engine only showcasing more products that the business can purchase. Both require sophisticated AI models, but the first clearly creates value for the customers.”

We have the tools, we are just unsure how to apply them. “AI adoption depends on the value and ROI it generates compared to the effort to train a model, which needs the right resources and skills to set up a strong data pipeline” says Raheja. “Machine learning outputs change with the data, algorithms and its evolution. The fear of an unknown decision causing an unforeseen liability is another factor that makes mainstream businesses cautious.”

As an example, Raheja adds, “an AI driven loans application denying certain sections of the population after a model update could be an error, or inherent bias introduced in the data and model.”

A lot of this has to do with the timeliness of AI data as well. “AI is actually a perishable good,” says Sengupta. “If your AI takes months to create, it is trained on months-old data and the world has changed in the intervening time period — thus the AI is no longer current. You need to create and deploy AI in days, not months, if you want to get value, and then iterate as the world changes.”

There are many innovative business cases that can be developed that can help drive AI adoption. Examples cited by Raheja include using AI “to analyze customer data being input into the system for formatting and semantic checks. Natural language processing and automated chatbots for customer relationship management are other use cases.”

Keep in mind that “AI adoption is constrained by the availability of data scientists, and we can’t train our way out of that problem,” Sengupta adds. We are trying to teach business users to speak AI instead of teaching AI to speak business. Before sending business users to learn Python, stop and say: ‘why can’t the AI understand my business needs and generate the Python code automatically?’ The Internet revolution didn’t happen because everyone learned how to code to interact with the World Wide Web; it happened because the Netscape browser could be used by almost anyone.”



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