How AI is making real contributions (right now) to business models

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There are about a dozen stories today about how AI will benefit an enterprise. Applications are legion in sales, marketing, payroll and many other fields. But still, there is a precious little discussion about how organizations are moving forward with their AI projects. Are they really fulfilling these promises, and can there be some concrete examples of AI at work that can be replicated elsewhere?

Judging by Gartner’s hype cycle, most organizations are close to completing the development and experimentation phases of their early AI programs and now want to operate them within the business model. This is a turning point for technology as it represents a leap from expectation to reality. Without tangible results in the real world, such as increased productivity, lower cost, or some other positive outcome, AI may be pushed back into the lab for further refinement or possibly slow death.

A positive outlook for AI

According to the MIT Sloan Management Review, however, 2022 is becoming the year when AI finally begins to deliver solid returns on investments over the past few years. For example, in 2019, only three of the 10 companies surveyed reported minimal value from their AI efforts, with failures largely attributed to the difficulty of advancing technology in a manufacturing environment. This year, more than 90% of people are reporting tangible returns on their AI investments and planning to advance their strategies.

Surveys are all good and good, but where are the success stories of who is benefiting from AI and how? Investment firm Vanguard is one such example. Its retirement planning department, Vanguard Institutional, needed a way to deliver information, service offerings and other content to customers not only in general but on an individual basis and on a scale. Using the natural language platform developed by Parsado, the company can now target individual customers with specific phrases, formatting and consistency, leading to a 15% increase in conversion rate.

Marketing, in fact, seems to be the starting point for AI in the production environment. Desite Analytics and AIA have recently posted five examples of how companies are using technology to differentiate themselves in increasingly crowded and noisy business environments. Laz, for one, has recently used Deep Fake technology to allow users to customize video messages from Argentine soccer player Lionel Messi to share with friends. Mattress firm Tomorrow Sleep set to work on their content marketing programs to identify ways to improve organic traffic – and increased from 4,000 visits per month to 400,000 per month within a year.

Operationally balance your McDonald’s orders

AI’s ability to optimize the supply chain has also begun to focus not only on product sourcing and distribution but also on customer-content operations. McDonald’s recently acquired an Israeli company called Dynamic Yield, which provides personalization software that can expect traffic volumes and choices for everything from ordering kiosks to ordering food and beverage offerings based on multiple criteria such as weather and public events. At the same time, however, it can read inventory levels to promote things that are plentiful and frustrate rare items, bringing supply and demand into greater alignment in a highly dynamic fashion.

These are just some of the ways in which AI can be put to practical use. Undoubtedly, there is still a long way to go before technology can enter the economic mainstream, and for that time there will certainly be more examples of AI failure than success.

But the distinctive factor between AI and earlier forms of digital technology is its ability to adapt to changing circumstances. This means that when it fails, or just doesn’t meet expectations, it can be easily retrained to get the best results – no need to go back to the drawing board to rewrite the whole code that could address the problem. Or may not. Not even relevant anymore.

In the digital world, AI makes the old adage “If you don’t succeed in the beginning, try, try again.” And even when that threshold of success is finally met, AI can be constantly refined to take that success to higher and higher levels.

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