How AI (Now) Really Contributes to Business Models


We’re excited to bring Transform 2022 back in person on July 19 and virtually July 20 – August 3. Join AI and data leaders for insightful conversations and exciting networking opportunities. Learn more about Transform 2022

There are dozens of stories these days about how AI will benefit the enterprise. Applications in sales, marketing, payroll and many other areas are legion. But there is still little talk about how exactly organizations are doing with their AI projects. Do they really deliver on these promises, and are there perhaps some concrete examples of AI at work that can be emulated elsewhere?

Judging by Gartner’s Hype Cycle, most organizations are almost done with the development and experimentation phases of their initial AI programs and are now trying to operationalize them within the business model. This is a critical step for technology as it represents the leap from expectation to reality. Without tangible results in the real world, such as increased productivity, lower costs, or any other positive outcome, AI could be pushed back to the lab for further refinement or possibly a slow death.

Positive outlook for AI

However, according to MIT Sloan Management Review, 2022 will be the year when AI will finally start to deliver solid returns on investments made in recent years. In 2019, for example, only three out of 10 companies surveyed reported even minimal value from their AI efforts, with the failures largely attributed to the difficulty of pushing the technology into production environments. This year, over 90% report solid returns on their AI investments and plan to expand their strategies in the future.

Surveys are all well and good, but where are the success stories that pinpoint exactly who benefits from AI, and how? Investment firm Vanguard is one such example. The pension plans division, Vanguard Institutional, needed a way to deliver information, service offerings and other content to customers, not only in a generic way, but also on a personalized basis and at scale. Using a natural language platform developed by Persado, the company can now target individual customers with precise wording, formatting and relevance, leading to a 15% higher conversion rate.

Marketing, in fact, appears to be the first hotbed of activity for AI in production environments. Discite Analytics and AI recently published five examples of how companies are using the technology to differentiate themselves in an increasingly crowded and noisy business environment. For example, Lays recently used deep fake technology to allow users to modify video messages from Argentine footballer Lionel Messi to share with friends. Mattress company Tomorrow Sleep put AI to work on their content marketing programs to find ways to improve organic traffic — seeing a jump from 4,000 visits per month to over 400,000 per month within a year.

Operating balance of your McDonald’s order

The ability of AI to optimize the supply chain is also starting to attract attention, not only in terms of product sourcing and distribution, but also in customer-facing operations. McDonald’s recently acquired an Israeli company called Dynamic Yield, which provides personalization software that can do everything from recommending food and drinks at the order kiosk to anticipating traffic volumes and preferences based on multiple criteria, such as weather and public events. At the same time, however, it can read inventory levels to promote items that are plentiful and discourage scarce items, thus aligning supply and demand in a very dynamic way.

These are just some of the ways AI is being put to practical use. The technology undoubtedly has a long way to go before it reaches the economic mainstream, and there will certainly be many more examples of AI failures than success for some time to come.

But the distinguishing 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 live up to expectations, it can easily be retrained to produce a more optimal result – no more going back to the drawing board for a full code rewrite that may or may not address a problem that might not even be relevant anymore.

In the digital world, AI enables the old adage, “If you don’t succeed at first, try again.” And even when that threshold for success is finally reached, AI can be continuously refined to take that success to ever higher levels.

This post How AI (Now) Really Contributes to Business Models was original published at “”


Please enter your comment!
Please enter your name here