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AI as an enabler of human potential? You can bet on it.

Renowned theoretical physicist and futurist Steven Hawking was torn on the value of artificial intelligence. At one point, he said, “AI could be the biggest event in the history of our civilization. Or the worst. We don’t know if we will be helped by AI … or conceivably destroyed by it.”

But just before his death earlier this year, Hawking appeared to change his AI calculus: “Perhaps we should all stop for a moment, and focus not only on making our AI better, but also (focus) on the benefit to humanity.”

There, in a nutshell, from one of the most brilliant minds of the century is the AI conundrum. Is it, as Tesla founder Elon Musk said, “more dangerous than nuclear weapons?” Or is it more likely to “inject more pride and dignity into work focused on enhancing our communities,” as suggested by author and former president of Google China Kai-Fu-Lee?

AI and human potential by the numbers

Recent data strongly weighs in favor of AI’s massive capabilities to boost human potential and fundamentally change the nature of work in ways not seen since the Industrial Revolution and the invention of electricity. These data are further bolstered by the experiences of early users of AI platforms, showcased at Inforum 2018 this week in Washington, DC.

  • A recent study from PwC found that two-thirds (67%) of executives believe AI will help humans and machines work together to be stronger by using both artificial and human intelligence.

  • Another PWC study found that by 2030, advances in AI will add an astonishing $15.7 trillion to the global economy, while boosting the gross domestic product of local economies by 26%.

  • Accenture, whose research finds that a full 85% of business and IT executives anticipate making significant investments in one or more AI-related technologies by 2020, also concludes that organizations “will use AI to amplify human existence and improve how we live and work.”

One application of AI that is richly laden with advancing the human condition is that of the plight of medical researchers throughout the world. The Internet has given them instant access to one another’s detailed papers and studies on various diseases and maladies. But who has time to read it all, or even a tiny fraction?

Enter AI platforms, which can effectively “read” hundreds or even thousands of related papers in hours instead of months, cherry picking the salient facts and common findings, and presenting researchers with a summary that they would otherwise never have time to produce — ultimately accelerating key discoveries and treatment advances.

Coleman @work

On display at Inforum 2018 were numerous examples of AI-at-work, particularly Coleman, Infor’s enterprise-grade and industry-specific AI platform unveiled last year. Named after physicist/mathematician Katherine Coleman Johnson of lunar landing fame, Coleman was developed specifically to maximize human potential.

Coleman essentially sits atop the Infor CloudSuite stack, providing advice and data context flowing up from the other stack components, including analytics, network, cloud, industry, and platform. As is the case with many leading technology companies, Infor has made extensive use of Coleman in several discrete business areas.

For example, human resources has traditionally made do with a limited set of data — usually HR data exclusively — as well as standard analytics tools to serve employees. Coleman changes the HR equation markedly. Harnessing diverse data types from a wide variety of sources (employee records, past performance, Myers-Briggs and other test indicators, job openings, projections by Coleman of job openings, peer feedback or raves, social media data — to name a few), Coleman works its magic.

Coleman then can cross-reference employee profiles with job openings to match employees to jobs they may not even know were open. Coleman can advise employees on training and experiential criteria that can better qualify them for advanced positions. In doing so, Coleman effectively takes the boss out of the career path equation, putting the employee in charge. Succession plans are no longer stiflingly linear, the way they have been for decades. And for the company, employees are less likely to leave for promotions elsewhere, saving significant costs in replacing valued, trained workers.

Taking guesswork out of SCM

Another demonstration of Coleman’s ability to optimize human potential and benefit the enterprise showcased at Inforum is in production management and supply chain — areas ripe for enormous potential savings. The potential impact of AI and related machine learning on SCM forecasting is virtually revolutionary, according to Forbes.

Traditional SCM systems are as good as the normalized data within the system. By contrast, Coleman’s tentacles extend into virtually every data store in the organization, and to additional external stores as well. For example, let’s say Coleman reads a news feed about a potential port/shipping slowdown or stoppage resulting from an approaching storm. That triggers Coleman to analyze the impact of such a possibility on users of the goods emanating from that port.

From here, Coleman can send appropriate alerts to logistics managers, along with suggested actions or preparations based on existing and future product orders, parts inventories, demand projections, and so on. These managers, accustomed to flying by the seats of their pants in such circumstances, are now armed with real-time data and updates of that data, with corresponding changes in tactics and strategies coming directly from Coleman — all available on a simple, color-coded display. In the future, perhaps entire discrete SCM operations will be deeded over to Coleman, leaving time for managers to focus on more strategic functions and fewer operational chores.


Naysayers and doomsday analysts will continue to pound on AI for its alleged capability for destroying human potential. Both the research and the early experiences in the real world strongly suggest otherwise.

Source: Infor AI

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