Let’s also do some use cases for AI.
As highlighted by The New York Times, researchers are leveraging AI to accelerate the drug repurposing process, which involves searching existing medications for new treatment applications. This method is not new, but machine learning is dramatically speeding up the exploration of treatment options for individuals with limited choices. Further insights from The Wall Street Journal reveal that AI enhances the efficiency of analyzing scientific journals and databases, making it nearly ten times faster to prioritize genes and proteins for potential Alzheimer’s drug development. A notable case shared is that of Joseph Coates, a 37-year-old diagnosed with a rare blood disorder, who is now in remission thanks to an unconventional AI-assisted treatment.
American Express is leveraging artificial intelligence to enhance operational efficiency, achieving a remarkable forty percent reduction in IT escalations and an eighty-five percent improvement in travel assistance for its elite customers. The company has integrated generative AI into its internal IT support chatbot, which now provides more intuitive and personalized assistance, significantly decreasing the need for employee IT tickets to be escalated to live engineers. Since its launch in October 2023, this AI-driven solution has enabled faster issue resolution, allowing employees to return to work more quickly. Additionally, over eighty-five percent of travel counselors at Amex report that an AI tool, designed to offer personalized travel recommendations, saves them time and enhances the quality of their service.
It’s not all sunshine and rainbows. Bloomberg News is navigating the complexities of integrating artificial intelligence into its journalism, reporting a rocky start in generating A.I.-summaries for articles. This year alone, the outlet has had to correct over three dozen A.I.-generated summaries, including a significant error regarding President Trump’s auto tariffs. Although Bloomberg claims that 99 percent of these summaries meet their editorial standards, challenges in accuracy remain prevalent, with some summaries misattributing information or presenting incorrect facts. While many news organizations, including Gannett and The Washington Post, are experimenting with A.I. technologies, concerns from journalists persist that readers may rely solely on these summaries rather than engaging with the full articles. Bloomberg’s editor-in-chief, John Micklethwait, acknowledges that the effectiveness of an A.I. summary is contingent on the quality of the original story, underscoring the indispensable role of human journalists in the reporting process.
Why do we care?
The core insight is recognizing where AI offers genuine business value versus where its implementation still faces significant challenges.
The use of AI to accelerate drug repurposing and development is a practical, high-impact application, with the potential to revolutionize how treatments are discovered and applied. The key here is speed and precision, as AI can rapidly analyze vast datasets to identify new treatment possibilities.
American Express showcases AI’s ability to enhance internal support and customer service, reducing IT escalations by 40% and boosting travel assistance efficiency by 85%. This points to a mature, pragmatic use of AI that delivers measurable outcomes.
Bloomberg’s struggles highlight the flip side of AI adoption. Despite impressive accuracy rates, errors in AI-generated summaries have real consequences, especially in news reporting. This exemplifies that even when AI adoption seems promising, maintaining quality control remains essential.
AI’s potential is vast, but its implementation must be strategic, tailored, and well-governed. Identifying the right use cases, maintaining human oversight, and preparing for potential inaccuracies will ensure that AI projects deliver sustainable value rather than becoming liabilities.
By focusing on tangible, measurable improvements and acknowledging the limitations, MSPs can help clients make smarter, more informed decisions about where to integrate AI into their operations.
