Leveraging artificial intelligence (AI) to achieve business goals involves strategic planning, implementation and continuous optimization, which includes conducting a technology assessment, building strong teams of experts and setting specific goals with routine follow-up. With the evolution of AI accelerating, developing an effective, data-driven implementation plan is critical to harness its benefits.
Given the current economic challenges for much of the technology industry, it would be natural for the customer success (CS) management community to aggressively adopt AI and large language model tools in hopes to scale delivery capacity in the presence of headcount limitations, but this is not the case.
We’ve recently experienced a shock wave throughout the healthcare system caused by an age-old threat to the digital world – cyber hacking. Change Healthcare, an automated clearing house that is part of UnitedHealth Group and the backbone of the country’s healthcare payment system, experienced a ransomware attack from a hacker group on February 21, 2024, causing them to stop service altogether for a few days to recover from the menace and allow the FBI to investigate the crime. Ransomware and associated data breaches are not unknown to the healthcare industry.
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Large language models (LLMs) are an exciting new frontier for delivering services but also require a new kind of tech stack that supports app development, deployment and maintenance, with a focus on quality and accuracy. We’ve learned that the three essential elements of the LLMOps tech stack are (1) observability, (2) compute and (3) storage.
Clinical trial research is experiencing a groundbreaking revolution. Trial data has always been essential for testing drugs, devices and treatment protocols. Common trial challenges such as timelines, trial costs and compliance have traditionally impacted the amount of data that was collected. Now, thanks to artificial intelligence (AI) and machine learning (ML), researchers can access, manage and analyze data faster, cheaper and more compliantly than ever before. With the aid of ML advancements, trials yield actionable data in days or weeks instead of months or years.
Contrary to the frenzy of hyperbolic rhetoric preaching artificial intelligence (AI) as an economic imperative for nations, the way it is entangled with all aspects of our lives calls for inquiry into its meaning beyond a passive prop whose affordances unfold as we handle it.
More and more marketers are embracing data scientists as part of their teams. They are also partnering with data consultants to help with buyer journey mapping and customer acquisition strategies. In some cases, the marketing and creative process has been somewhat stifled in favor of data crunching and analytics. Although I advocate leaning into data analytics to assist with new customer prospecting, I’m also recommending we elevate the “art” of audience building. In a crowded marketplace, brands need to accentuate creativity to stand out.
Summary of what's inside the Winter 2023 issue of Analytics magazine.
Long before any weather forecasts had been made for late August/early September, I had decided not to attend the 2023 Burning Man due to too many family and work commitments. And while many of my friends were dealing with unexpected logistical challenges to get back from the desert, I too was focused on optimization. My week began with a class session in the required first-year MBA Business Analytics course that I have been teaching for several years. More than a decade ago, I stumbled upon a teaching technique that I have come to call the Don’t Worry Exercises (DWEs). My DWEs are in-class group exercises in which students are given a moderately challenging business problem, typically accompanied by some associated data.
While the hyper-excitement around AI in healthcare is taking everyone by storm, the fall of Babylon Health became a stark reminder that risks abound in this space and it would be wise to tame this giant with caution and adequate wisdom.
Companies go to great lengths to protect their top executives. Keeping them safe, healthy and happy so they can perform their duties without unnecessary distractions is critical for the productivity of the company. At one time, executive protection meant providing bodyguards and secure transit and fortifying executive offices against external threats. As more executives work from home, efforts have extended to bolstering home defense systems. Still, there’s a missing element. In today’s digital world, it’s also necessary to protect executives online. That should include protecting their personal data.
The Washington Post reports that artificial intelligence (AI) chatbots “hallucinate.” That is, they are so driven to respond and to value response over everything else that they lie and experience a form of self-denial in which they come to accept those lies as truth. Examples of this are appearing daily in the news.
Based on my experience both as a strategy consultant and data scientist in industry, I believe that data science’s last-mile problem in corporate strategy is highly solvable. This article outlines what I think data scientists and strategists can do to close this gap and maximize their impact.
So, you’ve successfully navigated the daunting tech job market and secured your first role as a data scientist? Congratulations – the hardest part is over! Or is it? Here's some advice for early career data scientists.
There are challenges and threats inherent to most decision-making processes, and AI is no different. The key is to assess the level of risk and take steps to build a strong, trusted solution similar to how you train and build trust in an employee’s ability to make daily decisions on their own.
The need for greater analytics adoption is influencing investment decisions, which will create ecosystems for effective integration of analytics, leading to a growth in diverse analytic applications.
The U.S. healthcare system has been moving for years to consolidate independent practices. Hospitals are swallowing them up, which should mean better communication, more efficient patient care, and lower costs. But that doesn’t seem to be the case according to some of our research findings.
An analysis of the disparity between a novel and its adapted television series using natural language processing.
