More broadly, according to a recent report by Gartner, over twenty percent of digital workplace applications are expected to utilize artificial intelligence-driven personalization algorithms by the year 2028. This shift aims to create adaptive experiences for employees. Despite this positive trend, a Gartner survey conducted with over five thousand employees revealed that only twenty-three percent of digital workers were completely satisfied with their work applications in 2024, a decline from thirty percent in 2022. Tori Paulman, Vice President Analyst at Gartner, emphasized the need for workplace applications to be as intuitive and empowering as popular consumer apps. To achieve this, technology leaders are urged to implement best practices in AI personalization, ensure transparency in how algorithms work, prioritize key business outcomes, define clear requirements during vendor selection, and continually monitor and adapt these systems to meet employees’ evolving needs.
While concerns about the Chinese AI model DeepSeek are prevalent, research indicates that U.S.-based AI chatbots may be collecting even more user data. According to a study by Surfshark, Google Gemini tops the list as the most data-intensive AI chatbot, gathering 22 out of 35 user data types, including sensitive information such as location, contacts, and browsing history. In contrast, DeepSeek ranks fifth, collecting an average of 11 unique data types. Almost a third of chatbots share sensitive user data with third parties.
A recent survey by Writer reveals that the adoption of generative artificial intelligence is causing significant internal conflict within enterprises. Two-thirds of executives report increased tension and division, with 42 percent stating that the technology is tearing their companies apart. Nearly 70 percent of leaders note that applications are being developed in isolation, and over 30 percent of employees, including 41 percent of Generation Z, admit to undermining their company’s AI strategies by refusing to use AI tools. Additionally, while three-quarters of companies are investing at least one million dollars annually in generative AI, only one-third are seeing substantial returns on investment. Only 37 percent of executives without a formal plan feel successful in AI adoption, compared to 80 percent with one.
And we like use cases – The Information covers how Moderna has merged its Information Technology and Human Resources departments, driven by the rising use of artificial intelligence, particularly ChatGPT, in writing performance reviews. Brice Challamel, the company’s vice president of AI products and platforms, noted a significant increase in ChatGPT usage among employees during performance review periods, prompting the creation of a custom ChatGPT version to aid in drafting these evaluations. Moderna, which invests over one million dollars annually for ChatGPT access for five thousand employees, reports that staff members send approximately 1.2 million messages each month to the AI platform.
Why do we care?
The transition from hype to practical implementation is proving messy, expensive, and divisive.
AI integration isn’t just about adding features—it’s about solving problems. If businesses deploy AI without understanding employee workflows, productivity won’t improve. Providers who help bridge this gap—by tailoring AI tools to actual work habits—will have an edge. AI adoption isn’t just a technical challenge; it’s a cultural and operational one
The Surfshark study highlights a massive blind spot in AI privacy discussions. While fears about foreign AI models like DeepSeek persist, U.S.-based AI systems—especially Google Gemini—are collecting far more user data. businesses blindly adopt AI chatbots without understanding data privacy implications, they expose themselves to compliance risks, regulatory fines, and potential reputational damage. AI vendors are not transparent enough, leaving a gap for services that audit and mitigate AI-related data risks.
Moderna’s decision to merge IT and HR due to AI adoption is a real-world use case of AI’s operational impact. AI isn’t just changing how work is done; it’s restructuring corporate functions. AI-driven automation is reducing repetitive, administrative tasks. Expect other companies to consolidate or rethink traditional departments. Providers who offer AI-powered workflow automation and cross-functional AI strategy will have a competitive advantage. AI governance and bias detection services will be critical differentiators.
