With a deep understanding of market analysis and international business trends, Priya Jaiswal has become a recognized authority in the fields of banking and finance. Her work provides critical insights into how technological shifts, particularly artificial intelligence, are reshaping the economic landscape. As AI tools rapidly move from the fringes to the mainstream, her perspective is essential for understanding the real-world implications for American workers and businesses.
This conversation explores the tangible ways professionals are now using AI, from the sales floor to the classroom, and examines the uneven pace of its adoption across different industries. We will delve into the gap between the promise of a massive productivity boom and the current, more task-oriented uses of these tools. The discussion will also shed light on the specific vulnerabilities facing certain segments of the workforce and the surprisingly optimistic sentiment many employees hold about their future alongside AI.
With nearly a quarter of American workers now using AI frequently, we see varied applications, from store associates to teachers. Could you share some of the most effective ways professionals are integrating these tools and what specific, day-to-day problems they are solving with them?
It’s truly remarkable to see the pace of this integration. We’re not just talking about coders and analysts anymore. Think of a 70-year-old store associate at Home Depot who uses an AI assistant on his phone every hour to answer complex customer questions about electrical supplies. He feels his job would suffer without it, as it replaces “I don’t know” with immediate, useful information. In a completely different field, you have a high school art teacher who uses AI chatbots to refine her notes to parents, adjusting the tone to be just right, which she says has led to fewer complaints. And in finance, a young investment banker uses it daily to synthesize vast documents that would otherwise take hours, freeing him up for higher-level analysis. These aren’t abstract concepts; they are practical solutions to everyday challenges of communication, knowledge gaps, and time management.
While tech and finance sectors show high AI adoption, fields like retail and healthcare have been slower. What are the key operational or cultural barriers preventing wider AI use in these service-based industries, and what practical steps could help bridge this adoption gap?
The disparity is quite telling. In sectors like retail or healthcare, the core value proposition is often the “human interface,” as one employee put it. There’s a strong cultural belief that empathy, personal connection, and the physical presence of a caring human are irreplaceable. A pastor I read about articulated this perfectly when he said you want a human being, not a machine, to hold your hand when you’re dying. This sentiment creates a natural hesitation to automate roles centered on human-to-human interaction. Operationally, these jobs are less computer-based and more physically engaged, making direct AI integration less straightforward than in a desk job. Bridging the gap requires a mindset shift from replacement to augmentation—demonstrating how AI can handle inventory, scheduling, or data entry to free up a nurse or a retail worker to spend more quality time with the person they are serving.
We see many employees using AI for practical tasks, from synthesizing data to refining emails. How do these task-oriented applications align with the broader economic promises of a major productivity boom, and where might we be overestimating AI’s current impact on company-wide output?
This is the central tension right now. The economic narrative is one of revolutionary, system-wide productivity gains, but the reality on the ground is more incremental. While about four in ten users are leveraging AI to consolidate information or generate ideas, these are largely individual efficiency gains. An employee saving an hour on a report is a clear benefit, but it doesn’t automatically translate to a systemic boom, especially when not all economists are convinced of the scale of this impact. We might be overestimating the immediate effect because we’re focusing on the tool rather than the workflow. Until organizations fundamentally redesign processes around AI capabilities, instead of just bolting them onto existing tasks, the impact will remain localized. The real leap in productivity will come from rethinking how work itself is structured, not just from writing emails faster.
Research has identified over six million workers, primarily women in administrative roles, as highly exposed to AI’s effects but less equipped to adapt. What are the most significant risks they face, and what specific strategies can employers implement to support their reskilling and career transitions?
This group faces a perilous combination of high exposure and low adaptability. The research is stark: about 86% of these 6.1 million workers are women, often in administrative and clerical roles. The primary risk is that their core job functions—scheduling, data entry, correspondence—are precisely what current AI is very good at automating. Compounding this, they tend to have fewer transferable skills, lower savings to weather a job loss, and are often located in smaller cities with fewer alternative career paths. An income shock for them could be devastating. Employers have a critical responsibility here. The most effective strategy is proactive, internal reskilling. This means identifying adjacent roles where their institutional knowledge is valuable—like project coordination, client relations, or data verification—and creating dedicated training pathways to help them transition before their current roles become obsolete.
Many employees report not being concerned about AI replacing them, viewing it as a tool that complements the human element of their work. What factors contribute to this sentiment, and in which specific job functions might this optimism be overlooking potential long-term risks of automation?
This optimism is largely rooted in the current, complementary nature of AI use. When a tool helps you find information faster or draft a better email, it feels like a partner, not a replacement. Only half of employees now believe it’s “not at all likely” AI will take their job, which is actually down from about 60% in 2023, but it’s still a majority. This sentiment is strongest in roles where empathy, creativity, and physical interaction are key. The pastor who wouldn’t use a “soulless” machine for his sermons or the retail worker who emphasizes the “human interface” are perfect examples. However, this optimism might be shortsighted in roles involving complex data analysis, pattern recognition, or even creative generation. While an artist might use AI for inspiration today, the rapid improvement in generative tools could diminish the market for certain types of commercial art or design in the long run. The risk lies in underestimating the speed at which AI will move from being a simple assistant to a capable collaborator and, eventually, a potential substitute for entire workflows.
What is your forecast for AI adoption in the American workplace over the next three to five years?
Over the next three to five years, I foresee a significant deepening and broadening of AI integration. We’ll move beyond the early-adopter phase, where roughly a quarter of the workforce uses it frequently, to a point where basic AI literacy becomes a standard job requirement in most office-based professions, much like knowing how to use a web browser is today. The use of specialized, internal AI tools, like the one we see at Bank of America, will become commonplace in large corporations, tailored to specific business needs. However, the adoption curve will remain uneven; service and manual labor sectors will continue to lag, focusing instead on AI for back-office optimization rather than front-line roles. The most critical shift will be the growing pressure on companies not just to provide access to AI, but to actively train their workforce on how to use it effectively and ethically to achieve tangible business outcomes. The conversation will evolve from “Are you using AI?” to “How well are you using AI to drive results?”
