The statistics are staggering: 58% of employees now use AI-driven tools in their daily workflows.
Yet these same workers are interrupted every three minutes and then need another 23 minutes to regain focus after each disruption.
We've crossed a threshold where AI is simultaneously enhancing efficiency and fragmenting attention, a paradox that defines our current moment in workplace evolution.
In a recent panel for at the Idea Citizen Summit, Evante Daniels, Chief Strategy Officer for SEEQR, Eric Trowbridge, Director of Digital Solutions at Blue Federal Credit Union, and Thru Shivakumar, CEO of Cohesion, explored how leaders can navigate this transformation without getting lost in the noise. Their insights reveal a nuanced picture of where we really stand in the AI revolution and what it means for the future of work.
The Scale of Change We're Experiencing
"We haven't seen something like what's happening right now since probably the industrial revolution," Trowbridge observes. "The average person, if they've never done AI before, takes only about 10 hours to start getting to know how it works. But now over 50% of the workforce is using AI in some capacity within one or two years. This is unheard of."
This rapid adoption rate puts us in unprecedented territory. ChatGPT has had the fastest user adoption rate of any technology ever, with 800M active users at last count. The internet took decades to reach mainstream adoption, mobile technology transformed over about ten years, and now AI is reshaping work in a matter of months. Yet as Shivakumar points out, we're still in the early stages: "We're very much at the beginning of how all of this is going to transform us."
The Trust Question: Humans vs. Machines
One of the most interesting dynamics emerging is the question of trust. Shivakumar challenges the common skepticism: "People will raise their hand and say 'Well how can you trust AI?' I'm like 'Well how do you trust humans?' There's errors everywhere we go. I actually trust the computer more than I trust the human in doing a calculation."
This shift in trust relationships is fundamental. As organizations grapple with AI adoption, they're not just implementing new tools, they're recalibrating fundamental assumptions about reliability, accuracy, and decision-making authority.
The Hardware-Software Gap
Trowbridge identifies a crucial insight about why we're experiencing so much disruption: "The big problem is that the hardware has not yet caught up to the brain of AI. Right now we're basically just taking what we currently have and we're just slapping AI in it, and it's not making it any better. It's making it even more busy and more distracting."
This explains much of the current friction. We're using AI through interfaces designed for pre-AI workflows, creating the very interruption problems we're trying to solve. The future, according to Trowbridge, lies in entirely reimagined hardware that embeds AI more naturally into our work environment.
Practical Implementation: Starting Small, Thinking Big
Both leaders emphasize a measured approach to AI adoption. Rather than trying to transform everything at once, successful organizations are focusing on specific domains and going deep.
At Blue Federal Credit Union, they've chosen to focus on member experience and employee experience. "Pick one to three domains and go very deep with those," Trowbridge advises. "We've got about 10 or 13 agents already built out there, helping my developers look at API code, sprint tasks, and timing."
Shivakumar's approach with her team centers on elevating expectations: "We can do a lot more, we expect each other to do a lot more, and we expect that the mundane shouldn't be done by us. All of that should be elevated to a very different level."
The Human-Centric Approach
Perhaps most importantly, both leaders stress that AI should enhance rather than replace human judgment. Shivakumar explains: "You should never just use AI. Don't cut and paste anything and just use that as a whole. Learn how to prompt, how to use it, how to leverage it. But add sentiment, put feeling and emotion and have that human-centric piece of strategy."
This 80/20 rule—where AI gets you 80% of the way there, but human expertise and judgment complete the final 20%—appears to be a practical framework for most current applications.
Addressing the Skills Question
The fear of job displacement is real, but both leaders see it as an opportunity for human skills to become more valuable. "If your entire job is something that a computer can do faster, it will be replaced," Shivakumar states bluntly. "But you have to figure out what value you can add on top of that. Where is that emotional intelligence, where is that experience level, where is the strategy that you bring to the table?"
Trowbridge envisions a future where AI handles routine tasks, freeing humans to focus on uniquely human capabilities: "We actually get back to humanity. We can think, we can reason, we have emotions, critical thinking. We need to learn how to speak better and write better."
The Infrastructure Reality
Shivakumar brings a grounding perspective from the physical world: "We are far from where the physical office can go. We don't even have the foundation built when it comes to spaces and places. You're still using plastic key cards to get into 90% of buildings."
This infrastructure gap represents both a challenge and an opportunity. The $64 trillion infrastructure investment needed by 2050 isn't just about AI—it's about aligning our physical and digital systems for the future of work.
Building Organizational Buy-In
Successfully implementing AI requires more than just technology—it requires cultural change. Both leaders emphasize the importance of organic adoption and peer-to-peer learning.
Shivakumar's team hosts knowledge-sharing sessions where employees demonstrate AI applications to each other. Trowbridge is planning an "AI agent app store" where employees can showcase their innovations. These approaches create excitement and peer pressure that drives adoption more effectively than top-down mandates.
Looking Forward: The Next Three to Five Years
The immediate future will likely see continued friction as hardware catches up to AI capabilities. But both leaders are optimistic about what comes next. Trowbridge predicts we'll see AI agents embedded in everything from glasses to ambient computing environments, reducing the screen-based interruptions we experience today.
Shivakumar focuses on the foundational work still needed: "We're still in the foundational side of AI, creating the groundwork, getting the data, cleaning and structuring the data so that we can apply AI to that eventually."
The Takeaway for Leaders
The message for leaders is clear but nuanced: AI is here to stay, the transformation is massive, but we're still in the early stages. The key is to start experimenting now while building the infrastructure and cultural foundations for deeper integration later.
"As long as the company or you are using it and trying to learn it and figuring it all out, you are already way ahead of everyone else," Trowbridge concludes. "Even just tinkering around with it, creating some fun images, trying to take one little tiny piece of your job and converting it to AI. You're already ahead of the game."
The AI revolution isn't coming. It's here. We still have time to shape how it unfolds in our organizations. The question isn't whether to adopt AI, but how to do it thoughtfully, focusing on human-centered outcomes rather than just technological capability.
As Shivakumar reminds us: "The tools shouldn't inform the requirement. Start with what am I trying to accomplish, and then how am I going to use these tools to get there. Data for the sake of data is not really worth it until you figure out what decision can be made and or influenced."
The future belongs to organizations that can balance innovation with stability, efficiency with humanity, and transformation with trust. Those who start experimenting today—even in small ways—will be best positioned to thrive in the AI-enabled workplace of tomorrow.
