Some Thoughts on Artificial Intelligence and The Workplace
Some initial thoughts on how artificial intelligence is impacting leadership, talent and the workplace
Later this spring, I’ll be delivering a few keynotes around artificial intelligence and it’s impact on talent, leadership and the workplace. In an effort to better understand how AI is impacting my own industry and profession, I’ve been doing a lot of researching, reading and speaking with people who are leading the adoption and deployment of AI inside of organizations. My research and synthesis is still ongoing, but I wanted to devote a few editions of this newsletter to share a work in progress of some of the my most interesting ideas so far, and what I think they mean.
1)People who use GenAI are more effective and efficient (with a caveat..)
In a very interesting field experiment, research from the Boston Consulting Group (BCG) highlights a critical insight: AI is a powerful leveler of performance. Employees with lower baseline proficiency benefitted the most from AI-powered tools, experiencing a significant boost in productivity and accuracy. Furthermore, while using Gen AI “moved” the performance curve to the right (everyone improved when using GPT-4 against those who did not) the performance effect is not universal—some individuals may see limited or even negative impacts, particularly if AI is misused or if over-reliance reduces critical thinking.
What This Means: All else equal, people benefit from using AI tools for their work, and I would argue it makes sense to use it versus not at all, but that has limitations and we need to be intentional about what we use it for, as well as how we use it. As the BCG study notes, Using GenAI improved performance for some tasks but not for others - we are going to have to use this as a framework for identifying how/where/when/why we should be using AI in our tasks, workflows, and deliverables (ex: Is it the first draft? Is it to augment?
Is it after the human did the task first?) of the core insights from the BCG study was that AI seemed to be good at certain tasks and worse at others. This is also what makes it difficult (as of now) to let AI do full end-to-end deliverables which require a mix of types of task, some more routine and others more complex.
2)If we over rely on artificial intelligence, we may hinder our abilities and produce lower quality work
AI can reduce the effort required for critical thinking, but this convenience comes at a cost. Research from Microsoft and Carnegie Mellon University suggests that while AI automates tasks and accelerates access to information, it also risks diminishing user engagement and problem-solving skills.
When employees become overly dependent on AI-generated insights, they may neglect the deeper analytical thinking, and may also feel frustrated due to the lack of engagement.
What This Means: This ties nicely with BCG’s research. BCG’s research is great from a “technical” level but this research is strong from a “philosophical level” about why we should be more intentional about how we design work for people, and what we use AI for.
Just because AI can do something doesn’t mean we should let it even if it may lead to a better output. As this research shows, there are clear ramifications on humans who use AI either by over relying on it, or by using it for the wrong things. I think this research also shows that while we can take things off our plate by giving it to AI it doesn’t mean we should.
Daniel Pink, the NY Times Best-Selling Author found in his research that humans are motivated by autonomy, mastery, and purpose, and that ties to the work we design for employees. While not everyone has a job or wants a career for the same reasons, I think this research shows that we should design work and tasks for humans that do offer the ability to be challenge, demonstrate mastery and skill and actually allow humans to use their strengths and talents.
3) We already acknowledge (today) how transformative and disruptive artificial intelligence could be
There is an overused quote from William Gibson who says “The future is here, it's just not evenly distributed.” That sort of rings true with where we are with AI today. McKinsey’s research on Superagency found that 70% of employees expect Gen AI will change 30% or more of their work.
We don’t know exactly how things will play out but we are already anticipating significant transformation and change.
What This Means: Many of us are already seeing the effects of AI in our work. We should pay attention and be alert about what happens next, and depending on your interest and appetite level, take actions to put yourself in a position to be successful versus falling behind. The implication from the research in this statistic is that employees are both A) using AI and B) ready for AI to change the way that we work.
I don’t know if I fully agree with that implication especially part B, but what I would offer is this: People have the ability to shape the choices and decisions about AI and work, and if you are someone that either A) is concerned about how this could impact you or B) wants to take a more proactive role in making this work to your favor, you should take this as your signal that now is the time to build your action plan.
4) We’re still in the foundational stages of using AI in the workplace
In a survey of executives about how they are using AI in their organization, it looks like for now most companies are really focused on productivity gains and transactional uses of AI. A 2025 BCG AI Radar study categorizes AI investments into three main areas:
Deploy – AI applied to individual productivity, such as automating repetitive tasks. (44%)
Reshape – AI used to enhance business processes, redesigning workflows and improving team efficiencies. (27%)
Invent – AI as a tool for company-wide innovation, driving new business models and strategies. (29%)
Currently, more than half of AI investments focus on productivity and process efficiency rather than innovations. This signals that many organizations are still viewing AI through a transactional lens, rather than a transformational one, today.
To be sure, this is not uncommon for many other adoption curves of new technology, and in many respects, we are still in the early miles of the AI marathon. Furthermore, we are also experiencing a proliferation of advancements and evolutions of many aspects of AI itself, ranging from the models, products and applications, which also requires companies to evaluate the risk of going too far too fast.
What This Means: We’re still very early and foundational in terms of how AI is being deployed and evolving tasks, workflows, and processes. And while some of that has been impressive (other parts have been disappointing) it looks like it’s just the beginning.
5) Employees are looking today for foundational enhancements to their job using Artificial Intelligence
Just like many organizations are thinking about AI making transactional enhancements, the same is true for employees. In my own research, I’ve found that most people are looking at the benefits of AI in the short term as ways to make transactional improvements and enhancements to their job.
In a 2025 survey that my firm conducted, I asked participants what was the most important business benefit that AI could provide to their job today, and the majority of people selected either efficacy (improving existing outputs) or time (getting back time) Today, it seems as if most people are looking for getting back time (46%)
What This Means: I think this serves as an opportunity to think bigger, and more expansively and creatively about how we can use new tools to change how we work for the better. I don’t have to look for the litany of challenges that exist in most of our jobs today. Finding transactional enhancements, or saving 10 minutes here, or a task there, is all well and good.
But I think this serves as an opportunity to think bigger and more expansively. What are the things we can’t do that we really want to do? What are things we can stop doing, and now focus on?
What is something that we always dreamed of doing but couldn’t do but might be able to do because of AI. And what is a long-standing problem or challenge that we haven’t solved but we might be able to? These are the kinds of questions I’d love to start asking for us individually.
6)How AI Will Impact Jobs and Humans
There are two quotes that often get repeated that I have always had questions about but have kind of let go. The first is “AI won’t take your job but someone who uses AI will.” The second goes something like “AI can take away the non-human tasks so we can focus on the things that make us uniquely human.” I have always been a little skeptical of these because it sounds exactly like something that is helpful to say at an industry conference or on a webinar to attract customers. Helpful to a certain extent in the moment, but when you actually unravel it falls short on substance, and that would hard to be widely applicable across a complex world of work.
Let’s tackle quote number 1: “AI won’t take your job but someone who uses AI will.” First off, if we are acknowledging the honest reality, it will take some jobs - that has been true for almost every single technology revolution. Second, while it’s true that there are benefits to using AI in a job, the reality is more likely than not AI will be used to tackle, reimagine or wholesale transform specific parts of your job, namely tasks, workflows, deliverables or specific activities. Furthermore, what that looks like, is less likely to be determined by someone else using a tool, and more likely to be determined by the choices that are made in your organization as well as in your industry, and that has such a wide variability of outcomes.
For example, take a look at this chart from the World Economic Forum about the potential automation of tasks by industry/role - there is a variation of potential outcomes broken down by industry.
To the second quote about “AI can take away the non-human tasks so we can focus on the things that make us uniquely human.” - I would love for this to be true, and again, it sounds great in theory and I’ve always wondered about the practicality of the framework. To that end, this metastudy on 100+ experiments that studied human+AI collaboration done by the MIT Center for Collective Intelligence found out to their surprise that this quote doesn’t really hold true.
Check out this quote in an article about the study:
A recent paper from researchers at the MIT Center for Collective Intelligence found that on average, AI-human combinations do not outperform the best human-only or AI-only system.
“This was our most surprising finding,” said MIT Sloan professor Thomas W. Malone, CCI’s director. “Some of the most important and interesting use cases for AI involve a combination of humans and computers. Many people would have assumed the combination would be quite a bit better, but it was statistically significantly worse.”
And more:
The researchers found that the combination of humans and AI, on average, outperformed the baseline of humans acting on their own, but it did not perform better than the baseline of AI on its own. Notably, the average performance scores for the combination of humans and AI were lower than those of the best human or AI systems.
Turns out, the answers are much more nuanced. There are times when AI performed better on a task (ex: spotting fake hotel reviews) and times when humans+AI performed better (classifying images of birds) than humans on their own or AI on it’s on.
McKinsey Global Institute came to an adjacent conclusion with some of it’s own analysis, which found that automation might impact some of those “uniquely human” tasks and jobs than previously thought:
What This Means: I have no clue other than to say that A) we don’t know B) we are figuring it out and C) We have agency to make choices about how we want to create the way we do work with these tools and technologies
Conclusion
These are just some of the more interesting pieces of insight that I found in my research. In future editions I will try to unpack what this actually means for managers and leaders as well as employees. We are in an interesting world of work, and there’s a lot more to be explored
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