IESE Insight
Hack yourself: Reimagine your job and start experimenting with AI at work
Generative AI isn’t here to take your job — it’s here to rebuild it. Learn how to stop, improve and reinvent what you do through human-machine collaboration.
Good news: generative artificial intelligence (GenAI) may replace your job — the old tasks, that is, and in the process help you put your old job back together in radically reimagined new ways.
While AI’s ultimate impact on the labor market is uncertain, we know that it has not yet triggered widespread job destruction. In fact, PwC’s 2025 Global AI Jobs Barometer — which analyzed nearly a billion job postings and thousands of company financial reports — found that employment and wages are increasing across almost every AI-exposed occupation.
Still, AI will reshape work and organizations in profound ways. To understand how AI may substitute or complement your work, it’s useful to consider your role as a system of tasks, some of them core, which differentiate you from everyone else, and some non-core, which are more standard. Core tasks will be more difficult to substitute with AI, while non-core tasks may lend themselves to substitution.
What’s important is that you radically reimagine your work, a process I call “hacking yourself.” The mandate is threefold:
- Stop doing things. Identify tasks that AI can perform as well as, or better than, you can. Offload these to the technology. What are your pain points? If drafting texts to answer emails is your pain point, GenAI can do it for you.
- Do things better. Think of when AI can act as a natural complement to tasks you will continue to do — but you will do them better, thanks to the help of AI. What are the stoppers that prevent you from doing higher-level tasks? Many of us struggle to come up with new ideas. GenAI can serve as a helpful brainstorming partner.
- Do new things. This is the most disruptive part of the exercise. Imagine your job in the future, and imagine the new things that you could do with technology. You need to reformulate the basic hypothesis of your job. What have you never dared to do? If you work in a consumer goods company, for example, AI may allow you to design, test and launch new products virtually in a matter of days, by generating product concepts, simulated markets and automated iterations, opening up the possibility of entirely new markets.
Start experimenting with AI — independently
The next step is to access an advanced GenAI tool such as ChatGPT or Claude, and begin experimenting. Once executives do this, new use cases tend to emerge quickly, revealing where AI can deliver real value.
This part can be daunting because you will do it without the help of the IT department, at least initially. AI’s rapid evolution requires individual experimentation before institutional adoption. Some experiments will fail; others will boost only personal productivity. A few will be worth sharing with your team. And a small percentage will be worth scaling, at which point IT can help formalize and expand them.
For example, creating a custom GPT is an accessible option even for non-coders. Consider your data sources and formats, upload relevant documents, connect applications like email, and define a workflow for your AI agent. If the first iteration falls short, refine it. Experimentation is integral to mastery.
This mindset can be uncomfortable for leaders accustomed to structured processes or who leave experimentation to entrepreneurs or techies. Yet this is the nature of the new technological wave — it demands adaptability and experimentation.
Build a culture of AI innovation and measure it
Starting in this way matters, because relying solely on top-down initiatives often yields solutions that aren’t very useful to most employees or that underperform existing standard large-language models (LLMs).
For instance, BloombergGPT, a 50-billion-parameter model, was shown to lag behind ChatGPT on many financial tasks, despite Bloomberg’s data and technology might.
The role of the organization must be to influence, not impose. Organizational culture matters in innovation, and managers are key to creating an environment of openness and experimentation. Employees must know that they have the license to experiment, especially since GenAI is a general purpose technology that doesn’t come with a specific use case.
This experimentation, however, should be structured. Establish clear metrics and KPIs. If AI reduces your email time from 60 to 50 minutes, capture that 10-minute gain. If 80% of translations are automated, measure the cost savings.
Equally important is transparency. In many organizations, employees use AI informally. Leaders should normalize open discussion about AI experiments and create incentives to share learnings.
Most organizations will need to pair this bottom-up innovation with top-down strategy. While individuals explore ways to enhance their work, leadership must evaluate how AI can reshape the broader business model. Both approaches should progress in tandem.
In September 2025, OpenAI released a study on how people were using ChatGPT. Work-related usage had fallen to 27% of all usage, from 47% a year earlier. Writing — the automated production of emails and documents, along with editing, summarizing and translating — was by far the most common use case at work. Much of that is the “stop doing things” and “do things better” phases. Now it’s time to “do new things.”
MORE INFO: “An executive guide to generative AI (II): unleashing your creative potential” by Sandra Sieber is available from IESE Publishing.
Another version of this article was also published in Forbes. Read more insights from IESE Business School’s global experts at Forbes.
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