天美传媒 Insight
Hack yourself: Reimagine your job and start experimenting with AI at work
Generative AI isn鈥檛 here to take your job 鈥� it鈥檚 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 鈥� the old tasks, that is, and in the process help you put your old job back together in radically reimagined new ways.
While AI鈥檚 ultimate impact on the labor market is uncertain, we know that it has not yet triggered widespread job destruction. In fact, 鈥� which analyzed nearly a billion job postings and thousands of company financial reports 鈥� 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鈥檚 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鈥檚 important is that you radically reimagine your work, a process I call 鈥渉acking yourself.鈥� When I run this exercise with executives, we divide the activity into two phases with different roles: the architect and the builder. The mandate of the architect phase 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?
- Do things better. Think of when AI can act as a natural complement to tasks you will continue to do 鈥� but you will do them better, thanks to the help of AI. What are the stoppers that prevent you from doing higher-level tasks?
- 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?
In each of these areas, you should identify the pain points and the elements that add value. Define in detail the specific tasks generative AI will need to do, and the context and data it will need to do them.
Start experimenting with AI 鈥� independently
The second phase is the building phase. You鈥檒l need access to an advanced generative AI tool such as ChatGPT or Claude. Begin experimenting. Once executives do this, new use cases tend to emerge quickly, revealing where AI can deliver real value.
If you鈥檙e using ChatGPT, the options at present are essentially four: creating a prompt, project, assistant or agent. Prompting is the most basic, one that most of us know by now, and is a matter of learning to include the right context and information for one-time interactions that produce useful responses. Creating an agent is the most ambitious option, because it executes tasks, connects to data sources, runs code or uses external systems. Agents can automate multi-step workflows.
As an initial exercise, many executives find that creating a project or custom GPT is most within reach. Projects are useful for when you鈥檙e looking to organize work, files or ongoing conversations around a specific issue. Multiple steps or documents are involved. A custom GPT creates a GPT for repeated use and automation, and allows you to define the tone, rules and knowledge base, creating something like an assistant.
This part can be daunting because you will do it without the help of the IT department, at least initially. AI鈥檚 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.
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 鈥� 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鈥檛 very useful to most employees or that underperform existing standard large-language models (LLMs).
For instance, despite Bloomberg鈥檚 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鈥檛 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 . Work-related usage had fallen to 27% of all usage, from 47% a year earlier. Writing 鈥� the automated production of emails and documents, along with editing, summarizing and translating 鈥� was by far the most common use case at work. Much of that is the 鈥渟top doing things鈥� and 鈥渄o things better鈥� phases. Now it鈥檚 time to 鈥渄o new things.鈥�
MORE INFO: 鈥溾�� by Sandra Sieber is available from 天美传媒 Publishing.
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