In a previous article, I explored one perspective on the strategic division of labour in knowledge work with Generative AI (Gen AI), focusing on the importance of cultivating critical, creative, and structured thinking of humans. Here, I want to offer a complementary perspective—one that delves into how integrating Gen AI into our work is redefining the role of a human in collaborative knowledge work with Gen AI.
The folk at Board of Innovation, propose that the human shift in innovation knowledge work in the age of AI, is from Creator to Editor. However, I propose a more nuanced approach, where the Creator role is split into two parts: Conceptualiser and Generator, followed by Editor.
By understanding and strategically dividing work to these three roles, you can harness Gen AI to enhance your productivity. The previous framework of three essential human thinking skills intersects with this role division too.
The Conceptualiser takes the lead
As the Conceptualiser, I put creative and structured thinking to work as I take the lead. My role is to set the parameters for what Gen AI will generate. What is my vision of something to be created? What inputs are necessary to bring about this vision? Who is the audience for what will be created? What are the considerations for the format and medium? How do I want to proceed with the work: the steps I want to include and the sequence to follow? What should be explicitly left out or ignored? Even what questions can I put to the Gen AI to get it to help me think better or differently about what I am envisioning. This role requires me to tap into my humanity and use my creative and structured thinking to set the tone, the scope and the desired outcomes – as well as admit what I don’t yet know. It’s an old saying but a goodie: Garbage in means garbage out!
Conceptualising is often not light, quick nor inconsequential work! This article started out as me handwriting notes on blank sheet of paper in an airport café – there was some doodling involved! My thinking cogitated while I waited to board my flight. Once cruising altitude was reached, I did a stream of consciousness writing on my laptop (some statements, some questions) – knowing that I could dump this quality raw material into a Gen AI when back in internet range to get a first draft to work with. I did some seriously heavy conceptual lifting! And not just today.
I’ve been musing on these two frameworks for a while. The first framework has been for years and only expressed in verbal conversations arising from my own reflections about frustrations in less-than-effective collaborative endeavours. The second framework came from reading something a few months ago and it not sitting quite right. And with reflection, as I tangled with the critical question – what was my personal policy on attribution with Gen AI assisted (aka assisted computing) work? (I mused on thought experiment of the rise of ubiquitous technology like the word processor and printing – when I no longer had to do the heavy lifting of handwriting my essays for university or typing and re-typing my business work when iterations where required. Did I give public attribution to the word processor or the printer for generating the printed word?)
The Generator produces output
Gen AI becomes my powerful assistant, handling the labour-intensive aspects of knowledge creation by producing drafts, generating ideas, providing reference material, or even simulating different scenarios based on the guidelines I’ve established. I have not handed over control to Gen AI, I am steering the work and using Gen Ai’s capabilities to execute the vision I crafted (or is emerging) as the Conceptualiser.
The Editor checks and shapes the work
After Gen AI has generated its output, I step into the role of Editor. This is where my critical thinking becomes essential. As the editor, I scrutinise the Gen AI’s output for accuracy, bias, and relevance. It’s up to me to evaluate the content – which can be a philosophical and values-based consideration, as much as a functional consideration. I then request the necessary adjustments to ensure that the final product meets the desired quality. Sometimes I ask ChatGPT to critique the output it gave me, or to defend the position it took, and to justify with evidentiary sources why it gave me that output.
This role highlights the importance of critical thinking, as Gen AI can be confident, compelling, and wrong. My job as the Editor is to determine how well the output reflects my original vision, provide course-correcting guidance through as many iterations are needed to refine what is generated. When I am working with a Custom ChatGPT, this also means identifying further training that might be needed so I do not have to repeat myself on the big constant parameters I want automatically applied in the future.
I am also shaping the output further into the form of what will be shared with others. Does it work as a post, as an article? What further changes are needed about form, once I am satisfied the substance is right? I may need to interface with other tools to ‘build’ the final output – this could be as simple as putting the text into a MS Word document and applying Styles, page numbering and cross referencing. Or I might be switching to another Gen AI tool, because I the output of a script I created, will be the input for a video to be created. And the cycle might begin again as I assemble various elements, text, image, audio etc into a coherent output for a particular audience.
A new paradigm for collaboration
These three roles—Conceptualiser, Generator, and Editor—represent what I see as a strategic approach to human-Gen AI collaboration in knowledge creation work. By applying critical, creative, and structured thinking at the appropriate times, I can maximise the value of Gen AI tools while retaining control over the final output and amplifying my human contribution. This approach enhances my productivity and allows for greater creative freedom. I can focus on high-level conceptual work without being bogged down by the mechanics of content generation.
The collaborative relationship
Ethan Mollick (and his colleagues) have an interesting perspective on human-Gen AI collaboration. I have been following Ethan’s writing/thinking for a while and the article he wrote about Centaurs and Cyborgs caught my eye. Here’s an extract:
“Centaur work has a clear line between person and machine, like the clear line between the human torso and horse body of the mythical centaur. Centaurs have a strategic division of labor, switching between AI and human tasks, allocating responsibilities based on the strengths and capabilities of each entity. When I am doing an analysis with the help of AI, I often approach it as a Centaur. I will decide on what statistical techniques to do, but then let the AI handle producing graphs …
On the other hand, Cyborgs blend machine and person, integrating the two deeply. Cyborgs don’t just delegate tasks; they intertwine their efforts with AI, moving back and forth over the jagged frontier [where Gen AI is scarily good … and where it also falls short]. Bits of tasks get handed to the AI, such as initiating a sentence for the AI to complete, so that Cyborgs find themselves working in tandem with the AI.”
Arguably the Conceptualiser – Generator – Editor division of labour is to operate as a Centaur. However, in reality I find myself cycling through iterations of these roles and behaviours. And if I were to step back far enough, it could look like Cyborg behaviour as the boundaries blur and there is co-intelligence in the creation process.
Why does this matter? Because we should each be thinking about how we want to work with Gen AI on different tasks, and the choices we make, so our work is productive, interesting and meaningful … and even responsible/ethical.
Attribution in human-Gen AI collaboration
A key consideration in this new paradigm is how we attribute creative ownership in a human-Gen AI collaboration. If I’m primarily responsible for the Conceptualiser and Editor roles, while Gen AI handles the Generator role, it might seem that Gen AI’s contribution is purely functional. However, if Gen AI plays a significant role in shaping the conceptual framework, like ‘give me 5 ideas of what I could write about’, or critically evaluating the output ‘be a sceptic and point out the bias and gaps that should be addressed’, I believe it may warrant shared attribution. This raises important ethical and practical questions about how we recognise and credit the contributions of both humans and Gen AI in the creative process.
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As knowledge workers, we need to rethink our roles in the age of Generative AI. By strategically dividing labour between ourselves and Gen AI, and by applying our cognitive skills across the roles of Conceptualiser, Generator, and Editor, we can redefine what it means to create, generate, and refine knowledge. This approach not only enhances our ability to produce quality work but also ensures that we remain relevant and valuable in a rapidly changing workscape.
Author
Helen Palmer is Founder and Knowledge Producer at Quello. She’s been innovating with knowledge since an attempt to make a new kind of cookie without a recipe when she was nine. She’s been tinkering with Generative AI because she is curious about new ways of doing things and challenging the status quo. She shares her ideas and thoughts to inspire others to make meaningful change in their corner of the world.
Attribution
Except where otherwise noted, this content is released under a Creative Commons BY-NC-ND 4.0 International licence so it can be freely shared with attribution to the creator (Quello); it cannot be used for commercial purposes; and it cannot be modified.