entaur and Cyborg Practices in Adopting AI

In many industries and for many types of analytic tasks, the discussion is no longer about whether to

adopt AI but rather about how to use AI$.$ Two predominant models that demonstrate a successful

approach to using AI are Centaurs and Cyborgs.

Centaur behavior. Named after the mythical creature that is half$-$human and half$-$horse, this approach

involves a similar strategic division of labor between humans and machines closely fused together. Users

with this strategy switch between AI and human tasks, allocating responsibilities based on the strengths

and capabilities of each entity. They discern which tasks are best suited for human intervention and

which can be efficiently managed by AI$.$ From a frontier perspective, they are highly attuned to the

jaggedness of the frontier and not conducting full sub$-$tasks with genAI but rather dividing the tasks into

sub$-$tasks where the core of the task is done by them or genAI. Still, they use genAI to improve the

output of many sub$-$tasks, even those led by them.

Cyborg behavior. Named after hybrid beings, as envisioned in science fiction literature, that seamlessly

blend machine components with human biology, this approach is about intricate integration. Users do

not just have a clear division of labor here between genAI and themselves; they intertwine their efforts

with AI at the very frontier of capabilities. This manifests at the subtask level, when for an external

observer it might even be hard to demarcate whether the output was produced by the human or the AI

as they worked tightly on each of the activities related to the sub task.

Centaur Practices. Use individual$$s knowledge of the current strengths of generative AI relative to theirs

to switch between human and AI for each of the modules$/$sub$-$task of the tasks throughout the

workflow. Examples of behavior$/$practices:

$$ Mapping Problem domain: Asking AI for general information related to the problem$$s domain

for the human to use for their sub$-$task.

$$ Gathering methods information: Asking AI for specific information on methods that the human

is employing to solve their sub$-$task

$$ Refining human generated content: Users provide their own output and use AI to refine its

presentation.

Cyborg Practices. Use AI for each of the sub$-$tasks throughout the whole workflow. Apply principles

based on current knowledge about how to best elicit useful outputs from AI and$/$or continually question

AI and experiment to reach a better output. For example:

$$ Assigning a persona: Instructing AI to simulate a specific type of personality or character

$$ Requesting editorial changes to AI output: Asking AI to make editorial changes to the outputs AI

has produced

$$ Teaching through examples: Giving example of correct answer before asking AI a question

$$ Modularizing tasks: Breaking down tasks into multiple sub$-$steps for AI to execute

$$ Validating: Asking AI to check its inputs, analysis, and outputs

$$ Demanding logic explanation: Asking AI to explain a confusing output; or why a particular

recommendation was made

$$ Exposing Contradictions: Pointing out logical or factual inconsistencies

$$ Elaborating: Asking AI to bring more breadth of details and nuance on an interesting or

unexpected point

$$ Directing a Deep dive: Directing AI to focus on a particular data point, content or task

$$ Adding user$$s own data: Adding data after an output is generated to re$-$do the analysis in

iterative cycles

$$ Pushing back: Disagreeing with the output and ask AI to reconsider

Adapted from: Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects

of AI on Knowledge Worker Productivity and Quality, Dell'Acqua et al$.,$ Harvard Business Review Working

Paper Working Paper $24-013,2023$