18: Serendippidus
In the last email (subject: Ducks in a row), we acknowledged that for organisations to leverage AI, they need to understand their work. Hence why work design is becoming a more valuable skill.
So, how do we ‘design work’?
Well, I want to start by highlighting something important:
The requirements for reliable performance are the same whether we’re designing work for humans or AI.
Isn’t that reassuring?
Because everything we’ve ever learned about performance improvement suddenly becomes even more valuable.
Hence my optimism for those in L&D who have prioritised performance ahead of learning. Regardless of whether or not we’re AI experts (and I’m definitely not), we’re experts at the thing that makes AI effective.
Talk about right place, right time?!
So, let’s get back to the question - how do we design work?
Well, before we discuss that, let’s make sure we’re speaking the same language. Because there isn’t a globally recognised taxonomy for this stuff.
So, when it comes to the ‘steps’ within a workflow, I’ll use the word ‘task’. Meaning the individual things we do to achieve the outcome. Meaning the most granular step within a workflow.
Actually, if I were to be more precise, what we’re concerned with is not the most granular step - it’s as granular as it needs to be. We shouldn’t be deconstructing tasks for the sake of it. Because if we got really anal, we’d be listing out every mouse click. Which is helpful when you’re teaching pensioners to send an email (and I know this because my first training job was teaching a class called ‘Computers for the Terrified’). But less useful when you’re helping someone write an executive report.
And so, we should only get as granular as needed - and that’s determined by whoever we’re designing for (could be an AI agent, could be Sandra in Finance).
So, let’s use the term ‘tasks’ to describe the smallest, atomic unit of activity.
In my earlier email (subject: Guidance Inc.), I used ‘making a video’ as the workflow example:
Brainstorm video ideas
Research high ranking titles
Write script
Design storyboard
Setup camera and lighting
Record video
Edit video
Write description
Publish to YouTube
Share on socials
In that workflow, an example of a task is ‘brainstorm video ideas’ (which could probably be made more granular. but should suffice to explain the concept).
Now, to effectively design a task that can be executed to the desired standard by someone else or by AI, we must consider what they need to execute it. And to figure that out, let’s use our ‘making a video’ workflow as an example.
In fact, let’s get even more specific and imagine this video is helping salespeople run a great product demo. We might need to modify this workflow slightly e.g., if the video is for salespeople, we wouldn’t put it on YouTube. But, the example is close enough.
Now, we can group the requirements into four categories - context, instruction, guardrails and blueprints/templates.
Let’s consider what that might include for the ‘brainstorm video ideas’ task:
1. Context
Purpose: generate ideas for a video
Audience: salespeople who deliver product demos
Intended outcome: identify a video idea to improve delivery of sales demo
2. Instructions
Focus: generate ideas to help salespeople prepare, customise or deliver better demos
Output: five discrete ideas
Selection: identify the strongest idea and provide rationale behind decision
3. Guardrails
Scope: focus on one specific demo problem per idea
Exclusions: do not repeat topics covered in previous videos
Feasibility: make sure each idea is specific enough for one short video
4. Blueprints and templates
Template: working title, audience problem, required behaviour change
Criteria: specific, useful, relevant, distinct
Reference: examples of previous video ideas in the same format
When we start to put this level of structure around each task, we begin to see what’s needed to design the work. And this list is not exhaustive; it’s just an example of what could be considered.
Not only that but writing clear instructions forms the basis of AI prompts.
So, if the tasks in question could be done by AI, we’re actually prompt engineering. How cool is that?
But I can already hear the objections - how can we possibly do this for every task?
We’ll tackle that next.
Yours,
- Ant
