I recently listened to a HBR podcast that introduced the idea of “workslop”. i.e. AI generated work that doesn’t quite hit the mark in terms of quality. While the podcast focused mainly on leadership and inter-personal interactions; I think we can view this though a engineering lens too.
There are a handful of points I can think of that expand on this concept:
– Set expectations via Acceptance Criteria/MVP
– What does success look like end to end?
– Be open to collaboration + fixing bugs
– NFR’s
– The AI component
Set expectations via Acceptance Criteria/MVP
Setting expectations from the getgo is super helpful in preventing slop in general. i.e. what does it look like if this project is successful?
To give a concrete example: At work I oversee the card tokenization platform which takes a credit card number; tokenizes it into a alias called a token; and said alias can be shared across the company ecosystem without exposing the actual card number. Something that I think has made this successful is having a clear vision of what this does.
1) “Store” card numbers securely [or not at all in the case of FPE: Format preserving encryption]2) Ability to support multiple LOBs / internal customers3) NFRs such as logging, auditability who’s accessing creds via PAM solution4) … the list goes on …
What does success look like end to end?
Something to consider is viewing the usage of the work item from the customer’s (or internal customer’s) perspective. For example the customer will need to access the product, learn how the product works, do a quick proof of value, then move to using the product in a wider capacity [i.e. go to prod or increase adoption]. This is a wider workflow where all the steps are needed; i.e. if you just stop at “Look I have a product!” but the customer can’t access they product then you’re dead in the water (even if your product itself is awesome!).
Considering my experience with the tokenizer, an example of enabling the customer end to end would be making the answer to “Why doesn’t my access doesn’t work?” a easy to triage workflow.
1) Seeing a 401 error? Your creds might be wrong … try re-copying from the PAM2) Seeing a 403 error? You might not have permissions in the system … try opening a service desk ticket to the tokenizer access team3) Not seeing any response? Try running `nc -zv example.com 443` to see if the port is open. Not open? try opening a ticket to the networking team
As we can see some of this is outside my wheelhouse, but if it isn’t working then my product [i.e. tokenizer] can’t be used. While tokenizer itself isn’t a AI based solution this directly translates over: What does the end to end workflow look like and how do people access it and triage when things go wrong?
Be open to collaboration + fixing bugs
When AI or a colleague produces work that misses the mark, how you respond matters as much as identifying the gap. Even with the best people working on the best products issues come up from time to time. Do we throw them under the bus and disparage them? No! As Dale Carnegie put in chapter 1 of his book `How to win friends and influence people`:
“Let’s realize that criticisms are like homing pigeons. They always return home.“.
Put more succinctly being disparaging with your critiques typically backfires and makes people defensive.
Some tools and phrases you can use to diffuse this are:
– I noticed an opportunity for improvement where…
– I really like XYZ but when it comes to ABC I think we’ve missed the mark a bit. Can I suggest DEF so we can enable ABC?
– Fix an issue where…. [i.e. treat a deficiency through a bug remediation workflow]
While AI is a tremendous opportunity for automation we’ll still need to collaborate with each other. Using communication skills + balancing candor with tact can set everyone up for success by making issues visible without throwing people under the bus.
NFRs – Non Functional Requirements
In addition to getting the product to work and “do the thing” we also need to examine non function items that need to be in place for the product to be successful.
i.e.:
– Are there latency requirements?
– Do we need to send logs to a logging solution?
– Do we need to enable login through a company approved SSO solution?
– How do we know when customers are running into issues? Is this self inflicted by the customer or a issue with the product?
As humans we are able to “eyeball” a product and see if it’s suitable to our use-case. AI however will make products but might not know about specific NFRs like what SSO provider being used etc…
The AI component
You may have noticed that I haven’t mentioned AI much at all in the article. That’s because historically development of products has been human centered. AI has this magical effect of decoupling effort and quality but the discussion still has to be had: What is quality?. Historically this has been “tribal knowledge” where all the humans were aware of this through osmosis of their lived experience working on the product. AI however doesn’t necessarily understands this. As society adopts AI more and more we’ll need need to agree on what quality means and educate the AI through technologies such as `Agents.md` or `spec driven development`.
While I didn’t mean for this to be a ad for spec driven development here we are 🙂
Naturally this will materialize into a handful of industry trends:
– AI writes code that misses the mark a bit [i.e. workslop]
– Increased need for collaboration which’ll require more interpersonal skills
– Through the use of technologies to educate AI what quality means AI will get better over time
– Humans will need to drive home what quality means; meaning more conversations
– The loop continues; work items that miss the mark act as opportunities to improve both the product and definition of quality
Links
– https://podcasts.apple.com/us/podcast/hbr-ideacast/id152022135?i=1000754491130 — HBR Ideacast 1069 – The hidden causes of AI Workslop in the workplace
– https://www.linkedin.com/posts/adam-clark-04a928145_my-thoughts-on-workslop-activity-7439424512061816832-std0?utm_source=share&utm_medium=member_desktop&rcm=ACoAACMy1DoBrebQnjGLDS2FxMNJwQeZPDjYoVg — My linkedin post w/ image slideshow