will turn into our digital assistants, serving to us navigate the complexities of the fashionable world. They may make our lives simpler and extra environment friendly.” Inspiring and utterly unbiased assertion from somebody who already invested billions on this new know-how.
The hype is actual for AI brokers, and billions are pouring in to construct fashions that can make us extra productive and extra inventive. Exhausting to disagree once I fortunately get pleasure from my morning espresso whereas Cursor is coding my unit checks. But, asking individuals in my community how they use AI of their day-to-day, their solutions usually point out anecdotal use instances, anyplace from “I take advantage of it to inform bedtime tales to my son” (I assume that may not even be a use case for those who had extra creativeness) to “I take advantage of it to optimize my schedule” (Movement AI, please cease focusing on me for the love of god).
As a Knowledge Scientist, my thoughts goes forwards and backwards between two conclusions. The FOMO a part of me that doesn’t wish to be late to the Robotic revolution social gathering, and the cynical one which thinks that there’s nonetheless an extended technique to go earlier than synthetic intelligence truly turns into clever. To seek out out which aspect of my schizophrenic character I ought to guess on, I’m going to make use of a easy but highly effective framework: reviewing all of the initiatives I’ve labored on for the reason that starting of my profession and assessing how 2025 state-of-the-art AI fashions might have helped.
As we speak, we return to 2018. I’m a candid summer season intern at some of the disruptive startups in America: Hire the Runway.
What the Challenge was about
The Hire the Runway success middle in Secaucus, NJ, was once the largest dry cleansing facility in the USA.
Within the Summer time 2018, as an Operations Analyst intern, I used to be given a reasonably arduous drawback to consider: on a regular basis, the success middle was receiving hundreds of items again from throughout the nation. All of the objects needed to be first inspected, then would undergo an intensive cleansing course of, earlier than being dried or receiving some particular therapies. This might be:
- Recognizing if the garment was stained throughout the rental
- Urgent if it was too wrinkled and needed to be ironed
- Repairing if it had been broken
Most of those duties had been executed manually by totally different departments, and required specialised employees to be accessible as quickly as the primary batch of items had been reaching their division. With the ability to predict days forward what quantity of items must be processed (and when) was essential for the success middle planning squad, with the intention to be sure that each operations crew could be staffed appropriately.
The complexity of the movement made it even trickier. It was not solely about predicting the inbound quantity, but additionally assessing what a part of this inbound quantity would require particular therapies, the place and when bottlenecks might seem, and understanding how the work executed at one division would impression the opposite departments.
The 2018 Answer
At this level you could marvel: given the complexity and the stakes of the undertaking, why was it within the fingers of a younger inexperienced intern? To be honest, throughout my 10-week summer season internship, I solely scratched the floor and wrote an insanely sophisticated Pyomo script that was later refined by a extra senior Knowledge Scientist, who spent two years on this undertaking alone.
However as you possibly can think about, the answer was this enormous optimization mannequin taking as an enter the inbound quantity forecast for daily of the week, the typical UPH (items per hour, i.e the variety of items that may be processed in an hour) at every division, and a few assumptions on the proportions of items that may require particular therapies. The primary constraints had been on the timing and regularity of the shifts, and the variety of full time contracts. The mannequin would then output an optimized labor planning for the week.
How AI might have helped
Let’s re-clarify issues first: you’ll not see phrases like “AI-enthusiast” or “LLM believer” in my LinkedIn bio. I’m fairly skeptical that AI will magically clear up all our issues, however I’m fascinated with seeing if with at present’s know-how, one other strategy could be potential.
As a result of our strategy was, you might say, fairly old skool, and required months and months of refinements and testing.
The primary restrict is the static facet of the answer. If one thing surprising occurs throughout the week (e.g a snow storm that paralyzes the logistics in some elements of the nation, delaying among the inbound quantity), quite a lot of assumptions of the mannequin must be modified, and its outcomes have gotten out of date.
It is a answer that requires knowledge scientists to go deep into the weeds, as a substitute of counting on an out-of-the-box framework, to depend on quite a lot of assumptions and to spend time sustaining and updating these assumptions.
May AI give you a very totally different strategy? No.
For this specific drawback, you clearly want an optimization mannequin, and I’m but to examine an LLM having the ability to deal with a mannequin with such complexity. One might suggest a framework with an AI agent appearing as a Common Supervisor, and counting on sub-agents to deal with the planning of every division. However that framework would nonetheless require brokers to have instruments that permit them to resolve a posh optimization mannequin, and the sub-agents would wish to speak because the state of affairs of 1 division can have an effect on all of the others.
May AI considerably improve the “human-generated” answer? Doable.
It’s at this level fairly apparent to me that LLMs wouldn’t make the issue trivial, however they may assist enhance the answer in a number of areas:
- Initially, they may assist with reporting and choice making. The output of the optimization mannequin might need a enterprise sense, however making a choice out of it is likely to be arduous for somebody with no robust understanding of linear programming. An LLM might assist interpret the outcomes and recommend concrete enterprise selections.
- Secondly, an LLM might assist react quicker to sure surprising conditions. It might for instance summarize data on occasions that would have an effect on the Operations, reminiscent of unhealthy climate in some elements of the nation or different points with suppliers, and as such, suggest when to rerun the planning mannequin. That’s assuming it has entry to good high quality knowledge about these exterior occasions.
- Lastly, it’s potential AI might have additionally helped with making actual time changes to the planning. As an illustration, it’s usually predictable primarily based on the garment traits whether or not they would require particular care (e.g a cotton shirt will at all times must be ironed manually). Having a VLM scanning each garment on the receiving station might assist downstream departments perceive how a lot quantity they need to anticipate hours prematurely.
May AI allow Knowledge Scientists to keep up and replace the mannequin? Sure!
It’s actually arduous to disclaim that with instruments like Copilot or Cursor coding and sustaining this mannequin would have been simpler. I might not have blindly requested Claude to code each constraint of the Linear Program from scratch, however with AI code editors being smarter than ever, modifying and testing particular constraints (and catching human errors!) could be simpler.
My conclusion is that an LLM in 2018 wouldn’t have trivialized the undertaking, though it might have enhanced the ultimate answer. However it’s not unattainable to imagine that a number of years (months?) from now, brokers with enhanced reasoning capabilities might be refined sufficient to begin cracking most of these issues. Within the meantime, whereas AI might velocity up mannequin iterations and changes, the human judgment on the core stays irreplaceable. This serves as a beneficial reminder that being a Knowledge Scientist isn’t nearly fixing mathematical or pc science issues—it’s about designing sensible options that meet evolving, usually ambiguous and never so nicely outlined real-world constraints.
Article 100% human generated
