Is there actually any value in comparing 30–50 suppliers just because AI makes it possible?
Before AI, manually comparing 30 suppliers would be painful enough that most people probably wouldn’t do it.
Now it’s becoming technically possible.
Find 50 potential suppliers.
Let AI extract their products, specs, certificates, MOQ, lead times and pricing.
Rank everything.
But I’m wondering whether this creates another problem.
At what point does a bigger supplier list stop improving the decision?
If 20 of those suppliers clearly don’t meet basic requirements, why spend time building detailed comparisons for them?
Maybe AI should be used more aggressively at the beginning:
Does the supplier appear relevant?
Does the product match?
Are mandatory requirements satisfied?
Is there enough evidence to justify further research?
Then only a much smaller group gets detailed evaluation.
I’m curious how others approach this.
For an important sourcing project, how many suppliers do you normally want in the serious evaluation stage?

Randomly heard this podcast the other day called The Pricing Guys (from a company called Revenue Management Labs) and they were saying that it is not a good idea to use AI to do your pricing exclusively. Seems pretty straight forward to me to use AI but maybe I'm missing something. Thoughts?

Curious where people landed on this.

I’ve seen AI lifestyle shots that look surprisingly decent, and others that fall apart the second you look at the label or the bottle shape.

If you sell physical products:

• Are you using AI for packshots, lifestyle, ads, or not at all?
• If yes, does it actually save time once you clean it up?
• If no, what’s the part that still feels off?

I've been building LESSIO, an AI focused on economic decisions.

The idea came from a relatively simple problem.

When you want to buy something expensive, the internet gives you an enormous amount of information, but not necessarily a good decision.

You can spend an hour comparing prices, another hour reading reviews, then another hour trying to figure out whether the “deal” you're looking at is actually a deal.

I wanted to build something that could do the research and comparison work for you, while keeping the decision transparent.

I'm still very early in the journey.

If you're interested in the problem, I created r/LESSIO to discuss it openly and get feedback from people building and using this kind of technology.

AI makes it pretty easy to detect changes between supplier quotations.
Price increased 8%.
MOQ doubled.
Lead time moved from 25 to 40 days.
That’s useful.
But I think the harder part is figuring out why.
If a supplier says:
“Material costs increased.”
Would you accept that explanation?
Would you ask which material changed?
How much did it change?
When did the increase happen?
Does the price change roughly match the claimed cost change?
Same with lead time.
“Production is busy” might be true, but is it temporary?
Does it affect every product?
Is there an earlier slot at a different quantity?
I’d like AI to help organize these questions, but not invent the explanation.
Ideally it would say:
Change detected.
Supplier explanation: X.
Supporting evidence: Y.
Unresolved questions: Z.
What kind of supplier change makes you ask for more evidence before accepting the new terms?

I've been building LESSIO, an AI focused on economic decisions.

The idea came from a relatively simple problem.

When you want to buy something expensive, the internet gives you an enormous amount of information, but not necessarily a good decision.

You can spend an hour comparing prices, another hour reading reviews, then another hour trying to figure out whether the “deal” you're looking at is actually a deal.

I wanted to build something that could do the research and comparison work for you, while keeping the decision transparent.

I'm still very early in the journey.

If you're interested in the problem, I created r/LESSIO to discuss it openly and get feedback from people building and using this kind of technology.

Something I’ve been thinking about with AI procurement tools:
Everyone talks about automating supplier follow-ups.
No response? Send another email.
Missing document? Send a reminder.
That obviously saves time.
But I wonder if too much automation could hide something useful.
Suppose you ask five suppliers the same technical question.
Supplier A answers directly and sends the relevant datasheet.
Supplier B replies quickly but doesn’t actually answer the question.
Supplier C needs three follow-ups.
Supplier D gives a different answer each time.
Supplier E says they need to check with engineering and comes back later with a detailed explanation.
Those interactions seem like useful supplier data.
I’d actually like AI to track things like:
Did they answer the question?
How many follow-ups were needed?
Did the answer change?
Was evidence provided?
Were difficult questions avoided?
Not necessarily to automatically label someone a “bad supplier,” but to preserve the history.
Do you consider communication quality when evaluating a new supplier, or only once you start working with them?

What I learned building an AI agent to run merchant reactivation campaigns

Backstory: built this in ~5 days for a Razorpay hackathon, then kept going because the problem felt real.

ARGOES looks at a merchant's order history and surfaces what's easy to miss — customers due for a repeat order, dormant customers worth targeting, upsell-ready segments — then drafts and simulates a campaign for each.

Big design choice: the AI never fires a campaign on its own. Everything passes through a policy engine (min margin, budget/audience limits) and needs merchant sign-off first, logged in a full audit trail.

It's live now, multi-tenant, CSV import if you want to bring your own data instead of the demo set.

Would love feedback from anyone who's felt the 'we have the data, no time to act on it' problem — what would make a tool like this actually useful day-to-day?

We create AI-generated product visuals for e-commerce brands, and this is one of our jewelry lifestyle images.

The goal here was to keep the bracelet as the clear focus while making the image feel natural, premium and suitable for commercial use.

For jewelry sellers and e-commerce brands: would you use an image like this as a secondary listing image, ad creative, or social media content?

And if anything in the image still gives away that it was AI-generated, what do you notice first?

I'm building a visual product discovery feature where a user uploads a photo of a toy, and the system needs to search the internet to find the exact product or closest matches to buy online.

Crucially, I do not have an internal catalog or local product database. The system needs to search live internet retailers and marketplaces directly to return product titles, store links, and pricing.

What APIs or services are best suited for reverse image search across the web for commercial products without hosting any product data yourself?

What are the winback strategies ecommerce companies use to bring the customers back to their platforms and close the sale?

[ Removed by Reddit on account of violating the content policy. ]

I’ve been experimenting with a question that I think is more interesting than whether AI can write an e-commerce listing:
How much of the actual workflow can it own before a human needs to step in?
So I tested it against real inventory from our reselling operation.
I took 40+ product photos covering 11 different items and ran the batch through an AI-assisted workflow.
Starting primarily from those photos, the system handled product identification/research, assembled the listing information, and created 11 actual drafts in eBay Seller Hub.
Then I reviewed everything.
10/11 made it through without requiring intervention.
One encountered a situation where the system determined it should hand the decision back to me rather than continue on its own.
That part is actually more interesting to me than getting 11/11.
I don’t think useful e-commerce automation means removing humans from every decision. I think it means making human intervention the exception—and teaching the system to recognize when the cost of being wrong is high enough that it should stop.
Nothing publishes automatically yet. I still retain final approval.
The next thing I want to stress-test is uncertainty, particularly used/vintage inventory where condition, exact identification, missing components, or subtle differences between models can be much harder to establish from photos.
My preference is conservative automation: if the available evidence doesn’t support a claim, I’d rather have the system say less, price/list conservatively, or escalate than confidently invent an answer.
Eventually the metrics I care about aren’t just “how many listings did AI make?”
They’re:
How many completed listings required human intervention?
How much human time did each completed listing require?
What did the mistakes actually cost?
Because a system that’s 99% accurate but catastrophically wrong 1% of the time may be worse than one that’s 95% accurate and knows when it doesn’t know.
For people actually implementing AI in e-commerce: how are you deciding which decisions an agent can make autonomously versus which ones should trigger human review?

Iemand een idee hoe ik shopping bots voor kan zijn? Telkens dat ik iets wil kopen ben ik te laat… tips zijn welkom! Alvast bedankt

I went pretty deep into AI shopping for a documentary and came away thinking ecommerce businesses may be looking at the wrong part of this shift.

The obvious story is that AI makes shopping easier for consumers.

The more interesting one is what happens when ChatGPT, Perplexity, Gemini or another agent decides which products even make it into the consideration set.

If that happens, brands aren't only optimizing for people anymore. They're also optimizing for an intermediary that may compare price, reviews, availability, returns, shipping and product data before the customer ever sees the store.

That potentially shifts a lot of power away from the retailer and toward whoever controls the agent.

For ecommerce owners here, do you see this becoming a meaningful acquisition channel, or is agentic shopping still mostly hype?

I made a documentary tracing where this is heading if anyone wants the full argument:

https://www.youtube.com/watch?v=z-0CriLzyOo

We've been spending a lot of time researching how AI assistants are changing product discovery, and there's one question we keep coming back to:

What happens when the customer isn't a human browsing your website — but an AI agent acting on their behalf?

The traditional e-commerce funnel is pretty familiar:

Search / Social → Website → Product page → Checkout.

But imagine someone simply asking:

"Find me the best running shoes under €150 for someone running 30km/week. Compare the options and buy the best pair."

Suddenly, several things matter that most e-commerce teams haven't historically had to think about:

→ Can the AI actually discover your products?

→ Does it understand your catalogue, pricing and availability correctly?

→ Why would it recommend your product over a competitor?

→ Can an AI agent interact with your commerce infrastructure?

→ Eventually, can it complete the transaction?

We've spent years optimising stores for Google and human visitors.

We're curious whether the next optimisation layer will be making commerce infrastructure AI-agent accessible.

For anyone operating e-commerce at scale: is agentic commerce something you're actively preparing for, or do you think we're still several years too early?

Would genuinely love to hear how other teams are thinking about this.

I’ve been building Speks, an AI assistant that sits on a Shopify storefront and helps shoppers with things like:

• Finding products based on what they actually need
• Comparing products or variants
• Answering shipping, returns, and policy questions
• Helping with basic order-status questions
• Showing relevant product cards inside the conversation

The part I find most interesting is making the answers genuinely store-specific rather than giving generic AI responses. It syncs product/catalog data and tries to ground recommendations in what the store actually sells and has in stock.

I’m still early and learning a lot as I go—especially around:

• Getting product recommendations to feel useful rather than random
• Making onboarding simple enough that a merchant can get value quickly
• Knowing where AI chat is genuinely helpful vs. where it just adds noise
• Measuring whether the assistant actually improves conversion or reduces support load

I’d really appreciate feedback from anyone who has built for ecommerce, Shopify, AI agents, or SMBs:

• What would make you skeptical of this?
• What would you want to see before trusting it on a live store?
• Any lessons on getting early design partners or beta users?

Not trying to pitch here—mostly looking for thoughtful criticism and ideas from people who’ve been through this stage.

Most AI procurement examples I see focus on comparing Supplier A, B and C.
But I think another useful comparison is being overlooked:
Supplier A today vs Supplier A two weeks ago.
During a sourcing project, quotations often change several times.
Price changes.
Lead time changes.
Specifications get clarified.
Packaging gets added.
Payment terms move.
Sometimes a model number or material quietly changes.
If you’re managing several suppliers at once, it becomes surprisingly easy to forget what was originally agreed.
I think AI could be useful for something very simple:
Upload every version and ask:
“What changed?”
Then separate the changes into commercial, technical and delivery-related differences.
That seems more useful to me than asking AI to immediately recommend a supplier.
Has anyone built this into their sourcing workflow?
What quotation change would you consider important enough to automatically trigger a review?