Zowie

Mixed feelings — automation promise is real, but a poorly disclosed AI reply mid-ticket left at least one user feeling misled.

“You've got to be kidding me. We've been going back and forth on this one support ticket for the past week on what should be a simple question that a human can likely solve within an hour, and today I see an internal AI response in our email thread. I wasn't sure if the past few emails have been from a human or AI either. If your support ticketing strategy is to replace people with AI, I'd make sure that at the minimum, the correct context is provided & there is an escalation path that can be initiated by the user to talk to a human. Making customers talk to AI while pretending it's a human is kind of insulting.”— @zeyu1337 on X

Strong lean positive — people see clear enterprise and service-business use cases for AI agents beyond simple chat.

“Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out: * Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted? * Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts). * Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important. * Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down. * Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out. * Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills. * The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI. Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.”— @levie on X

Enthusiasm for concrete automation workflows like invoice chasers and CRM loggers replacing manual busywork.

“5 more that belong on this list: 1. Overdue invoice chaser. Service businesses sit on thousands in unpaid AR because chasing payment feels awkward. An agent that sends polite, escalating follow-ups collects money already earned. 2. Call-to-CRM logger. Sales teams hate data entry, so pipelines rot. An agent that turns call transcripts into clean CRM notes and next steps keeps deals from slipping. 3. Compliance renewal tracker. Insurance agencies and trades lose accounts over expired COIs and licenses. An agent that tracks expirations and chases renewals 30 days out prevents the fire drill. 4. Dormant customer reactivation. Every business has a list of past buyers nobody contacts. An agent that segments them and sends personalized win-back offers is found money. 5. Warranty and claims intake. Messy claim emails become structured, routable tickets with photos and details attached. Fewer back-and-forth emails, faster resolutions. Every one of these is a $1,500-3,000 build plus $300-500/mo retainer to maintain the system and keep it up to date. Pick one vertical. Sell the same build 10 times.”— @coreyganim on X

Subscription and pricing models discussed favorably as smart, monetizable strategies.

“The ecom cheat code nobody talks about because it sounds too boring to be real Consumable products with subscriptions to women 35-55 on Facebook Not sexy. Not exciting. Not going to get you 500 likes on ecom Twitter. But printing $20-50K/month for every operator who swallowed their ego and did it Skincare refills. $35-50/month subscription. Churn under 15% when you text before each shipment asking if they want to skip or swap. They almost never skip because the product is part of their routine now Supplements for menopause symptoms. $40-65/month. This demographic doesn’t cancel subscriptions because they grew up in an era where you committed to things. The guilt of canceling something that “might be helping” keeps them subscribed for 8-12 months average Coffee. Pet supplements. Cleaning products. Vitamins. Anything that runs out and needs replacing The math that makes this stupid profitable: acquire the customer once at $15-25 CAC on Facebook. She pays $40/month for 8 months. LTV: $320. CAC: $20. That’s a 16x return on ad spend. Not 1.6x. Not 2.6x. 16x Compare that to your current model: acquire a customer at $18 CAC. They buy once at $30. Never come back. LTV: $30. You spent $18 to make $30. Your entire business is a hamster wheel of acquiring new customers every month because nobody comes back The subscription checkbox is free to add. Shopify supports it natively. Recharge or Loop handle the backend. One afternoon of setup. And suddenly your business generates revenue while you sleep instead of starting from zero every month But you won’t do it because selling skincare subscriptions to 45 year old women on Facebook doesn’t look good in a TikTok flexing your lifestyle The operators that are printing don’t need to flex. Their subscriptions deposit every month whether they post or not (check out my free tool if you want to scale your ecom store with AI https://t.co/4vYVL0VEPV…)”— @alecsandrull on X

Latency is called out as a dealbreaker for live customer service, alongside praise for pre-conversation AI coaching.

“"The frontier labs will keep owning discovery. Open source will increasingly own production." Insightful take on a tired topic "When you're running AI agents in production for customer service, latency makes or breaks the product. A conversation where every turn takes 8 seconds is not a product anyone will use. So you need small, fast models. Each model call does need to know the capital of Lithuania or high school physics. But small models out of the box aren't good enough for the quality bar our customers hold us to. They only get there through heavy fine-tuning on the exact task. The frontier labs don't really sell this combination. You can't fine-tune their best models the way we need to, and their small models aren't ours to shape. Small + fine-tuned means open weights. The cost savings are real but secondary, and enterprise comfort with self-hosted models is a nice side effect, not the reason."”— @sarthakgh on X

Light discussion around how hidden or underused features can still signal strong product value if adoption is deep.

“Your best feature might be hiding in the wrong place. Danielle Olean, Director of eCommerce at Box, shared a funnel insight that applies to any product. Say only a small number of users find a feature, but those who do use it at a high rate. That test isn't telling you the feature failed. It's telling you the feature works and the placement is wrong. Everything has a flow, even an AI chatbot. Find it, start it, complete it. Measure those three steps and the problems reveal themselves.”— @AshleyGrowthB on X

Concern centers on not knowing when you're talking to AI versus a human agent in support threads.

“Goldman Sachs new report "An AI Job Apocalypse?" Says AI could displace 15mn workers, but not cause mass unemployment. Estimates AI could lift productivity and GDP by 15% after full adoption. The clearest pressure sits in customer support, back-office work, claims, billing, and entry-level tasks. Goldman’s own labor model finds a small current drag of 16K jobs monthly. Younger workers look more exposed because AI can absorb the basic tasks that teach judgment. College graduates are not collapsing yet, but their industries adopt AI faster than others. Joseph Briggs, GS global economists still expects more than 9% of workers to face displacement across 10 years. MIT professor Daron Acemoglu is more cautious, expecting 2-4% job losses across 5 years. His concern is that current AI replaces routine office tasks more often than it supports workers. Neil Thompson of MIT adds a missing filter: capability does not mean reliability, access, or cheap deployment. A chatbot can answer broadly, but businesses need accuracy, private data, and repeatable workflows.”— @rohanpaul_ai on X