Repetitive Purchases Will Become Automated

As we finish looking at the effect of agentic AI, we see a shift to control repeat sales. A lot of online shopping is repetitive anyway.

Things like:

• Pet food
• Groceries
• Skincare
• Office supplies
• Vitamins
• Household products

AI agents will increasingly learn buying patterns and automate reordering before users even think about it.

For many people, routine shopping may eventually become something they approve rather than actively manage.

Websites May Become Secondary

This feels like the biggest long-term shift.

Right now, e-commerce revolves around apps, product pages, filters, and storefronts.

But AI agents increasingly sit between users and the web itself.

Instead of navigating stores manually, users simply describe what they want and let the AI handle the research, comparison, and filtering.

The websites still exist, but users interact with the AI first.

That changes online shopping from a browsing experience into a task-completion experience.

What Stood Out to Me Most

The biggest shift is not that AI helps people shop faster.

It is that AI reduces the amount of manual decision-making users have to do.

The biggest bottleneck on the internet used to be finding information; now it's filtering out the noise. The tools changing the web aren’t making browsing shinier; integrated frameworks like Chatbase AI agents are removing the need to browse the worst parts of the web altogether.

For years, online shopping depended on people navigating huge amounts of information themselves.

AI agents increasingly turn that process into something conversational, filtered, and automated. And once people get used to that convenience, traditional browsing starts feeling surprisingly inefficient.

What are your thoughts?

Agentic AI usage is on the rise, and we are trending towards a digital slave format, where AI does the heavy lifting. According to one of the top AI agent creation platforms, Chatbase, AI is fast becoming a top companion for shoppers.

Shopping Recommendations Will Feel More Personal

Current recommendation systems are still fairly basic.

Most stores mainly track clicks and purchase history.

AI agents will likely build much deeper context over time, including:

• Your sizing preferences
• Products you returned before
• Budget habits
• Favorite brands
• Durability preferences

Things you avoid buying again

That means recommendations become less generic and more useful with continued use.

AI Agents May Reduce Impulse Buying

This is something I did not expect.

A good AI shopping assistant can actually slow down bad purchasing decisions.

Instead of encouraging every purchase, it may warn users:

1. “This product has high return rates.”
2. “The price usually drops next month.”
3. “Buyers report durability issues.”
4. “You already own something similar.”

That creates a more deliberate shopping experience instead of purely emotional buying.

What are your thoughts? Have you used AI agents in shopping or your business?

What surprised me most is that AI agents are not just improving shopping recommendations. They are starting to replace parts of the shopping process itself.

After testing a few AI shopping assistants and watching how people actually use them day to day, here are the biggest ways I think AI agents will change online shopping.

Here are the first three.

1. People Will Browse Less and Ask More

Most online shopping still depends on users opening dozens of tabs and comparing products manually.

AI agents change that behavior quickly. Instead of:

• Reading 50 reviews
• Comparing specs manually
• Searching Reddit threads
• Watching comparison videos

People increasingly ask:

1. “Which one is better for daily use?”
2. “Find the best option under this budget.”
3. “What do buyers consistently complain about?”
4. The AI handles the research layer while the user focuses on the final decision.
5. That alone removes a huge amount of friction.

2. Price Comparison Will Become Automatic

One thing people waste a lot of time on is checking multiple stores for the same product.

AI agents can already monitor:

• Price drops
• Alternative sellers
• Coupons
• Bundle deals
• Refurbished options
• Regional pricing differences

Instead of manually hunting for discounts, users will increasingly let AI track pricing continuously in the background.

That will probably make overpaying much less common over time.

3. Reviews Will Matter More Than Advertising

AI agents tend to analyze patterns in reviews instead of reacting to ads or sponsored listings.

That changes how products get evaluated.

For example, if thousands of buyers repeatedly mention:

• Poor battery life
• Cheap materials
• Sizing issues
• Bad customer support
• High return rates

An AI agent can surface those patterns immediately.

That makes real user feedback much more influential than polished product pages.

What are your thoughts? What do you think?

There are so many AI customer support platforms out there that it is becoming hard to track them. So, today I wanted to hear from you and find out what you are using.

Here is an example of what I am looking for: the name of your AI platform, and why you are using it.

• Absolutely no links.

I am using Chatbase, which is an ideal platform for businesses of any size. It trains on your own content or knowledge base. Plus, you can add voice chat and have the best handoff to a human agent.

Please share yours.

The traditional IVR "phone tree" is officially becoming a relic of the past. We’ve all been there, stuck in a loop, shouting "representative" into a phone while a robotic voice tells us to listen closely because "our menu options have changed."

By 2026, the data shows that 51% of customers will ditch a brand after just one bad automated experience. The friction of legacy IVR doesn't just annoy people; it actively kills customer loyalty.

The shift to "Intent-Aware" Voice AI

We’re seeing a massive move toward Real-Time Voice Interaction. Unlike the old systems that rely on rigid keypad inputs, modern Voice AI agents use natural language processing to understand intent and sentiment.

If you're looking at how call centers are modernizing, here’s what’s actually moving the needle:

• Zero Wait Times: Autonomous agents can handle thousands of calls at once. No more "peak hour" hold times.
• Resolution over Deflection: The goal has shifted from "getting people off the phone" to End-to-End Resolution. Modern agents can actually check order statuses or process data in real-time.
• Multilingual Scalability: Platforms are now supporting 95+ languages natively, allowing support to scale globally without adding massive headcount.

The Economics of the Switch

For those running support ops, the math is becoming hard to ignore. A human inbound call costs roughly $12–$15, whereas a sophisticated AI agent can resolve that same task for under $1.00. It’s less about replacing people and more about orchestration, letting AI handle the routine Tier-1 inquiries so humans can focus on high-stakes, empathetic cases.

Platforms to Watch

If you're researching tools for this, Chatbase has become a solid standard for an Enterprise-Grade AI Agent Platform. It’s built to ingest your own proprietary data (PDFs, docs, site info) and act as a technical extension of your team rather than just a basic FAQ bot. It’s particularly useful because it integrates directly into existing CRMs and phone systems via SIP/Twilio protocols.

Final Thought

Every week a legacy IVR stays in place, a business is likely losing revenue from calls that never got resolved. Moving to a 24/7 autonomous voice layer is quickly becoming a competitive necessity for market trust and operational efficiency in 2026.

Has anyone else made the jump to voice AI for their inbound calls yet? Curious what your resolution rates are looking like.

At the National Retail Federation (NRF) 2026, Google CEO Sundar Pichai announced that users will soon be able to shop directly within Google’s AI mode and on Gemini. This move marks a significant step in Google’s agentic commerce strategy, positioning the company to compete more effectively with OpenAI, Microsoft, and Perplexity. Agentic artificial intelligence is capable of autonomous decision-making and actions. (April Pad)

This means that shoppers will make purchases directly from the search pages guided by an agentic AI. This is already happening, although not in all markets.

1. OpenAI has introduced agentic features within its platforms, such as ChatGPT, enabling users to research products, compare options, and make purchases directly through conversational interfaces.
2. Microsoft’s AI agents can handle the entire transaction process, including payment and delivery arrangements, allowing shoppers to complete purchases with minimal effort.
3. Perplexity’s agentic artificial intelligence is designed to interpret complex shopping intents and autonomously locate, evaluate, and purchase products for users.

How will Google's agentic AI affect SEO? What are your thoughts? Are we ready for this?

Last year, during an interview with The Logan Bartlett Show, Salesforce's CEO, Marc Benioff***,*** disclosed that during 2025, the company replaced over 4,000 customer service agents with AI. Did he make an error?

There have been reports from the company showcasing good yields that have transformed it into a case study for the "execution gap" of corporate AI adoption.

1. AI agents handle 50% of customer service: Salesforce’s internal AI agents successfully scaled to handle 1.5 million customer queries. Chief AI Office
2. Growth Skyrockets: Revenue Increased by over $1.2 billion. April Pad
3. Stock Plunges: The market in general is still wary of AI and its security. Salesforce's stock experienced a steep decline. LinkedIn Christian H.

What are your thoughts? Do you support this massive deployment of AI agents in customer support?

I will start with a simple question. How is agentic AI changing your business?

To make sure we stay on topic, here is what we are talking about.

According to April Pad, Agentic AI refers to advanced artificial intelligence that can complete complex, multi-step tasks without human interaction. It is capable of planning, reasoning, and using external tools to meet human-oriented goals.

What exactly have they done?

1. Achieved a 30%–50% acceleration in business processing: Answer calls and take messages, monitor and interpret data to make trades, screen resumes and conduct interviews,
2. Enhanced Financial Advising: Unlimited advice from AI agents now streamlines this market by 66%.
3. Cybersecurity: The Internet is suddenly safer with agentic AI moving in a matter of milliseconds to control threats.
4. AI Software Development: Any novice can build advanced apps for their business without relying solely on agentic AI.
5. Checkmate: The dawn of agentic NPCs has changed how games are developed. These AI simulate virtual worlds and build human-like relationships.

You can get your own Agentic AI on platforms like Microsoft, Chatbase, and Lindy.

Now, back to the main question. How has agentic AI affected your business or your life?

We have been ranking all types of AI customer support chatbots for different business models. But what features make them rank above others? Why would this subreddit and tons of other valued forums recommend these above all the rest.

Here are the top reasons why platforms like Chatbase rank number one, or Fin ranks number 2.

1. Multilingual support with auto-detection: Do you remember when we had to select our language from a drop-down button for a website to change? That is the past, and your chatbot platform must offer this service.
2. Voice AI with phone support: What if your chatbot could talk? Would you just bother it from time to time? Well, chatbots are now handling incoming calls, and some customers won't notice the difference.
3. Unified omnichannel agent: another feature that sets these sites apart is its usages on multiple websites. Can your chatbot do that? Well, it should offer this important characteristic, especially for people with more than one domain.
4. Revise and response optimization: How much editing can you do with your chatbot's response? Top-tier platforms allow users to improve chatbot responses.
5. Human takeover and escalation: Can you jump into your chatbot's conversations? The system should convert chats into tickets, etc., or seamlessly allow a human to intervene in the chat.

These and other 15 features rank chatbot platforms. What do you think? How does your chatbot stand up? Which other feature do you look for?

I'm taking a class in UX Design for AI. For my capstone project I will be redesigning the AI customer service agent experience. It would be very helpful if you could answer these questions for me as part of my research.

• How clear was the communication from the AI agent?
• Did the AI agent understand the context of your conversation?
• How did the AI experience compare to your previous experience with human agents?
• Did you feel the AI agent was easy to navigate and use?
• How would you rate your level of trust in an AI customer service agent? (5 = high level of trust / 0= no level of trust)

Feel free to provide any other information about your experience with AI customer service agents.

Thank you!

Here’s my current stack: Chat automation: Intercom + custom GPT flows Helpdesk: Zendesk (still the backbone tbh) Knowledge base: Notion + embeddings for retrieval Email support: partially automated with PlusVibe (mainly for follow-ups, routing, and keeping responses consistent) QA optimization: reviewing convos weekly + tweaking prompts What i’m still struggling with is getting AI to handle edge cases without escalating too early, avoiding that obviously AI tone in replies, and keeping knowledge base + AI responses actually in sync. Any tips that you guys can share? I'd love to learn.

When evaluating AI support tools for SaaS, focus on capabilities that directly impact accuracy, scalability, and resolution quality. Besides these, there is more to watch for. Let’s see them. 

1. Documentation training capability: The tool should be able to learn from your help center, product docs, and past tickets to deliver accurate, context-aware answers. This is critical since most AI tools rely on internal knowledge to resolve queries effectively.
2. Omnichannel support: It should work across websites, Slack, MS Teams, Instagram, Facebook Messenger, email, and intranet, ensuring users get consistent support everywhere. Modern tools deploy one AI layer across multiple channels to handle queries seamlessly.
3. Workflow automation: Beyond answering questions, the tool should trigger actions like ticket routing, account updates, or task execution—turning conversations into real outcomes.
4. Accuracy and hallucination control: Strong AI systems use techniques like knowledge retrieval (RAG) and context grounding to minimize incorrect responses and ensure reliability.
5. Human hands off: The tool should seamlessly transfer complex queries to human agents with full context, avoiding repetition and improving resolution quality.

 

If you do not know what to look for, you can end up with just about anything. What are your thoughts? What other features do you look for?

Last 4 months I’ve been obsessing over how companies are actually using AI agents in production.

Not the look my chatbot can answer FAQs demos.

Real workflows.

After reading dozens of founder/operator discussions here and other AI communities, one pattern keeps repeating:

The best companies are NOT replacing humans.

They are removing repetitive conversations before humans even touch them.

Here’s what people are actually deploying right now:

• AI answering repetitive support tickets
• AI qualifying inbound leads
• AI booking appointments automatically
• AI collecting customer info before routing
• AI handling after-hours support
• AI generating sales summaries for reps
• AI syncing CRM + support workflows
• AI doing first-response triage before escalation

One founder shared their AI support + sales setup:

→ checks service availability
→ collects customer details
→ generates estimates
→ sends Stripe payment links
→ schedules appointments automatically

Another interesting trend

The biggest ROI isn’t full automation.

It’s response speed.

Most customers just want
• instant acknowledgement
• clear answers
• fast routing
• no waiting 6 hours for basic replies

And honestly

AI agents are becoming less like chatbots and more like junior employees following SOPs.

But there are still huge problems nobody talks about enough

1. Bad training data = terrible answers If your docs/help center are messy, the AI becomes messy too.
2. Sales agents fail when they sound robotic People hang up fast if the interaction feels scripted.
3. Most companies automate too much too early The sweet spot seems to be: AI handles repetitive work Humans handle emotional/complex situations
4. Guardrails matter more than the model Wrong refund Wrong pricing Wrong escalation = instant trust loss

What’s crazy is how fast this changed.

1 year ago
AI chatbot usually meant frustration.

Now some companies are seeing AI fully resolve tickets without human involvement.

I think the next big shift is

Every business will eventually have
• Support agent
• Sales agent
• Internal ops agent
• Knowledge agent

Kind of like hiring digital employees.

Curious what everyone here is seeing in real deployments.

What AI agent use case is actually working for you business today?

 It’s the era of short attention spans and customers with little patience. Fewer people have the time and patience to endure the time-consuming, complicated, and often error-prone customer support systems that were the norm a few years ago.

1. , Chatbase is an AI-powered chatbot solution that businesses can deploy to automate their customer support operations. What makes Chatbase a stand-out option in the market is its simplicity and ease of use.
2. HubSpot Chatbot: HubSpot is more of a general CRM automation tool than a customer support chatbot. It offers chatbots as part of its wide toolkit, but it focuses more on general engagement than deep customer support automation. You won’t be far off the mark if you think of HubSpot as a jack of all trades within the sales and marketing automation space.
3. Fin: Fin is a customer service AI chatbot by Intercom,, which is heavily powered by OpenAI’s large language models. It can understand complex queries, ask clarifying questions, and break down complex topics. Fin pulls information from a provided custom knowledge base to ensure it has deep knowledge of a topic it is discussing with a customer seeking support assistance.
4. Zendesk: Zendesk is one of the most popular customer service automation tools in the market. It has a robust suite of features that help businesses automate customer support alongside an AI-powered chatbot that can be configured to perform a range of customer service tasks.
5. Salesforce Einstein GPT can collect and unify customer data across various teams, making it possible for your customer support unit and other departments to work in sync. However, configuring and deploying Einstein GPT can be quite technical, making it a bit challenging for an average small business to deploy.

How do you vote?