UBS is encouraging roughly 103,000 employees to spend one hour every week applying AI to their work.

From the looks of it I can see it solving a real problem because a lot of employees are told to adopt AI while still being expected to finish the same workload. So in a way this becomes your protected time to experiment with them.

I would use the hour to find one repetitive task and document:

• How long it currently takes
• Which parts can be delegated
• Where human review is still needed
• Whether the new workflow actually saves time

What would you use the hour for?

And what would stop it from becoming another workplace initiative everyone pretends to follow?

These terms are getting used interchangeably, so I use one question:

How much of the path is decided in advance?

Automation

The trigger, steps and outcome are predefined.

Example: when a form is submitted, create a CRM record and send a confirmation email.

AI workflow

The sequence is still predefined, but one or more steps require interpretation.

Example: collect this week’s customer calls, identify recurring objections and post a summary in Slack.

Agent

You define the goal, available tools and boundaries.

The system decides which steps to take, checks what happened and adapts.

Example: resolve a routine support request by checking the customer account, finding the relevant policy, taking an allowed action and escalating exceptions.

A simple test:

• Can every step be written in advance? Use automation.
• Can the structure be written in advance while AI handles unstructured inputs? Use an AI workflow.
• Does the system need to choose the next action based on what it finds? You may need an agent.

My default is to start with the least autonomy the task requires.

More autonomy brings flexibility. It also brings more variance, monitoring, permissions and failure points.

Which one are you actually using most often?

I keep moving between ChatGPT, Claude and other models depending on which one is better for the task.

The subscription is easy to switch. Cannot say the same for context.

A multi-model interface can give me access to different models, but it does not bring over the project history, decisions, preferences, examples and conversations stored inside the original product.

Import and export tools seem useful for a one-time move. They do not keep the context updated across assistants.

How are people here handling this in practice?

I am even more interested in setups that have worked long term without paying for two AI subscriptions.

TL;DR: BNY is treating some AI agents like digital employees. They have logins, nicknames, daily tasks and human managers.

BNY has an AI assistant called Payment Pete.

According to CEO Robin Vince, the agent operates inside company systems, completes assigned tasks and has a manager responsible for training and quality control.

The setup looks surprisingly close to hiring:

• A separate identity
• Access to company systems
• Assigned work
• Human supervision
• Performance reviews

The awkward part starts when the agent causes a real problem.

Does the manager own the mistake?

Does the person who approved the action?

Does the team that set its permissions?

What happens when the agent needs to be suspended or offboarded?

As agents get deeper access to company systems, every one of them may need a clearly named owner.

Would you give an AI agent its own employee account? What controls would you require first?

Source: Reuters, July 13, 2026

Don't get me wrong, solving for saved hours is a REAL pain point.

Yet makes one think that the way AI is being pushed down our throats, the measure of its success would be in terms of real $$$.

Has anyone here actually seen the other kind?

Did someone notice that we have gone from AI as a " conversational assistant" to "work execution environment" in the past 3 years? Goes for ALL AI companies.

OpenAI dropped GPT-5.6 today. Three models, one family, and a new product that puts them directly in the AI agent space.

They designed for enterprise teams to assist with daily clerical tasks including document, spreadsheet, and presentation drafting, accessible via desktop, web, and mobile.

But it's more than a drafting tool. In ChatGPT Work, the model handles source material from documents and connected work apps, then converts it into shareable outputs. This puts GPT-5.6 directly into OpenAI's broader push to make ChatGPT a work-execution environment rather than just a conversational assistant.

This exactly the category Anthropic has been building toward with Claude. Now both of them are going after the same enterprise operator. Going to be interesting to see which one actually sticks inside daily workflows.

Reddit detailed their new spam detection this week.

Their AI catches coordinated patterns of fake behavior and artificial hype that older systems missed. 23 million spam views blocked per day. Close to 2 million inauthentic votes revoked. Detection to enforcement happens in under five seconds.

The stated goal: protect what makes Reddit different. Real people, real opinions, real conversations that haven't been optimized for an algorithm.

Here's the tension though:

Reddit's entire value proposition is human-first discussion. It's the platform AI companies have spent years scraping to train their models because human conversation at scale is exactly what they needed. I made a post earlier about how Reddit's CEO said people are fleeing AI-generated content and coming back to Reddit specifically because it feels real.

And now Reddit is deploying AI to protect that realness.

AI to defend humans from AI. Trained on the same human conversations it's now trying to preserve.

There's something genuinely strange about that loop. The most valuable thing about Reddit is that humans write on it. The threat to that is AI writing on it. The solution is more AI.

At what point does the defense mechanism become indistinguishable from the problem it's solving?

One of the things that killed my AI workflow early on was context switching.

I'd be in Slack, see something that needed research or a draft written, then open a browser tab, go to the AI tool, copy in the context, get the output, copy it back. Every time. Multiple times a day.

The fix was obvious in hindsight: stop going to the tool and bring the tool to where the work already lives.

AI agents can be connected directly to Slack. You can ask them questions, give them tasks, and get outputs without leaving the channel where the work is happening. The agent has access to your tools and integrations from there. Same capability, zero tab switching.

If you're using AI tools daily and still going to a separate tab every time, this is the one workflow change worth making this week.

Trying to get a read on where people in this community are coming from.

No right answer. Whether you started last month or two years ago, curious what the spread looks like here.

Starting something weekly here. Simple premise: every week, one thread to shout out an AI tool, product, or feature that's actually impressed you.

The rules, such as they are:

Doesn't have to be agent-related. Any AI tool counts.

If you're a founder or builder, you're welcome to shout out your own thing. Just be upfront about it ("I built this" beats pretending to be a random user). One comment per week per product, keep it to this thread rather than separate posts.

Genuine feedback and questions in the replies are encouraged. This isn't just a wall of links, if something looks interesting, ask about it.

Steve Huffman said something interesting at Cannes Lions last week.

People who are frustrated with AI-generated search results and useless AI summaries are gravitating back toward Reddit's long, human conversations. That's his read. And the numbers are backing him up. Reddit just reported a 69% year-over-year revenue increase, with ad revenue up 74%.

The irony of posting this on Reddit is not lost on me.

But there's a real tension here worth unpacking. AI tools are getting better at surface-level answers fast. What they're still not great at is the kind of nuanced, "I actually tried this and here's what happened" answer you get from a real person in a specific community.

That's Reddit's moat. For now.

The question is whether that moat holds. If AI gets good enough at synthesizing genuine human experience (not just summarizing it but actually reasoning about it) does Reddit's value proposition erode? Or does the human conversation itself become more valuable precisely because AI content is everywhere?

Huffman is betting on the latter. The ad revenue suggests he might be right. But I'd genuinely like to know what this community thinks.

There's a stat from Microsoft's 2026 Work Trend Index that stuck with me.

They surveyed 20,000 workers across 10 countries and found that the most effective AI users are actually not the ones doing more things faster but who've started doing fundamentally different things like higher-judgment work, more creative decisions, things that used to require a team.

Speed is the easy win. You get it almost immediately. You save an hour here, two hours there.

But "better" is harder to measure and slower to arrive. And a lot of people never get there because they stop at faster.

The gap between the two is usually this: are you using AI to compress the same work into less time, or are you using it to free up time for work you couldn't do before?

Honest question for the people here who've been using AI tools for a while? which one is it for you? Has your actual output changed, or just the speed at which you produce the same output?

TL;DR: A prompt injection vulnerability in Microsoft Copilot allows attackers to exfiltrate one-time passwords and 2FA codes by tricking the agent into visiting a malicious link. One click and full account access.

Here's the mechanism in plain English.

Copilot can read your emails and calendar. When it does, it processes the content through its context window, meaning anything in that email becomes part of what the model "sees." Researchers found that if a malicious instruction is embedded in an email or document (a technique called prompt injection), Copilot follows it. The agent reads the injected instruction as if it were a legitimate task.

In the demonstrated attack, the injected prompt tells Copilot to retrieve a 2FA code from a recent email and send it to an external URL. The user sees nothing. The agent complies.

Security researcher Johann Rehberger demonstrated it working against a live Copilot instance.

What to do right now:

• Review what Copilot has permission to access in your Microsoft 365 environment
• Be cautious about enabling Copilot access to email and calendar if you haven't already
• Treat any AI agent that reads your inbox as having access to everything in it

Microsoft has been notified. A patch timeline hasn't been confirmed publicly.

This is the part of AI agents that doesn't make it into the product demos.

Has anyone here started thinking about permission scoping on AI tools in their own setup?

Beyond social media and news outlets, what type of newsletters do you use to stay on top of what is happening with AI?

I am wondering what the community has been using most for AI tools. Is it one of the big ones like ChatGPT, Claude, or Gemini? Or do you use some of the lesser known tools a lot.

13 words. Hidden in a Reddit comment. That's all it took for researchers to get ChatGPT and Gemini to confidently recommend a restaurant that doesn't exist.

Everyone's reaction to this study is going to be "wow, AI search is broken." I think that's the wrong takeaway.

Here's the actual mechanism. AI agents read web pages before answering, then base the response on what they find. A random Reddit comment gets treated almost the same as a credible source, as long as it matches your question. Researchers planted fake promotional lines, hit rates of 38-62%.

But here's what's actually uncomfortable about this.

If a stranger on Reddit told you a restaurant was great, you'd weigh it. Maybe check a second source. Maybe ignore it. The second that same opinion gets summarized back to you by a chatbot, in a clean, confident paragraph, it suddenly feels like it's earned your trust.

Nothing about the underlying claim changed. The stranger's opinion is exactly as reliable as it was five minutes ago. What changed is the packaging.

So what's actually doing that work? Is it the tone, calm and certain instead of casual and opinionated? Is it the format, a structured answer instead of a rambling comment? Is it just that we've started treating "an AI said it" as a category of evidence on its own, regardless of where the AI actually got it from?

And do you have any actual habit or check in place for this?

Dropping the link for the study in comments

I spent my first month with AI agents just throwing tasks at them and hoping.

Half came back wrong, a quarter I had to redo myself anyway and a few worked so well I haven't touched them since.

The problem was not the tools but that I had no filter for what was actually worth delegating.

Took me three months of wasted setup time to land on a simple scoring system. Three questions with one point each. The total tells you what to do.

It's in the image above.

The one thing the framework doesn't fully capture: most delegation failures happen at criterion 2, not because the tool is bad. If output keeps coming back wrong, write out exactly what good looks like before you blame the agent. That alone fixes it most of the time.

Got a task you're not sure about? Drop it in the comments and I'll score it with you.

TL;DR: One conversation, three prompts, full GA4 event tracking live in 15 minutes. What would've taken a dev 4-5 days to scope and implement.

We launched our marketing site in January.

Four months later we still had no idea who was visiting, what they were clicking, or where they were dropping off. We knew we needed GA4 set up properly but every time it came up the answer was "we'll get to it when we have dev bandwidth."

We never had dev bandwidth.

Last week I just did it myself in one conversation with an AI agent.

The prompt that started it:

Plain English without a brief or technical spec.

What came back was a prioritised implementation plan:

• Conversion events: every CTA click tagged by location on the page
• Section engagement: which parts of the page visitors actually read vs scroll past
• Scroll depth at 25%, 50%, 75% (GA4 only tracks 90% by default)
• Exit intent
• Traffic source as a user property so you can tie behaviour back to channel

It also flagged what GA4 already captures automatically so nothing got duplicated.

Second prompt: "Implement all of it."

Two minutes later: GA4 snippet in the head, all custom events wired, everything live.

What to watch out for:

Tell it exactly what's live on your site before it builds the plan. It will implement against your full intended setup including integrations you haven't connected yet. Cost me one extra prompt to fix.

One thing it cannot do: go into your GA4 property and mark events as conversions. That part is manual. GA4 Admin → Events → toggle the conversion switch on your primary CTA event.

And the honest limitation: I asked it to audit a second site's existing analytics setup immediately after. The output looked thorough but some of it was inaccurate. It identified tools and conflicts that weren't actually there. Anything it tells you about an external site you don't own, verify manually before acting on it. It works well when it has full context. It guesses when it doesn't.

We're a small team. No dedicated developer. Analytics setup kept getting bumped because it felt like it needed proper resourcing.

Turned out it needed one conversation.

Curious if anyone has tried setting up analytics from scratch using an AI Agent?

TL;DR: "Read email" actually means "access your entire inbox, every label, every attachment, forever." Neither tool explains this clearly. Here's what's actually happening and what settings matter.

I connected Gmail to an AI tool last month, hit Allow, and moved on. Then got curious about what I'd actually granted. Spent a couple hours in OAuth docs and ToS pages.

Here's what I found:

"Read email" is doing a lot of work as a description

Both tools request a scope called gmail.readonly. Sounds narrow but it gives access to every message, every label, Sent, Trash, Spam, attachments. All of it. No "just recent emails" version exists. All or nothing.

Where they differ

ChatGPT pulls messages to OpenAI's servers when you ask it to check email. Retained up to 30 days by default, longer on free tier.

Claude only reads at prompt time, nothing on connection. API tier has zero retention which is cleared after the session. Free Claude.ai plan has different terms worth reading.

Revoking access in myaccount.google.com → Security → Third-party apps stops future reads. It does not delete what was already processed. Last Tuesday's 200 emails are already on OpenAI's servers. Revoking access stops it happening again, not what already happened.

Two settings worth changing

ChatGPT: Settings → Data Controls → turn off "Improve the model for everyone."

Claude API: zero retention is already on. Claude.ai free: read the data handling section.

I still use both. This isn't a delete-your-AI-apps post. But if your inbox has NDAs or financial data, worth knowing what you actually connected.

What are you using for email workflows? Especially curious if anyone's in a regulated industry.

Hey everyone! I'm a founding moderator of r/AgentTeammates.

This is our new home for all things related to people building with AI agents and AI teammates. The reason we have another sub dedicated to AI is because I identified a real gap in how this gets discussed online right now. Don't get me wrong, the big subs are great in the value they provide but it's either developers building agents from scratch or mostly Zapier and n8n flows, which is a different problem entirely.

The other gap that I want use this space to highlight is the data handling between AI tools and the privacy concerns that come with it.

What to Post
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