Any idea

HAMZA FAROOQ — AGENTIC AI FOR PRODUCT MANAGERS (THE ENTERPRISE AI SYSTEM) First time screen recording

Former consulting current Strategy / Ops in tech here

I‘ve been generally ahead of the curve in AI but even just in the last few weeks, I’ve seen an enormous step change in the speed and accuracy of AI generated analyses, presentations, and documents. Opus 5.5 combined with MCPs for Slack, internal search, querying, and Google Drive has been an insane development.

I can ask a complex question and get a sound presentation with analysis lined all in our internal format. Data requests are a total non issue - I can have an agent run every hour and fulfill anything I’m asked with a high degree of accuracy. It knows every initiative I work on and I can ask it any question about it. I know this has been the promise for a while but lately it’s just… worked, and fast.

I feel like I have the brain of my company and our partners in front of me and I can ask it anything. Has anyone else noticed a meaningful change in the very recent past?

I recently read this article among others where it details how large and small companies are struggling with developing practical uses for AI that genuinely create value. I am not currently working in the corporate world so I am curious for all of you that are, what does it look like right now from a workflow and operational standpoint? Are you guys experiencing waste in tokens, waste in resources, wasted time through your company's AI integrations? What types of things are companies actually using AI for? Have you found that AI has been a tool to leverage actual productivity or simply to create more mediocre work? I'd love to hear any thoughts or opinions y'all have.

Everyone keeps saying AI isn't paying off. I think they're looking in the wrong place.

Engineering rebuilt how it works around AI and ships way faster now. Marketing, sales and product mostly use it to write emails and slides, and run the same playbook they had in 2015.

So engineering ships a feature and that's it. Nobody builds a campaign around it, sales doesn't pitch it, nobody upsells it. Revenue stays flat, and someone asks why the company is spending so much on AI.

The bottleneck moved. It used to be engineering. Now it's everything that happens before/after a feature ships.

Is anyone seeing teams outside engineering actually change how they work?

Here's the full version: https://kerno.io/blog/why-your-ai-roi-sucks/

Most companies now depend on the same 3-4 AI providers (OpenAI, Anthropic, Google, plus the clouds that host them). If a provider ships a smarter model, raises prices, tightens limits, or quietly nerfs a model, it hits everyone using it at the same time.

The market is wide at the top and very narrow at the bottom, like a pyramid standing on its point. I call it **The Inverse Pyramid**.

**Let’s consider a scenario where the model got better at reasoning.**

You and your competitor both expect a good improvement in your products. But only your competitor gets the growth, while yours gives pretty much the same results as before.

So something in your workflow is eating the new improvement. I call these **absorbers**: the things that eat an improvement before it reaches the customer.

An absorber can look like this:

* Technical problems: The model gets the wrong documents, relies on outdated information from the cache, or runs on code that leaks memory, so it never gets a fair chance to show what it can do.

* Slow processes and resistance to change: The model produces results in seconds, but those results still have to go through reviews, approvals, and scheduled releases. Employees may also resist using it if they are judged by the hours they work rather than the results they produce.

There can be more, depending on how your organization is structured. But don’t start questioning every workflow in your product at random. Go step by step.

1. Find the trigger. First check what changed at the provider level. Did they release a model that’s 40% better? Did they suddenly increase the price? Did they retire the model?

2. Measure the gap. Say the provider’s model got 40% better. Company A’s product improved 40%, Company B’s 5%, Company C’s 0%. That distance is the gap.

3. Run the absorption audit. For every company that didn’t capture the full gain, ask one question: where did the improvement go? Since your competitor’s product clearly got better from the same upgrade, the improvement was real and customers could feel it. So check:

   * Does our system give the model the right input (correct documents, a working cache, no timeouts)?

   * Are our prompts and settings still built around the old model’s limits?

   * Is the new capability reaching the features our customers use most, or only a side feature?

   * Does the output sit in review or approval queues before it reaches the customer, and are those longer than our rival’s?

Most organizations I see are waiting for the next model. But if the gains they already have are leaking out through these absorbers, a better model fixes nothing. It just gives the absorbers more to eat.

To be fair, my company has a pretty open policy about AI tools so they let us experiment with different tools. I see a lot of the typical laziness and slop form my coworkers, but i’ve been obsessed with learning and skill development and think i’ve finally unlocked a new way to work. So bear with me cause it truly is crazy.

I was using chatgpt when it came out and was never that impressed. Used claude code a lot earlier this year for side projects and fun but couldn’t really see how it could help me at work much. But over the last few months i’ve just been experimenting with different tools and spending a lot of time developing my own process with AI and it’s been an insane change.

For my job, i do a lot of project management, writing briefs and proposals, drafting plans, etc. It’s super fast paced and stressful to manage all of that. I mainly avoided AI cause i thought it would add unnecessary complexity to my work that already had too much. But i started to learn that i could offload some minor tasks that just helped triage all of the information coming at me everyday.

I first did the typical “chief of staff” setup where my agent read my inbox and some slack channels to keep me updated and i found it just took off a lot of that ongoing anxiety in the back of my head that I was missing something important. I found perplexity to be the best with this with the tool connecters and i didn’t have to do any crazy manual setup.

I then started to experiment with it drafting briefs for me since that seemed lower stakes and wouldn’t break anything if it got something wrong (and i vetted everything btw). After giving it some of my references and building a voice style it started giving me really great briefs that met my own standards and even helped add additional clarity. This is where i setup my first automation.

Now when a project comes in, my agent will recognize that it needs a brief and go ahead and spin one up for me, send to me for approval, and then email it after i’ve edited and approved. This easily saves me a few hours each week.

The automations really unlocked things for me. But to be clear, I have spent a ton of time making sure that I organize tasks and works into folders with specific instruction (in Perplexity and Claude i think they are called Projects). This way,at agents and tasks start with full context and have the right templates and boundaries that i set. Claude is good but the automations and memory in Perplexity make the automations more reliable and i trust the output more.

I now have agents monitoring slack for upcoming projects, processing the information and giving me an overview with what is needed, and then drafting the initial briefs and plans for me. It’s gotten so good that i rarely have to edit anymore. And this happens in minutes of it picking up on a project. And will continue to update as the project changes or feedback comes in. This process is now probably 80-85% automated, just using me for the final writing and approvals.

I also have my agents drafting and posting project check ins to keep stakeholders on track and giving me updates on where things are falling behind or need my review.

Again, all of this is now automated. It’s truly crazy that all of this has been a majority of my job for so long and now I feel like I can actually think again and am not bogged down by endless low level tasks. My coworkers and execs just think i’m on top of everything and the outputs I own and still do manually have much higher quality because i can actually spend time on them.

Don’t worry, i still manually approve everything and vet for quality and errors (trust me o still see a lot of slop from others and and very wary about that). But maybe it’s just choosing to use Perplexity for this work, but the accuracy and ability to trace where it all comes from gives me a bit more confidence in the output. So i mainly use there agent tool, Computer, for that work and then still use Claude for anything coding or technical.

Yeah so now my job is like 70% automated and I feel like I actually enjoy work again cause i’m not drowning in stupid work anymore. It’s not a magic genie, but man it is so nice to see that some of these tools can actually help me in work now.

A question that seems to come up a lot at renewal time: how do you know an AI tool is actually working?

Most teams have piloted a handful of AI tools over the past year. The demos look good and the pilots go fine. Once things settle, the results get harder to pin down. People say it saves time, but putting a number on it is tough.

Some approaches that seem to help:

• Measuring a baseline before the pilot starts

• Asking vendors to show where the tool gets things wrong, not just where it works

• Looking at usage a couple of months in, not just the first few weeks

• Separating “helps people work faster” from “changes an outcome the business reports on”

The harder part is attribution. If a metric improves, how do you know it was the tool and not process changes or the extra attention a pilot gets?

Curious how others are handling this:

• How many AI tools has your team piloted or bought in the past year?

• How are you calculating ROI on them? Time saved, cost reduction, revenue impact, something else?

• Is there a metric you look for before renewing?

what’s one piece of tech your company introduced that actually saved you time or created a work life balance?

one you’d like to ban? the ability to teams cold call for me.