Asif D.

✔️Clients don’t want your flashy AI tools. They want data that backs your performance. In the latest episode of Between the Briefs, we explore how this translates into the world of legal amidst an industry-wide tech revolution. Tune in as Ashley Gibbs, Senior Director of Product Value, Engineers and Solution Sales Executives at Litera joins Adrian Cea and Joe Stephens for a candid conversation on why data quality makes or breaks AI success and is critical to drive positive change in tech adoption. Spotify: https://bit.ly/4gCOQ02 Apple: https://apple.co/47WBTfn YouTube: https://lnkd.in/gHjcNV8W #thestenostory #technologyinlaw #betweenthebriefs

2

Dear Claude/OpenAi/Gemini Product Designers: please give us the option to choose Enter = send or Enter = newline and Cmd/Ctrl + Enter = send Why is this not a toggle under Personalization? Nothing breaks flow like accidentally launching a half-baked prompt because muscle memory hit “Enter.” PS: I’ve been using this Chrome extension successfully: “Enter Key Control for ChatGPT / Gemini / Claude.” [LINK in COMMENTS] There are also more technical workarounds: • Userscripts (Tampermonkey) — override default Enter behavior • Browser extensions — cleanest fix • CLI users — Shift+Enter or Ctrl+J for new linesDear Product Designers...

2

1 Comment

After a years in CRO & UX research, I've watched countless brilliant insights gather dust in Slack channels and PowerPoint decks. Here's an uncomfortable truth: Most organizations are measuring research all wrong. We obsess over insight quality and delivery speed, but completely ignore what happens next. It's like a pharmaceutical company celebrating drug discovery while ignoring whether patients actually take the medication. The real value isn't in the research – it's in what gets implemented. I'm seeing a dangerous pattern emerge: "recommendation breakage." Teams invest heavily in testing, user research, generate valuable insights, then watch 60-80% of recommendations vanish into organizational silos. Meanwhile, teams launch changes based on incomplete guidance, marketing scrambles to fix messaging for flawed experiences, and researchers burn out wondering why their work doesn't seem to matter. The solution isn't better insights – it's better accountability. Starting tracking Recommendation Adoption Scores (RAS) alongside traditional research metrics. That way you can identify what is executed vs. cherry-picked, and where value leaks in their delivery process. This shift changes everything. When we measure implementation, not just insights, research transforms from a cost center to a measurable business driver. Teams that embrace this accountability will have a massive competitive advantage over those still celebrating beautiful research reports that nobody acts on. The future belongs to organizations that close the loop between discovery and delivery. #UXResearch #ProductStrategy #BusinessImpact #ResearchOps #DataDriven #ProductManagement #UserExperience #OrganizationalEffectiveness

3

If you're in product ops you'll immediately recognize this dilemma. A massive copy-paste challenge to maintain a commercialization/marketing view of the world...and a product view of the world. Made 300% harder because someone wants a "simple calendar" of launches. The reality is: 1. Initiatives have a M:1 relationship with Launches 2. Different launch tiers have different launch milestones 3. Calendars must include milestones, launch, and initiatives 4. Initiatives can have multiple releases 5. ...but not all releases are launch related 6. Oh, there are massive internal "launches" that marketing doesn't care about And this is why spreadsheets prevail. God-awful spreadsheets with: 1. Dozens of fields (like 6 dates for milestones) 2. Random manual fields to list multiple initiatives 3. Terrible VLOOKUP date madness 4. Product themes that are distinct from anything product uses A classic example of how there is a "right" model, but the UX complexity kills everyone, so they opt for something crappy. (PS: I love these problems. Hit me up if this resonates)

43

8 Comments

Across the teams I support as a Fractional CPO, one theme often emerges: Product Managers are confident in delivering features, yet much of the real work sits in the ambiguous spaces that aren’t always talked about as much. Here are four lessons I wish more PMs learned early: 1. Strategy always beats speed. Shipping quickly is irrelevant if the direction is wrong. 2. Users don’t behave logically. They decide based on emotion, context, and instinct. Our products need to meet them there. 3. Alignment is a core product skill. If leaders aren’t truly aligned, even the best roadmap stalls. 4. Your job isn’t to solve everything. Impact comes from choosing what not to solve so the team can focus on what actually matters. These rarely appear in product playbooks, yet they’re often the difference between teams that stay busy and teams that genuinely move the business forward. What’s a lesson you had to learn the hard way in your PM journey? #productthinking #productmanagement #fractionalcpo #leadership #uxstrategy

3

Across the companies I've seen, the most common blockers of product *outcome* velocity are 1. unclear/unaligned/unfocused strategic context 2. structure/process that isn't based on cross-functional product teams with agency 3. teams are not working in a truly agile way (Note most execs would rank them in the opposite order.)

3

1 Comment

𝐓𝐡𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 𝐭𝐨 𝐀𝐈 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐈𝐬 𝐇𝐞𝐫𝐞 Meta's product managers are dropping the PM title. They're calling themselves "AI Builders" now, and they're writing code. Not production code. Prototypes. Proofs of concept. Working demos, built without engineering support. Business Insider covered this shift on February 19, 2026: https://lnkd.in/gUtfvsMJ Meta, Google, and LinkedIn are all seeing this pattern. People who define products are now building them, using AI coding tools to turn ideas into functional outputs in hours. The term circulating right now is "𝐯𝐢𝐛𝐞 𝐜𝐨𝐝𝐢𝐧𝐠." You describe what you want in plain language. The AI writes the code. You iterate. No computer science degree required. This changes something fundamental for HR and operations leaders. Your non-technical staff are not locked out of building anymore. The question is whether you know how to spot the ones ready to make this shift. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝟑 𝐰𝐚𝐲𝐬 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 "𝐀𝐈 𝐁𝐮𝐢𝐥𝐝𝐞𝐫" 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐭𝐞𝐚𝐦: ☀️ Watch who experiments without being asked. AI Builders self-direct. They test tools on their own time. They bring demos to meetings, not decks. ☀️ Look for people who think in systems. You do not need engineers. You need people who understand inputs, outputs, and logic. Operations and process-oriented staff often have this instinctively. ☀️ Find the ones who are comfortable with "good enough for now." Vibe coding produces rough prototypes. People who need perfection before sharing will struggle. People who ship and iterate will thrive. The job title is changing. The skill set is widening. The teams that recognize this early will move faster than the ones waiting for a formal role description to catch up.

5

Product roles aren’t dead. They're more important than ever, especially as your company and product mature. The more product you build, the more complex the product work you have to do becomes. When you're first starting out, your product requirements can look something like "Build search," and frankly, any search is fine because any search is better than no search at all. As you build more and more product, you have to start thinking about: 1️⃣ All the existing workflows that any feature impacts and how the pieces fit together 2️⃣ Making sure that existing customers have a migration path when you make a change 3️⃣ How you're going to support the feature in the future (you probably ought to think about this the whole time, but when you’re starting out, it’s better just to move fast) Success of the work you do also looks really different. You have many fewer fully greenfield product opportunities. Instead, success often looks like moving the needle incrementally for both your existing customer base and growth areas for your business. How you think about product staffing needs to change at that point as well. I was the only product person at Starbridge for over a year, well past our first 200 customers. In December we had two amazing PMs join the team, Julia and Nicolas, which felt like it might be too much product staffing at the time. It has very quickly proven to still not be enough, given how much we have to do and how fast our engineering team ship. Even with that staffing, we were still running into bottlenecks in our ability to go deep on designing a beautiful and usable product. Last week, Eddie and Carpenco joined our design team to complement our incredible Head of Design, Serhii. Our entire product team is in New York this week, and in the last two days it feels like we've thought through product work that would have taken us a month before. It's trendy to say that product and even design roles are becoming less important in an AI-native world. I disagree: as it becomes easier to write code and, as a result, ship things, figuring out what to ship and how to build it in the right way is more important than ever.

48

5 Comments

If you’re applying for a PM job in this day and age, the following are all anti signals: - Strategic product leader - “Innovation” expert - Cross functional gtm expert - Literally any mention of Agile Or Scrum Please just start with what you actually shipped and what the impact was.

186

20 Comments

I've been mentoring a few #productmanagers who either: 1. want to get another PM job, 2. want to get a better role in #PM, or 3. just moved to the country and have a hard time finding a PM role due to cultural differences. Like every other tech roles, PM is one of the most affected by #AI. Lots of panic within the industry and tons of speculation whether how it would evolve as a role. I really enjoyed this take as it shows how PM roles are being described in a different way and what it might mean for those who are #jobhunting

2

A product team we worked with was juggling multiple external vendors - development, design, QA - across different time zones. Deadlines were missed, communication was inconsistent, and the PMs spent more time chasing updates than building the product. We introduced an AI-powered vendor management tool that: - Monitors timelines and flags missed deadlines - Analyzes code quality and rework percentages - Tracks communication patterns, clarity, and collaboration - Measures response speed to ensure smooth coordination The outcome? - Managers had a single dashboard for all vendors - Problems were caught before they snowballed - Teams could focus on product delivery instead of firefighting The bigger picture: managing external teams isn’t just about keeping tabs, it’s about predicting issues and improving collaboration. At MintyMint, we use AI and data insights to help companies manage vendors seamlessly, reduce risks, and deliver faster. 👉 DM me if you’d like to explore it more. #MintyMint #AI #VendorManagement #Productivity #Collaboration #ProjectManagement #TechTools #DigitalTransformation

7

2 Comments