Content Luis is Following

How optimistic do you feel about your software team’s current roadmap? Do you think your team can achieve the major milestones?

Very optimistic

Somewhat optimistic

Not optimistic at all

Not sure, our roadmap isn’t finalized yet

N/A, we don’t have a roadmap

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What is the most useful tool for simplifying the Developer Experience, reducing Cost and speed up the Time to Market of software application development that your organization will use in 2025?

Internal Developer Portal

Internal Developer Platform

Cloud Application Platform

Low/No Code Platform

AI Agents

Cloud Vendor Tools

None of them (please add a comment)

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Is GenAI going to replace junior developers? In which type of projects would you DEFINITELY use GenAI, and in which ones would you avoid it as much as possible?

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We’ve all been there—dealing with scope creep and the headaches it brings, especially when working on large website projects where so much is up in the air. I’d love to hear about your experiences with scoping out these big projects, especially using enterprise CMS/DXP (Adobe, Sitecore, Acquia). How do you tackle the uncertainties that come with them? Have you ever had to push forward without a super thorough discovery phase? If so, what strategies did you use to keep things on track? Which horror stories do you have to share related to Scope Creep? I’m curious about what’s worked for you and what hasn’t. Have you tried any specific tools or methodologies that made a difference?

We are currently developing an Open Source (OS) Policy for our company. To guide us through this process, we are following several Gartner publications. However, we are also looking for real-world implementation cases. Do you know of any examples of OS Policy implementation that you could share with us?

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AI and Research in Marketing and Communications: How Are You Ensuring Quality and Truth Over Speed? As AI becomes embedded in our day-to-day marketing operations, from customer insight to content strategy, it’s clear we’re gaining speed and scale. But are we sacrificing veracity and quality of output in the process? One of the biggest challenges I'm seeing is the rise of hallucinated content in AI-generated research.   - How do we ensure AI systems are surfacing trustworthy, contextually accurate, and well-sourced information? - Are we building enough verification layers into our AI-driven content and research workflows? - And how are you educating teams (and execs) to challenge AI outputs, rather than accept them at face value? - What frameworks, tools, or governance models have you adopted to maintain integrity and depth in AI-led research and insight creation? - Have you had success (or struggles) balancing speed vs. substance in AI-powered strategies? Your thoughts, examples, and recommendations would be incredibly valuable in helping others try to navigate this shift responsibly.

Luis Fernandez, Executive Director, Tech at VML | Gartner Peer Community