As execution becomes faster, however, a different constraint becomes more visible: deciding what actually matters. For Jurgita Baronienė, Senior Project Manager at Adroiti Technologies, this has become one of the most important project management skills today.
“In today’s context, as a project manager, the most important skill I’ve had to develop is deciding what doesn’t matter. Otherwise, we lose speed, efficiency, business and team focus, and end up exhausted by the constant noise and flood of information. The list of things that don’t matter is approaching infinity. Which is exactly why only the things that matter should take up space,” says Jurgita.
So, what are those things? For Jurgita, they come down to four areas: speed, quality, efficiency and costs.
Speed: AI is already changing the delivery flow
At Adroiti, AI skills, agents and agentic teams are no longer something we discuss as a future possibility. They are tools our teams build, test and use in their everyday work.
“We recently mapped how the AI skills, agents and automations our team has built and actually uses cover the whole delivery flow – from requirements and development to QA and operations:
- Requirements: skills that turn raw feedback, meeting transcripts or rough ideas into properly scoped, dev-ready tickets, plus a ‘code review for specs’ that runs every ticket through eight reviewer personas before refinement.
- Development: multi-agent code reviews where parallel agents cross-check each other’s findings to reduce false positives, agents that read PR comments and fix them directly, and hooks that spin up an entire agentic team of engineers, QA and a team lead for bigger tasks.
- QA: skills that generate test cases from a ticket, convert them into automated test specs, push them to our test management tool without duplicates and log results back to Jira.
- Operations: automated release notes, retrospective summaries, morning briefings that replace five open tabs with one page, and weekly product-data reviews that surface trends and anomalies on their own.”
For Jurgita, the point is not simply to accumulate more AI tools. It is to remove repetitive work so that every team member can create more value through thinking and decision-making.
“The list keeps growing because everyone is empowered to experiment, and failed attempts aren’t treated as failures. They’re one more step towards the right outcome. And because we’re a team, knowledge and discoveries are shared in one common library, so every skill one person builds makes everyone else faster too,” says Jurgita.
Quality: the definition has not changed
While AI is changing how quickly work can be done, Adroiti’s understanding of quality has not changed.
“We still want everything we ship to work flawlessly: a platform that’s easy to understand, a system that’s reliable, and data that is accurate and covers our needs.”
Faster execution does not automatically mean better delivery. AI can increase the amount of work a team can produce, but quality still depends on what ultimately reaches the user and how well it works.
Efficiency: are we working on the most important thing right now?
For Jurgita, efficiency has a straightforward definition: delivering value to the user, or to the operational team, in the shortest time possible.
“Put another way, it means constantly checking, at both team and individual level, whether we’re working on the most important item right now. We can’t always answer that alone, but we can always find out by asking and leaning on the team.”
When technology makes it possible to execute more and faster, efficiency is therefore not just about doing more. It is about making sure that time and capacity are directed towards the work that creates the most value.
Costs: not necessarily spending less, but creating more
The fourth consideration is cost. Companies have always had to think about how they allocate resources, but AI is changing the nature of that calculation.
“The question is how much more value we can create with the resources we already have by using AI – and how many times we can multiply the value we create by investing in it,” Jurgita explains.
This is also why she does not frame the rise of AI primarily as a question of people being replaced. Instead, a different set of questions is constantly running in the background: How much more value can we create with the same resources by using AI? How much can an investment in AI multiply that value? And how do we measure it?
It still starts with the team. Speed, quality, efficiency, and costs may be priorities, but for Jurgita, one condition comes before all four.
“And if I’m being completely honest, everything above only comes after you have a professional team, driven by results and the value it creates. Without that, everything else isn’t just unimportant. It’s simply impossible.”
As execution becomes easier, deciding what matters gets harder.
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Adroiti is a Lithuanian technology company building AI-powered systems and running senior engineering teams that deliver real, production-ready results.