From Scrum to AI-Native Delivery: What Changes, and What Should We Watch?

[#1]

What happens to Scrum when the team is no longer the only thing producing the work?

This is not just a theoretical question for me. I expect to experience this transition firsthand.

Many teams are moving from a Scrum-centered operating model, often scaled through frameworks such as SAFe, toward a more AI-native way of working.

But I don't think this is mainly about AI writing code faster.

The bigger question is: what happens to the delivery model when AI becomes an active participant in the process?

AI can increasingly generate code, tests, documentation, prototypes and technical alternatives.

As a result, the bottleneck may move away from implementation and toward:

• clearer product intent
• better specifications
• faster validation
• architecture and decision-making
• security and governance

This raises an interesting question:

Is the user story still the primary unit of work?

Or are we moving toward something closer to specification-driven delivery?

The better the specification, the more useful AI becomes.

The worse the specification, the faster AI helps us build the wrong thing.

Team structure may change as well. Smaller teams could become much more capable with AI, but simply reducing team size creates risks: knowledge concentration, overload on senior people, weaker reviews and less resilience.

If AI increases the amount of work one person can initiate, the bottleneck may simply move from execution to review, architecture and decision-making.

This also makes me question traditional Agile metrics.

If AI can dramatically increase the amount of code a team produces, does velocity still tell us much?

And this is where I am particularly curious about Jira.

If the flow of work evolves from:

Epic → Story → Task → Sub-task

toward something closer to:

Intent → Specification → AI-assisted execution → Validation → Learning

should the way we model work in Jira evolve as well?

Will Jira remain primarily a system for tracking work, or could it become an orchestration layer between people, AI agents, specifications, code, tests and outcomes?

I don't think Scrum is obsolete. Transparency, feedback, collaboration and adaptation may become even more important.

But perhaps the emphasis is changing:

Less ritual. More clarity.

Less focus on output. More focus on validated outcomes.

Less coordination of human effort alone. More orchestration of human and AI capabilities.

For me, the biggest risk is treating AI-native delivery as simply a tooling upgrade.

AI can accelerate delivery, but it can also accelerate unclear requirements, poor decisions and weak quality gates.

So I am curious to hear from others:

Are you adapting Scrum for an AI-native environment, moving beyond Scrum, or building a hybrid model?

What should replace, or complement, story points and velocity?

And perhaps the question I am most interested in:

How should Jira evolve to support this new way of working?