What challenges have you faced when prioritising AI features in your team’s backlog?

Agile Artificial Intelligence Ethics

Stelian ROMAN Project Manager| MicroSafety Carlingford, New South Wales, Australia

As artificial intelligence (AI) becomes integral to modern products and services, development teams face mounting pressure to deliver innovative features rapidly. The excitement around AI capabilities is often matched by ambiguity and scepticism—especially when it comes to how decisions are made about which features get built, tested, and launched first. Transparency in backlog prioritisation is not just a best practice; it’s essential for building trust among stakeholders, ensuring alignment with organisational goals, and fostering a culture of accountability. In this blog post, we’ll explore why transparency is so vital when prioritising backlogs for AI features, examine the common challenges teams face, and offer actionable recommendations for making the process more open and effective.

·What challenges have you faced when prioritising AI features in your team’s backlog?

·How does your organisation ensure transparency in product development decisions?

·What tools or practices have helped your team align on AI feature priorities?

Blog post: Transparency in Backlog Prioritisation for AI Features

ProjectManagement.com - The Agile Enterprise

Posted: Jul 3, 2026 12:48 AM

Luis Branco CEO| Business Insight, Consultores de Gestão, Ldª Carcavelos, Lisboa, Portugal

An important dimension of this discussion is that transparency in AI backlog prioritisation may need to reveal more than why one feature was placed ahead of another.
It should also make visible how mature the evidence behind that priority really is.

AI features are often prioritised using criteria that appear objective: expected value, feasibility, strategic alignment, or user demand.
But behind those scores may sit very different levels of evidence, from validated user needs to executive assumptions or enthusiasm around an emerging capability.

That distinction matters.
A prioritisation process can be perfectly transparent and still give false precision to weak assumptions.

Perhaps the next step is not only to make prioritisation criteria visible, but also to expose the confidence level behind them.
A transparent backlog shows the decision.
A mature one also shows how much confidence the team should place in it.

Posted: Jul 3, 2026 4:36 AM

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What challenges have you faced when prioritising AI features in your team’s backlog?

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