By Javi Hernández (ERNI Spain)
AI tools are already shaping our present. The behaviours and patterns we once recognised in people, teams and organisations are evolving rapidly. This leads to a natural question: What are the new responsibilities and competencies of Project Managers, Product Owners, Agile Coaches and Scrum Masters – today and in the near future? But I’d like to pose a different question: Are these roles really changing at their core?
Though the tools are expanding, the core purpose remains – and our purpose as leaders in delivery also hasn’t changed: Help individuals, teams and organisations deliver value more effectively and flexibly in complex, changing environments. AI doesn’t replace that purpose – it enhances how we pursue it. It opens new opportunities to overcome both longstanding and emerging challenges in more innovative and efficient ways. Let’s explore a few concrete examples of how.
Example 1: Automating what should be automated
When working with an organisation through a Value Stream Mapping exercise – used to identify where and how value flows and where it gets stuck – we often uncover repetitive, low-value tasks. In the past, automating these tasks might have been expensive or impractical, especially when some level of customisation was needed. Today, AI allows us to create lightweight agents or assistants that reduce effort and free up teams to focus on work that truly adds value.
That reclaimed time becomes a powerful lever for:
- innovation
- impacting the end user
- creating new business opportunities
Example 2: Managing technical debt with smart support
In long, complex projects, parts of the code or product can become overcomplicated. Over time, modifying those areas slows down development and generates bugs. Using delivery metrics – such as Cycle Time to track how long it takes to implement changes – we can identify hotspots in the codebase.
Teams then discuss this in retrospectives and might decide to apply the “Test-As-You-Touch” principle: using TDD (Test-Driven Development) whenever that part of the product is touched.
With AI, this becomes even easier:
- writing initial test cases
- understanding legacy code
- validating the quality and coverage of tests
AI becomes not just a helper – but an enabler of better engineering practices.
Example 3: Writing better user stories and refinements
User stories are standard in many organisations, but writing, refining and breaking them down into manageable work items is time-consuming. AI can’t replace the conversations and insights that teams generate during refinement sessions or reviews – but it can support the process:
- taking structured notes during sessions
- suggesting questions teams might not consider (e.g., related to security or compliance)
- helping rephrase or rewrite raw ideas into clear stories
AI tools can help us reduce time spent on low-value admin work and instead focus on tasks like:
- product discovery
- prioritising based on customer feedback
- validating business hypotheses early
How we apply this at ERNI
These aren’t hypothetical cases; they’re real examples of how we at ERNI are applying AI within our teams and services to:
- increase delivery performance
- reduce operational waste
- accelerate our customers’ capacity to innovate
The core roles may not be radically transforming – but the tools, skills and focus areas of each role are evolving fast. We see AI not as a replacement – but as a strategic partner in overcoming challenges with speed, clarity and precision. Watch how we explore this further at ERNI Academy (Video in Spanish).