The mention of AI in Scrum practices sparks heated debates. We witnessed this firsthand when we discussed using AI in estimation tools. There were (valid) concerns: that this violates fundamental Scrum principles and would make teams to lose the valuable conversations that estimation sessions provide.
These concerns aren't surprising. After all, Scrum is built on the foundation of human collaboration and collective intelligence. The idea of introducing AI into this deeply human process naturally makes many practitioners uneasy. And they're right to be skeptical about this - estimation isn't just about arriving at a number. It's about team alignment, shared understanding, and collective commitment.
But as AI continues to evolve and integrate into our daily work, it's time to discuss its role in estimation. We'll discuss this without advocating for or against AI in estimation. Instead, we'll discuss how AI might fit into estimation practices while preserving the core values that make Scrum effective.
Understanding How Estimation Works
Before we discuss AI in estimation, lets first understand why Scrum estimation works the way it does. At its core, estimation in Scrum isn't just about predicting how long something will take - it's a process of building shared understanding.
When a team estimates user stories, they're engaging in much more than just assigning story points. They're sharing context about the technical challenges ahead. The senior developer might point out integration complexities that others hadn't considered. A junior developer's questions might reveal assumptions that need clarification. The QA specialist might raise testing scenarios that expand the scope of work. Each perspective adds a layer of understanding that makes the final estimate more meaningful.
This is why Scrum emphasizes team-based estimation. The magic isn't in the final number - it's in the conversation that leads to it. When teams estimate together, they:
- Uncover hidden complexities and dependencies
- Share knowledge across experience levels
- Build consensus about implementation approaches
- Create shared ownership of the work ahead
- Calibrate their understanding of velocity
The resulting estimate isn't just a metric for planning; it's a token of shared commitment based on collective understanding. This process embodies the Agile principle of "individuals and interactions over processes and tools" in its purest form.
3 Misconceptions About AI in Estimation
1. The first is the belief that AI will replace team discussions. Scrum masters may worry that they will lose the conversation from the Dev Team. This is because they see AI as an oracle that simply spits out estimates, eliminating the need for team input. In reality, current AI tools are more like research assistants - they provide context and insights that can enrich team discussions, not replace them.
2. Another misconception is that AI-Powered estimation automatically means pre-assigning tasks. For instance, someone might say "How can the tool predict if a Senior or Junior developer is going to work on a task?". Modern estimation tools don't dictate who should do the work; they help teams understand how different experience levels might approach a task. This information feeds into the estimation discussion - it doesn't bypass it.
3. Perhaps the most significant misconception is that using AI violates Scrum principles. While Scrum emphasizes "individuals and interactions over processes and tools," this principle doesn't reject tools entirely - it prioritizes human collaboration. After all, we already use various tools in our Scrum practices, from JIRA to planning poker apps. The question isn't whether to use tools, but how to use them in a way that enhances rather than diminishes human collaboration.
How Can AI Support Estimation?
So what exactly can AI contribute to estimation? Rather than replacing human judgment, AI tools can serve as a facilitator that support the estimation process in several meaningful ways.
Think of AI as a team member who has analyzed thousands of similar tasks and can offer relevant context. When a team is estimating a new feature, AI can highlight patterns from past implementations, potential complexity factors, and various technical considerations. This isn't about dictating estimates - it's about ensuring the team has more information to discuss during their estimation sessions.
For example, when estimating a new authentication feature, AI might point out various implementation aspects: session management, security protocols, password policy enforcement, and user feedback handling. This prompts the team to consider these elements in their discussion. A junior developer might learn about security considerations they hadn't encountered before. A senior developer might spot patterns similar to previous projects. The entire team benefits from having this additional context while maintaining full autonomy in their estimation.
The key here is that AI serves as an input to the conversation, not an output that bypasses it. Just as a team might reference documentation, past sprint data, or architectural diagrams during estimation, AI-provided insights become another tool in their decision-making toolkit. The final estimate still comes from the team's collective judgment, informed by their expertise, experience, and understanding of their specific context.
This supportive role aligns with how many teams already use tools in their Scrum practice - to enhance rather than replace human collaboration. The goal isn't to make estimation faster or automated; it's to make team discussions richer and more informed.
Can AI Help Teams Estimate Better Without Pre-Assigning Tasks?
One aspect of software estimation is how different experience levels approach the same task. A feature that might take a senior developer two days could require a week for someone just learning the codebase. This isn't a problem to be solved - it's a reality to be understood and accounted for in our estimations.
This is where AI can provide valuable perspective. Instead of saying "this task should go to a senior developer," AI can show how different experience levels might approach the work. For instance, it might highlight that a particular API integration typically requires deep knowledge of authentication protocols, or that a UI component involves accessibility considerations that junior developers often encounter for the first time.
Some critics argue that surfacing these experience-based insights might lead to implicit task assignment, eg, "When you assign tasks upfront, this is utilization and introducing anti-patterns,". This is a valid concern, but it misses an important distinction. Understanding task complexity across experience levels isn't the same as pre-assigning work. Instead, this knowledge helps teams:
- Have more realistic discussions about capacity and timing
- Identify mentoring and pair programming opportunities
- Plan for knowledge transfer during implementation
- Consider technical debt implications
- Account for learning curves in their estimates
The goal isn't to create separate estimates for junior and senior developers, but to expand the team's understanding of the work ahead. This aligns perfectly with Scrum's emphasis on continuous learning and team growth.
How AI in Estimation Supports Scrum Values
AI in estimation raises an important question: Can we add AI while staying true to Scrum's fundamental values? What raises this question is the belief that Scrum is built upon the collective intelligence of the team.
The answer is in how we approach this . The Agile Manifesto's principle of "individuals and interactions over processes and tools" wasn't written to exclude tools entirely - it was written to prevent tools from overshadowing human collaboration. When we examine successful Scrum implementations, we see teams using various tools while keeping human interaction at the center of their practice.
Consider how a healthy team might use AI in their estimation process:
The team gathers for planning poker, armed with their expertise and understanding of the product. AI-powered insights become part of their reference material, alongside documentation and historical data. They discuss each story, debating different approaches and sharing concerns. The AI's input about complexity factors becomes one voice in a larger conversation. The final estimate still emerges from team consensus, shaped by their collective wisdom and specific context.
This approach maintains crucial Scrum elements:
- Team autonomy remains intact - the AI informs but doesn't decide
- Face-to-face conversation stays central to the process
- Collective intelligence is enhanced, not replaced
- Team members continue learning from each other
- The estimation process remains adaptive to team context
We're not choosing between AI and Scrum values - we're finding ways to support those values with modern tools. Just as digital planning poker tools didn't eliminate team collaboration, AI assistance doesn't have to diminish the human elements that make Scrum effective.
Best Practices for Using AI in Estimation
If we decide to incorporate AI into our estimation process, how do we do it effectively without removing the collaborative nature of Scrum? Here are practical guidelines that teams can consider.
If we decide to incorporate AI into our estimation process, how do we do it effectively while preserving the collaborative nature of Scrum? Here are practical guidelines that teams can consider.
1. Start with the team conversation. AI insights should join the discussion only after the team has had a chance to form their initial thoughts. This prevents anchoring bias and ensures the team's expertise remains the foundation of the estimation process. Let developers share their initial impressions, concerns, and questions before referring to AI-powered context.
2. Use AI insights as discussion prompts, not decisions. When AI flags potential complexity factors or experience-level considerations, treat these as conversation starters. "The AI suggests this might involve complex state management - what's everyone's experience with similar features?" This approach maintains team ownership while benefiting from additional perspective.
3. Keep calibrating and questioning. Teams should regularly assess whether AI insights align with their reality. If the AI consistently misses important factors or overemphasizes certain aspects, adjust how you use its input. Remember that your team's context is unique, and no AI model can fully capture that.
4. Maintain healthy boundaries. Some parts of the estimation process should remain purely human. Team dynamics, sprint commitments, and final estimation decisions should always come from the team. AI should inform these elements, not determine them.
5. Use AI to enhance learning, not replace it. When AI surfaces technical considerations or potential challenges, use these as teaching moments. Junior developers can learn from the patterns AI identifies, while senior developers can validate or challenge these patterns based on their experience.
Conclusion
Using AI in estimation isn't about replacing human judgment—it's about enhancing it.
By providing valuable insights into task complexity, AI can help teams make more informed decisions without undermining their flexibility or collaboration. As long as it’s used to support rather than dictate the process, AI can be a powerful ally in building better estimates and fostering team growth. Ultimately, the question isn’t whether it’s okay to use AI, but how we can use it responsibly to strengthen our Agile practices.