Keeping AI as a partner (not a replacement) requires more than technical safeguards.
It demands human intention, clarity of purpose, and relational maturity.
Agile values individuals and interactions, not out of nostalgia, but because real value creation happens in living ecosystems, where trust, empathy, and shared learning are irreplaceable.
In my practice, we treat AI as a team member with a defined role, clear boundaries, and ethical purpose.
Not a “technical miracle,” but a cognitive collaborator, serving the team’s collective intelligence.
AI can:
- Speed up backlog grooming, but it does not decide what matters to the customer.
- Detect patterns in retrospectives, but it doesn’t replace honest dialogue.
- Suggest technical improvements, but it must never silence team voices.
Real-world example:
In a recent project, we used AI to synthesize scattered stakeholder feedback before a critical release.
AI revealed useful patterns, but it was the team, through open discussion, that decided what to prioritize.
AI proposed.
The team decided.
Purpose guided.
This triad is central to our regenerative decision-making model (RCPCV™):
AI proposes | Team decides | Purpose guides
Here lies the ethical boundary:
- If AI doesn’t build trust, doesn’t stimulate dialogue, and doesn’t respect shared vision -
then it’s not collaborating. It’s automating.
And Agile is not about automating interactions.
It’s about growing together with awareness, responsibility, and purpose.
How are you integrating AI into your Agile teams without losing what makes us human?
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5 replies by Elaine Alexander, Eslam Hassan, Farhan Liaquat, Mounir Ashour, and Shashi Kumar
Jan 25, 2026 2:13 AM
Mounir Ashour
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سؤال رائع وإجابات من خبرات الزملاء ممتازة ومفيدة جداً , أضف اليها أن الذكاء الأصطناعى ليس بديل عن الإنسان بل هو آداة حديثة لإضافة مقترحات متعددة حسب البيانات المدخله الى البرنامج والاختيار النهائى من المقترحات هو للانسان حسب الحاجه والموقف والمصلحه
Feb 03, 2026 7:19 PM
Shashi Kumar
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AI proposes | Team decides | Purpose guides - good phrase..
Mar 24, 2026 5:53 PM
Elaine Alexander
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Using AI to do researcher on published work to provide background on content
PMO Leader | Speaker & Mentor | Content Leader – PMOGA Latin America
Hub| Catholic University of UruguayMontevideo, Montevideo, Uruguay
Using AI as a support, not as a substitute: facilitating communication, automating repetitive tasks and enhancing decision making without replacing human interaction. Integrating it as a collaborative tool, not as a protagonist.
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1 reply by Verónica Elizabeth Pozo Ruiz
Oct 16, 2025 11:39 AM
Verónica Elizabeth Pozo Ruiz
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I agree that AI should be used as a collaborative tool to assist us in our tasks, but not to replace the original and creative work humans produce.
It's about using AI as a support rather than a guide. Trust and ethics are all values that can be proposed by AI, But the team of agile that can decides the priorities. Saving Changes...
To maintain AI as a partner rather than a replacement in Agile environments, it's essential to adopt a human-centric approach that leverages AI's strengths while preserving the core values of Agile methodologies. Here's a comprehensive strategy:
1. Position AI as a Collaborative Tool
AI should be viewed as an enabler that augments human capabilities, not as a substitute for human roles. For instance, AI can automate repetitive tasks like code generation and testing, allowing team members to focus on higher-value activities such as problem-solving and innovation .
2. Enhance Decision-Making with AI Insights
Integrate AI to provide real-time analytics and predictive insights, aiding Agile teams in making informed decisions swiftly. AI can analyze vast datasets to identify patterns and trends, supporting teams in anticipating risks and adapting to changes effectively
3. Maintain Human Oversight and Ethical Standards
Establish clear guidelines to ensure AI outputs are transparent, verifiable, and aligned with ethical standards. This includes validating AI-generated content, preserving data privacy, and ensuring that human judgment remains central in decision-making processes .
4. Foster a Culture of Continuous Learning
Encourage team members to view AI as a learning partner. Promote experimentation with AI tools and foster an environment where feedback is used to refine AI applications, ensuring they evolve in line with team needs and project goals .
5. Integrate AI into Agile Practices Thoughtfully
Incorporate AI into Agile ceremonies and workflows in a manner that complements existing practices. For example, AI can assist in sprint planning by analyzing past performance data to suggest realistic goals, without dictating the direction of the sprint.
6. Empower Scrum Masters as AI Facilitators
Scrum Masters should take an active role in guiding the team in the ethical and effective use of AI. This includes facilitating discussions on AI's role, addressing concerns, and ensuring that AI tools are used to enhance team collaboration and productivity .
By implementing these strategies, Agile teams can harness the benefits of AI while preserving the human-centric values that are foundational to Agile methodologies. This approach ensures that AI remains a supportive partner, enhancing the team's capabilities without overshadowing human input.
Saving Changes...
Heather FitzpatrickSystem Analyst III| CareSourceMiamisburg, Oh, United States
In Business Analyst training for Prompts we call out all the skills the BA brings to AI.
Skills of a Business Analyst in Documenting ADO Feature Requirements and Deliverables
·Data Analysis Skills
·Ability to interpret and analyze raw data to identify trends, patterns, and insights.
·Skilled in using data visualization tools to present findings clearly and effectively.
·Requirements Gathering
·Proficient in conducting interviews, workshops, and surveys to gather business needs and expectations.
·Capable of translating stakeholder desires into clear, actionable requirements.
·Process Mapping
·Expertise in mapping current and future state processes to identify gaps and opportunities for improvement.
·Ability to use process modeling tools to create visual representations of workflows.
·Documentation Skills
·Strong writing skills to document feature requirements, user stories, and acceptance criteria in a clear and concise manner.
·Familiarity with ADO (Azure DevOps) for tracking and managing feature requirements and deliverables.
·Stakeholder Engagement
·Skilled in building relationships with stakeholders to ensure their needs are understood and met.
·Ability to facilitate discussions and workshops to validate requirements and gather feedback.
·Critical Thinking and Problem-Solving
·Aptitude for identifying potential issues and proposing effective solutions.
·Ability to evaluate multiple options and make informed decisions based on data and stakeholder input.
·Technical Acumen
·Understanding of relevant technologies and systems to effectively communicate with technical teams.
·Familiarity with software development processes and methodologies, such as Agile.
·Change Management
·Ability to assess the impact of changes on processes, systems, and stakeholders.
·Skilled in developing change management plans to support smooth transitions.
Then we call out all the ways the BA can use AI as a "leverage" to those skills sets.
How AI Can Assist Business Analysts in Organizing Requirements and Identifying Deliverables
By leveraging these skills and AI capabilities, Business Analysts can enhance their efficiency and effectiveness in documenting ADO Feature Requirements and Deliverables, ultimately leading to better alignment with business goals and improved project outcomes.
·Automated Data Processing
·AI can quickly process large volumes of raw data, identifying key themes and insights that may be missed manually.
·Tools like natural language processing (NLP) can analyze stakeholder feedback and extract relevant requirements.
·Requirements Organization
·AI-powered platforms can categorize and prioritize requirements based on predefined criteria, making it easier for Business Analysts to focus on high-impact items.
·AI can help create structured templates for documenting requirements, ensuring consistency and clarity.
·Collaboration Enhancement
·AI tools can facilitate collaboration by providing shared platforms for stakeholders to contribute to and review requirements in real-time.
·AI chatbots can assist in answering common questions from stakeholders, streamlining communication.
·Trend and Pattern Recognition
·Machine learning algorithms can identify trends and patterns in stakeholder feedback, helping Business Analysts to anticipate needs and refine requirements.
·AI can analyze past project data to predict potential challenges and suggest mitigations.
·Validation and Review Support
·AI can assist in cross-referencing requirements against business goals and objectives to ensure alignment.
·Automated tools can generate reports summarizing requirements and deliverables for review with business owners, saving time and enhancing accuracy.
·Acceptance Criteria Generation
·AI can help draft initial acceptance criteria based on the documented requirements, providing a starting point for discussions with stakeholders.
·AI tools can suggest best practices for writing clear and measurable acceptance criteria.
·Feedback Analysis
·AI can analyze feedback from stakeholders on proposed requirements, highlighting areas of concern or confusion for further discussion.
·Sentiment analysis can gauge stakeholder reactions to requirements, helping prioritize revisions.
We have two slides on how the BA needs to stay in control around the requirements gather, verification, and validation of the AI generated requirements.
1. Focus on the Unique Business Analyst Skills you bring to the role
·Critical Thinking: Business Analysts excel in analyzing complex situations and making informed decisions that AI cannot replicate.
·Stakeholder Engagement: Building relationships and understanding stakeholder needs is a human-centric skill that fosters trust and collaboration.
·Contextual Understanding: Business Analysts possess the ability to interpret nuances and context in requirements that AI may overlook.
2. The Importance of the Human in the Loop
·Oversight and Validation: AI-generated outputs should always be reviewed and validated by Business Analysts to ensure accuracy and relevance.
·Enhancing Outputs: Business Analysts can refine and enhance AI-generated content, infusing it with insights and context that AI lacks.
·Decision-Making Authority: The final decision on requirements and features lies with the Business Analyst, ensuring they remain in control of the process.
3. Cross-Product Delivery Knowledge
·Holistic Understanding: Business Analysts are equipped with knowledge across products and processes, allowing them to identify dependencies and impacts effectively.
·Integration of Insights: This knowledge enables them to integrate AI outputs with existing systems and processes, ensuring comprehensive and coherent requirements.
·Facilitating Collaboration: Business Analysts can act as liaisons between technical teams and stakeholders, ensuring that AI-generated features align with overall business objectives.
4. The Evolving Role of the Business Analyst
·Adaptation to Technology: Embrace AI as a tool that complements and enhances their role, rather than a replacement.
·Focus on Strategic Value: With AI handling routine tasks, Business Analysts can focus more on strategic analysis, stakeholder engagement, and driving business value.
·Continuous Learning: Emphasize the importance of upskilling and adapting to new technologies to remain relevant in a rapidly changing environment.
5. Remember: Empowering Control with AI
·AI as an Ally: Position AI ChatGPT as a supportive ally that enhances the Business Analyst's capabilities without diminishing their control.
·Reinforce Your BA Confidence: Business Analysts are encouraged to leverage AI to streamline processes while retaining their critical role in shaping business requirements and ensuring quality outcomes. Saving Changes...
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
Great example, Fabian. Change resistance often reveals more about fears than facts, people fear loss of control or relevance. I’ve found that mapping emotional stakeholders early and involving them as “change partners” helps transform resistance into advocacy. Transparency, small wins, and visible sponsorship make adoption natural rather than forced. It’s not just managing change, it’s guiding people through uncertainty with empathy and structure.
A common refrain I've heard for a couple of years now is that AI isn't going to replace you, but somebody that knows how to use it might. As a Project Manager, you can learn everything there is to know about GenAI and how to use it effectively in your job and on your projects, but ultimately, the decision of whether you or a team member gets replaced by AI is out of your control.
Fortunately, the signals are indicating that mass disruption of the job market due to GenAI is unlikely, at least in the near term. A recent post on LinkedIn referenced reports from Yale University Budget Lab and The Brookings Institution making statements to the effect that it hasn't happened yet and cited further sources indicating that widespread technological disruption in the workplace generally takes decades.
According to a recent report from MIT's Media Lab, "The State of AI in Business 2025", 95% of corporate AI initiatives fail, largely due to companies pursuing hype, not strategy. Where GenAI is likely to find the most success is in areas like back-office automation, procurement, finance, and operations - changing the nature of the work being done without replacing people.
An article in Forbes discusses the MIT report, and it gives a quote I may have to borrow - Technology doesn't fix misalignment. It amplifies it. Automating a flawed process only helps you do the wrong thing faster. Add GenAI and you risk runaway damage before anyone realizes what's happening.
The message behind these reports is that, applied effectively, GenAI can change the nature of work and that, if you're paying attention, you have time to prepare. It's too soon to say what that change will be. So, make sure that when you use GenAI, you're using it to enhance the right things. If you use GenAI and cause failure and loss, can you blame GenAI?
To answer the last part of the question - how do you ensure that AI strengthens teamwork - here are some considerations:
- Create GenAI working agreements. For example:
* GenAI is for drafts; humans own the final product
* Always verify facts before using GenAI outputs
* Be transparent about GenAI content
- Encourage team members to experiment with GenAI and share what works with each other
- Capture lessons learned about GenAI usage and share what is and isn't working with a broader audience
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1 reply by michele oksa
Nov 11, 2025 1:23 PM
michele oksa
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Aaron, I enjoyed reading your post. I think the way AI presents information in a human-like communication style combined with its ability to search vast amounts of data to create responses and recommendations can give us a false sense of security and trust in its output. If we walk away from AI because we fear it, it becomes easier for organizations to believe that AI is “smart enough” on its own to do our jobs. But if we lean in, showing that we understand and can manage both AI and its output then we can demonstrate why it isn’t yet capable of replacing many roles. We can highlight where it’s strong, where it’s weak and requires human intervention, and what it would need before it could truly take on certain tasks. I’ve challenged my team to treat AI like an intern: assign it specific tasks, validate its work, train it, and decide where it brings real value for continued “employment.”
I see AI as a teammate that amplifies—not replaces—human strengths. In agile, AI can handle data, trends, and routine tasks so teams stay focused on empathy, creativity, and collaboration. The key is transparency—let AI inform decisions, not dictate them. People remain the heartbeat of agility
Saving Changes...
Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
The problem is the high confusion outside there about what agile is. Unfortunately most of the people and most of the organizations confuse agile with agile applied to software. The last is just an implementation of agile. Agile is defined as something related to enterprise architecture and using agile you will gain into agility. AI is a key component to implement agile to gain into agility. This is the real from more than 30 years ago. By the way: people and organizations are confusing AI with generative AI. The last one is just a subset of the first one. Saving Changes...
AI is a change initiative so it's important that we keep everyone onboard to maximize the benefit. The value has to be realized by reaping benefits of AI and passing it on to the team to help them perform better. Saving Changes...
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