Culture and Methodologies

Introduction It is hard to ignore that hiring processes are becoming increasingly lengthy and intricate. What troubles me is the apparent lack of genuine innovation in this space. While candidates are required to invest more effort and energy into these processes, I find it difficult to see the proportional increase in value or impact for companies. In this article, I aim to share my thoughts, personal experiences, and some strategies that have worked for me. Last year, I participated in several hiring processes that felt remarkably familiar. As I navigated through the steps, it became evident that the approach, structure, and overall experience closely mirrored what I encountered four years ago. The Purpose In essence, the hiring process bridges the opportunity and talent. For companies, it is a critical mechanism for identifying individuals who can contribute to their mission, culture, and business objectives. For candidates, it is a gateway to showcase their skills, experience, and potential in pursuing meaningful careers. Ultimately, it all boils down to impact for both sides. The aim is not to impose a rigid, one-size-fits-all structure but to design a process that identifies the right match while embracing flexibility. Moreover, I believe the hiring process isn’t inherently designed to be equitable for all candidates—its purpose is to serve the broader needs of the organization by selecting individuals who can deliver the greatest value We could summarize the purpose in four goals: Primary Goal - Attract Talent that Drives Business Value and Impact: The main objective of hiring is to bring in individuals who can contribute directly to the organization’s success. Whether a manager elevates team performance or an engineer creates scalable solutions, the focus is always on generating measurable impact. Primary Goal - Assess Cultural Fit: Verifying that candidates align with the company’s values and culture is essential to building cohesive teams and ensuring long-term success. Secondary Goal - Facilitate Cultural Integration: The hiring process also serves as an introduction to the company culture, laying the foundation for effective onboarding and alignment with organizational goals. Secondary Goal - Enhance Employer Branding: Every interaction in the hiring process contributes to the company’s reputation. A positive experience, even for those not hired, can turn candidates into promoters and encourage interest in future opportunities. If values such as effort, commitment, and resilience are central to the organization, designing a more extensive and rigorous hiring process can reinforce these ideals from the outset. However, this must be carefully balanced to ensure it remains fair and engaging for candidates. The Value of Efficiency and Referrals I have always believed that if I can identify and hire a talented individual within an hour, it’s far better than stretching the process over two or more hours. This approach not only saves valuable time for everyone involved but also increases the likelihood of securing the right candidate quickly. This is why referrals hold significant value for me. When a candidate comes recommended by a trusted colleague, it often carries more weight than an extended interview process. A referral provides a level of pre-vetted confidence that is difficult to replicate in a standard hiring pipeline What Some Companies Forget The hiring process is more than just a way to fill roles; it is a bidirectional process that sets the tone for the relationship between the candidate and the company. It is the first step in engaging potential employees and ensuring they feel a connection to the organization. Losing candidates during this process isn’t just a missed opportunity; it’s a sign that something fundamental may be lacking. A critical yet often overlooked aspect is making candidates feel inspired and valued throughout the journey. Ideally, the hiring process should make people fall in love with the company and its mission. Unfortunately, this is rarely the case in my experience. Interviews often feel more like rigid tests, with interviewers focused on assessing whether a candidate meets their criteria within a limited time, rather than fostering a meaningful conversation. In Search of Potential What truly matters in the hiring process is identifying the potential of individuals to excel in the role they are being considered for. Many hiring processes place an excessive emphasis on experience, the prestige of previous employers, or familiarity with specific procedures. While these elements can provide some context, they often miss the bigger picture: the candidate’s capacity to grow, adapt, and create value. Some processes overly prioritize adherence to industry best practices, but these practices are not always synonymous with generating real impact. Instead, the focus should shift to uncovering the unique qualities and untapped capabilities of candidates. It’s about looking beyond the surface and evaluating their ability to bring fresh perspectives, innovation, and drive meaningful change. The goal should be to identify those who can rise to challenges, learn rapidly, and contribute in ways that drive the organization forward—qualities that may not always be apparent in a resume or even during a conventional interview. Not everyone is suitable for hiring Not everyone is well-suited to the role of identifying and hiring top talent. Some individuals have an exceptional ability to recognize a candidate’s potential by going beyond standard interview questions and focusing on the subtleties that reveal true capability. A person may excel as an engineer or project manager but may not have the skills or mindset required to be an effective hiring manager. The ability to hire successfully involves more than just technical knowledge or following a checklist—it requires insight, intuition, and a nuanced approach to evaluating candidates. Interestingly, many companies lack data on who within their organization has the highest success rate in hiring. Ensuring a Positive Candidate Experience A successful hiring process is about more than just finding the right person for a role; it’s about creating a positive experience for everyone involved. Even candidates who are not ultimately offered a position should come away feeling respected, informed, and encouraged. Here are a few reasons why fostering a great candidate experience is so important: Feedback and Continuous Improvement: Providing clear, constructive feedback helps both the company and the candidate learn and grow. From the company’s perspective, this feedback loop can reveal areas of the hiring process that need refinement whether it’s clarifying job descriptions, improving interview questions, or streamlining communication. For the candidate, receiving honest feedback can help them prepare for future opportunities and develop professionally.Future Talent and Promoters: Sometimes a candidate may not be the right fit for a specific position right now, but they could be a strong match for a future opening. By ensuring they have a positive experience, you keep the door open for them to return down the line. Moreover, satisfied candidates, even if they don’t receive an offer, often become informal promoters of your brand. Their positive word-of-mouth can enhance your company’s reputation and help build a community of talent that you can tap into in the future. Ultimately, investing in a positive candidate experience is an investment in your company’s long-term success. It ensures that you attract high-quality applicants, maintain good relationships with potential future hires, and continuously refine your hiring process to better meet the needs of both the organization and candidates alike. Developer’s Technical Challenge When evaluating developers, technical exercises serve as an excellent way to assess problem-solving skills, coding proficiency, and overall software engineering understanding. Two key considerations often arise: Embracing AI Tools Speed and Automation: Utilizing AI-based tools in coding tasks can help automate repetitive, low-value activities, allowing developers to focus on impactful tasks such as designing robust architectures, optimizing performance, or ensuring scalability.Conceptual Understanding: Simply using AI to generate code is not enough; understanding fundamental principles and how AI-derived code fits into broader software design is crucial. Candidates should demonstrate they can harness AI effectively while still applying best practices and solid engineering judgment. Demonstrating Seniority Through Quality Depth Over Quantity: A common misconception is that senior developers must produce large volumes of code. In reality, seniority is better demonstrated by crafting solutions that are both concise and of high quality, employing appropriate design patterns and robust testing methods.Clear Documentation: Often, exercises are submitted with minimal or unclear documentation. It’s important to articulate why you’ve made certain design decisions. Different teams employ different patterns, and they all have merits and drawbacks. Explaining your chosen approach, the trade-offs, and the reasoning behind those decisions will help interviewers understand your thought process and technical depth. By striking the right balance, leveraging AI judiciously, and ensuring your work reflects your true capabilities, you can showcase both the breadth and depth of your expertise. Clear communication, thoughtful design choices, and purposeful documentation will not only help you stand out as a candidate but also demonstrate how you approach and solve complex problems in a real-world setting. Sharing My Experience Keys to a Successful Interview I would like to share some tips that I have found to be crucial for success in the hiring process. These tips are based on my experience as a candidate and as a hiring manager, and I believe they can be valuable when facing recruitment and selection challenges. Attitude and Mood: An interview is your moment to shine, and energy and motivation play a critical role in making a positive impression. In my experience as both a hiring manager and a candidate, attitude accounts for more than 50% of the outcome of an interview. However, this can be challenging for individuals feeling burnt out in their current roles. Before an interview, it’s essential to engage in a motivational exercise to visualize a better future and bring your best self forward. Practice: Interviewing is a skill, and like any skill, it improves with practice. The more you engage in interview processes, the better your chances of landing your dream job. I recommend participating in interviews for roles that may not be your top choice to familiarize yourself with the structure and develop confidence. Many processes are mechanical, and preparation can give you an edge. Reading is Essential: Today’s job market is saturated with best practices, methodologies, and standardized approaches. It’s almost guaranteed that you’ll encounter questions related to these topics during an interview. Keeping up-to-date with industry trends and practices is not only beneficial for interviews but also for professional growth. Understand the Company: Demonstrating genuine interest in the company is key to building a connection during the interview. Research their culture, mission, and values to determine if it’s a place you truly want to work for. This preparation not only helps you decide if the company is a good fit but also shows enthusiasm and commitment, leaving a lasting impression on your interviewer. The Hiring Process That Captivates Me Among the various hiring processes I’ve experienced, my favorite ones have been short, well-structured, and focused, even if they involve multiple steps or areas of review. I believe a comprehensive hiring process can be effectively completed within a week, including a technical test. The processes that have truly captivated me are those where, in a single day, I’ve had multiple interviews back-to-back, covering nearly all the steps. This approach offers several benefits: Maintains Focus: It allows candidates to remain concentrated and engaged in the process, with a sense of continuity that enhances their ability to deliver value.Improves Interviewer Collaboration: Interviewers benefit from having fresher discussions, and sharing insights while the candidate’s performance is still top of mind.Speeds Up Decision-Making: A condensed timeline facilitates quicker, more informed decisions, reducing delays that can lead to losing strong candidates. On the other hand, I’ve often encountered hiring processes that require candidates to complete lengthy technical challenges, sometimes involving deep dives into the company’s product. These exercises can feel excessive, especially for experienced professionals. I believe a simple, well-designed exercise that takes a couple of hours to complete is sufficient to evaluate the essential skills and fundamentals. A candidate with 20 years of experience shouldn’t need to dedicate weeks to a hiring assignment to prove their capabilities. Streamlining and simplifying the hiring process not only respects the candidate’s time but also demonstrates a company’s commitment to efficiency, focus, and innovation in its approach to talent acquisition. Conclusion The hiring process is the beginning of a journey, not its guarantee. True fit can only be discovered through the challenges and experiences shared during that journey. While losing potential talent is a cost we cannot afford, we must also acknowledge the high impact of hiring individuals who may misalign with our culture, a mismatch that is often difficult to detect during the process. I strongly believe that the hiring process is overdue for a transformation. It requires bold rethinking, evolution, and innovation. It’s time for companies to challenge the status quo, to bring the same level of creativity and problem-solving to hiring as we do to our technical challenges. By embracing innovative approaches, we can shape a future where hiring is not just a gateway but a meaningful, impactful step in building exceptional teams.

By Miguel Garcia CORE

TL, DR: AI Interview Analysis When you step into a new role as Scrum Master or agile coach for a team under pressure, you’re immediately confronted with a challenging reality: you need to understand the complex dynamics at play, but have limited time to process all the available information. This article explores how AI interview analysis can be a powerful sense-making tool for agile practitioners who need to quickly synthesize unstructured, qualitative data, particularly when joining a team mid-crisis. The Challenge: Information Overload With Limited Time Imagine you're the new Scrum Master in a startup for a team under severe pressure: Series B funding on the line if the next release failsTechnical debt accumulatingBurnout signals flashing red On your desk are transcripts from personal interviews with your new teammates, business stakeholders, and leadership team members. You need to understand what's happening — fast. But who has time to manually analyze hours of conversations when there are fires to put out? On the other hand, is becoming busy and picking low-hanging collaboration fruits the best choice? The scenario isn't hypothetical; I have been there plenty of times with a significant difference, though: I never had generative AI become my most valuable ally in making sense of a chaotic situation; back then, AI interview analysis wasn’t a thing. The Challenge: Making Sense of the Chaos — Quickly Typically, these interview transcripts contained critical insights about team dynamics, technical challenges, product vision, product delivery, and organizational pressure points. As a new team member, you often enjoy the benefit of the doubt as you're not yet part of the status game within the organization, and interviewees use the opportunity of a private interview to vent frustrations and explain politics. However, manually analyzing hours of conversation transcripts requires significant time — a luxury you rarely have when joining a team already facing immense delivery pressure, technical challenges, unclear priorities, or stakeholder misalignment. AI Interview Analysis Key Terms: The Sensemaking Challenge Before diving into how AI helped, let's clarify a few terms: Sensemaking: The process of building a coherent mental model from fragmented, incomplete data to understand a complex situation.Unstructured data: Information that doesn't fit neatly into predefined fields, like interview transcripts, open-ended survey responses, and meeting notes.Generative AI: Language models that can interpret, summarize, and extract patterns from human language inputs.Coachable entry points: Specific opportunities where a Scrum Master can intervene to improve outcomes or dynamics. How You Can Use AI as a Sensemaking Amplifier Instead of skimming the interviews or picking just a few to read deeply, you can use a generative AI tool to help analyze all the qualitative data at once. Here's my approach: I provided the AI with the complete interview transcripts.I prompted the AI to identify patterns, contradictions, emotional signals, risk and conflict issues, and potential intervention points.I asked it to anchor all observations with source references, for example, a quote.I reviewed the AI's synthesis and followed up with targeted questions. In my experience, the results are often surprisingly helpful. The AI does not just summarize — it helps to recognize patterns that might have taken days to identify manually. How AI Interview Analysis Amplifies Sensemaking Rather than skimming a few interviews or spending days deep-diving into all of them, generative AI offers a third option: comprehensive analysis with accelerated insights. Pattern extraction and thematic analysis: Generative AI is really good at identifying recurring patterns across large volumes of text. When applied to interview transcripts, it can quickly surface: Common pain points mentioned by different team members: Recurring themes across various roles and departmentsContradictions between what leadership says and what teams experienceUnderlying assumptions that may be causing misalignment The AI doesn't just count keywords — it understands context and can recognize when people discuss the same issue, even if they use different terminology. Emotional Intelligence and Sentiment Analysis Beyond factual content, generative AI can detect emotional undertones in language, a critical capability when assessing team health: Identifying signs of burnout or frustrationRecognizing enthusiasm or engagement around specific topicsHighlighting areas where team members express uncertainty or concern, detecting variations in how different team members feel about the same issues This emotional awareness helps Scrum Masters prioritize interventions based on process issues and human needs. Identifying Contradictions and Misalignments One of the most valuable functions of AI analysis is spotting contradictions that might otherwise go unnoticed: When leadership's expectations differ from what teams believe is possibleWhen different teams have conflicting understandings of prioritiesWhen stated values clash with described behaviorsWhen different stakeholders have incompatible definitions of success These contradictions often represent the highest-leverage intervention points for a Scrum Master. The Benefits of AI Interview Analysis: Beyond Time Savings While saving time is valuable, using AI for interview analysis offers additional benefits: Reduced cognitive load: Instead of holding dozens of details in your mind simultaneously, the AI handles the initial pattern extraction, allowing you to focus on understanding implications and planning responses.More comprehensive analysis: When time is limited, manual analysis often means sampling — reading some interviews thoroughly while skimming others. AI can process all available information, ensuring insights from every interview are considered.Reduced confirmation bias: As humans, we naturally notice information that confirms our existing beliefs. AI doesn't have these same biases, making it more likely to surface insights that challenge our assumptions.Evidence-based coaching: With comprehensive analysis, your coaching interventions become grounded in a broader understanding of the system rather than anecdotal observations. This increases both the effectiveness of your interventions and your credibility with the team.Faster trust building: Recognizing and acknowledging specific challenges team members face — even those not explicitly brought to your attention — demonstrates empathy and builds trust more quickly. For example, Claude is excellent at creating sociograms and identifying isolated team members who need your help. Practical Implementation: Using AI Interview Analysis as Your Sensemaking Partner If you're a Scrum Master or Agile Coach facing a similar challenge, here's how to leverage generative AI effectively: Gather diverse input data: Collect a variety of unstructured data sources, such as interview transcripts, Retrospective notes, meeting minutes, survey responses, and even Slack conversations, anonymized if appropriate. Please remember: the quantity of information or its inherent messiness is much less of a concern now.Create effective prompts: Your prompts to the AI should be specific and structured, addressing the use case, not just fishing around. Consider meta-prompting to support the task (see the Agile Prompt Engineering Framework above): "Analyze these team interviews to identify: - Common pain points mentioned across different roles, - Contradictions in how different people describe priorities, - Emotional indicators of team stress or burnout, - Potential process improvements implied by the concerns raised." Request evidence-based analysis: Ask the AI to ground its observations in the data by providing examples, quotes, or patterns rather than making unsupported generalizations.Iteratively refine your understanding: Use the AI's initial analysis as a starting point, then ask follow-up questions to explore specific areas of interest or concern. You'll need to talk with your AI buddy.Synthesize your understanding: Remember that AI is a thinking partner, not a replacement for your judgment. Use its output as input to your sensemaking process. Ethical Considerations and Limitations While leveraging AI for interview analysis offers significant benefits, it's crucial to maintain awareness of its limitations: Privacy and confidentiality: Ensure team members understand how their interview data will be used and processed.Compliance: Ensure that your approach meets your organization's compliance standards.Verification is essential: Always verify critical insights through direct conversation rather than relying solely on AI analysis. If you have any doubts, please ask the interviewees.AI has limitations: Current AI systems may miss subtle cultural nuances or specialized technical terminology. Also, remember that the customization of your AI account, your GPT (ChatGPT), your Gem (Gemini), or your Project (Claude) will affect the analysis!Human judgment remains central: AI provides input to your thinking and is not a replacement for your coaching expertise. Conclusion: AI as a Team Intelligence Ally Generative AI offers Scrum Masters a powerful new tool for quickly understanding team dynamics when joining a new organization or project. By handling the initial cognitive heavy lifting of pattern recognition, AI allows you to focus your energy on building relationships and facilitating meaningful improvements where it matters most. The goal isn't to automate understanding — it's to augment your natural coaching abilities by quickly surfacing patterns and connections that would take much longer to identify manually. Your time is better spent coaching, facilitating, and connecting with team members than manually analyzing text. Let AI help with the heavy lifting so you can focus on what truly matters: supporting your team during challenging times. The next time you're faced with a stack of interviews and limited time, consider how generative AI might help you make sense of the situation more quickly and comprehensively than traditional methods alone. P.S.: This sketched process also massively helps with customer interviews when you are in product management.

By Stefan Wolpers CORE

When you think of secrets scanning, most people immediately think about source code repositories on platforms like GitHub, GitLab, and Bitbucket. While the codebase is a source you absolutely should monitor, this is just a part of the overall secrets security story. Indeed, secrets leaking in code are a major concern. In the GitGuardian’s 2025 State of Secrets Sprawl Report, the scale of this issue has surged dramatically. In 2024 alone, over 23.7 million new hardcoded secrets were added to public GitHub repositories — a 25% increase from the year before. And that’s just GitHub. However, that is not the full story. The report also shows that today’s many critical secrets exposures are not in your code; they’re in your collaboration tools. Platforms like Slack, Jira, Confluence, and other project management and productivity tools have become high-risk zones for leaked credentials. Even worse, secrets found in these systems are more likely to be critical, harder to detect, and almost entirely distinct from the plaintext credentials found in source code. Secrets Are Sprawling Everywhere Work Happens In a traditional security mindset, secrets management begins and ends in the repository. Once your team has adopted a vault solution and you are scanning for the code and CI/CD pipelines, you are well on your way to secrets management maturity. If you have gotten your developers to standardize on prevention tools like pre-commit git scanning or embedded tools in their favorite code editor, you have achieved a major milestone towards better secrets security. The reality is that secrets are leaking in every tool your team touches, not just code and CI/CD platforms but across your full digital workspace. Messaging apps, ticketing systems, internal wikis, and even container registries are now active battlegrounds for credential exposure. In our 2025 State of Secrets Sprawl Report, we uncovered the fact that: 38% of secrets found in collaboration tools were classified as critical or urgent, compared to 31% in source code.Only 7% of secrets overlap between SCM (Source Code Management) and collaboration tools—these are mostly completely separate exposures. That last fact is extremely alarming and suggests that these secrets might be properly stored otherwise but are being copied in plaintext into workflow tools outside the codebase. Let's take a closer look at what systems are involved. Messaging Platforms: Slack and Microsoft Teams Chat is fast. Chat is informal. And that’s exactly why it’s dangerous. Slack continues to be one of the most notorious hotspots for secrets leakage, especially in real-time incidents, engineering huddles, or postmortems. But it's not alone anymore. Microsoft Teams, now a core platform in many enterprise environments, faces the same risks. Developers often share quick-fix credentials to help teammates debug without thinking through what happens if they get breached. Too often, once service accounts or API tokens are posted in threads, they are completely forgotten, yet these threads remain persistent in enterprise environments for years. Message history is retained indefinitely in many orgs, exposing secrets that are sometimes never rotated. Security teams often lack access or scanning capabilities in these messaging ecosystems. And unlike code repos, there's no concept of pull requests or reviews, just a never-ending stream of text, files, and unmonitored links. Ticketing Systems: Jira and ServiceNow While Jira has long been a central planning platform for many dev teams, ServiceNow is quickly emerging as a critical risk vector — particularly in IT operations, security, and support workflows. In both systems, secrets surface when support teams paste credentials to reproduce bugs or assist customers. Engineers all too often attach logs containing sensitive headers or tokens, which makes sense on the surface, as teams race to find root causes and fight fires, but the trail of secrets they leave behind is a nightmare waiting to happen. Both platforms are often perceived as “internal only” or “safe,” but history shows otherwise. Tickets are overlooked in access audits, are difficult to monitor at scale, and become long-term repositories for forgotten secrets. In fact, 6.1% of Jira tickets analyzed in the study contained secrets, many of which were still valid at the time of detection. ServiceNow presents similar risks, especially due to its heavy integration with automation workflows and non-technical users who may not recognize a secret when they see one. Documentation Platforms: Confluence Confluence remains a critical part of the modern collaboration stack, providing a quick and easy way for everyone to document their knowledge in a searchable and centralized platform. Unfortunately if teams are also placing their plaintext credentials in their internal wiki, it becomes a major liability. It’s a common place to find environment configuration guides with real secrets embedded. It is easy to add architecture diagrams that include access tokens or database connection strings for convenience. For teams that are rapidly expanding or folding in new members due to an acquisition, onboarding documentation might contain credentials “just to get people up and running.” These documents are persistent, rarely updated, and often overlooked in security reviews. Once a secret lands in a Confluence page, it’s indexed, searchable, and available to anyone with access permissions, which are often broad by default. If an attacker gains access, secrets are the first thing they will look for. Why Collaboration Tools Are So Dangerous for Secrets There are three key reasons collaboration platforms are especially dangerous environments for secret sprawl: 1. They Weren’t Built With Secrets in Mind These tools prioritize productivity and speed, not secure information handling. Unlike source control management platforms, tools like Slack or Jira lack any native secrets scanning, access scoping, or pre-submit protections. While pre-commit scanning is possible to automate in a developer workflow, preventing the pasting of a plaintext credential into a text field in a ticket or chat window is all but impossible to prevent. 2. Too Many Hands, Too Little Awareness Secrets don’t just leak from developers. Product managers, support staff, QA engineers, and basically everyone else with access can unknowingly paste sensitive credentials into a ticket or thread. Once posted, those secrets can live forever, buried in the backlog. 3. No Effective Lifecycle for What Gets Shared In a codebase, a hardcoded secret can be flagged, rotated, and replaced. In Slack? That secret may be reposted across multiple channels, shared in screenshots, or even pinned. It’s invisible to most traditional secrets detection tools and completely outside normal code review workflows. The False Sense of Security in Private Spaces One of the most dangerous assumptions organizations make is believing that because a space is private, it’s secure. But private Jira tickets, internal Slack channels, and restricted Confluence spaces are not immune to compromise. Phishing attacks, token theft, and lateral movement can give attackers access to internal tools, where secrets are just sitting there, often unmonitored. In GitGuardian’s analysis, private repositories were 8x more likely to contain secrets than public ones. The same trend holds true across productivity tools. People behave more carelessly in private spaces, assuming obscurity equals security. How to Handle Secrets Leaks Outside of Code Like with everything else in security, the solution requires that we align people, processes, and tools. This starts with awareness. Even reading this article is a step in the right direction. Making sure your team is aware of the dangers is a very positive first step toward eliminating the problems before they start. However, awareness alone is not the solution. The playbook for addressing this challenge also involves: Deploying real-time secrets detection across Slack, Jira, and Confluence using tools that are purpose-built for collaboration platforms.Consolidate alerts across systems — don’t treat a secret in Slack and Jira as separate incidents if they’re the same credential.Act fast: Valid credentials are often exploited within hours of exposure. Rotation and revocation workflows should be automated where possible.Establish internal playbooks for handling secrets found in non-code environments, and assign clear ownership for remediation. Demand More Productivity and Fewer Leaks From Collaboration Tools Your team’s productivity stack is becoming your largest unmonitored attack surface. It’s not just that secrets end up on these platforms; it’s that they’re shared in high-urgency situations, by a broader group of users, and with far fewer safeguards in place. From debugging to deployments to onboarding, secrets are copied, pasted, and forgotten in spaces never meant to secure them. And attackers know it.

Conflict resolution is not just a process but a skill for team leaders to address disagreements between two or more parties to drive consensus. Where different personalities and perspectives work toward shared goals, conflicts are certain to arise, and this can be particularly challenging for software development teams that thrive in collaboration and innovation. Whether it’s disagreements over technical approaches, mismatches in priorities, unclear requirements, differences in work styles, conflicting points of view, or arguments over standards, unresolved conflicts may have undesirable impacts on the core strength, collaboration, or productivity of a team. No matter how challenging it may seem, it becomes crucial for you as a tech lead to redirect disputes toward constructive disagreements so that your team of developers can work in sync with each other. To emphasize the importance of addressing conflicts head-on, the words from David Augsburger resonate deeply: “The more we run from conflict, the more it masters us; the more we try to avoid it, the more it controls us; the less we fear conflict, the less it confuses us; the less we deny our differences, the less they divide us.” In this post, we will look at 7 actionable strategies to encourage software development teams to look beyond conflicts and focus on delivering high-quality projects while preserving their peace of mind and innovation. 7 Conflict Resolution Strategies for Software Development Teams “Nothing can destroy the effectiveness of a team more quickly or more completely than unresolved conflicts between the members.” Watts Humphrey While a constructive exchange of differing views opens the route for betterment, rigid disagreements can quickly escalate and undermine a team’s ability to collaborate toward achieving the shared vision. That is the critical moment when tech leaders or managers must step in to guide their teams to rise above their differences to achieve their visions. And there exist effective conflict resolution strategies that you can implement to facilitate constructive discussions, keep everyone aligned, and skillfully manage interpersonal dynamics to keep your teams moving forward with purpose and confidence, making projects better and successful. Here are a few actionable steps that you can take to navigate and resolve conflicts effectively: 1. Identify the Cause of the Conflict According to 63% of individuals, identifying the underlying causes of conflicts is the essential first step toward resolving them. Conflicts don’t always manifest as major blow-ups — these smaller incidents can be just as impactful. So, start by observing subtle signs that might seem minor at first glance, such as stress, burnout, withdrawal, missed deadlines, unmet expectations, or a noticeable lack of motivation. Additionally, when the matter seems ambiguous or recurring, engage with developers to know whether it comes from technical issues or dynamics with other team members, so you can address the problem effectively. This is how, through insightful questioning, behavioral pattern analysis and resolving surface-level issues can prevent conflicts from escalating. Objectify facts and data in the process of dealing with situations, avoiding letting personal judgments, assumptions, or emotions influence the approach. 2. Promote Active Listening “When people respond too quickly, they often respond to the wrong issue. Listening helps us focus on the heart of the conflict. When we listen, understand, and respect each other’s ideas, we can then find a solution in which both of us are winners.” Dr. Gary Chapman That does not only apply to the development team but also to you as a tech lead. In conflict resolution, listening attentively to others when they are speaking is of utmost importance. When the team fails to listen to each other, be the impartial facilitator without forming opinions or intervening prematurely. Avoid being shallow just to appear involved; instead, gain a deeper understanding of the intent behind their concerns to uncover both apparent and hidden perspectives. This will help you respond more thoughtfully. To gain your team's trust and make them feel heard and valued, demonstrate true empathy and pay close attention in every interaction. Also, intervene diplomatically when any team member, intentionally or unintentionally, dominates or monopolizes the discussion. 3. Encourage Open and Transparent Communication Effective communication not only resolves but even prevents conflicts from occurring in the first place. By creating a safe space for your development teams to be vocal and vulnerable, you can address any underlying issues that could mess up their peace of mind. The right conversations, however, hold the potential to put out sparks before they escalate to blow out the team dynamics. You can achieve such clarity by pushing for real-time 1:1 conversations, regular check-ins, daily stand-up meetings, and cross-functional collaborations. This would challenge your team to collaborate way beyond the usual, build trust, and address challenges proactively. Besides that, you can encourage the usage of comprehensive project management and communication tools to enhance coordination and collaboration, especially for teams working in remote or hybrid work environments. It's important to remember that there may be situations where team members resist change or form opposing groups. Regardless of their discomfort or reluctance, you must bring all parties to the table for constructive discussions focused on resolution. Stay neutral, and avoid letting bias or side-taking cloud your judgment. 4. Create a Culture of Collaboration and Respect In development teams, especially cross-functional ones, where cohesive efforts are required to push against goals, it is necessary for teams to be on the same page and get along with each other. By creating a culture where team members learn to collaborate and respect each other, conflicts can be minimized. Host regular stand-ups so that your team gets to set aside their differences and let go of pride to come together and find common ground. Remember, conflict resolution is a joint effort. You can’t expect results if anyone in the team is unwilling to cooperate. To deal with disputes, you have to be the driving force that brings them together and helps figure out the resolution. By doing this, you create a safe space for the developers’ team to have healthy, respectful discussions, so that they remain focused, engaged, and aligned with the team's goal to optimize their workflow. 5. Step in the Role of Mediator Developers do require autonomy at a personal level, and they must be encouraged to find solutions to problems, discuss issues, and resolve personal differences on their own. However, there might be instances where self-resolution won’t be of good use. Team members with clashing personalities, varying experience levels, or conflicting work styles, can compromise the team's progress and productivity. In such situations, you must step in to take control of the matter and bring your own insight to guide the judgment after considering both sides. It's important to make it clear that your role is to facilitate, not to impose your own decision. Your intervention could be as simple as having a one-on-one conversation, setting up a neutral meeting, or something else for that matter. What matters is that everyone involved knows that you are impartial and are there to help resolve the issue. By doing so, you can bring everyone to a mutually agreed consensus so that work can proceed with efficiency. 6. Achieve a Balanced Resolution “Let’s not do it your way or my way; let’s do it the best way.” – Greg Anderson We know that when conflicts arise, we can expect both healthy debate and rigid opinions. While the former is easy to navigate, when faced with the latter, everyone becomes competitive to defend what they think is right. Regardless of whether the matter is brought to you or the team resolves it independently, encourage everyone to collaborate on finding a balanced solution that aligns with everyone’s interests. Educate them that it is not about winning or losing, and they should not take the conflict to their ego. As when teams work together effectively, it is the mutual benefit that should be prioritized and not individual preferences. 7. Escalate the Issue When Needed If, despite multiple rounds of discussions and your intervention, the resolution seems impossible and the matter does not appear to die down, bringing up the matter to high-ups is the right thing to do. There will be times when team members will doubt your intention. And that's normal. Because people tend to get blinded by the rage they hold against one another, leading them to not trust anyone but their own instincts. By handling escalation thoughtfully and professionally, you can encourage team members to trust in the process, and the organization’s culture as well. Senior management, HR, or at worst, CEOs are stakeholders who should be engaged when the issues escalate beyond your control. While the first two should be treated as the first point of contact, after your efforts, executive leadership should be involved only when the organization’s values or objectives are at stake. Conclusion: Turning Conflicts Into Opportunities Conflicts are bound to happen where either of the two or both parties are unwilling to compromise, find their way the right way, or simply collaborate to take the sole credit. While there might be several other reasons such as poor communication or unclear expectations, conflicts among development teams are no different. These can ruin team dynamics and stagnate both teams' and project's growth. To make sure the team remains working in sync, tech team leaders should enact solid conflict resolution strategies to improve creativity, potential expression, and productivity. From finding the root causes to knowing when to take matters into your own hands, you will minimize disruptions and prevent them from escalating to levels that are detrimental. It ensures that your team stays aligned, motivated, and ready to take on whatever obstacle may arise. Lastly, no matter how overwhelming conflicts may seem, remain calm during the entire process. Patience is the key to dealing with difficult conversations and promoting an inclusive environment.

TL; DR: Optimus Alpha Creates Useful Retrospective Format In this experiment, OpenAI’s new stealthy LLM Optimus Alpha demonstrated exceptional performance in team data analysis, quickly identifying key patterns in complex agile metrics and synthesizing insights about technical debt, value creation, and team dynamics. The model provided a tailored retrospective format based on real team data. Its ability to analyze performance metrics and translate them into solid, actionable retrospective designs represents a significant advancement for agile practitioners. The Rationale Behind Analyzing Team Data In this conversation, I explored Optimus Alpha’s capabilities in analyzing team performance data and designing evidence-based retrospectives. The exchange began with a familiar scenario: a Scrum team stuck in a “feature factory” mindset with stakeholders not embracing agile practices. While Optimus Alpha initially provided a solid retrospective format based on this limited information, the real test came when I introduced comprehensive team performance metrics from my AI for Agile Bootcamp cohort class. The synthesized data of MegaBrain.io’s fictitious MB1 team includes metrics on technical debt, value creation, stakeholder satisfaction, and team autonomy across multiple sprints. This simulated dataset represented the complex reality many Scrum teams face — balancing predictability with technical health, stakeholder demands with value creation, and feature delivery with sustainability. What follows is a remarkable demonstration of how Optimus Alpha absorbed this complex information, identified patterns that would typically require an experienced agile practitioner, and then transformed those insights into a tailored retrospective design with supporting materials. The conversation showcases analytical power and the ability to convert data insights into practical tools for team improvement. So, let’s get into the conversation with Optimus Alpha. Analyzing the Team Data With the Help of Optimus Alpha Round 1 I ran this chat on OpenRouter.ai on April 12, 2025. “SW” refers to myself, “OP” to Optimus Alpha. The text is unedited to preserve its authenticity: SW: My Scrum team has had a hard time; we are stuck in feature factory. Our Product Owner is little more than a Jira monkey, and no one is listening to me, the Scrum Master. Can you suggest a retrospective format my team can use to invite our stakeholders and help them understand that we could be more effective if we would only embrace agile practices? OP: Absolutely, and thank you for sharing your challenge so openly — feature factory symptoms and disengaged roles are all too common. It’s great that you’re ready to create space for everyone, including stakeholders, to reflect and learn together. Here’s a retrospective format designed to include stakeholders and shift the conversation toward agile value, not just output: Optimus Alpha — Retrospective Format #1: “From Feature Factory to Value Factory” Aim: Help the whole group — team and stakeholders — reflect on how you work, see the costs of “just pushing features,” and imagine better ways of working together. 1. Setting the Stage (10 min) Welcome everyone, explain the purpose: “We want to reflect together on how we’re working — not just what we’re building — and explore ways to better create value for our customers and business.”Establish Working Agreements (respect, honesty, curiosity).Remind folks: retros are blameless. 2. The Metaphor: “Factory Floor” Visual (5 min) Draw a simple assembly line on a virtual board or whiteboard.On one side, put “Inputs” (ideas, requests); in the middle, “Our Work,” and outputting “Features.”Ask: “Where does customer value show up here?” (Get a few responses from both teams and stakeholders.) 3. Group Activity: “What’s Working, What’s Not?” (15 min) Split everyone into breakout groups (mixed team and stakeholders).Each group answers: What do we do well right now?Where do we see pain, rework, or missed opportunities for value?Use sticky notes (digital or real), and post answers on the board under “Working” and “Not Working.” 4. Share and Cluster Key Themes (10 min) In the main group, everyone shares stickies.Scrum Master (you!) clusters themes — often you’ll see “no time to validate with users,” “little feedback,” “unclear priorities,” etc. 5. Reflection — Are We Doing Agile or Just Doing Stories? (5 min) Present the Agile Manifesto values or Scrum pillars briefly.Invite all: “Where are we supporting these values today? Where are we struggling?”Give a couple of minutes for silent reflection, then discuss. 6. Co-create Experiments (10 min) In pairs (ideally a stakeholder and a team), brainstorm one small experiment for the next sprint that could address a pain point (e.g., getting quick user feedback, clarifying the value of one backlog item, joint story mapping session, etc.).Each pair shares their idea briefly. 7. Closing and Commitment (5 min) Summarize: “If we want to escape feature factory mode, we all have a role.”Ask each participant: “What will I do differently next sprint?”Agree on 1–2 experiments to run, and how you’ll follow up. Tips and Rationale Stakeholders see they’re part of the system — their requests, engagement, and feedback matter.Makes visible the costs of just shipping features (technical debt, missed value, burnout, lack of learning).Creates joint ownership for change, rather than feeling like the Scrum team is just “whining.” Resources You Might Share Feature Factory SignsAgile ManifestoAtlassian’s Retrospective Techniques Let me know if you want some invitation language for your stakeholders or facilitation tips for handling tricky discussions! Note SW: All links are correct topic-wise and technically, although I had to add the title tags manually. Also, this is not just a good retrospective — it’s a transformational opportunity if followed through. It reframes roles, builds bridges, and ignites reflection on what really matters. For a Feature Factory, it’s a worthwhile approach: small risk, huge potential upside. Round 2 SW: Do you need more information from me to improve your retrospective suggestion? OP: That’s a great offer — thank you! Here are some clarifying questions that could help me refine my suggestion, making it more targeted and actionable for your specific context: 1. Stakeholder Context Who are the stakeholders you want to include? (e.g., internal business partners, external clients, managers, VPs…)What do you think “they” understand about Agile or Scrum now?How engaged or disengaged are they in your team’s work or results? 2. Team Dynamics How large is your team? (Size can affect activity facilitation)How long has your team been working in Scrum?How are your team members currently feeling about your process? Are they frustrated, disengaged, or burned out? 3. Pain Points and Symptoms Could you specify a couple of concrete “feature factory” symptoms you’re seeing? (e.g., relentless backlog churning, little feedback from users, no time for reflection/learning, missing sprint goals, etc.)Are there past attempts at improvement or retros that didn’t land well? 4. Goals for the Retrospective What’s your top wish for the outcome? (e.g., awareness/education, spark for change, specific agreement on next steps)Are you hoping to target a specific agile practice or value (focus, transparency, collaboration, delivering value, etc.)? 5. Risk Appetite Would your group be open to a bit of “creative tension,” or do you need to be more diplomatic and gentle in surfacing issues? The more context you can share, the more tailored (and likely effective) I can make the retrospective format, fitting your team’s culture, goals, and challenges. If you’d like, answer whichever of these jumps out, or just share anything else you think is relevant! SW: Before I answer your questions, please analyze the following team performance information and reconsider your questions. (Note: Here, I copied the synthesized team data from the Excel file into the context window.) OP: Thank you for sharing this comprehensive team performance data. It gives a richer, more nuanced picture than any qualitative conversation alone. Before updating questions or tailoring my retrospective advice, here’s a quick analysis of key patterns: Key Patterns and Insights 1. Feature Factory and Agile Health Experiments per sprint: Variable, sometimes 0, often 2–7 (average ~3–4), so the team is experimenting, but not always consistently.Technical debt %: Frequently high (14–25%), showing this is a persistent drain, likely slowing new value delivery.Education time: Fluctuates widely (as low as 1–2 hours, up to 11+), suggesting learning is sometimes deprioritized.Customer interactions: Moderate, but not always present every sprint (often 3–10). Not bad, but more could drive value.Stakeholder satisfaction: Mostly good, though dips into the 4–6 range sometimes with spikes to 9–10 — indicative of variability. 2. Agile Values and Team Dynamics Scores on autonomy, value creation, NPS, professional satisfaction, psychological safety, courage, respect, openness, and focus mostly trend between 5 and 9, but all dip into the 3–5 range at times, signaling periods of stress (see sprints 5–6, 15, etc.).Diary entries frequently mention: Management interferenceTechnical debt slowing progressPositive retros/workshops that seem to spike morale/value scoresStakeholder tensions, executive pressure, and production outages 3. Other Patterns Predictability is high (60–90%), but that doesn’t always equate to satisfaction or value.Escaped defects, incidents, and new/lost team members are low: this is a stable team, but with recurring process pain. What This Means for Your Retrospective The Team is NOT “Just a Feature Factory” There is some experimentation, customer contact, and educational investment.However, periods of “feature factory stress” (management interference, tech debt response mode, executive pressure) very clearly drive low scores in autonomy, satisfaction, and value creation. When the Team Works Well High autonomy and psychological safety, productive retros, stakeholder workshops, customer feedback => spike in both business and team satisfaction. Biggest Opportunities Consistent focus on value over pure output — variability is still high.Creating a more stable environment for learning, retrospection, and customer feedback, not just monthly or in “good times.”Better defense against management micromanagement and shifting priorities.Bridging perception with stakeholders — communicating value, not just throughput. Revised Follow-Up Questions With this data, fewer clarifying questions are needed, but I’ll tune them: What are your goals for the next retrospective, given these patterns? Awareness/education for stakeholders?Specific improvements (tech debt, stakeholder engagement, value focus)?Who do you want stakeholders to hear from? The team as a whole?Specific customer voices or data?Are there “untouchable” topics, or are you willing to openly discuss management interference and tech debt as a group?What timeframe do stakeholders care about most (week-to-week, quarterly outcomes, etc.)? How much appetite do they have for participating?In your view, which ONE success from the data is worth celebrating with stakeholders, and which ONE pain would you like to see acknowledged or solved? If you answer even a couple of these, I can create a high-impact, data-driven retrospective format (perhaps even using this data as a visual centerpiece to spark joint learning and action among stakeholders and the team). Would you like a sample format that incorporates this data as a mirror for your group? Or do you want to further discuss the questions above? SW: Regarding your follow-up questions: Help the stakeholders understand that the team could create more value for customers and the organization if we leave the feature factory and embrace agile product development. It is about education, opportunities, trust building, overcoming technical debt.The team as a whole.No.At the moment, they are focused on the team meeting its commitments as defined in their year-long product roadmap, a problematic approach in a complex environment.Celebrate: High predictability despite the circumstances. Pain point: technical debt accumulation. Please revise your suggested retrospective format. OP: Thank you for the clear answers! Here’s an improved retrospective format tailored to your needs, data, and objective to educate and inspire stakeholders, shifting them from feature factory thinking to agile, value-driven product development. Retrospective Format #2: “Beyond Commitments: Building Lasting Value” Audience: Scrum team and stakeholdersDuration: 60–75 minutesGoal: To build trust and educate stakeholders on how greater agility, autonomy, and focus on technical health can boost business value — beyond simply hitting features on a roadmap. 1. Welcome and Framing (5 min) “We’ve been very predictable, but our team sees even more potential for value — if we work together to modernize how we deliver.”Ground rules: curiosity, no blame, focus on opportunities.State purpose: “Let’s look at the real story our data tells, and find ways to unlock even more value for our customers and business.” 2. Celebrating Predictability (5 min) Show a simple chart of your team’s high predictability.Acknowledge: “We’re proud to honor commitments, despite challenges. That’s a real team achievement worth celebrating!” 3. Looking Deeper: The Value vs. Output Exercise (10 min) Show two simple graphs: a) Predictability (high, steady)b) Technical debt/Value creation/Stakeholder satisfaction (variable/fluctuating)Prompt: “What do you notice? What stories hide behind the numbers?”Invite both team and stakeholder reactions. 4. Short Team Stories: What’s the Cost of Feature Factory Living? (10 min) Team members each briefly share: A time when technical debt or backlog pressure forced a tradeoff.A time when autonomy/conversation with customers led to real value or insight.Ask: “What might have been possible, if we’d invested differently?” 5. Joint Brainstorm: Obstacles to Value (15 min) Use two columns (“What helps us create value?” / “What blocks it?”) The team and stakeholders add stickies or notes.Common “blockers”: technical debt, rigid roadmaps, lack of room to learn or adapt, excessive management intervention.Common “helpers”: customer feedback, refactoring, team autonomy, experiments, stakeholder trust.Cluster and discuss. Highlight technical debt as the recurring pain point. 6. Opportunities for Change: Data-Driven Reflection (15 min) Show a visual of technical debt trend and its correlation with value creation or satisfaction (even rough/qualitative is fine: “Our biggest dips in value/satisfaction align with tech debt crunch periods…”)Prompt stakeholders: “If we could reduce tech debt and invest in technical health, what new doors might open?”“What would make you more confident to allow the team to explore, experiment, or adjust course?”“How might we move from only delivery, to creating value that lasts?” 7. Pick 1–2 Team + Stakeholder Experiments (10 min) Examples (modify as needed): Block 10% of time for tech debt/health work every sprint; review outcomes with stakeholders.Monthly customer feedback or “value alignment” review — invite a stakeholder to join.Jointly define what “real value” means for a sprint or feature.Hold quarterly review: “Is our roadmap still the highest-leverage way to deliver value?”Agree to try one or two for the next sprint/month. 8. Commitment Rounds and Closing (5 min) Each person (including stakeholders): “One thing I commit to do, say, or advocate for in service of value creation, not just feature delivery.”Thank everyone for honesty, curiosity, and partnership. Facilitation Pointers Use rich data visuals — a couple easy graphs (predictability, tech debt, value creation, satisfaction) spark honest discussion.Encourage the team to speak frankly about technical debt consequences.Invite stakeholders to reflect on what *they* would gain if the team could work with more agility and autonomy (“would we get to market faster, respond to changes better, attract and retain top talent?”).Model mutual respect: “Everyone here wants to deliver business value — but the path isn’t always just through features.” Optional Pre-Work Consider sending the predictability and tech debt charts (and maybe a value creation graph) to stakeholders ahead of the session, with a note: “We’re incredibly proud of our team’s reliability, yet we see a chance to unlock even more business value by working smarter. Looking forward to exploring this with you in our next session!” Let me know if you want: Example data slide templatesStickies/board templates for Miro/Mural/JamboardMore story prompts for your team This format will help you shift the conversation from mere throughput (“predictable factory”) to real, meaningful, sustainable value — for both customers and the business. Note SW: The revised retrospective format is an interesting outcome: Adding real team data helped shift our retrospective from general discussion to focused conversations about what really affects results. Instead of just talking about feelings or processes, we looked at facts — like predictability, tech debt, and team satisfaction. This shift would make it easier for everyone, including stakeholders, to see where we are stuck and what is possible. It turned vague concerns, addressed by the first format, into clear opportunities for smarter decisions and better long-term outcomes. This development perfectly reflects one of the outstanding capabilities of large language models: analyzing large quantities of data, identifying patterns within them, and suggesting next steps. And Optimus Alpha proves to be really good at this. Of course, I asked to deliver all of the before-mentioned artifacts only to be disappointed; Optimus Alpha could not deliver yet. Conclusion: Optimus Alpha’s Retrospective Formats Optimus Alpha’s ability to analyze team data and design targeted retrospectives demonstrates the transformative potential of AI for agile practitioners. Tools like Optimus Alpha can help teams escape the feature factory trap and build more sustainable, value-focused ways of working by quickly identifying patterns in complex metrics and translating them into actionable formats. The model’s insight into the relationship between technical debt and value creation, team autonomy, and stakeholder satisfaction provides evidence-based foundations for retrospectives that might otherwise rely solely on subjective experiences. This approach represents a new frontier for data-informed facilitation — where LLMs serve as information processors and insightful partners in team improvement, easing communication with more data-oriented stakeholders. As agile practices continue to evolve, the opportunity to augment your retrospectives with AI-assisted data analysis could be game-changing: no more “gut-feeling-driven” change requests! Have you experimented with analyzing team data using AI tools? How might an LLM like Optimus Alpha help you identify patterns in your team’s metrics that could inform more impactful retrospectives? Please share your experiences and thoughts on how this approach might enhance your team’s journey toward greater business agility and sustainable value delivery.

By Stefan Wolpers CORE

People worry — sometimes with justification, but often without — about small things that might have tipped an interview or job hunting interaction against them. Were they dressed “right”? Too dressed up? Not enough? They joined the zoom call 1 minute late, did that ruin the whole thing? Did they answer every single question? Did they ask too many questions themselves? And on and on and on. My perspective — built from 35 years in tech as well as trying to help friends find their own new roles from time to time — is that most interviews are a series of moments that all add up. No individual moment is a deal-breaker. Instead, how a candidate “did” often comes down to the overall sense the interviewer feels after the entire conversation. (I would say “vibe” here, but my kids have been very clear that the word is absolutely forbidden for my personal use.) However — and again, based on years of experience — I have found ONE question with the potential to immediately disqualify you. It’s a question you’re almost certain to hear, sometimes more than once, regardless of the industry, company, or role. “Why do you want to leave your current job?” What’s So Bad About THAT?!? It’s such a simple question, even trivial. And it’s neither a trick question nor an interviewer’s gambit to get the candidate to divulge something against their own interest. It’s also highly relevant, which is part of why it’s so dangerous to candidates. Here’s why: I’m here to tell you that there’s not a single manager walking the planet who gives the smallest fig on the oldest tree why you want to leave your current role. What they — and the company behind them — really want to know is, “Will you leave US the way you’re leaving THEM?” So, what is your answer? Obviously something like “my boss was a complete jerk” or “they made me work too hard” is a supremely poor choice. But other answers might seem more innocent and fail the test. What are some common reasons people leave their jobs? To be sure, having a bad boss is at the top of the list, as is poor work-life balance. But there are plenty of other common reasons: A toxic corporate cultureNo chance for advancementPoor compensationUnchallenging workA job that’s really 3 jobs in a trenchcoatUnclear objectives, whether it’s for the company, the team, or your job specifically. Obviously, there are many more, but I’d like you to look at that list of reasons — as well as any you may have mentally added — from the perspective of the person on the other side of the desk. Unable to fit in with a teamUnwilling to put in the work for a promotionUnsatisfied with salary after negotiationIncapable of completing jobs as assignedRequires explicit direction and supervision I’m not saying either list is 100% accurate. On the other hand, none of them are 100% fabrications, either. As with so many things, the truth lies somewhere in the middle, where nuance, context, and details matter. But an interview isn’t always a place where we can rely on the chance to provide nuance, context, or details. And, as I say, this is the ONE question that could lose you the job. Consider Your Next Words Carefully So what do I suggest? While you might be tempted to invent a complete fabrication, don’t. Not only is lying unethical, but it’s also a bad habit to get into. On the other hand, this is a job interview, not therapy. You don’t owe anybody (except, perhaps, your therapist) your innermost thoughts and justifications. My suggestion is to take a minute (before the interview) and come up with your own list of reasons for leaving. Odds are, you have more than one. Most folks do because it’s rare for someone to leave a job because of a single issue. It’s usually a combination of things, each compounding upon the next, until what you have is a work environment that no level of hard conversations, compromises, or lateral moves will resolve. Once you have your list, look for ones that, as much as possible, leave both you and your current employer as blameless as possible while also emphasizing the immutability of them. Then wrap that reason around a series of positives before you drop your “but”. For example: “You know, my team was great. Everyone was an absolute champ and a lot of fun to work with. And the company was doing some interesting stuff. In fact, that’s what I was doing — working on some of the latest initiatives. BUT… once we’d gotten the new system up and running, there was nothing on the horizon. The company wanted me to move into an operational-type role, and I’m just not a “keep the joint running” kind of person. I’m energized by learning new things and finding new applications for what I’ve learned. It’s really not that I wanted to leave XYZ company as much as they needed me to move out of the work I was hired to do, which is the work I love doing and what I was hoping I’d be able to continue doing here.” That’s the general structure. Other ideas you might build your answer around include: The company had been fully remote and is now pushing for in-office a few days a week.The project you were hired to work on ended, and there was no new project to move to and you’re just floating between smaller jobs.The company trained you in a new technology, but there’s no role on the team responsible for that tech, so you’re effectively stuck in your old role waiting for someone to leave while all those new skills go unused (and fading from memory). The point is to make a case where your reasons for leaving aren’t anyone’s “fault” but simply a matter of circumstances that both you and your current employer would have preferred to avoid, and which your new employer can safely say, “We don’t do that kind of thing here.” And thus, your reasons for leaving your old spot aren’t a pattern so much as an unfortunate fact of business. The Get Out of Jail Free Card There is one group of folks who have, whether they realize it or not, the perfect response. Everyone who’s been touched by the epidemic of layoffs has, in one of the biggest examples of real-world irony I’ve ever seen, a hall pass on the interview-killing question. “Why do you want to leave your current job?” “Well, to be honest, if I had my choice, I’d still be there today. I loved the work I was doing, my team, and the mission. But 3 weeks ago, 2,300 folks from XYZ were shown the door, and I was part of that group. I know the phrase, 'it’s not personal, it’s just business' is a cliche, but when 2,299 other people are also laid off, I’m pretty sure it really WASN’T personal.” While layoffs might have been a certain kind of scarlet letter once upon a time, it’s now become so common that it’s hard to find someone during the interview process who wasn’t, themselves, touched by it, which translates to a possibility for empathy. Thanks, Leon. One More Thing to Stress About Not really. I didn’t write this to fuel your anxiety during what is almost certainly a deeply anxious experience. I wrote it because I’ve seen too many folks blithely answer this question, never realizing how much damage they did to their chances. Now that you are aware of it, you can prepare for it. In the meantime, if you have other thoughts on the subject, questions, corrections, or kudos, I’d love to hear them in the comments.

Modern authentication protocols, such as SAML and OpenID Connect (OIDC), rely heavily upon federation as the guiding principle to secure front-door authentication. Federation is an elegant approach for web-based applications to isolate authentication from the application using a trust established between the centralized identity provider (IDP) and a subscribing application. Armed with asymmetric key-based digital certificates, federation ensures that an application can securely leverage an external IDP service for authentication and free itself from the burden of handling user interaction during the authentication process. With federation comes the concept of single sign-on (SSO). Suppose the centralized IDP has already established a secure authentication session. In that case, multiple applications can benefit from being able to single sign-on and bypass redundant login, improving user experience and reducing authentication frictions. Limitation With Current Protocols However, federation exhibits limitations with modern applications, especially native mobile applications. Consider the scenario of an insurance provider's mobile application using biometric authentication with a central IDP. Once a user logs in to the application, she might want to go to a banking partner web application, for instance, by clicking a link inside the application. For a seamless user experience, the expectation is to transparently log the user in with SSO to the partner web application. The partner web application can be configured to use the same central IDP, but federation does not work since the mobile app does not have any easy way to share an existing IDP session with the web app. The de-facto technique for web SSO is using browser cookies, something that the native mobile app can not share with the mobile's system browser. As a result, the partner application that opens in the system browser does not know there has been an existing session, and SSO does not happen. Instead, the user would have to re-authenticate to the partner app. A potential solution is to use a shared identifier other than a browser cookie. That approach works well for SSO between mobile apps. Since both apps reside on the same device, they can securely share a session identifier using other forms of secure storage, such as the keychain in iOS devices. There is a standard specification for native-to-native SSO with OIDC. Unfortunately, there is no obvious way to implement SSO in a native-to-web scenario using industry standards. Often, individual applications resort to insecure ad hoc approaches. Identity Bridge Concept The identity bridge framework attempts to provide an architecture that applications can use to implement native-to-web SSO securely, staying close to the industry standard OIDC protocol. Since the protocol does not have a mechanism to support SSO out of the box, it needs a few additional constructs. First, the bridge relies on the central IDP's ability to delegate authentication to another IDP. This capability is known as inbound federation. Most commercial and open-source IDPs support inbound federation. Technically, communication between the two IDPs can use any federation protocol (SAML or OIDC) independently. However, the framework recommends OIDC since the industry is increasingly adopting it for new applications. Also, a service that we will call the Bridge service needs to be deployed. It will act as the inbound IDP communicating with the central IDP using OIDC. The bridge does not need a user interface; it will simply work as a proxy redeeming the mobile app authentication token for a web SSO session from the central IDP. A Model Bridge Framework Here is a model of the basic architecture for the bridge: There is a lot to unpack here: The user opens the mobile app and authenticates with the central IDP.Central IDP issues an authentication token to the application. For OIDC, the ID Token can be considered as the authentication token.The user then clicks on a web link (for the target web application) inside the mobile application.The web link opens in the system browser. The link also contains the authentication token as a parameter.The web application extracts the authentication token.The web app application initiates an OIDC authentication with the central IDP. First, it redirects to the IDP's /authorize endpoint. It also passes the authentication token in the login_hintparameter.The central IDP initiates another OIDC authentication flow, with the bridge acting as the inbound IDP. It passes on the login_hint parameter, which contains the authentication token from the mobile app.The bridge then generates a temporary authorization_code for one-time use and redirects back to the central IDP with the authorization code.The central IDP makes an HTTP POST call to the bridge's /token endpoint.The bridge validates the authentication token obtained from the mobile app passed through the login_hint parameter in the previous step. Using information from the source authentication token, it generates a new JWT token, signs it using its private key, and returns it to the central IDP.Next, the central IDP calls the keys endpoint of the bridge to obtain the public key to verify the signed JWT token.After the JWT is validated, the central IDP creates a web session and completes the authentication process by redirecting back to the web application. Security Considerations and Limitations Security Risks and Challenges Although the framework was designed using the standard OIDC protocol, it is not without its limitations. A proxy service must be deployed and maintained by adding an additional component to the solution. The vanilla architecture does not deal with many token security aspects. If additional measures are not taken with a leaked mobile authentication token, bypassing authentication in the web application is possible. Security Enhancements One implementation consideration for protecting the token is not using the original authentication token from the mobile application. Mobile applications tend to have long-lived tokens, and using them to perform SSO with a web application significantly increases the risk of session compromise. For example, the following strategy can be adopted: Before starting the web SSO, obtain a separately scoped ultra-short-lived ID token from the primary IDP. Use the new token to perform SSO instead of the original token for the mobile application. The ideal solution would be direct protocol support for native-to-web SSO. This would help avoid additional components and reduce the implementation complexity. Several promising works are underway to create an industry specification in this space. Implementation This section details a prototype implementation using Okta as the primary IDP. The bridge essentially acts as an OIDC server and implements three primary APIs. 1. /authorize This is the OIDC authorize endpoint. Okta will redirect to this endpoint using HTTP 302. The endpoint accepts the parameter login_hint, which carries the native token generated when the native app authenticates with Okta. For the prototype, the ID token issued by Okta is used as the native token. JavaScript app.get("/authorize", (request, response) => { customNonce = request.query.nonce; response.redirect( request.query.redirect_uri + "?code=" + request.query.login_hint + "&state=" + request.query.state ); }); Instead of generating a transient authorization code, the native token itself is passed back to the Okta redirect endpoint using the code parameter. Okta also generates a nonce value in the response. The nonce must later be included in the token. 2. /token OIDC token endpoint. Okta calls this endpoint to redeem the authorization code for a token. Communication occurs between Okta and the bridge. The resulting token is not reused in the application context. JavaScript app.post("/token", async (request, response) => { let originalClaims = jwt.decode(token); let claims = {}; claims.nonce = customNonce; claims.sub = originalClaims.sub; claims.ver = originalClaims.ver; claims.iss = originalClaims.iss; claims.aud = originalClaims.aud; claims.email = originalClaims.sub; customKeystore = jose.JWK.createKeyStore(); let result = await customKeystore.generate('RSA', 2048, {alg: 'RS256', use: 'sig' }); publicKey = result.toJSON(); privateKeyPEM = result.toPEM(true); publicKeyPEM = result.toPEM(false); customJWK= jwt.sign(claims,privateKeyPEM, { algorithm: 'RS256', header: { typ: 'jwt' } } ); var responseData = { access_token: customJWK, token_type: "Bearer", expires_in: 3600, scope: "openid", id_token: customJWK, }; response.send(responseData); }); In the prototype, the implementation effectively copies the claims of the native token to create a new JWT and includes the nonce value generated during the authorization step. It then signs and sends the token to Okta. To sign the token, it generates an ephemeral key pair for a one-time use. 3. /keys OIDC key endpoint. Okta uses this endpoint to fetch the public key of the bridge to verify the signed token issued at the /token endpoint. The implementation should return the ephemeral public key and then discard the keypair. JavaScript app.get("/keys", (request, response) => { let keys = {"keys":[publicKey]} response.send(JSON.stringify(keys)); }); A working prototype of the bridge service using Okta is available here. Sample applications for testing: Native app and Web app. Security Analysis The identity bridge is transparent to various applications. It communicates only with the primary IDP using OIDC, which is a secure federation protocol. The bridge must send a signed token to the IDP. It generates ephemeral key pairs for signing. Because every authentication is aided by a unique random keypair, a complex key management is not required. The bridge must ensure that the key pair is discarded after use or is in a failure condition. The authentication token used should have a sufficiently small value to reduce the potential for token leakage and guard against token replay attack. To further reduce the risk of token leakage, another idea is to use a very short duration token from the primary IDP generated just before initiating the SSO, instead of using the primary authentication token from the native application. Additionally, the bridge should be configured to accept requests only from whitelisted IPs related to the primary IDP. Real-World Use Cases Let us look at a few real-world scenarios in which native-to-web SSO flow is common. Corporate Portal A corporate mobile application can have links to authorized applications that are web-based and open in a system browser. After employees log into their corporate web portal, they typically single sign on to the applications they are authorized for. To provide a similar feature when they access the portal through the company's mobile application, a native-to-web SSO flow is required, particularly for web-based applications. Online Travel Agency The mobile app for an online travel agency can have web links to its partner airlines and hotels. Customers can then log in to the mobile app and click on their preferred airline or hotel to directly access and manage their bookings from their respective websites. Healthcare The mobile app for a medical provider or hospital can allow access to the web-based patient portal (for example, Epic MyChart) without the patient needing to be authenticated in the patient portal again. Streaming and E-Commerce Many consumer-facing streaming and e-commerce applications provide core features through mobile applications such as streaming videos or allowing consumer shopping. They redirect users to the web interface for other features such as account and subscription management. native-to-web SSO will allow consumers to switch to the web interface without re-authentication, thereby improving the user experience. Vendor Portal Similar to corporate portals, organizations typically create mobile applications for B2B portals, such as vendor applications. Vendors can have access to several web-based applications through the portal and hence will benefit from the native-to-web SSO ability. Conclusion Today, applications increasingly use different platforms and devices to provide flexibility to users and allow them to access an application from any place and device. The idea is to bring the applications close to users and allow them to access them from any digital platform that they might be using. Properly authenticating and authorizing users without disrupting their experience and productivity in such an environment is critical. The OIDC bridge complements web-to-web federation and native-to-native SSO standards to provide authentication and SSO services across applications in all possible devices and platforms.

Scientific research is about finding quick, dependable, and efficient ways to accomplish goals. As the demand for high-quality data is essential for making accurate analysis, automation has become a necessity for streamlining the workflow. Automation is transforming research in many fields, helping with everything from collecting data to managing workflows, making research faster and more productive. We explore real-life examples of automated systems that are helping businesses and researchers innovate and accelerate discoveries. Why Automation Matters in Research Today As research grows increasingly data-intensive, automation practices provide an efficient way to process massive datasets and execute complex workflows to access insights faster. There is a growing need for accelerated computing solutions to power artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) workloads. GPUs and purpose-built accelerators have emerged as indispensable tools, especially for research fields that demand precision, speed, and scalability. Dynamic Allocation: Optimizes resource use by dynamically allocating based on workload needs.Real-time Monitoring: Provides instant visibility into performance, enabling proactive issue resolution.Cleaning Data: Executes data preprocessing upon ingest into feature-rich data points to fit business workflow.Workflow Orchestration: Automates complex workflows like data ingestion, evaluation, and notification.Error Reduction: Minimizes manual errors, improving reliability and consistency.Accelerated Innovation: Speeds up AI/ML development, enabling faster experimentation. Streamlining Data Collection and Preprocessing One of the most time-intensive parts of any research project is data collection and preparation. In fields like genomics, neuroscience, and environmental science, researchers often deal with hundreds or even thousands of data samples requiring manual logging, cleansing, and standardizing. The data cleaning process is not only labor-intensive but also prone to errors. Automation has changed the game in multiple ways. Automated Data Collection: Modern automation tools enable high-speed data collection and processing, with high-throughput screening analyzing thousands of samples daily for stronger insights. IoT sensors ingest real-time data for applications like environmental monitoring and material performance, tracking variables such as temperature, humidity, and chemical composition without manual intervention. A structured approach to data ingest and preprocessing accelerates analytics, streamlining research and decision-making.Data Preprocessing and Quality Control: Machine learning automates data cleaning, flagging inconsistencies in real time while GPUs preprocess vast datasets for faster insights, such as in genomics. AI-driven quality control ensures standardized, error-free data, reducing human error in screening, decision-making, and reporting. By automating these redundant tasks, AI frees up time for researchers to focus on higher-level work, making it an essential tool across industries.Simplifying Documentation with ELNs: Electronic Laboratory Notebooks (ELNs) automate experimental data capture, storing information in a digital, searchable format with version control and change tracking. Secure data storage prevents loss and ensures accurate reporting, while automated logging in biotech research has significantly reduced reporting errors, improving documentation and traceability.Workflow Management Systems: AI-driven workflow automation simplifies resource allocation, scheduling, and progress tracking, improving collaboration in large research projects. With AI’s influence expected to impact up to 80% of the U.S. workforce, by reducing manual workloads in data-intensive fields, workflow systems enable researchers to focus on innovation rather than repetitive administrative tasks. How Automation Shaped the Modern Computing Era The practical impact of automation in research is best illustrated by real-world applications, especially in recent groundbreaking history. Here’s a closer look at some industries that have made a huge impact with the help of AI. Pharmaceutical Research: Moderna’s COVID-19 vaccine case is a hallmark of how automation can be applied to streamline vaccine development. Automated mRNA sequence design, high-throughput screening, and robotic automation played key roles in accelerating the development process, allowing Moderna to deliver life-saving vaccines in record time.Environmental Research: NOAA uses automation in its climate monitoring system to transform environmental science. By combining satellite data and automating a data cleanup to efficiently feed machine learning, NOAA’s models can make more accurate climate predictions, crucial in addressing pressing global issues like climate change.Materials Science: Shell’s team leveraged NVIDIA DGX for engineering simulations such as subsurface imaging and digital rock analysis, which involves mapping pore structures in rocks. With a streamlined workflow pipeline and NVIDIA GPUs reducing high-fidelity CFD simulations, Shell is able to deploy and use efficient resources to explore sustainable materials like biomass and renewable feedstocks in industrial chemistry. Conclusion Automation in research has fundamentally redefined productivity. By automating processes like data collection, analysis, and documentation, researchers are achieving insights faster, with greater accuracy, and at a scale that was previously unimaginable. But it’s not just about speed — automation enables researchers to explore deeper, innovate further, and address global challenges with precision. Increased Integration of AI and ML: AI-driven experimental design and hypothesis generation will make research smarter and more proactive, driving new discoveries by modeling complex systems.Advanced Computing Resources: Accelerated computing could unlock new possibilities for complex simulations, especially in drug discovery and materials science.Enhanced Collaboration Tools: The future promises more interconnected research platforms, enabling real-time data sharing and cross-institutional collaboration on a global scale. The path forward lies in striking a balance between the benefits of automation and the irreplaceable value of human insight. Human creativity and automated efficiency can propel research into a new era of discovery and impact, one where innovation isn’t limited by time or scale but empowered by innovators and AI automation working harmoniously. AI agents have become a huge topic in the AI era for developing agentic AI that can plan and execute decisions based on a prompted goal.

The software development world is growing at a rapid pace, meaning that the quality of work should never be compromised while delivering projects on time. Agile outsourcing merges the iterative and flexible approaches of Agile development with the scope and breadth of outsourcing. This allows companies to change in response to market shifts, equate to the different skill sets available in the market, and ensure that work is being done continuously, which results in expedited delivery of software projects. Benefits of Agile Outsourcing in Software Development Agile outsourcing enables companies to react quickly to new obstacles and opportunities. By working with external partners, businesses have the ability to respond to scaling project demands by efficiently aligning resources with needs. This form of outsourcing is especially helpful in today's time-sensitive market. Working with nearshore outsourcing companies grants businesses access to a wide range of talent across the globe. Such collaboration incorporates expert skills into your projects which increases the rate of innovation and quality. Consequently, using such diverse skill sets makes tackling complex problems easier and adds insight into the development process. Third-party contracting is an effective strategy for businesses seeking to minimize operational and infrastructural costs. Deloitte indicates that 70% of corporations resort to outsourcing as the primary source of cost reduction. There are great savings that can be achieved with minimum effort by outsourcing parts of organizational processes. Such financial efficiencies are incredibly useful as they can be redirected to other critical areas of importance in the business. Source Accelerating Project Delivery through Agile Practices If you incorporate continuous integration (CI), you will start noticing and fixing problems much earlier, which will reduce release time and increase code quality. CI consists of developers integrating code into a shared repository several times a day. Automated builds and tests are then run. The feedback is immediate and can substantially improve the quality of code and speed to market. The combination of both synchronous (e.g., video calls) and asynchronous (e.g., chat) tools promotes transparency and trust within the team. Such open communication serves to keep everyone aligned toward common objectives while minimizing misunderstandings and enabling efficient cooperation. Having different team members in various time zones can translate into almost continuous software development. This helps to achieve significantly shortened product time to market as work is done 24/7. Source Challenges and Mitigation Strategies Engaging in a nearshore agile outsourcing journey greatly enhances various business aspects. However, potential issues must be addressed appropriately. Here’s how to mitigate challenges: 1. Decision on Outsourcing Vendor Competence Evaluation and Company Culture Fit For your partnership, your prospect should be a capable person who can do the technical aspects of your company. Otherwise, it would suggest that there is likely going to be a conflict that would cause a misunderstanding and subsequently delay the project. Out of scope, a research conducted stated that 25% of outsourced projects fail because of communications issues (16) or cross culture issues (9). To resolve this: Conduct detailed interviews. To solve the problem at hand, it is important that you have detailed conversations with the vendor so that you are proficiently able to analyze their knowledge and skill set as well as their understanding of your industry.Check for cultural alignment. Their clauses and practices should be able to assimilate within your company’s core values to be able to work productively together. Communication Skills Assessment This is a requirement that should be completed at all times. Any issues that have sprouted due to language barriers, coupled with differing time zone, will be a hindrance to the success of the project. Additionally, 44% of problems in outsourcing are a result of the lack of proper communication channels. To tackle the communication barrier, it is advisable to: Check written and spoken language skills. The vendor you choose should have fluency in the language you predominantly use to avoid any confusion from arising.Test communication responsiveness. Response rates should be measured for the initial phases of engagement to know what to expect in later interactions. 2. Streamlining Communication Procedures Setting Up Systematic Meetings and Reporting Regular meetings foster transparency and trust. Set up progress check-in meetings where goal alignment and concerns can be addressed. To achieve this, ensure that: Set up periodic catch-up meetings. Set up bi-weekly or weekly meetings centered on reporting progress or thoughts on issues raised so far.Send status reports. Routine reports should be drafted and distributed in as much detail as possible so that the intended goal can be detailed. Implementation of New Collaboration Platforms and New Tools The right tools could assist in minimizing blunders, especially with remote teams. The introduction of video conferencing and using project management tools helps to ensure that all team players are aware, and all hands are on deck. Remember that: Implementation of planning tools. Trello and Asana can be very useful as they can track completed tasks and timelines.Team communication. Install Slack and Microsoft Teams for instant messaging or notification purposes to address any urgent updates. 3. Division Of Work Preparing Detailed Project Reports As far as informed decisions are made, documentation from all parties needs to be considered, hence the reasons for documentation. To avoid misinterpretation, great detail and precision need to be established at the outset. As always, ensure that: Clarifying project goal. Elucidate on which tasks need be performed and the result that is expected.Defining success indicators. Number the aspects that assure quality so that they will be achieved. Expectations for a project will always be misguided, which brings about delays. This is an important consideration for every stakeholder. Outline applicable deliverables. The expectations for every milestone of the project should also be defined.Determine realistic timeframes. Set deadlines that are practical in order to be productive. Best Practices for Successful Agile Outsourcing Implementing agile outsourcing can crucially improve the factors within the software development process. To fully optimize your performance, the following best practices must be acknowledged: 1. Choosing the Most Suitable Partner for Outsourcing Appraising technical skills and culture. Be sure to take a closer look at a partner’s development and agile experience. For example, almost all companies today, around 94%, report the usage of agile, which means they are heavily adopting it. Moreover, they need to have a compatible culture in order to be able to work together successfully.Evaluating their communication skills. Agile project management relies heavily on direct communication. Therefore, fellow nearshore outsourcing agencies are preferred because, besides the language barrier, the time zone and culture will also be in unison as well. 2. Setting Up Strong Communication Structures to Avoid Miscommunication Scheduling regular follow-up meetings and progress updates. The flow of communication across the board needs to be regular in order for everyone to be on the same page. Frequent get-togethers need to be planned to update each other on the milestones achieved, challenges faced, and chances that have emerged.Using modern chat tools as well as project management software. Communication over project timelines and objectives can take place in near real time, and this leads to high transparency and effectiveness. 3. Itemizing Tasks and Functions as well as Classifying People Elaborate project documentation. A comprehensive documentation highlights project requirements, deadlines, and workflows which helps all participants to achieve desirable results.Managing your expectations with your deliverables. With the aid of your team and the outsourcing unit, try to reach an agreement on the goals of the project and what is supposed to be delivered. It is important for the project to provide the expected results. Conclusion Agile outsourcing is the integration of agile methodology’s flexibility along with the power of outsourcing to increase the speed of business software project completion. External teams enable companies to iterate, test, and release the software in record time, which improves competitiveness by speed to market. The method also permits changes to be made to the evolving needs of the project while maintaining the desired quality. The agile outsource approach enables resources to be consumed as they are needed for the targeted result expectation, achieving seamless cooperation and effective positioning to cope with swiftly changing market demands.

TL;DR: A Harvard Study of Procter and Gamble Shows the Way Recent research shows AI isn’t just another tool — it’s a “cybernetic teammate” that enhances agile work. A Harvard Business School study of 776 professionals found individuals using AI matched the performance of human teams, broke down expertise silos, and experienced more positive emotions during work. For agile practitioners, the choice isn’t between humans or AI but between being AI-augmented or falling behind those who are. The cost of experimentation is low; the potential career advantage, on the other side, is substantial. A reason to embrace generative AI in Agile? Introduction: The AI Dilemma in the Agile Community Interestingly, Agile practitioners are no strangers to skepticism about new tools. The Agile Manifesto’s emphasis on “individuals and interactions over processes and tools” has led some to dismiss generative AI (GenAI) as another buzzword that distracts from human-centric collaboration. Others fear it might worsen an already challenging job market. But what if avoiding AI is the riskier choice? The job market concerns are multifaceted and reflect broader anxieties about AI’s impact on knowledge work. Many Agile practitioners worry that AI could automate core aspects of their roles — from documentation and facilitation to coaching and analysis. In a profession already experiencing market fluctuations due to economic uncertainty and evolving organizational models, the prospect of AI-driven efficiency creates fear that fewer Agile professionals will be needed. Some practitioners also believe that organizations might reduce investment in human Agile talent or consolidate roles if AI can generate user stories, facilitate retrospectives, or analyze team metrics. These concerns are particularly acute for practitioners who have positioned themselves primarily as process experts rather than as the strategic business partners they are supposed to be. (Remember: We are not paid to practice [insert your Agile framework of choice] but to solve our customers’ problems within the given constraints while contributing to the organization’s sustainability.) Drawing parallels to the Y2K crisis — where preparation, not panic, averted disaster — adopting GenAI today represents a low-cost, high-upside strategy for Agile professionals. Early adopters will thrive if AI becomes foundational to work (as the Internet did). If not, the cost of learning is negligible. The evidence, however, increasingly points toward AI’s transformative potential. Rather than relying on theoretical arguments or anecdotal evidence alone, we can turn to rigorous research that directly examines AI’s impact on collaborative work. One particularly relevant study provides empirical insights into exactly how AI affects the kind of cross-functional collaboration at the heart of Agile practice. Generative AI in Agile Analysis: The Harvard Business School P&G Study The 2025 Harvard Business School study “The Cybernetic Teammate” by Dell’Acqua et al. provides compelling evidence of AI’s impact on collaborative work. Conducted with 776 professionals at Procter & Gamble, this large-scale field experiment examined how AI transforms three core pillars of collaboration: performance, expertise sharing, and social engagement. The study implemented a 2×2 experimental design where participants were randomly assigned to work either with or without AI, and either individually or in two-person teams on real product innovation challenges. This design allowed researchers to isolate AI’s specific effects on individual and team performance. The results were striking. Individuals with AI-matched teams’ performance without AI, suggesting that AI can effectively replicate certain benefits of human collaboration. Furthermore, AI broke down functional silos between R&D and Commercial professionals, with AI-augmented individuals producing more balanced solutions regardless of their professional background. Perhaps most surprisingly, the study found that AI’s language-based interface prompted more positive emotional responses among participants, suggesting it can fulfill part of the social and motivational role traditionally offered by human teammates. These findings directly address the concerns of Agile practitioners and provide empirical evidence that AI adoption can enhance rather than detract from the core values of Agile work. Debunking Myths: AI vs. Agile Values Myth 1: “AI Undermines Agile’s Focus on Human Interaction” This objection misunderstands both the Agile principle and AI’s role. The principle doesn’t reject tools; it prioritizes human connections while acknowledging that appropriate tools enable better interactions. The Dell’Acqua study demonstrates that AI-enabled individuals matched the performance of human teams in innovation tasks. Crucially, AI didn’t replace collaboration — it enhanced it. The P&G study also revealed that participants reported more positive emotions (excitement, energy) and fewer negative ones (anxiety, frustration) when using AI, mirroring the social benefits of teamwork. The researchers concluded that AI can “fulfill part of the social and motivational role traditionally offered by human teammates.” (Source: Abstract, page 2.) For Agile practitioners, this suggests that AI automates administrative work while potentially enhancing the emotional experience of the work itself. Myth 2: “AI Will Replace Agile Roles” The more realistic concern isn’t replacement by AI itself, but competition from AI-augmented practitioners. The P&G study found that when some professionals leverage AI to accomplish in minutes what takes others hours, the performance differential becomes significant. The research conclusively showed that AI doesn’t eliminate expertise — it redistributes it. In the P&G context, R&D professionals using AI proposed more commercially viable ideas, while Commercial professionals delivered solutions with greater technical depth. As the study authors noted, “AI breaks down functional silos” (Source: Abstract, page 2), allowing professionals to exceed their traditional domain boundaries. For Agile practitioners, this implies that AI won’t eliminate the need for facilitation, coaching, or product ownership — but it will transform how these roles operate, requiring practitioners to adapt their skills accordingly. Myth 3: “Using AI Is ‘Cheating'” This perspective assumes a static definition of human capability that never reflects reality. Knowledge workers have always integrated tools — from calculators to spreadsheets to project management software — to enhance their capabilities. The P&G study reinforces this view, showing that AI represents a continuation of this tradition, not a departure from it. The Y2K Parallel: Preparing for Uncertainty Like Y2K, no one knows if today’s AI hype will fizzle or redefine work. But consider: Cost of learning: Free and low-cost tools and 4-8 hours of practice can yield initial competence,Upside: Early adopters gain a significant career edge if AI becomes essential. The P&G study provides concrete evidence of these benefits, showing that AI users completed tasks 12–16% faster while producing higher-quality results. As the researchers noted, “Individuals with AI produced solutions at a quality level comparable to two-person teams” (Source: 1. Introduction, page 4), demonstrating substantial productivity gains with relatively minimal learning investment. Practical Applications for Generative AI in Agile Workflows Here’s how Agile practitioners can apply lessons from the P&G study to their workflows: 1. Enhancing Agile Events Sprint Planning Generate draft acceptance criteria for complex user storiesIdentify potential dependencies or risks in planned workSuggest task breakdowns for large epics The P&G study found that “AI can enhance collaborative performance by automating certain tasks and broadening the range of expertise available to team members” (Source: 2. Related Literature, page 7). In Sprint Planning, this translates to faster generation of comprehensive acceptance criteria, which teams can then review, refine, and customize based on domain knowledge — improving thoroughness while reducing time spent by up to 70%. (Note: You can also repeat these benefits during refinement sessions.) Retrospectives The study revealed that “GenAI’s ability to engage in natural language dialogue enables it to participate in the kind of open-ended, contextual interactions that characterize effective teamwork” (Source: 2. Related Literature, page 7). For retrospectives, AI can analyze previous retrospective notes to identify recurring patterns, generate discussion questions based on Sprint metrics, and suggest innovative formats tailored to specific team challenges. 2. Documentation and Communication User Story Refinement The P&G research demonstrated that AI-assisted participants produced “substantially longer outputs”(Source: 5.1 Performance, page 15) in less time than control groups. For Agile teams, this efficiency translates directly to user story refinement: Before: ‘Create a dashboard for the sales team.’After AI refinement: ‘As a sales director, I want a customizable dashboard showing region-specific sales metrics with trend visualization to quickly identify performance gaps and opportunities without manually compiling data. The dashboard should update in real-time, allow filtering by product line and period, and highlight variances against targets.’ Stakeholder Communications The study found that AI enables participants to “breach typical functional boundaries” (Source: 1. Introduction, page 4), allowing them to communicate more effectively across different domains of expertise. For Agile practitioners, AI can help convert technical updates into business-focused language, generate executive summaries of Sprint outcomes, and create tailored communications for different stakeholder groups. 3. Conflict Resolution The P&G experiment showed that “AI may also enhance collaborative team dynamics and transform the division of labor” (Source: 2. Related Literature, page 7). For conflict resolution, this suggests using AI to simulate stakeholder negotiations where it role-plays as a resistant Product Owner, helping facilitators practice persuasion techniques. This provides a safe environment for practitioners to hone their facilitation skills before high-stakes conversations. (Note: Try Grok to train arguing.) 4. Meeting Facilitation Given the finding that “the adoption of AI also broadens the user’s reach in areas outside their core expertise” (Source: 1. Introduction, page 4), meeting facilitation becomes another area ripe for AI enhancement. Practitioners can use AI to generate structured agendas, provide real-time suggestions for redirecting off-track discussions, and create comprehensive summaries and action items from meeting notes. Measurable Benefits of Generative AI in Agile The P&G study quantified several benefits of AI-augmented Agile practices that directly apply: Time Efficiency: The study found that AI-enabled participants completed tasks 12-16% faster than those without AI. For Agile practitioners, this could translate to a 40-60% reduction in time spent on routine documentation.Quality Improvements: Individuals with AI showed a 0.37 standard deviation increase in quality over the control group, comparable to the quality improvement seen in two-person teams (0.24 standard deviation). This suggests a potential 35% increase in clarity and completeness for Agile artifacts.Strategic Focus: The time saved allows practitioners to redirect 2-3 additional hours per week toward strategic activities and direct team support.Team Satisfaction: The study’s finding that “professionals reported more positive emotions and fewer negative emotions when engaging with AI” (Source: 1. Introduction, page 5) suggests that AI can reduce frustration with administrative tasks, potentially improving team health metrics. Current Limitations and When Not to Use Generative AI in Agile Despite its benefits, the P&G study acknowledges that AI has limitations. As the researchers note, “Our findings suggest that adopting AI in knowledge work involves more than simply adding another tool” (Source: 1. Introduction, page 5). For Agile practitioners, understanding these limitations is crucial. AI cannot replace: Building genuine human connections and psychological safety,Understanding team dynamics and interpersonal tensions,Making value judgments that require organizational context,Facilitating difficult conversations and conflict resolution. The P&G experiment involved “one-day virtual collaborations that did not fully capture the day-to-day complexities of team interactions in organizations — such as extended coordination challenges and iterative rework cycles” (Source: 7. Discussion and Conclusion, page 20). Thus, Agile practitioners should be aware of these limitations when implementing AI in ongoing team dynamics. Warning signs that you should revert to fully human approaches: When the AI’s suggestions lack the necessary organizational context,For highly sensitive situations involving personal conflicts,When the team needs emotional support rather than logical solutions,When building trust is the primary objective. Generative AI in Agile: Enhancing Collaboration, Not Replacing It The P&G study conclusively shows that AI enhances team collaboration rather than diminishing it. The researchers observed that AI served as a “boundary-spanning mechanism, helping professionals reason across traditional domain boundaries and approach problems more holistically” (Source: 7. Discussion and Conclusion, page 21). For Agile teams, this translates to several collaboration enhancements: Meeting preparation. AI can help team members organize thoughts and contributions before meetings, leading to more productive discussions where everyone participates meaningfully.Knowledge gap bridging. The study found that “workers without deep product development experience can leverage AI’s suggestions to bridge gaps in knowledge or domain understanding” (Source: 1. Introduction, page 4). This allows less experienced team members to contribute more confidently in discussions.Idea expansion. Dell’Acqua et al. observed that “individuals using AI achieved similar levels of solution balance on their own, effectively replicating the knowledge integration typically achieved through team collaboration” (Source: 7. Discussion and Conclusion, page 21). This helps teams break out of groupthink and consider a wider solution space.Documentation burden reduction. The study showed that AI-enabled participants produced significantly longer, more detailed outputs in less time. When one person volunteers to document decisions, AI can help them create comprehensive notes quickly, avoiding the common pattern where documentation responsibilities limit active participation. Getting Started: Simple Steps When using generative AI in Agile, start with simple steps: Experiment with prompts. Use role-based framing (“Act as a Scrum Master…”) and constraints (“Propose solutions under a 2-week deadline”). The P&G study found that participants who interacted more extensively with AI through iterative prompting achieved better results.Focus on repetitive tasks. Automate standup summaries, reports, or OKR tracking. The research shows that AI provides the greatest benefits for routine, structured tasks where domain expertise can be clearly communicated.Iterate. The study demonstrated that treating AI as a collaborative partner rather than a mere tool yielded superior results. Approach AI like a junior teammate — critique its outputs, refine prompts, and integrate feedback. Common Pitfalls to Avoid When using generative AI in Agile, avoid the following anti-patterns: Excessive secrecy. The P&G researchers found that transparency about AI usage was important for team dynamics. Being secretive can create distrust; be appropriately transparent.Over-reliance. The study noted that AI sometimes produced hallucinations, highlighting the need to maintain critical thinking and not accept AI outputs without verification.Tool fixation. Dell’Acqua and others emphasized focusing on outcomes rather than becoming obsessed with the tools themselves.Ignoring team dynamics. Consider how AI adoption affects team interactions and relationships, a concern highlighted by the researchers when discussing the social implications of AI integration. Conclusion: Evolve Agility, Don’t Betray It The P&G study provides compelling evidence that AI acts as a “cybernetic teammate,” augmenting human skills, not replacing them. As Dell’Acqua et al. conclude, “By enhancing performance, bridging functional expertise, and reshaping collaboration patterns, GenAI prompts a rethinking of how organizations structure teams and individual roles” (Source: 1. Introduction, page 5). Agile’s strength lies in adaptability, and dismissing AI contradicts that principle. By embracing AI now, practitioners future-proof their careers while staying true to Agile’s core mission: delivering value faster, together. The cost of waiting is your competitive edge. Start small, experiment often, and let AI handle the mundane so you can focus on the meaningful. The most Agile approach to AI isn’t blind enthusiasm or stubborn resistance — it’s thoughtful exploration, validated learning, and continuous adaptation based on results. This approach has guided Agile practitioners through previous technological shifts and will serve you well in navigating this one. Have you started embracing AI in your daily work? Please share with us in the comments.

By Stefan Wolpers CORE