Category: Agile & DevOps

Stop Context Switching, Start Shipping: How Rovo Gives Devs Back Their Focus

Posted on July 23, 2025 by - Agile & DevOps, Platform Adoption & Governance

Developers know the drill: time often slips away in the small moments. Searching for the right information. Jumping between Slack and Confluence. Digging through logs. Each piece of busywork pulls focus away from real priorities like coding, building, and shipping great products.

For years, Atlassian has given development teams a better way to collaborate and reduce friction through a central platform. Now, with Rovo, an AI teammate powered by your organization’s knowledge, those capabilities go even further. 

Powered by Atlassian’s Teamwork Graph, Rovo adds a connected layer of context with built-in AI across developer workflows. Rovo Search, Chat, and Agents help teams improve productivity, streamline workflows, and eliminate repetitive tasks.

Here are a few ways Rovo can supercharge your daily work:

In this blog, we’ll break down exactly how Rovo benefits DevOps teams, including more real-world examples of how teams are using it today.

Disrupting Focus: The Real Cost of Developer Busywork

While developers are under pressure to innovate faster, they’re spending 84% of their valuable time on tasks outside of coding. That time is lost across four key friction points: 

  • Constant context switching. Developers jump between tasks, tools, and conversations. These interruptions can cause up to 40% in productivity loss.
  • Manual, repetitive tasks. From searching for information to organizing Jira tickets, Atlassian research shows automating this type of work can save developers up to 1.5 hours/day.
  • Lack of visibility. Tool sprawl and complex, disconnected workflows force development teams to manually piece together the full picture. Up to 23 hours a week of employee time is spent on excessive documentation, meetings, and overhead tasks.

Collaboration breakdowns. Without shared context or a single source of truth, it’s hard to move fast. One-fourth of executives and teams spend a quarter of the workweek just searching for information.

How Rovo Reduces Developer Time Drains

Rovo is easily customizable and built directly into developers’ favorite tools, like Confluence, Jira, Compass, and Bitbucket, making it a seamless way to adopt AI and reduce friction.   

Rovo Search: Context That Spans Your Stack

Developers work best when they have uninterrupted focus. A simple process, like attempting to debug an API issue, could take hours without a central system. It also means jumping across five tools. With 23 minutes lost on every switch of context, developers could lose almost two hours in this case.

By using Rovo Search, developers can see everything in one place instead of manually switching context across tools like Jira, Slack, and Datadog. Ask Rovo, “Why is the API timing out?” and get related tickets, docs, and threads with context provided, instantly.

Rovo Chat: Ask and Get Instant Answers

Without connected data and systems, engineers spend their day acting as human search engines, asking and answering the same questions repeatedly: 

  • “Where’s the deployment runbook?” 
  • “Who changed the database schema?” 
  • “Why did we choose Redis here?” 

Using Rovo Chat, developers can simply ask Rovo for what they need. For example, by turning on Rovo in Confluence and Bitbucket and connecting it to Slack, a developer can chat with Rovo to ask questions like, “Why do we use Redis for session storage?” Rovo will pull any related information, from the original architecture decision and performance benchmarks to the team discussion that led to the choice. No meetings, pings, or emails required.   

Rovo Agents: Automate the Work That Slows You Down 

A 3am incident means starting the day by reviewing error logs in Splunk, finding recent changes in GitHub, and searching for similar incidents in Jira. It can take an entire team of engineers hours to piece together what happened. 


Instead, developers can set up Rovo Agents to automate this work and save time. Agents can summarize deployments, review code, surface similar past incidents, and identify code owners automatically, delivering the incident context to the right engineer, reducing bottlenecks and getting the team back to work faster.

Building Your Intelligent Development Ecosystem

While many teams thrive on Rovo’s out-of-the-box capabilities, the biggest gains can come from tailoring agents to your unique workflows. With Rovo Studio, you can build specialized agents with or without coding to automate the friction points impacting your organization most.

Some of the custom agent patterns engineering teams are building today are:

  • Code Quality Agents that learn your team’s standards and flag potential issues before a merge. 
  • Deployment Orchestration Agents that coordinate releases across your specific infrastructure stack. 
  • Knowledge Capture Agents that automatically document tribal knowledge from Slack discussions and code reviews. 
  • Onboarding Pathway Agents that create personalized learning journeys based on your actual codebase. 
  • Extended integrations beyond the Atlassian ecosystem—GitHub Enterprise, internal APIs, monitoring tools, and custom databases—turn Rovo into your engineering team’s central nervous system.

The key to starting is identifying your team’s biggest pain point and building from there. Teams getting the most out of Rovo aren’t trying to automate everything at once. They’re addressing pain points and perfecting workflows before moving on to the next stage. 

At Cprime, we design and implement these intelligent development ecosystems, from custom agent development to complex integrations, ensuring your AI transformation actually moves the needle on engineering velocity. The most successful Rovo implementations combine a deep understanding of engineering workflows with thoughtful agent design and integration strategy. 

Agile Practitioners Embracing AI: From Scrum Master to AI Enabler

Posted on July 17, 2025 by - Agile & DevOps, AI Transformation

Artificial Intelligence (AI) has evolved from speculation to enterprise reality, reshaping how work is orchestrated. This is especially true in dynamic, technology-centric environments that have long embraced Agile practices. The current wave of AI advancement is a force to harness for outsized impact. For Agile practitioners, and particularly for Scrum Masters / Agile Coaches, this signals an exciting evolution: a transition from facilitating Agile practices to becoming pivotal “AI enablers” who empower their teams to reach unprecedented levels of performance and innovation. 

This journey involves understanding how AI can amplify Agile practices and actively guiding teams to integrate these powerful new capabilities into their daily work. The integration of AI with Agile practices is a pivotal evolution, one that promises to redefine efficiency and creativity in product/service development.

The pervasiveness of AI discussions naturally creates a mix of anticipation and apprehension. 

Therefore, it is crucial to frame AI’s role constructively within the Agile context, highlighting it as an opportunity for growth and enhancement, rather than a threat to existing roles or practices. The shift for Scrum Master to become an AI enabler is a transformative journey, and understanding this new dimension to the role can provide a compelling roadmap for development professionals.

Understanding the Scrum Master’s Core Mission

Before exploring the fusion of AI with Agile practices, it is essential to re-establish the foundational role and mission of the Scrum Master. The introduction of AI does not seek to replace these core duties but rather to augment and enhance the Scrum Master’s ability to fulfill them. According to the Scrum Guide, “The Scrum Master is responsible for promoting and supporting Scrum as defined in the Scrum Guide. Scrum Masters do this by helping everyone understand Scrum theory, practices, rules, and values”. They are strategic enablers for the Scrum Team. Furthermore, the Scrum Master is accountable for “establishing Scrum” and for the “Scrum Team’s effectiveness”.

This definition is critical because it provides the inherent “why” behind a Scrum Master’s engagement with AI. If a Scrum Master is accountable for team effectiveness and the successful implementation of Scrum, then exploring and facilitating the use of tools and technologies that enhance these aspects falls squarely within their purview. 

The “true leader” characteristic is particularly pertinent when considering AI enablement. It implies adopting the use of AI themselves, then guiding and supporting the team’s exploration and use of AI, fostering a collaborative approach rather than imposing solutions. 

This aligns with the principle that AI adoption should be team-driven to ensure genuine buy-in and maximize effectiveness. A true leader facilitates this by providing necessary resources, removing obstacles to learning and adoption, and cultivating an environment where it is safe to experiment and learn from both successes and failures. 

Moreover, the Scrum Master’s responsibility to help everyone understand Scrum theory and practice can be extended to understanding how AI aligns with or can amplify Scrum values, such as using AI-generated reports to improve transparency or leveraging AI tools to help the team maintain focus on sprint goals.

AI Meets Agile

AI and Agile amplify each other. Fast, iterative practices meet intelligent acceleration. Agile provides a robust framework for iterative development, rapid response to change, fast learning, and continuous value delivery. AI, in turn, offers a suite of powerful tools and capabilities that can accelerate, automate, and enrich these Agile practices.

AI technologies can propel this agility to new heights, offering tools that automate tasks, predict trends, and facilitate decision-making. 

This powerful combination allows AI to amplify core Agile principles:

  • Transparency: AI-driven dashboards, automated reporting, and real-time data analytics can provide unprecedented visibility into project progress, impediments, and team performance.
  • Inspection: AI tools can analyze sprint data, identify patterns in team velocity or defect rates, and provide objective insights for more effective Sprint Retrospectives.This allows teams to inspect their processes with greater depth and accuracy.
  • Adaptation: By offering predictive insights, AI enables teams to anticipate potential roadblocks, forecast delivery timelines more accurately, and make quicker, more informed adjustments to their plans and priorities.

The integration of AI into Agile can also help address common challenges that teams face in their Agile journey. For instance, many teams struggle with estimation and maintaining a predictable delivery. AI tools, by analyzing historical team data, can significantly improve forecasting accuracy and help teams develop more realistic sprint plans.

In this way, AI can act as a supportive mechanism, bolstering Agile maturity. 

While AI can help to accelerate processes and enhance efficiency, Agile frameworks  like Scrum with their defined events, accountabilities, and artifacts provide the essential structure to ensure this acceleration is directed towards valuable outcomes. 

This structure prevents AI-driven speed from devolving into “faster chaos,” ensuring that efforts are channeled effectively, reviewed regularly through feedback loops, and adapted as necessary to meet evolving requirements.

AI as Your Team’s Superpower: Supporting Humans, Not Replacing Them

A prevalent concern surrounding the rise of AI is the potential for job displacement. However, within the context of Agile and knowledge work, the narrative is shifting towards AI as an augmentation force—one that enhances human capabilities rather than rendering them obsolete. This shift empowers teams by allowing individuals to focus on tasks that uniquely leverage human intellect and creativity. 

MIT economics professor David Autor articulates this perspective clearly: “AI will end up generally augmenting workers instead of replacing them,” and “Tools often augment the value of human expertise…They enable us to do things we could not otherwise do without them”.

This sentiment is echoed by MAPFRE, which states, “AI will never replace people, and human oversight will always be necessary”.

AI excels at handling repetitive, mundane, or data-intensive tasks, thereby liberating human workers to concentrate on:

  • Strategic thinking and complex problem-solving: AI can process vast datasets and identify patterns, but humans are needed to interpret these findings within a broader strategic context and devise innovative solutions to complex challenges.
  • Creativity and innovation: By automating routine aspects of work, AI frees up cognitive bandwidth for creative exploration, ideation, and the development of novel products and services.
  • Ethical considerations and nuanced decision-making: Many knowledge work tasks require human judgment, empathy, and ethical reasoning—qualities that current AI systems largely lack.

Benefits of AI Augmentation in Agile Contexts

The augmentation capabilities of AI translate into tangible benefits for Agile teams across various aspects of their work:

  • Accelerating Ideation and Innovation: AI accelerates innovation cycles and time-to-value. It can analyze vast amounts of market data, customer feedback, and emerging trends to help teams identify unmet needs and opportunities.  AI tools can assist in brainstorming sessions, help synthesize research findings, and enable the rapid creation of prototypes to test new ideas quickly.
  • Boosting Productivity and Velocity: In software development, AI tools are already demonstrating significant productivity gains. Developers can complete coding tasks up to twice as efficiently using AI assistants. AI can automate aspects of code generation, conduct preliminary code reviews, generate unit tests, and even assist in creating and maintaining documentation. For instance, AI testing tools have enabled teams to reduce test execution time by as much as 75% and decrease manual testing hours by 80%.
  • Unlocking Data-Driven Insights: Agile teams thrive on data, and AI can supercharge their ability to extract meaningful insights. AI algorithms can process large volumes of project data to deliver actionable intelligence, helping project managers and teams make faster, more informed decisions. For example, AI can look at data from previous projects and spot patterns that could affect current or future projects, leading to better planning, risk mitigation, and resource utilization. This capability extends to predictive analytics for better forecasting, early risk identification, and optimized resource allocation.

The “augmentation” narrative effectively shifts the focus from a fear of job loss to an opportunity for skill evolution. As teams begin to work more closely with AI, new skills will become necessary—such as effective prompt engineering for generative AI, the ability to critically evaluate AI-generated outputs, and an understanding of AI ethics. 

Scrum Masters, in their coaching capacity , can play a vital role in facilitating the development of these new competencies within their teams. The true value of AI is unlocked when human expertise guides its application and interprets its outputs. AI can provide the “what”—the data, the patterns, the initial drafts—but humans provide the crucial “so what”: the context, the strategic implications, and the final decisions. This symbiotic relationship, where AI processes information at scale and humans apply wisdom and contextual understanding, is central to successful AI integration. The Scrum Master can help the team understand and cultivate this productive balance.

The Scrum Master as an AI enabler

The core responsibilities of a Scrum Master—ensuring team effectiveness, fostering continuous improvement, and upholding Scrum principles—align perfectly with the opportunity presented by AI. Guiding the adoption and effective use of AI is not an additional burden but a natural extension of the Scrum Master’s existing role, enabling them to serve their teams even more powerfully in an increasingly AI-driven landscape.

Key Responsibilities of an AI-Enabling Scrum Master

The transition to an AI enabler involves embracing several key responsibilities:

  • Educating and Evangelizing: This involves actively advocating for AI’s strategic value and practical applications relevant to the team’s work. The Scrum Master can demystify AI, address concerns, and showcase success stories or specific use cases to inspire the team and stakeholders. This aligns with the Scrum Master’s established role of “helping everyone understand Scrum theory and practice, both within the Scrum Team and the organization”, now broadened to include AI’s role within that practice.
  • Facilitating Exploration and Experimentation: An AI-enabling Scrum Master creates the space and a culture of experimentation and safety for the team to explore AI tools and techniques. This might involve allocating time during Sprints for experimentation, organizing innovation spikes, or guiding the team in identifying small, low-risk experiments to test AI tools for specific problems. 
  • Coaching for Human-AI Collaboration: Effective use of AI is a skill. The Scrum Master coaches team members on how to work with AI tools. This includes practical guidance on tasks like writing effective prompts for generative AI, critically evaluating AI-generated outputs, and seamlessly integrating AI into existing workflows. 
  • Removing Impediments to AI Adoption: As with any new initiative, AI adoption can face obstacles. The Scrum Master, in their capacity as an “Impediment Remover”, works to identify and address these barriers. Impediments might include lack of access to appropriate AI tools, skill gaps requiring targeted training, resistance to change, or unclear organizational policies regarding AI usage and data security.
  • Championing Ethical and Responsible AI Use: With the power of AI comes the responsibility to use it ethically. The Scrum Master facilitates crucial discussions within the team about data privacy, potential biases in AI algorithms, the transparency of AI-driven decisions, and the overall ethical implications of their AI applications. This proactive approach helps ensure the team uses AI tools responsibly and in alignment with organizational values and regulatory requirements.

Categories of AI Tools for Agile Teams

We are now awash with AI tools, here are some categories you may wish to consider.

  • AI-Powered Delivery Management & Collaboration: A new generation of delivery management and collaboration platforms is embedding AI to streamline workflows.
    • These tools can automate task creation and assignment, summarize progress for stakeholders, generate reports, facilitate virtual brainstorming, transcribe meeting minutes, and generally improve team communication and coordination.
  • AI for Developers (Coding, Review, Testing): This is perhaps one of the most mature areas for AI application in Agile.
    • These tools assist with code completion, automated unit and integration test generation, intelligent vulnerability scanning, AI-assisted code reviews, and code refactoring suggestions, all contributing to faster development cycles and higher quality code.
  • AI for Backlog Refinement & User Story Generation: While still an emerging area, AI shows promise in assisting Product Owners and teams with the crucial task of managing and refining the Product Backlog.
    • This can help in drafting initial user stories, suggesting acceptance criteria, identifying dependencies, or even flagging conflicting requirements, allowing the Product Owner and team to focus on higher-level strategic refinement.

Of course remember, AI is augmenting how we do work, not replacing us, and certainly not replacing the knowledge work we humans do.  For example using AI to help ideate requirements / user stories is great for idea generation, it might be great to help explore requirements and help the team understand them.  But the actual decision of what the requirement is and what to do is the decision of a human.

The Future is Human-AI Collaboration in Agile

The trajectory of AI in the workplace points not towards an AI-dominated future, but one characterized by a synergistic partnership between humans and intelligent machines. 

This human-AI collaboration holds the key to unlocking new potentials for Agile teams, enabling them to achieve levels of creativity, efficiency, and value delivery previously unimaginable. 

McKinsey envisions a future where AI empowers teams to “spend more time on higher-value work and less on routine tasks”. 

This evolving landscape underscores the critical importance of a continuous learning mindset. The field of AI is exceptionally dynamic, with new tools, techniques, and capabilities emerging at a rapid pace. Agile teams, with their inherent emphasis on adaptation and improvement, are well-positioned to thrive in this environment. Guided by their Scrum Masters, they will need to continuously learn, experiment, and adapt their practices to harness the latest AI advancements effectively. 

Scrum Masters, by cultivating an environment of psychological safety, play a crucial role in enabling team members to openly discuss concerns, share learnings, and collectively build trust in new processes involving AI. As AI systems become increasingly adept at handling analytical and executional tasks, the uniquely human skills of empathy, complex communication, nuanced judgment, and strategic oversight will become even more valuable differentiators for Agile teams and their leaders. The future value proposition for human knowledge workers, including Scrum Masters, will increasingly lie in these higher-order cognitive and emotional capabilities.

Step Up, Scrum Masters – Become the AI enablers Your Teams Need

The integration of artificial intelligence into Agile ways of working presents a transformative opportunity, and Scrum Masters are uniquely positioned to lead their teams into this new era. The call is clear: embrace the challenge and the opportunity to evolve from Scrum facilitators to indispensable AI enablers. This evolution is not about adding an overwhelming new set of responsibilities, but about enhancing existing skills and leveraging powerful new tools to better serve teams and organizations in an increasingly AI-driven world.

The journey to becoming an AI enabler is, fittingly, an iterative one. Scrum Masters should approach AI adoption the same way they approach Agile itself: iteratively, incrementally, and with a clear focus on outcomes. Scrum Masters can encourage their teams to start small, experiment, learn from those experiments, and adapt their strategies accordingly. This iterative approach makes the prospect of AI integration less daunting and aligns perfectly with the Scrum Master’s existing mindset and the core principles of Agile.

By proactively engaging with AI, Scrum Masters not only drive measurable outcomes for their current teams—driving efficiency, innovation, and value—but also enhance their own career relevance and marketability in a rapidly changing technological landscape. 

Agile teams empowered by AI and guided by strategic leaders will define the future of work.

From Agile and Digital Transformation to AI Transformation: The Natural Evolution

Posted on May 13, 2025 by - Agile & DevOps, AI Transformation

In Technological Revolutions and Financial Capital, Carlota Perez plots the evolution of societal, industrial, and economic capital based on technological revolutions that occurred over the last few hundred years. She opines that these disruptive trends happen every generation or so.

It started with the Industrial Revolution in the 1770s, followed by the Age of Steam and Railways in the early 19th century, then the Age of Steel and Heavy Engineering in the late 19th century, bringing us to the Age of Oil and Mass Production in the 20th century. We conclude with the current Age of Software and Digital into the 21st century.

Each revolution has a regular sequence of three distinct phases:

  1. Installation Period: New technology and financial capital combine to create a “Cambrian explosion” of new entrants (“Cambrian explosion” is a biological term for a large diversity of life forms appearing over a relatively short time) 
  2. Turning Point: Existing businesses either master the new technology or decline and become relics of the last age
  3. Deployment Period: The production capital of the new technological giants starts to take over

Carlota also explains that she has observed from history that during the Installation Period, while there is an influx of financial capital to support the new entrants, this is followed by some form of “crash” or multiple crashes.

If we consider the Age of Oil and Mass Production, we had the Roaring Twenties, but in 1929, we had the Wall Street crash, which affected markets around the world. We then encountered the longest turning point in history, which is often when we see a period of political uncertainty. In the 1930s, we saw the rise of fascism in Europe through to the conclusion of the Second World War in 1945. Those that survived took advantage of the biggest boom in history, with the likes of Toyota coming to the fore in car manufacturing.

If we turn to the Age of Software and Digital, we had the dotcom crash that peaked in 2000 and the global financial crash in 2008. Carlota was on stage in Paris in 2019, presenting at Sogeti’s Utopia for Beginners’ Summit about our digital future, and she said:

“Maybe we will have another crash ahead, but after that, we should have the possibility of a sustainable global information technology Golden Age.” (Carlota Perez, 2019)

Bearing in mind this was in 2019. Then, starting in March 2020, we had the unprecedented Covid-19 pandemic. It was as if Perez predicted this months earlier.

Post Covid-19 pandemic, it is clear that we are now firmly in the Deployment Phase of the Age of Software and Digital.

We often get asked, “What is the next technology revolution?” We are neither futurists, nor clairvoyants. That said, we know that the rise of new technologies comes with the decline of the previous technology. We then have a period of bubble prosperity with financial capital supporting the new entrants.

If we follow the current financial capital, then we will see investment in artificial intelligence (AI), big data, and the cloud.

Research by PWC found 72% of executives believe that AI will be the most significant business advantage of the future.

The company Snowflake, which provides data warehouse-as-a-service, was the biggest software IPO in 2021 and implied five-year sales growth of 819%. In the cloud, there are three or four major providers, and the worldwide end-user spending on public Cloud services was forecast to grow to $332.3 billion in 2021.

So what is the ‘Natural Evolution’ from the Age of Software and Digital to the Age of AI? Is there any difference between the two ages? What do leaders have to consider in this new age if they believe that this will give them the most significant advantage?

AI-Native vs. Digital-First: The Divide That Matters

We have already seen and experienced those organizations that did not make the digital shift, especially in the retail industry, accelerated by the COVID-19 Pandemic. As Mik Kersten highlighted at the time in his book Project to Product:

“Those that master digital business models and software at scale will thrive. Many more, unfortunately, will not.”

In past disruptions, digital-native giants like Amazon, Google, and Netflix upended industries by mastering cloud, data, and software-driven scale. Today, AI-native challengers are outpacing even those digital leaders.

Companies that hesitate to make this leap will soon find their digital-first strategies obsolete. This marks a new competitive order; one that goes beyond traditional waves of digital transformation.

Digital-first companies still operate in a deterministic model, making decisions based on historical data and predefined logic that delivers the same outcome, for every customer, every time. AI-native enterprises function differently. Real-time intelligence drives every action, outcomes vary based on context, and individualization and immediacy define the customer experience.

AI-native enterprises distinguish themselves by how they think, operate, build, and engage, with AI embedded at the center of the business. It goes far beyond the tools they adopt.

What Do You Need to Do as a Leader?

Leading in an AI world demands a shift in mindset, skills, and culture, going well beyond a basic understanding of technology. Here’s what leaders need to focus on to truly embrace and thrive in an AI-driven world:

  • Adopt a Learning Mindset

      1. Stay curious about AI and emerging tech; leaders don’t need to be data scientists, but they do need to understand the fundamentals.
      2. Encourage continuous learning across teams to demystify AI and build confidence in using it.

  • Develop a Clear AI Strategy

      1. Connect AI initiatives to business outcomes, not just tech for tech’s sake.
      2. Define where AI can add the most value (e.g., improving customer experience, automating processes, augmenting decision-making).

  • Create a Culture of Experimentation

      1. Promote a test-and-learn culture where teams can explore AI use cases safely.
      2. Accept that failures are part of innovation, and celebrate learning from them.

  • Empower Cross-Functional Teams

      1. AI success lies at the intersection of tech, data, business, and people.
      2. Build diverse teams (data scientists, domain experts, designers, etc.) that can co-create AI solutions.

  • Champion Ethical AI and Data Responsibility

      1. Ensure AI is used ethically and responsibly: transparent, explainable, and bias-aware.
      2. Treat data as a strategic asset, and invest in governance, privacy, and compliance.

  • Focus on Augmentation, Not Just Automation

      1. Use AI to enhance human intelligence, not just replace it.
      2. Look for ways to empower employees through tools that make them smarter, faster, and more creative.

  • Lead by Example

    1. Be visible in your support for AI transformation.
    2. Model the behaviors you want to see: curiosity, collaboration, courage, and openness to change.

AI reshapes the very nature of enterprise leadership. The leaders who embrace this early and thoughtfully will shape the future.

Is Agile dead? Spoiler alert – NO!

The essence of agile ways of working is about being adaptive, collaborative, and focused on delivering value quickly and continuously. Here’s a breakdown of its core principles:

Iterative and Incremental Delivery

  • Work is delivered in small, usable pieces (iterations or sprints), allowing teams to adapt based on feedback and change.

Collaboration and Empowered Teams

  • Cross-functional teams work together closely, with shared ownership and accountability.
  • Stakeholders and customers are involved regularly to ensure alignment with business needs.

Continuous Learning and Improvement

  • Regular retrospectives help teams reflect and improve their processes.
  • Embrace fail-fast, learn-fast mindset to innovate without fear of failure.

Customer-Centricity

  • Focuses on delivering the highest value to the customer as early and often as possible.
  • Requirements evolve based on real user feedback, not assumptions.

Transparency and Visibility

  • Progress is visible to everyone through tools like Kanban boards, burn-down charts, and daily stand-ups.
  • Encourages honest conversations and quick surfacing of blockers or issues.

Adaptability Over Predictability

  • Plans are flexible and open to change, responding to new information is more valuable than following a rigid plan.

In short: Agile is about delivering value fast, working collaboratively, and continuously improving. It’s less about a strict methodology and more about a mindset that enables speed, flexibility, and resilience in a constantly changing world.

Are all these principles needed in an AI world? Yes, more than ever!

The Future is AI-Native

There have been many stages in digital evolution. Some slow and gradual, some sudden and disruptive. But few have had this level of impact.

AI-native enterprises are setting the pace. They build architectures that respond to change in real time, continuously optimize outcomes, and reduce friction across every level of operation.

This is the new standard. Companies that embed AI deeply into their DNA will unlock competitive velocity.

The next move is yours. How will you lead in the AI-native era?

Solution in Action: Platform Engineering Evolved with AI

Posted on May 5, 2025 by - Agile & DevOps, AI-Ready Foundations

With orchestrated AI agents managing tasks and communication behind the scenes, you can eliminate context switching and stay fully focused on what matters most.

Unlocking the Why: Purpose, Benefits, and Measurable Outcomes

Context drives efficiency. AI-powered automation and smart integrations have the potential to transform platform engineering by eliminating repetitive work and streamlining workflows for both developers and project managers. 

By connecting Atlassian tools like Jira and Confluence with intelligent agents such as Rovo, along with seamless integration across development tools, we automate essential tasks and remove friction. The result is faster delivery, fewer errors, and better collaboration. 

Our AI-powered solution shifts the focus of platform engineering to what matters most: enabling teams to spend less time on routine tasks and more time driving impact.

Bridging the PM-Developer Gap: From Problem to Solution

The Problem ->The Solution -> The Outcome ->
High context switching between tools causes inefficiencies.AI-driven integration of Atlassian tools with intelligent assistants like Rovo and Cline plugin.Reduced context switching, improving developer productivity.
Tedious manual processes slow down both development and project management.Streamlined task management, automated status updates, and smarter documentation.Faster task completion with minimal human intervention.
Managing large engineering projects across multiple tools is complex and error-prone.AI enhances developer flow while supporting project management with real-time insights.Improved project visibility for managers through automated reporting and proactive issue identification.

Versatile by Design: Real-World Use Cases Across Teams

See It in Action: Platform Engineering Evolved with AI

Engineering Excellence: Key Features

This AI-powered platform engineering solution enhances productivity, minimizes repetitive tasks, and ensures that developers and project managers can operate at their highest potential. With Atlassian tools at the core, AI accelerates workflows, enhances project visibility, and improves collaboration, leading to faster, more efficient software delivery.

  1. AI-Powered Task Understanding: Developers can access task information, requirements, and details without leaving their IDE, ensuring continuous focus and flow state.
  2. AI-Driven Code Implementation Assistance: AI helps developers with fast, contextually relevant code suggestions and solution implementations, speeding up development.
  3. Seamless Integration with CI/CD Pipelines: AI monitors and reports on deployment status automatically, keeping developers focused on their code rather than administrative tasks.
  4. Automated Sprint Documentation Updates: Rovo automatically drafts and updates Confluence documentation based on live Jira data, eliminating manual tracking.
  5. Streamlined Code Implementation: AI suggests full code structures and algorithms, enabling developers to work faster and smarter, while also enabling rapid refinement of solutions
  6. Comprehensive Dashboard Integration: Both PMs and developers can work from centralized Atlassian tools (Jira, Confluence, IDE), ensuring consistency and alignment across the team.
  7. Increased Data Quality: Centralized and automated workflows in Jira and Confluence lead to better data quality, which informs better decision-making and reporting.

Bottom Line Results

Solutions like this boost team productivity by up to 60% with AI-powered Atlassian tools. Reduce context switching, automate tasks, and accelerate development cycles, all while enhancing decision-making and workflow efficiency.

The Product Owner’s Guide to AI Features—Balancing Innovation with Value Delivery

Posted on April 30, 2025 by - Agile & DevOps, AI-Ready Foundations

AI is changing the game. But it doesn’t rewrite the rules of product leadership. The opportunity lies in choosing where intelligence creates measurable impact on experience, efficiency, and outcomes.

Product owners today are responsible not just for what gets built, but for why and how. That includes shaping conversations around AI features with purpose, grounding each decision in value, and speaking in terms that resonate from sprint planning to the executive level.

Anchor AI in Outcomes, Not Novelty

The most effective AI features start with value. These two principles help you filter hype from opportunity.

Value Earns Priority

AI should serve the product’s purpose. When evaluating potential features, the most important question remains: What outcome are we enabling? Whether it’s reducing friction, increasing precision, or unlocking personalization at scale, AI belongs when it clearly contributes to business and user value.

Start with what users actually need. Then assess whether AI is the most effective approach to meet that need. Smart recommendations, automated steps, and adaptive content are all strong candidates. The most valuable AI features often feel like natural extensions of a well-designed product. Not flashy, but quietly effective.

Simpler Ideas Can Scale

Effective AI doesn’t always mean advanced. A single well-targeted automation can save hours of user time and scale value across thousands of interactions. Features like smart autofill, behavior-based nudges, or next-action guidance often outperform more complex implementations. Focus on usability and repeatability, not novelty.

Connect the Dots Between Systems, Teams, and Goals

AI features depend on more than code. They require system thinking and team alignment to reach full potential.

AI Features Don’t Stand Alone

AI depends on structured data, consistent flows, and ongoing learning. That means success requires more than a good idea. It demands cross-functional alignment and a clear understanding of how each feature fits into the broader product system.

Product owners play a key role in connecting engineering, data, and design functions. You don’t just approve features. You orchestrate feasibility. That includes knowing what data is available, how models will evolve over time, and what technical or ethical constraints may apply.

Fit Features Into a Larger System

AI features perform better when they are part of an adaptive, connected experience. Look for opportunities to create feedback loops where the system learns and improves. Prioritize features that scale across use cases, expand system intelligence, or lay the groundwork for future automation.

Lead Conversations—Up, Down, and Across

Great product owners manage more than the roadmap. They guide conversations with stakeholders and delivery teams alike.

Translate Business Strategy Into Execution

It’s common for stakeholders to request “something with AI” without clarity on what that entails. Product owners are uniquely positioned to turn ambiguity into action. Anchor conversations around outcomes. Clarify the benefit. Focus on the impact.

Frame AI initiatives in terms of cost reduction, time savings, engagement, or strategic differentiation. Then work with delivery teams to translate those goals into manageable iterations. Being fluent in both business and technical priorities makes you a linchpin, able to ensure every AI investment has a purpose and a path to value.

Career Growth Through Language Alignment

For team-level product owners, fluency in enterprise language creates career momentum. By connecting user stories to strategic value, you position yourself as someone who can operate at scale. AI provides an opportunity to stretch beyond backlog grooming and into product strategy by asking better questions, championing responsible design, and guiding features that align with future-state operations.

Build Features That Scale With the Business

The best AI features adapt, evolve, and expand over time.

Prioritize Expandable Intelligence

Not every AI idea deserves a place on the roadmap. Look for features that extend value over time. Think systems that get smarter with use, experiences that adapt based on behavior, and automations that free up time across teams or functions.

Examples include:

  • Behavioral recommendations that improve with more interactions
  • Automation of repetitive tasks like classification or routing
  • Dynamic personalization that adjusts based on contextual data

These improve user experience while preparing your product to scale as your operating model becomes more fluid, more responsive, and more intelligent.

Design With Trust at the Core

Product value depends on user confidence. Trust must be embedded into every intelligent feature.

Responsible Design Is Product Excellence

Trust is essential for adoption. Regardless of performance, users need to understand what the system is doing and why. Transparency, fairness, and control should be baked into your feature design from the beginning.

Be clear about how the AI makes decisions. Give users appropriate visibility and options for control. Ensure the feature complies with privacy regulations and ethical expectations. Trust doesn’t just protect your product, it elevates it.

Position AI Workstreams as Strategic Progress

Whether you’re optimizing autofill or embedding real-time prediction, AI workstreams now shape how product teams contribute to enterprise evolution. The real challenge is building AI features that activate intelligence in ways that create lasting value.

Product owners are essential to that shift. You have the context, the access, and the influence to decide what gets prioritized and how it gets done. And in doing so, you have the opportunity to shape more than just a product. You can shape how intelligence flows through the business.

4 Insightful ServiceNow GenAI Use Cases to Reduce Manual Work for Agents with Case Summarization

Posted on August 21, 2024 by - Agile & DevOps, AI-Ready Foundations

Case Summarization for IT workflows is the latest inclusion in the ServiceNow Vancouver release powered by GenAI. This feature helps employees quickly locate and understand essential document details. It consolidates relevant information and touchpoints from IT, HR, and customer service cases into summary notes within seconds.

It is an exciting time for the tech industry. With all the buzz around AI, INRY, a ServiceNow elite partner, is in a great position to combine AI technologies like Generative AI (GenAI) on the Now platform. We are powered by proprietary ServiceNow large language models (LLMs) tailored to understand the Now Platform, workflows, and automation use cases. 

Case Summarization for IT workflows

The Large Language Model (LLM) for case summarization is based on a specialized version of the 15-billion-parameter StarCoder model. It was developed as part of ServiceNow’s co-led open BigCode initiative and tuned with Nvidia accelerated computing and DGX Cloud.

Case Summarization leverages generative AI to seamlessly filter information, including the data from IT environments, HR records, and customer service cases. The aim is to streamline and enhance the efficiency of IT operations. The feature enables faster handoffs between team members and achieves more efficient resolution processes.

Case Summarization: Focus on Value by Reducing Manual Efforts

The Case Summarization feature is designed to help employees focus on more valuable work, improve employee satisfaction, and reduce burnout. It reads and summarizes relevant information, including case or incident details, previous touchpoints, and actions by all involved parties, to create detailed case summary notes within seconds.

Objectives of Case Summarization

  • Reduce manual work for agents with overviews and insights to help them start work fast.
  • Easily summarize case or incident records with the click of a button
  • Quickly review pertinent information with an “at-a-glance” view
  • Keep data secure using ServiceNow’s native LLM

The technology reads and distils information from IT tickets, case files, service requests, and conversations for customer or employee issues to create summary notes in seconds. It helps automate manual tasks, reduces hand-off times between teams, speeds resolutions, and increases productivity for employees and customers.

Case Summarization models are trained on real-world data sets specific to the Now Platform, workflows, and automation use cases. The features enable faster and more reliable results and are now available to select customers through the ServiceNow Assist product.

4 Key Use Cases of Generative AI Case Summarization

One of the critical use cases for generative AI is summarization — refining all the actions and touchpoints in a case into a single summary. This could make handoffs smoother between agents and allow teams to resolve problems more quickly, improve processes, and increase alignment. 

Additionally, generative AI significantly simplifies navigating institutional knowledge and policies within the service platform’s knowledge base. This enhancement aids users in quickly finding relevant information and understanding organizational guidelines, thereby improving overall efficiency and user experience.

  • The capability helps streamline handoffs between representatives and accelerate reporting
  • Sharing case resolutions with other teams will become easy
  • The results can make IT workflows more data-driven
  •  Increase alignment and boost productivity across the organization

A recent study by Valoir revealed that when applied effectively, AI can substantially reduce a worker’s workload, possibly by up to 40%. This significant reduction in workload can lead to enhanced productivity and work-life balance for employees.

Benefits of Case Summarization

Case Summarization helps organizations realize immediate productivity gains. It simplifies processes and enables employees to focus on solving problems quickly and efficiently.

For instance, Case Summarization utilizes generative AI to meticulously read and filter key information from various domains, including IT, HR, and customer service cases. This process includes analyzing customer or incident details, previous touchpoints, actions taken, and the eventual resolution. Consequently, it efficiently automates the generation of summary notes, which are invaluable for future reference and streamlined operations.

The Take!

The new generative AI capabilities use specialized LLM optimized for the ServiceNow platform. The GenAI models are designed to learn ServiceNow workflows, automation use cases, and processes. They are currently available to a limited number of customers within the Vancouver release.

From Guesswork to ROI: The Critical Role of Metrics in AI-Driven Development

Posted on June 7, 2024 by - Agile & DevOps, AI-Ready Foundations

Companies across the globe are eagerly experimenting with various AI solutions. Pilots abound, some of them costing millions. Enthusiasm for this shiny new tech is at an all-time high. But there’s a problem: who’s measuring the actual return on investment (ROI) from these AI initiatives? Even after lengthy pilot programs with various AI tools like Github Copilot, many companies are considering expensive rollouts based, essentially, on hype and their teams’ gut feelings.

For savvy executives, that just won’t do.

This leap into AI—which reminds me of the early days of Agile adoption—begs the question: how can businesses assess of value of their AI investments without effective measurement?

The Importance of Metrics in Agile and AI

Without concrete metrics to gauge the improvements and ROI from AI tools, companies are navigating in the dark, making decisions based on hype rather than hard evidence. They’re risking financial resources, and (perhaps more importantly) they could miss out on genuinely transformative opportunities as a result. Without measurement, there is no visibility, and without visibility, there is no way to ensure that investments in AI are sound, strategic, and ultimately successful.

I clearly remember the path many organizations took in past years with Agile methodologies, and today’s rapid push toward integrating AI into software development processes is following the same course. Both require huge paradigm shifts in mindset, experimentation, and, crucially, a commitment to measurement. 

In Agile, metrics like velocity, sprint burndown, and release burnup are great for gauging team performance, project progress, and overall efficiency. You can base decisions on these metrics, adapt strategies, and continuously improve. Based on the same pattern, the successful adoption of AI in software development demands we establish clear, relevant metrics and figure out how to monitor them effectively.

The Challenge of Measuring AI’s Impact

Applied to software development, AI tools can increase productivity, which is little easier to measure. But they can also enhance code quality, reduce the incidence of bugs, and facilitate more innovative solutions by freeing developers from repetitive tasks. These indirect benefits, are harder to quantify and incorporate into an ROI calculation, even though we intrinsically know they’re valuable. So, we don’t only need to measure the immediate impact of AI on development speed and efficiency; we also need to somehow capture its broader contributions to project outcomes and team dynamics.

The Solution: Integrated Measurement with CodeBoost and Allstacks

Organizations need a solution that enhances developer productivity but also integrates seamlessly with tools for comprehensive metrics. That’s the key to navigating the complexities of measuring AI’s impact on software development. 

CodeBoost—our holistic framework, powered by CprimeAI—offers precisely this combination, letting you quantify the ROI of AI investments.

The CodeBoost framework does it all: 

  • Automating repetitive tasks
  • Suggesting code improvements
  • Facilitating faster debugging and code review processes 
  • Powering fast and high-quality user story generation

But there’s more. Beyond GitHub Copilot-style coding assistance, CodeBoost comes with industry-leading implementation and enablement services. It empowers development teams, getting them up and running quickly so you can see quantifiable results in as little as ten weeks. 

We’re talking immediate efficiency gains, as you’d expect. But also improved code quality, and developer satisfaction increases over time. 

But still doesn’t supply concrete measurement to prove all the claims I just made. That’s why the true power of CodeBoost lies in its seamless, baked-in integration with Allstacks. With comprehensive metrics automatically measured and monitored through Allstacks, the sky’s the limit.

Allstacks serves as the analytical backbone. You set a baseline at the start of a CodeBoost implementation, and Allstacks provides ongoing automatic reporting throughout the pilot and beyond. It tracks key performance indicators (KPIs) relevant to software development, such as time saved on coding tasks, reduction in bugs or errors, and improvements in project delivery timelines.

This ability is further enhanced by custom reporting capabilities that tailor metrics to your organization’s specific needs and goals. Adoption rate, decrease in time to market for new features, the reduction in technical debt, and more—Allstacks provides the flexibility to focus on the metrics that matter most.

With this integrated approach to measurement, there’s no question about the value of CodeBoost. Developers appreciate a quality tool that makes their lives easier, while executives have clear, data-driven insights into the ROI of their AI investment. 

It’s a win-win scenario.

What’s Your Next Step?

By setting clear metrics from the outset and leveraging ongoing, automatic reporting, you can confidently navigate the complexities of AI adoption, making informed decisions that align with your strategic goals.

We’re excited by the results we’ve already seen just months into the rollout of CodeBoost. If you’d like a custom demo of CodeBoost to see what it can do for you, just respond in the comments or reach out to me personally!

Achieve Greater Alignment with AI-Powered OKRs in Jira Align

Posted on June 5, 2024 by - Agile & DevOps, AI-Ready Foundations

OKRs in Jira Align FAQs addressed in this article:

  • What are OKRs in Jira Align? — OKRs in Jira Align are a framework for setting and measuring objectives and key results, helping organizations align their strategic goals with actionable outcomes.
  • How does AI enhance OKRs in Jira Align? — AI enhances OKRs in Jira Align by providing intelligent insights, real-time tracking, and predictive success analytics, making it easier to set, manage, and achieve strategic objectives.
  • What are the benefits of AI-powered OKRs? — The benefits of AI-powered OKRs include improved strategic alignment, dynamic tracking, predictive success insights, and better decision-making based on real-time data.
  • How can AI help in aligning program-level objectives with company-level objectives? — AI helps align program-level objectives with company-level objectives by analyzing relationships and providing insights to ensure all levels of the organization contribute to overarching strategic goals.
  • What role does AI play in generating well-formed OKRs? — AI suggests objectives and key results based on historical data and existing work, helping organizations set realistic and aligned OKRs that are grounded in actual projects.
  • How do AI-powered OKRs assist in adapting strategies? — AI-powered OKRs provide real-time visibility and predictive insights, enabling organizations to adjust their strategies based on current data and changing business conditions.
  • What future enhancements are planned for AI-powered OKRs in Jira Align? — Future enhancements include value funding, workstream management, advanced predictive analytics, scenario planning, and deeper integration with execution data.
  • Why is real-time tracking important for OKRs? — Real-time tracking is important for OKRs because it allows organizations to monitor progress continuously, identify potential issues early, and make necessary adjustments to stay on track.
  • How can organizations benefit from AI-powered OKRs in Jira Align? — Organizations can benefit from AI-powered OKRs in Jira Align by achieving greater strategic alignment, improving agility, making informed decisions, and driving continuous improvement in goal setting and execution.

Objectives and Key Results (OKRs) have become a cornerstone for organizations aiming to align their strategic goals with actionable outcomes. By providing a clear, measurable framework, OKRs enable enterprises to synchronize their long-term vision with day-to-day operations, ensuring that every team and individual is working towards the same objectives. 

However, the journey from setting these objectives to realizing their full potential can be fraught with challenges. This is where the integration of AI with OKR management, particularly through tools like Jira Align, can make a significant difference.

Cprime has developed an AI-powered solution that enhances the capabilities of Jira Align, making it easier for organizations to set, manage, and achieve their OKRs effectively. This innovative approach not only streamlines the OKR process but also provides real-time insights and intelligent recommendations, helping enterprises to stay agile and responsive to changing business conditions.

In this blog post, we will explore how AI-powered OKRs can transform your organization’s approach to goal setting and execution. We will focus on the practical applications of this technology, the future enhancements on the horizon, and how these advancements can help you get the most out of your investment in Jira Align. 

By the end, you will have a clearer understanding of how to leverage AI to unlock your agile future and drive greater strategic alignment across your enterprise.

This article is largely based on our recent expert-led webinar, “AI-Powered OKRs: Unlock your Agile Future with Cprime and Jira Align”. For more information, including a demo of the solution, watch the full webinar-on-demand at your convenience.

Understanding OKRs in Jira Align

Objectives and Key Results serve as a powerful framework for capturing and measuring expected business outcomes. At their core, OKRs consist of two components: objectives, which define the overarching goals, and key results, which provide specific, measurable indicators of success. This structure ensures that everyone in the organization is aligned and working towards the same strategic objectives.

There are numerous benefits of implementing OKRs:

  • They promote transparency by making goals visible across the organization
  • They foster alignment by ensuring that all teams and individuals are moving in the same direction
  • They simplify the goal-setting process with a clear, straightforward approach

However, to fully realize these benefits, it is crucial to adhere to best practices. OKRs should be value-based and specific, focusing on outcomes rather than tasks. They should also be revisited regularly to ensure they remain relevant and aligned with the evolving business landscape. Additionally, aligning OKRs across different levels of the organization, rather than cascading them, helps maintain strategic coherence and ensures that every effort contributes to the overall goals.

With this foundational understanding of OKRs, we can now explore how the integration of AI can enhance their effectiveness and help organizations overcome common challenges in setting, managing, and achieving their objectives.

The Role of AI in Enhancing OKRs in Jira Align

Integrating AI with OKR management, particularly through Jira Align, represents a significant advancement in how organizations can set, manage, and achieve their strategic objectives. AI brings a new level of intelligence and efficiency to the OKR process, providing several key benefits that enhance the overall effectiveness of this framework.

  • Intelligent Insights and Real-Time Tracking: AI-powered OKRs leverage historical data and industry benchmarks to suggest realistic and aligned objectives, ensuring ambitious yet achievable goals.
  • Dynamic Tracking and Predictive Success Insights: AI provides real-time visibility into OKR progress, allowing early identification of issues and enabling strategy adjustments to stay on track.
  • Better Alignment Across the Enterprise: AI analyzes relationships between objectives to ensure program-level goals contribute to overall strategic objectives, maintaining coherence and direction.

With these capabilities (and more!), CprimeAI-powered OKRs in Jira Align provide a robust solution for organizations looking to enhance their goal-setting and execution processes.

Read the white paper, “Misaligned to Mastered: How Cprime’s AI-Powered OKR Solution Amplifies Atlassian’s Jira Align Features” for full details on the new solution.

Practical Applications of AI-Powered OKRs in Jira Align

The integration of AI with OKR management in Jira Align offers several practical applications that can significantly enhance the way organizations set and achieve their strategic goals. Here are three key areas where AI-powered OKRs can make a substantial impact.

Aligning Program-Level Objectives with Company-Level Objectives

One of the most critical challenges in OKR management is ensuring that objectives at different organizational levels are aligned. CprimeAI helps bridge this gap by analyzing the relationships between program-level objectives and company-level goals. 

By providing insights into how these objectives align, AI ensures that every team and department is contributing to the overarching strategic objectives. This alignment is essential for maintaining coherence and ensuring that all efforts are directed towards the same long-term vision.

Generating Well-Formed OKRs

Setting realistic and aligned OKRs can be a daunting task, especially for organizations new to this framework. Our AI solution simplifies this process by suggesting objectives and key results based on existing work and historical data. This capability is particularly useful for organizations looking to reverse-engineer objectives from ongoing projects.

By analyzing the current work defined in the epic backlog, CprimeAI can generate several objectives and their corresponding key results that align with the strategic direction and goals of the company. This not only helps in setting well-formed OKRs but also ensures that they are grounded in the actual work being done, making them more realistic and achievable.

Adapting Strategies Based on Real-Time Insights

AI-powered OKRs provide dynamic tracking and predictive success insights, enabling organizations to adjust their strategies as needed. This real-time visibility into the progress of objectives allows for timely interventions and course corrections, ensuring that the organization remains agile and responsive to changing conditions.

For instance, if the AI identifies that certain key results are not on track to be achieved, it can provide recommendations for adjustments. This proactive approach helps organizations stay aligned with their strategic goals and make informed decisions based on the latest data.

With these practical applications, CprimeAI-powered OKRs in Jira Align offer a powerful tool for enhancing strategic alignment and agility. Importantly, though, it’s not a final solution.

Future Directions and Enhancements

The integration of AI with OKR management in Jira Align is an evolving journey, with several exciting enhancements on the horizon. These future developments aim to further improve decision-making, portfolio management, and overall strategic alignment within organizations.

Value Funding and Workstream Management

One of the key areas of focus is the introduction of value funding and workstream management features. These enhancements will enable organizations to better prioritize their efforts and allocate resources more effectively. By understanding which epics and initiatives provide the most value, organizations can make more informed decisions about where to invest their time and resources.

More Advanced Predictive Analytics and Planning

Additionally, the AI-powered solution will continue to evolve to provide more advanced predictive analytics and scenario planning capabilities. This will allow organizations to forecast the success of their OKRs more accurately and explore different strategies to achieve their goals. 

For example, if certain objectives are identified as having a lower likelihood of success, the AI can offer alternative scenarios and recommendations to improve the chances of achieving those objectives.

Deeper Integration With Execution Data

Another exciting development is the deeper integration with execution data. By seamlessly connecting OKR progress with execution metrics like sprint velocity and release progress, organizations can gain a holistic view of how day-to-day activities contribute to strategic objectives. This integration will enhance alignment and efficiency, ensuring that every effort is directed towards achieving the organization’s long-term vision.

These future enhancements will further solidify the role of AI-powered OKRs in driving strategic alignment and agility. By leveraging these advanced capabilities, organizations can stay ahead of the curve and continuously adapt to the ever-changing business landscape.

Could Your Organization Benefit From AI-Powered OKRs in Jira Align?

The integration of AI with OKR management in Jira Align offers a transformative approach to setting, managing, and achieving strategic objectives. By providing intelligent insights, real-time tracking, and advanced predictive analytics, AI-powered OKRs help organizations unlock their agile future and drive greater strategic alignment. 

Watch the full webinar on demand to gain a comprehensive understanding of AI-powered OKRs. Or, request a personalized demo to see firsthand how Cprime’s AI-optimized OKR solution can amplify Jira Align’s features and enhance your strategic planning processes.

Embracing Agility: Dealing with Mid-PI Feature Changes in SAFe

Posted on May 31, 2024 by - Agile & DevOps

Today I want to tackle a question that comes up all the time in my Implementing SAFe® class: 

“What do I do if someone wants to change a Feature mid Planning Interval (PI)?” 

This is a real-life scenario that we need to know how to handle effectively.

First things first, let’s remember that SAFe is a fractal model. What we do at the Team Level, we also do at the Agile Release Train (ART) Level, although the frequencies may differ. For instance, we have a Team Sync every day at the Team Level, but at the ART Level, we might have a Coaches Sync or an ART sync once or twice a week.

(SAFe® and Scaled Agile Framework® are registered trademarks of Scaled Agile Inc.)

Handling Changes at the Team Level 

Now, let’s consider a situation where someone outside the team wants to change a story within an Iteration, making the Iteration Goal obsolete. According to the Scrum Guide,

 “The Sprint Goal is an objective set for the Sprint that can be met through the implementation of the Product (Scrum) [/ Team (SAFe)] Backlog.” 

[In SAFe we refer to Sprints as Iterations]

Only the Product Owner has the authority to cancel the Iteration before the time-box ends, usually under the influence of Stakeholders, the Development (Scrum) / Agile (SAFe) Team, or the Scrum Master.

An Iteration cancellation should only happen if the Iteration Goal becomes obsolete, which might occur due to a change in company direction or market/technology conditions. However, given the short duration of Iterations, cancellation rarely makes sense. You’d hope that any directional change could be accommodated in the next Iteration, which is never more than 9 days away on a 2-week cycle. Plus, it gives the team time to consider and refine the new work for the next Iteration.

In my years of practicing Scrum, I’ve only canceled ONE Iteration, and that was to demonstrate the transaction cost of canceling an Iteration. 

When an Iteration is canceled, the transaction cost includes:

  •     Reviewing completed and “Done” Backlog items, re-estimating and returning incomplete items to the Team Backlog, 
  •     Holding a retrospective to learn what needs to be done differently so that future iterations don’t suffer the same fate,
  •     Finally, regrouping for another Iteration Planning to plan for the remaining days in the current Iteration.

People often ask me, “Can’t we just swap some stories out?” 

But I believe this sets a dangerous precedent. There’s a two-way commitment: the team works together to deliver the Iteration Goal, and in return, everyone agrees to leave the team alone for the duration of the Iteration to meet that goal. We can’t maintain this commitment if there’s a constant moving feast.

If the team starts conceding to this level of change, it will become the norm, leading to increased uncertainty at Iteration Planning and variability within an Iteration. 

However, it’s important to note that as the team works, they keep the Iteration Goal in mind. They can change the contents of the Iteration Backlog as long as they continue working towards the Iteration Goal. 

This is fundamentally different from someone outside the team changing a story in the Iteration Backlog.

Handling Changes at the ART Level

Now, let’s apply the same principle at the ART Level, where Teams have made a commitment to their PI Objectives. 

“Planning Interval (PI) Objectives are a summary of the business and technical goals that an Agile Team or train intends to achieve in the upcoming PI.”

Most PIs last 8 to 12 weeks, so variability within a PI is more common. However, canceling a PI because the  PI objectives are obsolete has a much higher transaction cost, like re-convening a 2-day PI Planning event for 5 to 12 teams!

Therefore, our first line of defense is to ask, “Can this wait until the next PI?” 

Depending on the PI length, we might only be a few weeks away from the next one, giving Product Management time to explore, refine, prioritize, and socialize the Feature(s) for the next PI. In most cases, this is a real option after reminding the company of the two-way commitment!

However, if the company changes direction or market/technology conditions change and continuing with the existing work doesn’t make sense, personally, I have swapped out a feature for a new one. 

But be aware, this is fraught with danger! 

We’ve just spent two days with the Teams PI Planning, collaboratively understanding dependencies and gaining alignment. We’ve created an ART Planning Board that visualizes these dependencies, so pulling out one feature and plugging in a new one is not that easy! It requires a significant level of impact analysis.

Dean Leffingwell, the creator of the SAFe Framework, advises that if you have too much variability in your work, you need a shorter batch size. Instead of a 12-week PI, consider a 10-week or even an 8-week PI. Yes, there’s a higher transaction cost for PI Planning, but as with all things, it’s a trade-off.

Alternatively, you can reserve more capacity within the PI and the teams for ‘unknown, unknown’ work – the things we don’t know we don’t know!

I hope this helps clarify how to handle mid-PI Feature changes. If you’re interested in learning more about SAFe, join one of our classes.

Happy SAFe journey, everyone!

Agile and AI: Navigating the Future

Posted on May 1, 2024 by - Agile & DevOps, AI-Ready Foundations

In the realm of software development, the integration of artificial intelligence (AI) with Agile methodologies marks a pivotal evolution. This fusion promises to redefine efficiency, innovation, and adaptability in project management and execution. 

As businesses seek to harness these technologies, understanding their potential to transform software development becomes crucial. This exploration delves into how AI can amplify the Agile framework, offering insights into a future where development processes are not just accelerated but also enriched with precision and creativity.

The Agile Evolution: Accelerated by AI

Agile methodologies revolutionized software development by introducing flexibility and responsiveness to rapidly changing requirements. The advent of AI technologies propels this agility to new heights, offering tools that automate tasks, predict trends, and facilitate decision-making. 

This synergy between Agile practices and AI doesn’t just speed up development; it enriches it with data-driven insights, making the process more adaptive and intelligent. By integrating AI into agile processes, teams can automate mundane tasks, allowing them to concentrate on innovation and problem-solving. This partnership also elevates the quality of the output. 

As we harness AI’s capabilities within Agile frameworks, we unlock unprecedented potential for innovation and efficiency in software projects.

Holistic AI Integration: Beyond Coding

Integrating AI across the software development life cycle (SDLC) transcends mere automation of coding tasks. It’s about embedding AI from project inception through to support, aligning it with every role and task for comprehensive efficiency gains. This approach ensures AI’s capabilities are fully leveraged, from enhancing planning with predictive analytics to refining testing through automated error detection.

Measuring Success: The Role of Metrics in AI Integration

The integration of AI into software development emphasizes the importance of metrics for tracking progress and evaluating effectiveness. Utilizing data from various systems teams can establish performance baselines and measure the impact of AI tools. 

These metrics offer insights into productivity enhancements and areas needing improvement, guiding teams towards optimized AI utilization. By quantifying AI’s contributions, organizations can make informed decisions, ensuring their investment in AI technologies drives tangible improvements in their development processes.

Tailoring AI for Software Development: The CprimeAI™ Advantage

CprimeAI exemplifies the shift towards custom AI solutions tailored for specific challenges in software development. By offering AI-assisted support and seamless integration with development tools, CprimeAI enhances both security and productivity. 

Its role-based access control ensures sensitive project information remains protected, while its integration capabilities streamline workflows. This specialized approach to AI integration highlights the importance of solutions designed with the unique needs of software development teams in mind, paving the way for more efficient and secure development processes.

CodeBoost™: Revolutionizing the SDLC with AI

CodeBoost, powered by CprimeAI, introduces a comprehensive framework for leveraging AI across the entire software development life cycle, from ideation to support. By aligning AI technologies with each phase of development, CodeBoost ensures that AI’s full potential is harnessed to enhance efficiency, quality, and innovation. 

This framework represents a paradigm shift in software development, where AI is not just an auxiliary tool but a core component of the development process. CodeBoost demonstrates the future of software development, where AI and agile methodologies converge to create a more dynamic, efficient, and effective development ecosystem.

For an in-depth demo of both CodeBoost and other use cases for the CprimeAI platform, watch our webinar-on-demand, A Framework for Development in the AI Age.