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Tag: innovation

GitHub Copilot Coding Agent for Jira Connects Planning to Pull Requests Without Leaving Your Workflow

The gap between “assigned” and “PR ready for review” just got shorter. GitHub announced on March 5 that its Copilot coding agent now integrates directly with Jira, letting teams assign issues to Copilot from their Jira workspace and receive draft pull requests in their GitHub repository — without anyone switching tools.

The integration, now in public preview, is part of a broader push by GitHub to embed its coding agent into the project management tools where engineering teams already plan and triage work. It’s a meaningful step in the shift from AI assistants that help you write code to AI agents that participate in your development workflow end to end.

How it Works

Assign a Jira issue to GitHub Copilot — either through the assignee dropdown or by @mentioning it in issue comments. The agent reads the issue’s title, description, labels, and comments to gather context, then works independently in a secure GitHub Actions environment to implement changes and open a draft pull request.

While working, the agent posts updates to Jira through an agent panel. If it needs more information, it asks clarifying questions directly in Jira. When the draft PR is ready, it appears in GitHub per your existing review and approval rules.

Setup requires installing the GitHub Copilot for Jira app from the Atlassian Marketplace and a corresponding GitHub app, connecting your GitHub organization, and configuring which repositories the coding agent can access. You’ll need Jira Cloud with Rovo enabled and GitHub Copilot coding agent on a paid plan.

The March 5 Announcement Wave

The Jira integration didn’t ship in isolation. March 5 was a significant release day for GitHub Copilot, and the full picture matters for understanding where the product is heading.

GPT-5.4 is now generally available in Copilot. OpenAI’s latest agentic coding model is rolling out across all paid tiers and supported IDEs. GitHub reports improved performance for multi-step, tool-dependent tasks. Enterprise and Business admins must enable access through a new policy in Copilot settings.

Copilot code review moved to an agentic architecture. Code review now uses an agentic tool to gather broader repository context, so feedback reflects how changes fit into the larger architecture. Copilot code review has reached 60 million reviews, now accounting for more than one in five on GitHub.

The coding agent itself has new capabilities. Recent updates include a model picker for agent tasks, self-review (the agent runs Copilot code review on its own changes before opening a PR), built-in security scanning (code scanning, secret scanning, and dependency vulnerability checks), and custom agents defined via files in .github/agents/.

Together, these releases show GitHub treating Copilot as an agentic development platform rather than an autocomplete tool. The Jira integration extends that platform into the planning layer.

“GitHub connecting its coding agent directly to Jira extends platform execution authority into the planning layer. This is a deliberate move: The platform that owns the arc from issue assignment to pull request is responsible for the development workflow. With more than 15 million Copilot users and the code already living in GitHub, adding Jira closes the last gap between where work is assigned and where it ships,” according to Mitch Ashley, VP and practice lead for software lifecycle engineering at The Futurum Group.

“The operational consequence teams need to address is straightforward. When agents handle routine issue-to-PR execution, code review becomes the primary delivery bottleneck. Investing in agentic development without equal investment in review capacity, CI reliability, and merge governance means the constraint moves; it does not disappear.

Why This Matters for DevOps

The value proposition is specific: Teams that plan in Jira and build in GitHub no longer need to context-switch to delegate well-scoped tasks to an AI agent. Bug fixes, documentation updates, test additions, and routine feature work can go from Jira assignment to draft PR without a human writing code.

That’s not the same as saying the agent replaces developers. Every PR the agent creates goes through your standard review process. The agent follows your branch protections, your required reviewers, and your CI checks. It participates in the workflow. It doesn’t own it.

But the operational implications are worth considering. When routine tasks can go from issue to PR autonomously, code review becomes the primary bottleneck. The same dynamic is playing out across the AI coding agent landscape — we wrote about it with Cursor’s cloud agents, which run 35% of internal PRs. Teams investing in agentic workflows need to invest equally in review capacity, CI reliability, and merge processes that can handle higher PR volume without sacrificing quality.

The security scanning built into the coding agent helps here. Having code scanning, secret scanning, and dependency checks run automatically before the PR opens means the agent catches vulnerabilities that might otherwise surface only during human review — or worse, after merge. GitHub notes that this scanning is included at no extra cost, a significant addition given that code scanning is normally part of GitHub Advanced Security.

The Competitive Landscape

GitHub Copilot isn’t the only agent bridging planning tools and code. Google’s ADK ecosystem added integrations with Jira, Asana, Linear, and Notion this week. Cursor’s cloud agents run independently in isolated VMs. Claude Code handles autonomous tasks, whether local or remote. The pattern is consistent: AI coding agents are moving from IDE features to infrastructure that connects across the development lifecycle.

GitHub’s advantage is distribution. Copilot has more than 15 million users, and the coding agent runs inside the same platform where the code lives. Adding Jira extends that platform into the most widely used project management tool in enterprise development.

The Jira integration is now available in public preview on the Atlassian Marketplace. It’s also available for GitHub Data Residency customers in any supported region.

March 9, 2026
Redefining Engineering Excellence: How Product Skills Amplify Your Impact in the Era of AI
The rapid evolution of artificial intelligence (AI) is transforming the landscape of multiple industries, including software development. The question isn’t whether AI will change the role of software engineers — it already has. The real question is: How can engineers amplify their impact and thrive in this new reality?

As a professional with over five years of experience in software engineering, including roles such as tech lead, engineering manager and platform engineering lead at one of the world’s largest job aggregators, I have seen firsthand how much engineers dislike repetitive and routine tasks. Today, AI allows engineers to delegate these tasks to a ‘personal junior engineer’ that everyone has always dreamed about. This raises one of the many important questions: What is next for software engineers?

This is, indeed, an interesting question. To address it effectively, I propose we apply first principles thinking — a method that involves deconstructing complex problems into their most basic elements. One practical exercise within this framework is the ‘Five Whys’ technique, which can help us understand how AI can enhance software engineers’ roles rather than replace them.

Let us break it down:
- Why do engineers write code?
To build new features
- Why do engineers need to build new features?
To solve problems or meet user needs
- Why do we need to solve problems or meet user’s needs?
To create value for the business and improve user experience (UX)
- Why do we need to create value for the business?
To ensure the company’s growth and sustainability
- Why is the company’s growth and sustainability important?
It provides jobs, drives innovation and contributes to the economy
- Why does this matter at all?
As Steve Jobs said, “Innovation distinguishes between a leader and a follower.” Innovation isn’t just about solving today’s problems; it is about imagining what doesn’t yet exist and having the courage to create it. The sixth ‘why’ reminds us that we innovate not just for utility or profit but to leave a mark — to contribute something meaningful to the world.

By drilling down to these fundamentals, we see that software engineering is about creating value and innovation, allowing engineers to impact not just the ‘how’ of software development, but also the ‘what’ and ‘why’. This strategic involvement requires human insight and creativity — areas where AI cannot replace us.

The Shift From Software Engineers to Product Engineers

Traditionally, software engineers have focused on delivering clean, efficient and scalable code. While technical expertise remains critical, the rise of AI is shifting the emphasis from execution to strategy and problem-solving. Developers who incorporate product thinking into their work will position themselves as indispensable assets to their organizations.

A product engineer is more than just a code expert. They bridge the gap between development and business impact by:
- Understanding product strategy and objectives and key results (OKRs)
- Understanding user needs and designing solutions that enhance UX
- Identifying opportunities to contribute to product strategy
- Developing a product mindset
Why AI Makes Product Thinking Essential for Engineers

AI is not just a tool for automating repetitive coding tasks — it is also reshaping how products are built, tested and deployed. As AI models generate code, optimize infrastructure and even predict software failures, engineers must redefine their role in the development process.

To remain valuable, developers must shift their focus from ‘How should I build?’ to ‘What should I build and why?’ By embracing product skills, engineers can:
- Become ‘Thinkers’, Not ‘Doers’: AI can generate code, but it cannot deeply understand user pain points, business objectives and long-term scalability. Product engineers connect the dots between technology and business value.
- Lead Cross-Functional Innovation: AI-powered automation frees up engineers to drive innovation rather than get stuck in execution mode. Product-focused engineers work at the intersection of technology, business and UX. They lead strategic initiatives rather than just implement them.
- Design With AI in Mind: The best engineers do not just use AI tools — they design for them. Understanding how AI impacts user workflows, data privacy and ethical considerations gives engineers a competitive advantage in building future-ready products.
Key Product Skills Every Engineer Should Develop

For engineers looking to stay ahead in an AI-driven landscape, developing product skills is no longer optional — it is essential. Here are the key areas to focus on:

Understanding Product Strategy and OKRs

Regardless of the role you hold in a company, you are hired to deliver value. Your salary is an investment and the company expects a return. Each organization defines value differently, but ultimately, it is linked to revenue — a clear indicator that users love the product and are willing to pay for it. They exchange money for the value they receive.

How can engineers focus on delivering this value? The answer lies in understanding product strategy. Product strategy isn’t just about deciding what to do; it is also about knowing where not to waste time and energy.

To gain this understanding, engage with the product team and be curious about OKRs and product strategy. Understand the company’s goals for the next year or five years and learn how the company plans to achieve them.

For example, imagine your company sets an OKR to increase e-commerce sales by 15%. The product strategy involves enhancing the mobile app’s checkout process to reduce abandonment rates. By understanding these business objectives, you now have guidance on how to deliver value. You can generate ideas to achieve the OKR, aligning your technical skills with business goals and contributing directly to the company’s success.

User-Centric Thinking

Now that we are clear on the goal, let us revisit the second ‘why’ in our exercise: Why do engineers need to build new features? The answer is simple — to solve problems or meet user needs. How can we solve user problems if we do not truly understand who our users are and what their problems might be?

This brings us to the second important skill: Having a strong understanding of user needs. There are many frameworks available to help structure this understanding. Two that I find particularly useful are Jobs To Be Done and Customer Journey Mapping.

Now, let us return to our example of the checkout process. We know the abandonment rate is high, but why? To answer this, I suggest approaching the problem from two angles:
- Qualitative Data: Ask questions such as, ‘Do we have user insights about the current pain points in the checkout flow?’
- Quantitative Data: Analyze metrics to understand, ‘Where are users experiencing friction in the checkout flow?’
For instance, imagine users report that the payment screen is loading too slowly. By comparing this feedback with quantitative data (e.g., drop-off rates), you can validate their concerns and identify actionable solutions.

Developing Product Solutions

As you dive deeper into product thinking, you will start to identify potential solutions to business challenges. For instance, you might hypothesize that optimizing page load times could significantly reduce checkout abandonment rates.

However, it is crucial to remember that this is just a hypothesis, not a proven solution. This is where your product management skills come into play. Instead of jumping straight to implementation, approach it strategically:
- Frame your idea as a testable hypothesis
- Use frameworks such as data, insight, belief, bet (DIBB) to structure your proposal
- Outline a plan to validate your hypothesis through experiments or A/B testing
By presenting your ideas in a structured way, you demonstrate both technical expertise and strategic thinking that align with business goals.

Product Mindset

I have learned that developing a product mindset starts with curiosity and a critical, nonjudgmental approach. I always ask myself: Is what I am building really valuable? How valuable is it? I try to think like an investor — would I put my own money into this? What would I get in return?

Stay curious, I say. Question everything. Just because we have always done something one way does not mean we should keep doing it. I love digging into data to see how customers actually use our product. Trust me, it can surprise you.

For me, it is about developing a real passion for the problem. I am fascinated by payments, always looking for ways to make things simpler for merchants. Payments are complex, but our job is to make them feel simple.

I have learned to be proactive. I treat the company like it is my own. I do not wait for features to work on — I know the company’s goals. I figure out how to hit those goals, come up with ideas and pitch them.

The Future of Engineering

Engineers are in a unique position to lead. My advice? Take initiative. Get buy-in. Drive the product forward. Think differently. That is how you make a real difference.
July 31, 2025
Agile and Innovation: Striking the Right Balance in Software Development

The tech industry is abuzz with a contentious debate: Is Agile stifling innovation? Security expert Moxie Marlinspike brought this question to the forefront, challenging the very foundation of modern software development practices. However, a closer examination reveals that the issue is more nuanced than it first appears.

Marlinspike argues that Agile’s focus on incremental development and short-term goals restrains creativity and limits the ability to undertake ambitious, long-term projects. At first glance, this critique strikes at the heart of Agile methodologies. Yet, the real culprit is not Agile itself but its misapplication within many organizations.

Potential Pitfalls of Overemphasizing Process

Beyond the surface-level critique that Agile stifles innovation lies a more complex reality: The problem is not inherent in Agile’s principles, but in how organizations implement them or not. The issue is an overemphasis on process; when teams get too caught up in Agile rituals and tracking vanity metrics, they can lose touch with the more creative side of their work. Instead of encouraging and promoting innovation, it creates a culture obsessed with ticking boxes rather than thinking outside of them.

Another key factor is the lack of ‘elbow room’. True innovation requires space for experimentation and exploration. When Agile methodologies are pigeonholed and held under the tight reins of deadlines and deliverables, the lack of space for creative thinking means these methods fail to boost the very innovation they aim to foster. Failing to understand the flexibility that Agile methods offer your team automatically sets you up for failure.

The growing number of less experienced developers entering the field might worsen this problem. Many newcomers lack the holistic understanding that comes with the experience required for effective software engineering and innovation. This can lead to a surface-level application of Agile principles without grasping their deeper intent. It is more about understanding the original principles, which promote adaptability and doing the right thing, than simply sticking to the rules.

Agile in Action

That being said, not all Agile views and experiences are steeped in doom and gloom. Real-world teams are proving that Agile can work effectively when done right. Companies like Spotify have molded Agile to align perfectly with their team’s workflows and take advantage of their culture that supports autonomy and has trust and safety in place. By adapting Agile methods to suit their work and creative space, the team successfully sculpted a way of working that works in their context. Agile methodologies are not a destination but a journey that needs to be redirected and replanned, depending on the unique dynamics of your team and business.

Similarly, navigating the dynamic world of digital streaming, Netflix adopted Agile methodologies to maintain adaptability, prioritizing experimentation and rapid iteration. They have adopted a flexible approach to creating content, working in shorter bursts that allow for constant tweaks based on feedback. This way, they can quickly adapt and keep their work in sync with what their audience wants. Again, by embracing Agile’s original principles of welcoming change, Netflix has transformed content creation from a rigid, linear process to a more expressive and responsive ecosystem of creativity.

The Future is (Still) Agile

The key to harnessing Agile’s innovative potential lies in striking the right balance between structured process and free-rein creativity. Organizations need to prioritize the human element of Agile — the collaboration, creativity and motivation of their team members — over rigidly sticking to unchanging rules and guidelines. This means embracing an environment where teams have the freedom to explore new ideas, take calculated risks and think beyond immediate KPIs. Metrics should not be used as a punitive tool, but as a guide to help better understand team performance.

A huge part of reimagining Agile is recognizing that an overemphasis on being process-oriented can be limiting, while real innovation and change come from returning to Agile’s core principles: Valuing each individual and interaction. This means moving beyond checklists and ceremonies to truly understanding and nurturing the human dynamics that drive exceptional work. It is about rebuilding organizational structures around the people rather than processes. This also means recognizing when structures are failing teams. A one-size-fits-all approach suffocates creativity and stifles individual potential. Instead, organizations must develop the sensitivity and flexibility to create bespoke environments that allow each team to thrive in its unique way.

January 24, 2025
Navigating Beyond the Apex: Reshaping IT Transformation and Organizational Evolution for Sustainable Growth
In my nearly four decades of navigating the ever-evolving landscape of information technology (IT), I have witnessed profound shifts in technology and organizational structures. While the DevOps movement has undoubtedly made remarkable strides, it is imperative to recognize that it may have reached a plateau. This article aims to delve into not only the challenges and successes of DevOps but also the broader considerations essential for sustainable growth in the rapidly changing IT ecosystem.

DevOps and its Inflection Point

The rise of DevOps has ushered an era of collaboration, accelerated delivery and enhanced quality. For example, the implementation of continuous integration and continuous deployment (CI/CD) pipelines has significantly streamlined the development and release processes. However, having weathered the ups and downs of IT transformation, it is clear that DevOps has hit an inflection point. Organizations that initially saw a surge in efficiency are now grappling with the need for a more profound reevaluation of their structures.

The Crucial Role of Long-Term Reorganization

My extensive IT tenure underscores the significance of organizational dynamics. Consider the journey of a large enterprise embracing DevOps. Shifting from traditional hierarchical structures to cross-functional, product-focused teams is exemplified by companies such as Spotify. This shift not only optimizes flow and cognitive load but also encourages innovation and adaptability.

Navigating the Short-Term vs. Long-Term Challenges

The allure of short-term gains often overshadows the need for sustained evolution. For instance, a company adopting DevOps practices may experience immediate benefits such as faster time-to-market. However, without a broader strategy, these gains may prove short-lived. Consider the case of Amazon, which, beyond its e-commerce success, continuously invests in long-term adaptability through innovations like AWS.

Unveiling the Pay Gap Predicament

Beyond IT, a concerning trend emerges — the widening gap between company productivity and the employees’ pay scale. Short-term cost-cutting measures can lead to damaging repercussions. A case in point is the tech industry, where some companies, despite high productivity, have faced criticism for unequal pay structures. Addressing this gap requires a commitment to equitable compensation and responsible business practices, safeguarding the foundation of a robust economy.

Human-Centric Approach

In the fast-paced world of IT, a human-centric approach is paramount. Consider the example of Google, where the emphasis on a positive work culture, innovative projects such as ’20% time’, and employee well-being contribute not only to technical success but also holistic growth.

Continuous Learning and Skill Development

Continuous learning is the bedrock of success in IT. IBM’s commitment to skills development, exemplified by initiatives such as the IBM Skills Gateway, ensures that employees stay relevant in an ever-changing technological landscape. This not only keeps employees engaged but also fosters a culture of innovation and adaptability, essential for sustained growth.

Metrics for Success

Establishing meaningful metrics is crucial for measuring the success of IT transformation. For example, Etsy, beyond traditional performance indicators, incorporates metrics such as employee satisfaction and collaboration effectiveness. This holistic approach provides a more comprehensive view of the transformation’s effectiveness.

Cybersecurity Integration

Embed cybersecurity discussions seamlessly into IT transformation narratives. The Equifax and more recent data breaches serve as a stark reminder of the repercussions of overlooking security. Integrating security measures into DevOps practices is crucial for safeguarding organizational assets and ensuring the longevity of IT initiatives.

Collaboration Beyond IT

Encourage collaboration not only within IT teams but also across different departments. The success of Salesforce, for instance, lies not only in its CRM software but also in its cross-functional approach, ensuring alignment with overall organizational goals and fostering innovation beyond traditional boundaries.

Ethical Considerations

Highlight the ethical considerations in technology and IT transformation. Google’s commitment to ethical AI, demonstrated by the creation of an external advisory council, showcases a proactive approach to address societal and ethical implications associated with technological advancements.

Community Engagement

Advocate for community engagement and participation in industry forums and events. Microsoft’s engagement in open-source communities and events such as Build not only contributes to the collective knowledge base but also fosters a sense of camaraderie among professionals facing similar challenges.

Adaptive Leadership

Stress the importance of adaptive leadership in navigating IT transformations. Satya Nadella’s leadership at Microsoft is a testament to adaptive leadership. His approach to leading through change, inspiring teams and making data-informed decisions has played a pivotal role in Microsoft’s resurgence as a tech leader.

Challenges and Addressing Factors for Hesitancy

While many companies recognize the importance of adopting a holistic approach to IT transformation, various factors can impede progress. A few reasons for hesitancy or resistance to moving in this direction include:
- Resistance to Change
Approach: Change management strategy emphasizing clear communication, employee engagement and highlighting the benefits of the transformation.
- Short-Term Focus and Pressure for Immediate Results
Approach: Communicating the long-term value and return on investment (ROI) of the transformation to help align stakeholders with a more sustainable vision.
- Lack of Awareness or Understanding
Approach: Education and awareness programs, along with showcasing successful case studies, can bridge this knowledge gap.
- Legacy Systems and Technical Debt
Approach: A phased approach, starting with critical systems and leveraging technologies such as containerization and microservices, can help manage the transition without overwhelming the organization. Don’t just simply ignore it.
- Fear of Disruption to Operations
Approach: Implementing changes gradually, during less critical periods and having robust contingency plans can alleviate these concerns.
- Inertia in Leadership
Approach: Promoting adaptive leadership workshops and showcasing success stories of organizations that have successfully transformed their leadership styles can encourage openness to change.
- Budget Constraints
Approach: Demonstrating the cost-effectiveness of the transformation over the long term and exploring partnerships can help overcome budget constraints.
- Greed and Short-Term Focus of Senior Executives
Approach: Aligning executive incentives with long-term organizational health, rather than short-term gains, can help mitigate this issue.
- Cultural Inertia
Approach: Fostering a culture that encourages innovation, collaboration and adaptability is crucial for a successful transformation.
- Uncertainty and Risk Aversion
Approach: Addressing uncertainty associated with transformative initiatives and potential risks by emphasizing the importance of bold steps, learning from failures and not preferring the status quo.

In summary, while many companies understand the benefits of a holistic IT transformation, various internal and external factors can contribute to hesitancy or resistance. Overcoming these challenges often requires a combination of strategic leadership, effective communication and a commitment to long-term organizational health.

Leaders must actively address resistance to change, align short-term actions with long-term vision and invest in the necessary resources and skills for a successful transformation. Furthermore, fostering a culture that embraces innovation, collaboration and adaptability is essential for navigating the dynamic landscape of IT and ensuring sustained growth. By addressing these factors head-on, organizations can position themselves to not only survive but thrive in the era of digital transformation.
July 10, 2024
Embracing the Future: Navigating the Waves of AI in DevOps

In the rapidly evolving digital landscape, the advent of generative AI and large language models (LLMs) has ushered in a new era of innovation and transformation. As IT organizations pivot toward DevOps, DevSecOps and SRE mastery, leaders find themselves at the crossroads of excitement and anxiety. This dual sentiment stems from the vast potential of AI to revolutionize operations and the inherent challenges that accompany generative AI’s integration. In this article, we’ll explore the reasons behind this excitement and anxiety, outline the top use cases for generative AI and provide strategic guidance to ensure a smooth DevOps and digital transformation journey.

The Catalysts of Excitement

1. Enhanced Efficiency and Innovation: Generative AI, with its ability to automate complex processes and generate new ideas, promises unprecedented efficiency. It enables IT organizations to streamline development pipelines, reduce manual errors and foster innovation, making the leap toward DevOps and DevSecOps not just a goal but a tangible reality.
2. Improved Decision-Making: Large language models offer sophisticated data analysis capabilities, turning vast datasets into actionable insights. This empowers leaders to make informed decisions rapidly, optimizing operations and enhancing service reliability in line with SRE principles.
3. Competitive Advantage: In the digital age, staying ahead means adopting the latest technologies. Generative AI provides a unique competitive edge, allowing organizations to deliver superior digital experiences, innovate products and services and respond to market changes proactively.

Top Three Generative AI Use Cases in DevOps

1. Automated Code and Test Generation and Review: AI-driven tools can generate code and test snippets, conduct code reviews and testing and suggest optimizations, accelerating development cycles and improving code quality.
2. Security Enhancement: By integrating AI into security protocols, organizations can predict and mitigate potential threats more effectively, enhancing their DevSecOps initiatives with proactive rather than reactive measures.
3. Incident Management and Resolution: AI can automate incident response workflows, predict outages before they occur and suggest remediations, aligning with SRE goals of maintaining high availability and reliability.

Sources of Anxiety

1. Ethical and Privacy Concerns: The potential for misuse of AI technologies, including privacy violations and biased decision-making, raises significant ethical questions, creating apprehension among leaders.
2. Skill Gaps and Workforce Impact: The shift toward AI-centric operations necessitates new skills and roles. Leaders worry about the existing workforce’s ability to adapt and the challenge of filling emerging skill gaps.
3. Integration and Compatibility Issues: Integrating AI into legacy systems and ensuring compatibility across the digital infrastructure poses technical challenges, complicating the transition to advanced DevOps and SRE practices.

Navigating the Digital Transformation Journey

To harness the benefits of AI while mitigating its risks, leaders should consider the following strategic actions:

1. Foster an AI-Ready Culture: Cultivate a culture of continuous learning and adaptability. Encourage your team to embrace AI tools and methodologies, emphasizing the importance of upskilling and reskilling.
2. Implement Ethical AI Guidelines: Develop and adhere to ethical guidelines for AI use, focusing on transparency, accountability and fairness. Ensure privacy and security measures are integral to your AI initiatives.
3. Invest in Talent and Training: Address skill gaps by investing in training programs and partnerships with educational institutions. Consider hiring AI specialists to bridge the gap between traditional IT roles and AI requirements.
4. Prioritize Seamless Integration: Adopt a phased approach to AI integration, ensuring compatibility with existing systems. Leverage APIs and microservices architectures to facilitate smoother transitions.
5. Establish Governance and Oversight: Create a governance framework to oversee AI initiatives, ensuring alignment with organizational goals and compliance with regulatory requirements. This should include monitoring AI performance and its impact on operations and workforce dynamics.

Summary: Seize the AI Opportunity

As we stand on the brink of a transformative era, the fusion of AI with DevOps, DevSecOps and SRE practices represents a significant leap forward. While the journey is fraught with challenges, the potential rewards are immense. By embracing AI with strategic foresight, ethical consideration and a commitment to continuous improvement, IT leaders can propel their organizations toward unparalleled efficiency, innovation and competitive advantage.

In conclusion, the integration of AI into DevOps to further accelerate digital transformation is not just an option but a necessity for staying relevant in the digital age. The journey requires careful navigation, but with the right approach, IT organizations can emerge stronger, more agile and better equipped to face the future. Let’s embrace this opportunity to redefine the digital landscape, ensuring our transformations are not just successful but sustainable and responsible.

Embrace the future; embrace AI.

February 20, 2024
10 Key Features of AI Code Assistants

One of the most potent tools that have emerged to streamline the coding process is code assistants. Code assistants are software tools designed to assist programmers by providing suggestions and automated features that help in writing code more efficiently and accurately. They act as virtual aides, making the coding process faster, more intuitive and less prone to errors.

Code assistants can be categorized into two types: Rule-based and AI-based. Rule-based assistants follow predefined rules and perform according to set guidelines. They are mainly used for syntax highlighting, code formatting and basic error detection. AI-based code assistants can learn from past coding patterns, predict what a programmer is trying to achieve and suggest appropriate code snippets. They are capable of understanding the context of the code and providing accurate suggestions, as well as generating entire blocks of code based on natural language instructions. Read this blog post for examples of popular AI coding assistants.

Whether you are an experienced software developer handling complex projects or a beginner, coding assistants can automate the tedious aspects of coding, allowing you to focus more on problem-solving and innovation.

The Growing Importance of AI in Software Development

As software development becomes more complex and time-sensitive, the role of AI in this field has grown significantly. AI-driven code assistants represent a major shift in how software is developed. They bridge the gap between human expertise and the efficiency of automated processes.

AI in software development not only speeds up the coding process but also enhances the quality of the code. These tools can analyze vast amounts of code, learn from it and apply that learning to assist developers in real-time. They can adapt to different coding styles and requirements, making them versatile tools for a wide range of programming tasks. Furthermore, as software projects increase in size and complexity, AI code assistants become crucial in managing and navigating these complexities. They offer intelligent insights, detect potential issues before they become problematic, and assist in keeping large codebases clean and manageable.

In addition to increasing efficiency, AI code assistants are instrumental in reducing the barrier to entry in software development. They make coding more accessible to people with varying levels of expertise, including those who are new to programming. This accessibility is essential in an era where the demand for software development skills is continuously rising. By providing real-time assistance and learning resources, AI code assistants are not just tools for current developers but also learning aids for future generations of programmers.

10 Key Features to Expect from Code Assistants in 2024

With the rapid advances in large language models (LLMs), the following features are becoming standard in coding assistants as we enter 2024.

Advanced Contextual Understanding

Modern code assistants are evolving to understand the context of code more intricately. Unlike simple autocomplete tools, these AI systems can analyze the broader scope of a project, recognizing patterns and offering suggestions that align with the overall architecture and design patterns. This advanced understanding not only streamlines coding but also aids in maintaining consistency throughout the codebase.

Real-Time Code Optimization Suggestions

AI tools can proactively recommend changes to improve performance and efficiency. For instance, a code assistant might suggest a more efficient algorithm or highlight redundant code blocks that can be simplified. This feature not only enhances code quality but also serves as a learning tool for developers, exposing them to best practices and alternative approaches.

Enhanced Debugging Assistance

Debugging is often a time-consuming aspect of development. In 2024, expect code assistants to revolutionize this process by offering enhanced debugging assistance. Through deep analysis, these tools automatically detect errors and anomalies, suggesting potential fixes. They can pinpoint the exact location of a bug and provide context, significantly reducing the time developers spend in the debugging phase.

Automated Refactoring

Refactoring is crucial for maintaining clean code, and AI-driven assistants now offer sophisticated automated refactoring capabilities. They intelligently suggest changes that improve code readability and maintainability, such as restructuring code or renaming variables for clarity. These suggestions will be particularly beneficial for managing and updating legacy code, ensuring it adheres to modern coding standards.

Code Documentation Generation and Update

Keeping documentation updated is a challenge. Code assistants can now automate this task by generating and updating documentation in real-time. As code changes, the documentation will reflect these updates, ensuring that it always stays in sync with the codebase. This feature will be invaluable in large teams where keeping track of changes can be daunting.

Predictive Coding Assistance

Moving beyond reactive suggestions, code assistants will offer predictive assistance. By analyzing coding patterns and project structures, these tools will predict a developer’s next steps and offer relevant suggestions even before they are explicitly requested. This proactive approach will speed up the development process, reducing the cognitive load on developers.

Understanding and Responding to Developer Queries in Natural Language

Developers can pose queries or express coding requirements in plain English, and the assistant will interpret and execute these requests. Chat-based interfaces offered by leading AI coding assistants take this one step further. This advancement will make coding more intuitive and accessible, especially for beginners or non-expert users.

Real-Time Suggestions and Conflict Resolution in Team Projects

Collaboration in coding projects can lead to conflicts and inconsistencies. Code assistants now offer real-time suggestions and automated conflict resolution for team projects. These tools will identify potential conflicts in merge requests and suggest optimal resolutions, enhancing team collaboration and maintaining code integrity.

Personalized Coding Assistance Based on Past Interactions

Personalization is becoming a significant feature of code assistants. By learning from past interactions with a developer, these tools will tailor their suggestions and support to align with individual coding styles and preferences. This adaptive learning approach ensures that the assistance provided is most relevant and efficient for each developer.

Enhancing Accessibility of Coding for Non-Expert Users

One of the most impactful features of code assistants is their role in democratizing coding. By simplifying complex coding concepts and automating mundane tasks, these tools will make software development more accessible to non-experts. Beginners and those learning new programming languages will find these assistants particularly useful, as they provide guidance and learning aids.

Conclusion

Code assistants powered by AI are becoming an integral part of the software development process. They not only make coding faster and more efficient but also reduce the chances of errors, making the resultant software more reliable. The role of code assistants is only expected to grow, bringing in more advanced and intuitive features. Whether you are a seasoned programmer or a newbie, embracing code assistants can be a game-changer for your coding journey.

February 6, 2024
How to Host a Successful Hackathon: Encourage Innovation and Deliver Value

In the tech world, hackathons are not just events; they’re incubators for real-world innovation and collaboration. They bring together not just coders, but visionaries eager to push the boundaries of what’s possible with technology. As these events become mainstream, organizations from tech giants to startups are leveraging hackathons not just to unearth coding prowess, but to kindle a culture of continuous innovation and ethical AI development.

If it sounds like a hackathon would benefit your organization, you’re likely correct – but a challenging, fun and well-organized hackathon doesn’t happen on a whim. First, you need to nail down ground rules, guardrails and goals to ensure the best return from the event.

Setting Goals and Determining Desired Outcomes

Before determining the objective or challenge of the hackathon, it’s important to consider your organization’s priorities and what you hope to achieve by hosting it. This can – and should – vary widely depending on the scope of your business.

If you’re a vendor of proprietary software, you might consider using the hackathon to gauge the accessibility and user experience offered through your project. It could also be an opportunity to encourage customers and partners to use your solutions to solve business problems in ways they hadn’t thought of before. If you’re questioning current cybersecurity measures, it could be beneficial to format the event around auditing and recording security discrepancies or vulnerabilities.

Hackathons also don’t need to be an externally facing event – if you have in-house talent, hosting an internal hackathon could make the most of your resources while bolstering camaraderie and chemistry among employees. Internal hackathons can enable goals in sensitive areas of your business that couldn’t be exposed to external developers.

Once you’ve determined the primary objective and attendee profile for your hackathon, you can determine the challenge you’ll pose to participants that will ultimately yield the desired outcome.

Creating the Perfect Challenge

The crux of a successful hackathon is a challenge that mirrors real business problems, engaging minds to develop solutions that are not only innovative but also practical and ethical. It’s about responsibly applying AI to do good in ways that resonate with your core values. At ABBYY, for instance, we’ve witnessed teams of professionals harness the low-code capabilities of our intelligent document processing platform to solve complex data puzzles within hours — not just as a testament to the power of AI but to the creativity and collaborative spirit of the participants.

These teams were comprised of professionals from Deloitte, PWC and Unilever, indicating a strong knowledge base of document-centric challenges. They worked to solve advanced scenarios involving handwritten forms with identity documents, claims processing, bank checks with signature extraction, unstructured data with named entity recognition, combined PDF, and bank statements.

With the vast experience of the competing teams and the accessible low-code/no-code interface of Vantage, the winners of the hackathon were able to develop and present robust solutions within a four-hour period.

Note the designation of “winners,” as hackathons are traditionally a competitive activity. Therefore, there needs to be guidelines for rules and adjudication while still ensuring creative freedom.

Crowning Winners and Encouraging Creativity

Competition, while central to the hackathon’s vigor, is curated to fuel creativity and not just to crown victors. It’s about recognizing those who think differently and championing the potential of AI to enhance — not replace — the human element in the workplace.

Hackathons often entail both development and presentation skills, as teams will pitch their completed solution. Judging criteria should consider all steps of this process and account for the competitors’ ability to illustrate the full breadth of their product. This ensures that all team members’ efforts are considered, encouraging deeper collaboration.

An example of comprehensive judging criteria could be:
• Completeness of solutions
• Functionality of solutions
• Uniqueness of solutions
• Clarity of presentations
• Teamwork and collaboration

Not every team will walk away a winner, but with a sufficiently engaging objective, each participant can leave the hackathon having learned something new, made a new connection, or otherwise developed themselves and their professional skills.

Finding Opportunities Through Feedback

Feedback from these events is gold dust. It’s not just about what went right; it’s about learning, adapting and constantly improving. It’s a dialogue that continues long after the hackathon ends, ensuring that each subsequent event is more aligned with your vision for AI’s role in society.

Were there opportunities for logistical and organizational improvement that could have limited the event’s potential? Were the assigned scenarios excessively challenging, or perhaps not challenging enough? Did participants expect more opportunities for networking? Ask your attendees for their thoughts on every facet of the event that could have impacted the value brought to them and your organization.

Participants in ABBYY’s recent hackathon found value across the spectrum of development and professional growth. “I feel like we learned many things, like executing new strategies and how we can interact with other technologies,” said Radhakrishnan R, associate software engineer at PWC. “The other great thing is we connected with many people. This was a good opportunity to build a great network – we saw how many people are working with this technology, and we got to meet them in person.”

Mutual and Multifaceted Benefit

For companies that sit at the forefront of AI technology, hackathons are an opportunity to test the waters of user experience and accessibility and to push platforms to solve business problems creatively and ethically. They are a testament to a commitment to compliant and explainable AI, aligning with the ethos that technology must be as responsible as it is revolutionary.

In essence, a hackathon, when done right, reflects a belief in the symbiotic relationship between humans and AI. It’s a platform where innovation meets practicality, where ethical AI development is not just a buzzword but a practice, and where every participant, regardless of the outcome, leaves with a richer understanding of AI’s transformative power.

December 11, 2023
BMC Mainframe Survey Results Show a Platform on the Rise

What if I told you that the oldest active computing platform is also one of the most innovative, seen as a platform for growth and new workloads, not just by seasoned professionals, but by a new generation of stewards, too? The 2023 BMC Mainframe Survey shows just that. In addition to an overwhelmingly positive perception and growth mindset regarding the mainframe, the survey reveals that the majority of those who are most enthusiastic about its future are relative newcomers to the platform.

Now in its 18th edition, the annual BMC Mainframe Survey gauges the opinions of more than 800 executive and technical professionals on a range of topics, from overall perception of the platform to top priorities, common practices and greatest challenges.

This year’s results show that 94% of respondents have a positive perception of the mainframe, with 62% citing it as a platform to grow and attract new workloads. This tracks the 62% who report adopting DevOps practices on the platform. It’s no surprise, then, that two of the top five priorities cited by respondents are efforts to enhance automation and modernize applications. Organizations around the world recognize that the mainframe can be a leader in innovation, powering their digital transformations.

Modern Management Approaches

This innovation goes beyond the software and services developed for the mainframe to include those used to manage the platform. It’s reported that 35% of respondents listed the implementation of cloud technologies as one of their top four priorities for the coming year, with 41% saying they are considering cloud-based storage and backup. With the growing volume of mainframe data and the emergence of new regulations and standards governing data security and recovery, organizations are turning to cloud-based data management to reduce costs and improve recovery options.

Year over year, the number of survey respondents reporting the use of AIOps hasn’t changed considerably, but those reporting its cross-platform use on both mainframe and non-mainframe systems has jumped 7% from 2021.

Organizations are recognizing the need to take advantage of the greatest business values of AIOps — cited in the survey as improved uptime and the ability to detect problems quickly, understand trends and respond proactively — as part of a holistic approach to enterprise IT operations management.

With increased cross-platform use, significant benefits and a decrease in the number of respondents reporting implementation challenges like cost, complexity and the need for specialized skillsets, BMC sees AIOps as an area ripe for significant growth in the coming year.

Investment Pays Dividends

Improving the availability, quality and efficiency of the mainframe, of course, requires investment. In our report on the results of the 2023 BMC Mainframe Survey, we compare the responses of maximizers (those planning to increase investment in the platform) with those of minimizers (those planning to decrease investment levels). The results are eye-opening.

Maximizers have invested in future-proofing their workforce in a variety of ways, and the data bears that out; 81% have ten or fewer years of experience on the platform. The new generation of stewards sees the mainframe not as a legacy platform, but one of continued growth and innovation, worthy of the investment required not just to keep it modern, but to place it at the forefront of digital transformation.

Another key difference between maximizers and minimizers relates to security, which was cited as the top overall priority of survey respondents for the fourth consecutive year. Maximizers are more likely than minimizers to have a dedicated resource to investigate security events and to have dedicated ransomware recovery capabilities in place. It should come as no surprise, then, that maximizers were 20% more likely than minimizers to report that their mainframe security controls for ransomware are extremely effective.

These results show that the differences between maximizers and minimizers go beyond outlook, producing tangible effects on outcomes. The maximizers increasing their investment in the mainframe report fewer challenges with staffing and skills shortages and a stronger stance against increasingly sophisticated cyberthreats. The minimizers decreasing their investment in the platform not only have greater difficulty in maintaining a skilled workforce, which increases the risk of falling behind competitively, but it also puts their organizations at serious risk of potentially catastrophic security breaches.

A Bright Future

In short, the results of the 2023 BMC Mainframe Survey reflect a positive outlook for the platform’s future. By investing in their workforces and the tools required to advance and protect the platform while adopting modern techniques and practices, organizations can reduce risk and promote innovation that delights their customers.

November 16, 2023
Technical Debt is a Major Threat to Innovation

A survey published today suggested that technical debt is starting to take a heavier toll as IT environments continue to become more complex to manage.

The survey of 400 IT leaders at organizations with 1,500 employees was conducted by Foundry, the market research arm of International Data Group (IDG) on behalf of Insight Enterprises, a provider of IT services. The survey found that 86% of respondents reported their organization was impacted by technical debt over the past 12 months, with the top areas affected being the ability to innovate (43%), meet service level agreements (41%) and avoid downtime (37%).

Technical debt is created when organizations cut corners and leave known bugs in the race to meet application delivery deadlines. Application code is often not as thoroughly tested as it should be. In some cases, it turns out that what was intended to be a minimum viable product doesn’t scale all that well. Quick fixes needed to improve performance are also frequently undocumented. Inefficient code also winds up consuming more IT infrastructure than required, which adversely impacts everything from cloud costs to the amount of carbon generated.

Juan Orlandini, chief architect for the Cloud + Data Center Transformation (CDCT) division of Insight Enterprises, said every organization struggles with technical debt; it will never be eliminated, but it does need to be contained, he added.

The challenge is instead of trying to keep the proverbial IT lights on during the COVID-19 era, IT teams are now being asked to innovate to advance digital business transformation initiatives, said Orlandini. A full 87% of survey respondents cited modernizing critical applications as a key success driver.

As a result, many organizations are embracing platform engineering to bring more structure to their DevOps processes, he noted. The challenge, however, is striking a balance between a more centralized approach to DevOps and maintaining the ability of developers to innovate, said Orlandini.

The issue, of course, is that in addition to massive technical debt, the latest generation of applications are more distributed than ever. The survey found 91% of respondents now rely on multiple public cloud providers for different workloads, with 54% of data residing on a public cloud. However, the survey also found on-premises IT environments are still relevant, with 20% planning to repatriate select public cloud workloads to an on-premises model over the next 12 months.

Nearly three-quarters (72%) of respondents also noted data volumes are growing faster than their ability to manage them, with 64% ranking implementation of a data governance program to improve data management as their top data-related objective this year. Just over a third (35%) of the data estate is now, on average, driven by edge workloads, with 29% of respondents reporting that at least 50% of data volumes now reside on the edge. Nearly two-thirds (64%) expected that to increase in the next 12 months.

A total of 85% of respondents also indicated their organization is using artificial intelligence (AI) to some degree to drive business insights and/or efficiencies, although only 36% reported having optimized their use of AI and machine learning (ML) across the enterprise.

Finally, cybersecurity also remains problematic, with 51% reporting they were impacted by a cybersecurity breach over the past 12 months. Just 20% resolved their issue in one day, while 31% needed less than a week. Nearly half (49%) required one week or more to recover.

It’s clear that IT isn’t becoming any easier to manage. IT teams need to find ways to build and deploy new applications while reducing technical debt so it doesn’t strangle future innovation.

February 16, 2023
What Devs Need to Know About R&D Tax Credits
The research and development (R&D) tax credit was created to reward innovative companies for building and improving products and processes, including software development. Yet software companies often overlook claiming this lucrative credit. Less than one-third of eligible companies take advantage of R&D tax credits from the U.S. federal government. While the software development community may already be aware of R&D tax credits, they often lack the knowledge and resources to make the most of the system. If you’re a software developer thinking of starting your own company or building your own application or you work for a company that would benefit from the credit, here are three important pieces of the R&D puzzle you should know to optimize and defend their R&D tax credits.

The History

Each year, the federal government provides billions of dollars to businesses that develop and improve technologies, products and processes. Beginning in 1981 as part of the Economic Recovery Tax Act, the federal government allowed for a temporary tax credit of up to 13% on spending for qualified research on products and processes that have been developed or improved through the application of either the physical sciences, biological sciences, computer science or engineering. This can include costs associated with developing a patent, a new product or service offering or even a new technology that is sold to third parties or used internally within the business.

In 2015, the Protecting Americans from Tax Hikes (PATH) Act permanently extended the R&D tax credit and expanded its benefits to startups and small businesses. Early-stage businesses can use the credit to eliminate up to $250K per year in federal payroll taxes.

The Required Documentation: Meeting the Four-Part Test

While the benefits associated with the R&D credit are significant, documentation is crucial. Historically, the cost and work involved in meeting the documentation requirements has kept many businesses from participating. Fortunately, IRS regulations outline a straightforward four-part test that creates a fairly low bar for qualification. As you read the test, you can see that the development and testing–even time spent questioning if the advancements might work–are things that should be considered.
1. Permitted Purpose: Are you developing or improving a product, process, formula or software?
2. Technological in Nature: Is your work within physical or biological sciences, engineering or computer sciences?
3. Elimination of Uncertainty: Are you asking questions like, “Can we develop it?” or “How do we develop it?”
4. Process of Experimentation: Are you systematically evaluating one or more alternatives?
Examples might include:
- Designing and developing new or improved technologies, algorithms, applications or databases
- Platform design and testing
- Compiling, programming and testing software source code
- Newly developed techniques (architecture, algorithms, databases)
- Software and hardware development to further communication/interaction
- Image processing, AI and other specialty tech design
- OS development
- Creating mockups, UX design or technical design work
- Testing automation to ensure quality during development
- Establishing functional relationships between software modules for internal use or to better serve clients
- Developing feature enhancements
- Improving internal processes and conducting QA testing during development
Although meeting the four-part test can be relatively straightforward, documenting it appropriately can be cumbersome. Technology can help streamline and simplify the process of claiming the credit.

The Applicability

External Vs. Internal

The government has different qualifications for different types of software development: External use/third-party software (like Microsoft) versus internal use software. Internal use software has stricter requirements in addition to the four-part test above, and must meet these extra criteria to be claimed for the credits:
1. Software must be highly innovative.
2. Similar products must not be available commercially.
3. Development must introduce significant economic risk.
The R&D tax credit is available mostly on wages paid to employees who carry out the R&D work, as well as a part of the wages of contractors who carry out R&D work, as long as they are not based outside the U.S.

Other items to note:
- The company must own the intellectual property rights to the software being created.
- Funded research is not considered for an R&D tax credit.
- Qualified expenses can range from supplies to cloud computing costs and costs associated with offsite servers.
- Both traditional IT coding and low-code platforms can be claimed under the tax credit. Low-code and no-code platforms allow businesses to build solutions without major coding investment using flexible templates that increase the experience and overall efficiency.
If you’re a software developer thinking of starting your own company or building your own application or you work for a company that would benefit from the credit, it’s a great time to consider taking advantage of it.
August 26, 2022