The Agile movement, which advocates for close, incremental collaboration between business teams and technology, is undergoing a remarkable transformation. Artificial intelligence (AI) has the potential to intervene, maintain composure for all involved, and liberate developers and IT specialists, allowing them to dedicate more time to managing the company effectively.
Since its inception two decades ago, the impact of AI on Agile has the potential to be the most fascinating advancement in the field. Agile Intelligence, a distinct form of AI, is likely to be a topic of future discussions.
Five strategies for effectively leveraging AI are also outlined.
AI’s influence on Agile is twofold. Just as Agile is influencing AI, a Lean approach is essential for constructing and operating AI-based systems. When AI and Agile are integrated, companies have the opportunity to enhance their software design and development processes.
Margaret Lee, the senior vice president and general director of online services and procedures management at BMC, states, “Artificial intelligence fosters closer connections among developers, operations, and users by providing rapid access to information and streamlining workflows.”
Moreover, ChatGPT and other prominent AI bots represent the pinnacle of AI technology.
Arguably, the most significant benefit of AI-enhanced collaboration is the time saved for both technology teams and users. Keith Farley, senior vice president at Aflac, believes that “AI can aid in numerous administrative tasks, thereby creating more opportunities for collaboration.”
He describes AI as a “force multiplier,” explaining, “When you bring two people together, you have two sets of opinions, experiences, and personalities contributing to the conversation. However, with AI in the mix, it’s akin to incorporating the ideas and perspectives of millions of individuals into your discussions.”
Farley emphasizes that integrating diverse viewpoints into conversations “allows us to think more broadly and comprehend different perspectives beyond our own biases, leading to improved products and outcomes.”
Lee from BMC notes that numerous IT professionals are already exploring AI-enhanced engagement due to its potential. She asserts that “IoT innovations and use cases, whether conceptual, causal, correlation, predicted—or a combination of these—are currently in progress with integrated AI.”
“AI-powered technology enhances the user experience by streamlining, expediting, and enhancing change management.” By swiftly sharing insights across groups like DevOps and SREs, AI promotes greater engagement for new software and process enhancements.
Furthermore, AI provides DevOps with more benefits than initially perceived.
Varun Parmar, the chief operating officer at Miro, suggests that AI can “foster collaboration and growth at scale.” The primary obstacles to innovation are technical hurdles such as outdated software and organizational challenges, particularly those related to cross-functional collaboration. Fear hampers innovation, and companies are often hesitant to prioritize technology.
According to Lee, “cross-team collaboration through predictive recognition and automated issue resolution before they arise, along with root-cause analysis of problems,” exemplifies AI-enhanced collaboration in action. By automating process management across departments, such as HR employee onboarding, AI is also enhancing collaboration.
Parmar from Miro believes that the ultimate outcome of this effort is that AI is “eliminating mundane bandwidth tasks that frequently burden teams across various industries.” This involves selecting the best tools to perform tasks like content clustering and summarization, code evaluation, and complex diagram creation.
With AI in the mix, teams can focus more on the development and partnership phases of a project, according to Parmar. “Teams spend less time on administrative tasks that sap momentum and focus, enabling deeper analysis of changes in client behavior that inform business or product decisions and bridging knowledge gaps during ideation.” Instead of taking hours or days, it eliminates individual bias in analysis within seconds.
Moreover, the future of these nine subjects is being reshaped by conceptual AI and machine learning.
According to Lee, Artificial Intelligence for IT Operations (AIOps) is one of the most critical emerging tools for IT departments. AIOps monitors real-time operational trends, promptly identifies and addresses incidents before they impact the organization. It facilitates root cause analysis and real-time incident correlation as part of the process.
Lee explains that AI also promotes change management by “analyzing relevant documents and processes, reducing risks, and advancing DevOps. It links change requests with the software development cycle and integrates CI/CD pipeline stages, enabling direct communication between shift managers and developers.”
However, Lee cautions that AI poses challenges for IT operations. Conceptual AI “promises to simplify processes by reducing the effort required to collect and correlate data across businesses.” Nevertheless, AI models must be trained on internal datasets for business use cases. “Organizations and clients can achieve unprecedented levels of operational efficiency.”
Generative AI “offers significant benefits, such as enhancing user experience and streamlining IT operations,” but Lee advises against hasty implementation. To avoid future complications, it is crucial to understand the limitations of AI and ensure appropriate training.
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Lee expresses particular concern about the impact on data integrity and quality. She warns that improper use of AI and ChatGPT in business applications with inadequate data could lead to serious consequences such as misuse, inaccurate results, or leakage of sensitive data. This may result in business disruptions, compromised data integrity, and customer dissatisfaction. Training models over time also pose challenges; for instance, using self-generated data could lead to model failures.
Nevertheless, Lee predicts that in the next 12 months, conceptual AI functionalities may be integrated into the majority of technology products and services, “ushering in conversational methods of interacting with and creating technologies, leading to their transformation.” Proficient teams can derive clear, actionable insights from AI solutions, identify risks, and provide problem-solving recommendations.
