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Agile Intelligence: AI gives tech and business collaboration a much-needed boost

Agile Intelligence: AI gives tech and business collaboration a much-needed boost

Agile Intelligence: AI gives tech and business collaboration a much-needed boost

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The Agile movement — which encourages close, iterative work between technology and business teams — is taking an intriguing turn. Artificial intelligence (AI) has the promise to step in, help keep everyone in sync, and free up developers and IT professionals, so they can spend more time running the business. 

The impact of AI has the potential to be the most interesting development in Agile since the practice was first outlined two decades ago. In the future, we might be talking another kind of AI — Agile Intelligence.

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Importantly, the impact of AI on Agile works both ways. Just as AI is impacting Agile, you also need an Agile philosophy to build and run AI-based systems. But where AI and Agile are used in combination, there’s the potential for businesses to supercharge their software design and development processes. 

“Artificial intelligence brings developers, operations, and users closer together through faster access to knowledge, streamlined workflows, and automated processes,” says Margaret Lee, senior vice president and general manager of digital service and operations management at BMC. 

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Perhaps the most compelling benefit of AI-boosted collaboration is the time given back to both tech teams and users. “AI can help with many administrative activities, so it automatically gives us more time for collaboration,” says Keith Farley, senior vice president at Aflac. 

He says AI essentially serves “as a type of superpower collaborator”: “For instance, when you bring two people together, you have two people’s thoughts, experiences and personalities to contribute to the discussion. If you have four people, then that’s four, and so on. But when you pull up a seat for gen AI, it’s like adding the thoughts and attitudes of a million diverse people to your discussion.”

Bringing these varied thoughts to discussions “will allow us to look more widely and understand diverse viewpoints beyond our own biases, which can result in better products and outcomes,” Farley adds.

Many IT professionals are intrigued by the potential of AI-boosted collaboration and are already experimenting, says BMC’s Lee. “AI innovations and use cases, whether generative, causal, correlation, predictive — or all working together via composite AI — are currently happening,” she says. 

“AI-powered automation improves the developer experience by simplifying and accelerating their work with improved change management. AI automatically shares insights across teams, such as DevOps and SREs, to foster greater collaboration for new applications and process improvements.”

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AI can help to “drive collaboration and innovation at scale,” agrees Varun Parmar, chief operating officer at Miro. “The biggest roadblocks to innovation are technological challenges, like legacy tools and organizational challenges, especially those related to cross-functional collaboration. Fear gets in the way of innovation, and companies are afraid to prioritize innovation.”  

An example of AI-boosted collaboration in action is “cross-team collaboration through predictive identification and auto-remediation of incidents before they occur while identifying the root-cause analysis of issues,” says Lee. “AI is also improving collaboration by automating workflow management across departments, such as HR employee onboarding.”  

The net result of this effort is that AI is “eliminating the tedious overhead tasks that often plague teams across companies,” says Miro’s Parmar. “This means finding the best software to perform tasks like creating technical diagrams, interpreting code, and clustering and summarizing content.”

With AI in the mix, “teams are spending less time on administrative tasks that drain momentum and concentration, and more time in the innovation and collaboration phases of a project,” Parmar adds. “It helps eliminate knowledge gaps for participants during brainstorming and facilitates a deeper research dive into consumer behavior trends that shape business or product decisions. It eliminates the human research bias in just seconds, rather than hours or days.”

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Lee says that one of the most important emerging tools for IT departmentsis is artificial intelligence for IT Operations (AIOps). AIOps helps to “monitor the operations environment in real time, automatically seeing and responding to incidents before they affect the enterprise,” she says. As part of the process, AIOps enables root cause analysis and real-time incident correlation.

AI also promotes change management, “analyzing relevant data and processes, mitigating risk, and advancing DevOps,” Lee continues. Integration with DevOps tooling, “links change requests with the software development lifecycle, and imports CI/CD pipeline stages, enabling direct communication between change managers and developers.”

However, AI does bring some risks to IT operations, Lee cautions. “If you look at generative AI, it promises to automate processes that reduce the work of gathering and correlating data across industries,” she says. “Organizations and customers can achieve a level of digital operational efficiency never seen before, but, in the case of enterprise use cases, AI models need to be trained on internal data sets.”

While generative AI “offers significant benefits, such as customer experience and streamlining IT operations, it must be implemented thoughtfully,” says Lee. “You need to understand the limits of AI and ensure proper training to avoid challenges down the road.”

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Lee is particularly concerned about the implications for data quality and integrity. “If companies apply AI and ChatGPT in the wrong use cases and with bad data, there can be severe consequences, such as misuse, flawed outputs, or leakage of sensitive data,” she warns. “This can cause business disruptions, compromised data integrity, and customer dissatisfaction. There are also issues with how models are trained over time — if they feed on self-generated data, it could lead to model collapse.”

However, Lee predicts most technology products and services will incorporate generative AI capabilities during the next 12 months, “introducing conversational ways of creating and communicating with technologies, leading to their democratization. AI solution technologies can provide Agile teams with clear, actionable insights, pinpoint risks, and provide recommendations to resolve problems.”

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