How can you keep things flowing and on-track when you’re developing complex artificial intelligence (AI) applications? With AI of course.
Today’s software developers are both enthusiastic users of AI-based tools as well as creators of AI systems. Seventy percent of the 90,000 developers Survey By Stack Overflow several months ago already using or planning to use AI tools in their development process.
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Many are involved in AI application development. A forty percent venture of IBM Survey Of the 8,584 IT professionals who reported actively deploying AI applications, another 40% were piloting or testing the technology.
In short, AI is becoming a valuable tool for building applications. Tools like Generative AI, GitHub Copilot, AgentGPT and Azure Machine Learning Studio cover many aspects of a developer’s work, from code generation to testing.
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But how do these tools fit into software lifecycle workflow, collaboration and management? Here, AI is emerging as a means of keeping people closer, in sync and enhanced through automation. The technology also provides an understanding of progress for developers, operations, teams, executives and business users.
In other words, collaborate to create AI; Employ AI to collaborate better.
AI enables collaboration in many ways, he says Veena Ammanath, Global Head of the Deloitte AI Institute. For example, in terms of DevOps, it “increases collaboration between developers and operations by automating tasks, enabling real-time problem detection, and promoting the use of shared metrics in DevOps processes.”
The growing use of AI can transform and strengthen both DevOps and agile methodologies, he continued: “It automates tasks, promotes data-driven decisions, and improves collaboration between development and operations teams.”
Using AI in development
First, let’s see why everyone needs to be on the same page in an AI project. Yes, the technology is automating many tasks associated with AI leasing and software, but developing AI projects requires a highly collaborative approach.
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The push into AI is “creating the need for teams to work together,” he says Steven Huels, Senior Director of AI at Red Hat. “For any AI project, a clear understanding of the business goals starts the process, which then helps data engineers and data scientists understand the data and model requirements.”
The models developed by these teams “then need to be deployed in applications, creating a need for collaboration between developers and data scientists to ensure that models are integrated into applications,” says Huels. “Then comes the DevSecOps approach, providing the ability to deploy AI-enabled applications where it makes the most sense for the business.”
A continuous process, such as Agile and DevOps for AI development, “extends the need to iterate quickly and automate as much of the process as possible, so that models are updated as new data is learned and reassembled and redeployed into applications,” Huels said. says
On the other hand, AI can significantly strengthen these collaborative strategies. For starters, AI can help “speed up the development and delivery of software products by automating or optimizing certain tasks and processes,” says Chad NigerCIO of Lumen Technologies.
“For example, AI can help teams code, test, debug, and deploy software faster and more reliably. [AI can] Improve the quality and functionality of software products by increasing or improving certain capabilities and resources.”
Furthermore, AI “can help teams monitor, analyze and improve software quality, performance and user experience,” notes Niger. It helps IT professionals “innovate and test new software products by creating or exploring possibilities and solutions. For example, AI can help teams create, design, and prototype new software features, functionality, and interfaces.”
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With AI tools, “we can iterate much faster through a sprint cycle,” he adds. “We can also experiment with new ideas and approaches, enabling innovation on a much broader and deeper scale without affecting the speed of the market.”
Enhancing business processes
AI is also playing a role in increasing the role of developers in business, “by increasing collaboration between developers and business stakeholders through data-driven product development and personalized user experiences,” says Deloitte’s Ammanath. “It aligns technical and business teams. For example, he noted, “AI helps developers analyze user behavior and tailor applications to meet business goals.”
At Lumen Technologies, the company uses AI in three ways to enhance collaboration, Niger said. For starters, AI affects employee engagement by “using AI-powered communication and collaboration tools to streamline information sharing and improve team collaboration.” Additionally, AI “influences personnel and processes within specific functions. Finally, AI is having a positive impact on customer engagement.”
AI enables team members to “create and share content more easily, automate and optimize business processes more efficiently,” he continued. “It enhances team communication by using transcripts to bring clarity and use the right words to eliminate ambiguity. All of this helps learning and development, and fosters team culture and engagement.”
The company also employs “AI-powered chatbots that can translate messages, summarize conversations and provide relevant information,” says Niger. “AI can help teams share data and insights more easily by creating visualizations, dashboards, and reports. By automating or optimizing certain processes, AI can help teams coordinate their tasks and workflows more efficiently.”
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While AI-enhanced collaboration is already happening in IT sites, the emerging technology is still a work in progress. Moving to AI-fueled collaboration means “organizations need to adapt and prepare for changes in how these teams work, integrating AI-driven metrics and managing AI tools,” says Ammanath. “This integration can increase efficiency and effectiveness, but it demands adjustments in work methods and embracing AI-driven insights and tools.”
“As the extent of AI adoption and integration may vary widely across industries and organizations, the potential for AI to enhance team collaboration is still a long-term view,” says Ammanath. “Addressing challenges such as bias, privacy and ethical considerations will shape the pace and effectiveness of AI-driven collaboration in the future.” will play a role.”
As Lumens Niger emphasizes: “AI will be a productivity booster for our people, but understanding the need for human review in the loop is also very important.”
In Lumen’s case, IT and business leaders are “piloting tools like Microsoft Copilot 365, Power Platform, Sales, GitHub, which are not only facilitating improved communication, but they’re also enabling collaboration at a much higher level of engagement,” says Naeger.
“These tools can shift focus during meetings from individuals taking notes to being active participants. These tools give us the ability to be in two places at the same time with quick access to copilot transcriptions and meeting summaries. And most importantly, it translates into how we serve our customers. We also engage with them to have important information to serve them at our fingertips.
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Other examples of AI-fueled collaboration include “improving communication through internal chatbots and virtual assistants, for more complex use cases that can help with decision-making based on large datasets,” Huels said.
Finally, AI and generative AIO can enhance collaboration through AI-powered chatbots and large language models that facilitate natural language interactions, helping businesses communicate more efficiently with customers, Ammanath said.