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 avid users of AI-based tools as well as builders of AI systems. Seventy percent of 90,000 developers surveyed by Stack Overflow several months back are already using or plan to use AI tools in their development processes.
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Many are involved in AI application development as well. Forty-four percent of enterprises in an IBM survey of 8,584 IT professionals report they have actively deployed AI applications, with another 40% piloting or experimenting with the technology.
In essence, AI is becoming a valuable tool for building AI applications. Tools such as generative AI, GitHub Copilot, AgentGPT, and Azure Machine Learning Studio cover many aspects of the developer job, from code generation to testing.
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But how do these tools fit into the workflow, collaboration, and management of the software lifecycle? Here, AI is emerging as a means to keep people closer together, in sync, and boosted by automation. The technology also provides an understanding of progress for developers, operations, teams, executives, and business users.
In other words, collaborate to build AI; employ AI to better collaborate.
AI enables collaboration in many ways, says Beena Ammanath, global head of Deloitte AI Institute. In terms of DevOps, for example, it is “fostering collaboration between developers and operations by automating tasks, enabling real-time issue detection and promoting the use of shared metrics in DevOps processes.”
The growing use of AI can both change and strengthen DevOps and Agile methodologies, she continues: “It automates tasks, promotes data-driven decisions and improves collaboration between development and operations teams.”
Using AI in development
First, let’s look at why AI projects need to have everyone on the same page. Yes, the technology is AI easing and automating many tasks associated with software, but developing AI projects themselves requires highly collaborative approaches.
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The push to AI is “creating the need for teams to work closer together,” says Steven Huels, senior director of AI at Red Hat. “For any AI project, having a clear understanding of business goals kicks off the process that then helps data engineers and data scientists understand the data and model requirements.”
Models developed by these teams “then need to be deployed into applications, creating the need for collaboration between developers and data scientists to make sure that models are integrated into applications,” says Huels. “Then the DevSecOps approach comes in, providing the ability to deploy AI-enabled applications where it makes most sense to the business.”
A continuous process, such as Agile and DevOps for AI development, “extends the need to iterate quickly and automate as many processes as possible, so that as new data is learned, models are updated and re-integrated into applications and deployed back out,” says Huels.
On the flip side, AI can significantly bolster these collaborative strategies. For starters, AI can help “speed up the development and delivery of software products by automating or optimizing some of the tasks and processes,” says Chad Naeger, CIO 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 performance of software products by augmenting or enhancing some of the capabilities and resources.”
Furthermore, AI “can help teams monitor, analyze, and improve software quality, performance, and user experience,” Naeger observes. It also helps IT professionals “innovate and experiment with new software products, by generating or exploring some of the possibilities and solutions. For example, AI can help teams create, design, and prototype new software features, functionalities, 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, which enables innovation at a much wider and deeper scale without impacting speed to market.”
Boosting business processes
AI is also playing a role in enhancing developers’ roles in the business at large, “fostering 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, she points out, “AI helps developers analyze user behavior and tailor applications to meet business goals.”
At Lumen Technologies, there are three ways the company leverages AI to enhance collaboration, Naeger says. For starters, AI impacts employee engagement by “using AI-powered communication and collaboration tools to streamline information sharing and improve team collaboration,” he says. In addition, AI “impacts employees and processes within specific functions. Finally, AI is having a positive impact on its customer engagements.”
AI enables team members “to create and share content more easily, automate, and optimize business processes more efficiently,” he continues. “It enhances team communications by bringing clarity and utilizing transcripts to leverage exact words to remove 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,” Naeger states. “AI can also help teams share data and insights more easily, by creating visualizations, dashboards, and reports. AI can help teams coordinate their tasks and workflows more efficiently, by automating or optimizing some of the processes.”
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While AI-enhanced collaboration in IT sites is already happening, the emerging technology is still very much a work in progress. The move to AI-fueled collaboration means “organizations need to adapt and be prepared for shifts in how these teams work, integrating AI-driven metrics and managing AI tools,” says Ammanath. “This integration can enhance efficiency and effectiveness, but it also demands adjustments in working methods and embracing AI-driven insights and tools.”
AI’s potential in fostering team collaboration is still a long-term vision, “as the extent of adoption and integration of AI can vary widely across different industries and organizations,” says Ammanath. “Addressing challenges like bias, privacy and ethical considerations will play a role in shaping the pace and effectiveness of AI-driven collaboration in the future.”
As Lumen’s Naeger emphasizes: “AI will be a productivity booster for our people, but it is also very important to understand the need for human reviews in the loop.”
In the case of Lumen, IT and business leaders are “piloting tools like Microsoft Copilot 365, Power Platform, Sales, GitHub, which are not only facilitating better communications, but they are also enabling collaboration at a much higher level of engagement,” says Naeger.
“These tools can take the focus during meetings away from individuals from being note-takers to active participants. These tools give us the ability to be in two places at the same time with Copilot transcription and quick access to meeting summaries. And most importantly, this translates to how we engage with our customers to have information that is important to servicing them at our fingertips.”
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Other examples of AI-fueled collaboration in action include “improving communications through internal chatbots and virtual assistants, to more complex use cases that can help in decision making based on large datasets,” says Huels.
Finally, AI and generative AI also can enhance collaboration through AI-powered chatbots and large language models that facilitate natural language interactions, helping businesses communicate with customers more efficiently, says Ammanath.
Artificial Intelligence
