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ZDNET Highlights
- Pressure grows for software that better aligns with business.
- Agile techniques have been stable for a decade.
- AI can accelerate Agile team output.
Agile has always had the best intentions: working side by side with the business to collaboratively build software that actually works, versus limiting development to technically driven, siled projects.
Of course, in practice, things don’t always go smoothly. For example, Agile was not great for large groups or organizations. Organizational politics and inertia often get in the way of this sought-after utopian business-IT alignment.
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Can AI help make Agile more agile? This is the hope.
That hope is reflected in Digital.ai 18th State of Agile ReportWhich says AI and AI agents are making things faster – and potentially improving the quality of software creation and delivery. And not a moment too soon – technology teams are under constant pressure to innovate in said software while also increasing the ROI of their products. The survey authors collected insights from approximately 350 participants, primarily Agile coaches and consultants from large enterprises with more than 20,000 employees.
In terms of adoption, Agile methods have been stuck at a plateau for almost a decade. In previous Digital.ai surveys, a consistent majority (between 52% and 60%) said their organizations were “using Agile practices but are still maturing,” and only one in ten reported a high level of agility in their organizations.
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These numbers haven’t changed in nearly a decade, and this year is no exception – only 13% say Agile is deeply embedded in business and technology, while 42% describe their culture as “better than nothing but can be more effective.” Agile is “cursed with faint praise exactly when organizations need it most.”
That’s because more than three in four technology managers (76%) cite increased scrutiny on the business impact and ROI of Agile. Only 49% have guardrails as AI adoption moves faster than oversight.
The hope is that AI is moving from merely a supporting tool to the orchestrator of the full software delivery lifecycle. “Instead of just assisting teams, these systems can reason, decide, and act autonomously to improve flow, quality, and speed at scale,” the report says.
very optimistic? Industry experts and observers are skeptical about how far AI can go in conjunction with business to improve the software development and delivery process.
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“AI is not something you can pull out of your toolbox and expect magical things to happen,” cautions Andrew Kum-Hsien, research director at Info-Tech Research Group. “At least, not right now. IT managers must be prepared to address the human, workflow, and technical implications that come naturally with AI, while being honest about what AI can do for their organization today.”
In other words, organize your AI implementation before you try to implement it to streamline your software development.
“Will it be a productivity tool or something else?” Kum-seen continued. “Then, managers must be equipped with the right tools and strategies to help teams overcome their fears, uncertainties, and doubts. Ultimately, we want to get in the water and not dive in without learning how to swim.”
Here are some guidelines for bringing AI to boost Agile software efforts:
1. Remember, it’s still early
Kum-Seen explained that his firm has not yet seen widespread adoption of AI agents performing autonomous activities across the software development lifecycle (SDLC). “Much of the value and breakthroughs we see today are at the individual, work level, where AI complements human team members.”
2. Start small and prepare
“Start small by introducing low-risk processes like test generation and documentation before exposing live code or customer data,” advises Zbigniek Sopuch, chief technology officer at Safetica.
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“A general rule of thumb before implementing AI into any process is to locate, examine, and then map the sources of all data throughout the organization, including code, logs, tickets, and customers, and determine what is out of bounds. Part of the strategy should include ‘govern first, then scale’: create a policy for what is allowed before adding more tools or users.”
3. Decide what can be improved by AI
Which areas of SDLC management can be enhanced through AI-powered Agile practices?
“There are a lot of benefits with AI, especially in terms of addressing tickets, including backlog analysis, sorting by impact, and bringing up priorities, which means developers can focus on pressure areas,” Sopuch said. “Additionally, AI enables better test creation by writing test cases directly tied to requirements or prior bugs, thus improving coverage and traceability.”
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“Documentation is also significantly improved with AI,” Sopuch said, with the ability to draft pull request summaries, user notes, and automatically issue updates from the code’s version history.
AI helps clearly show teams bottlenecks in software delivery, he adds, “empowering teams to fix those issues and patterns faster.” “Additionally, AI-powered dashboards and summaries not only allow cross-team visibility but also provide less technical organization members the ability to understand and engage in these processes.”
4. Keep humans in the loop
Since Agile aims to retain humanity in software development, AI needs to support this approach. This should also be a core component of AI-powered Agile development. “If leaders are unable to bridge their intentions for AI with the team’s concerns, they will likely view AI as inappropriate use and, perhaps, deliberately sabotage its implementation,” Kum-Seen said.
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Another important step, Sopuch said, is to “keep all AI explainable by ensuring the use of AI tools that clearly explain where their suggestions come from – no black-box code that can’t be easily verified.”
“Human oversight is an essential step. AI can write and refactor code, but humans must absolutely approve a merge, product push, or any exception. Everything in the process should be logged, including prompts, outputs, and approvals so audits can easily be done on demand.”
5. AI-enhanced Agile still follows the rules of software development
“I’ve seen a lot of silver bullets, like Agile and AI, come and go over the years,” said Laura Zuber, training and customer support manager for Quantitative Software Management Inc.
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“IT managers and professionals need to understand that implementing any software process improvement, that is, AI, will initially reduce productivity,” Zuber said.
“Gains in productivity will be realized when developers become skilled at motivating and teaching their chosen AI tool or agent. Developers must have enough experience to recognize bad and irrelevant code and continue working with the AI agent until it learns what they need or are looking for.”
6. Know the risks
The top risk in incorporating AI into the Agile development process is the same as with most other AI initiatives — potential data exposure, Sopuch said.
“Developers can accidentally include sensitive data in signals sent to external AI tools, such as API keys, credentials, and customer data. Shadow AI is also a risk, especially when it comes to SDLC management. By nature, people will look for solutions to make their jobs easier, and in this case, employees may install or use unapproved or uncertified AI tools without IT visibility, creating compliance and governance risks.”
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As Digital.ai reports, this AI-powered Agile wave is fundamentally different from all earlier waves – such as waterfall, DevOps, automation in the cloud, and mobile revolutions. “Agent AI isn’t a new tool; it’s a new teammate. It brings awareness and adaptability to every step of delivery, creating systems that learn from each interaction and continuously improve flow, quality, and safety.”
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