How to power up agility with AI-driven software development

The concept of "business agility" gets lots of airtime in professional circles. But the questions of “how fast” and “how agile” are about to get new answers as AI is applied to enterprise software development.

Generative AI has already established its utility in the realm of software development, most notably through code completion tools like GitHub Co-Pilot. However, these applications represent just the initial phase of a broader, more comprehensive transformation of the entire software development lifecycle (SDLC), from requirements analysis and system design to testing, deployment and maintenance.

Even with current AI capabilities, businesses can speed software development and uplevel developers’ productivity. However, when integrated across the entire lifecycle, future iterations of AI will completely change how enterprises even think about software development.

We see a future where AI-driven software development will enable organizations to adapt to market change in real-time, reduce costs and drive innovation—in short, be as agile as they need to be in today’s competitive landscape.

AI and real-time agility

The combination of human expertise and AI capabilities will become the cornerstone for speeding intelligent adaptation with a new approach to software development. For example, consider a typical scenario and how it would be completed today: An ecommerce business detects a downward trend in purchase volumes and, through contextual analysis, sees customers are being lured away by a key competitor’s newly enhanced user experience.

The business develops a range of competing concepts to improve its own user experience, tests them with users and refines the ideas into an enhanced design. It implements and deploys the preferred design within an A/B test structure, validates it via operational measurements and then adopts it as the new default experience.

Now consider how this could happen with AI accelerating each process step, automated in an integrated way, with humans in the loop to validate and refine recommendations. The entire cycle of issue detection, solution design, engineering and validation could be completed in days, or even hours instead of weeks and months.

What’s possible today with AI

While the future promises a transformative role for generative AI in the SDLC, even today's AI technologies offer substantial benefits that drive efficiency and speed in software development. These advantages manifest in various forms, each targeting a specific aspect of the development process:

• Automated code generation converts prompts into functional code with minimal manual intervention.
  
  
• Automated documentation creates and updates documents by annotating code.
  
  
• Debugging assistance analyzes code to offers suggestions for resolving issues, which reduces the time spent on troubleshooting.
  
  
• Code reviews proactively scan code changes to identify risks and flag areas for improvement.
  
  
• Refactoring recommendations safely and efficiently restructure code. This is particularly beneficial for long-term maintainability and significantly reducing technical debt.

These immediate benefits collectively contribute to a more streamlined and rapid development process in which teams can build, maintain and update applications at an accelerated pace, leading to quicker bug identification and feature delivery. The end result is a tangible advantage in time-to-market, positioning organizations to be more agile and competitive.

A look over the horizon

The future holds a host of groundbreaking possibilities that promise to further revolutionize the field of software development. These advancements are not merely incremental—they represent transformative shifts in how development processes are approached and executed.

• Enterprise-level code comprehension: Future iterations of AI will understand entire systems and codebases, not just isolated files. This comprehensive understanding will facilitate large-scale refactoring and simplification, easing management of complex projects and reducing system inefficiencies.
  
  
• Intelligent code deduplication: AI will become adept at identifying and eliminating redundant logic across different parts of a codebase. This will not only improve software maintainability but also substantially reduce technical debt.
  
  
• Closing the software-ops feedback loop. By connecting real-time operational metrics and production data with engineering needs, AI will identify opportunity areas to enhance business and technical performance.
  
  
• Leveraging reusable architectures: AI will recognize and effectively utilize reusable libraries, services and design systems, which will accelerate the development process and promote more efficient use of resources.

These future capabilities will collectively elevate the software development process to new heights of agility, productivity and impact. They will enable organizations to adapt more swiftly to market changes, reduce costs and drive innovation at a pace that sets them apart in an increasingly competitive landscape.

A new way of looking at software development

Generative AI’s role in software development goes beyond just improving coding speed and operational efficiency. It also has significant indirect effects that will change how companies handle software development and management.

• Move away from legacy systems: Businesses will have a greater ability to migrate away from complex and risky legacy applications that constrain innovation.
  
  
• Move from buy to build: Commercial products that offer a partial fit to business requirements can be replaced with more effective custom solutions.
  
  
• Accelerate change management: Increased speed and volume of change delivery will require new mechanisms for users to keep up.
  
  
• Focus on new skillsets: Businesses will require new capabilities to effectively integrate and exploit the range of fast-moving automation solutions available.

The focus in software development will also shift toward outcomes rather than just product features. Traditional metrics like "lines of code" will become less important, making way for frameworks that prioritize business outcome KPIs. This shift will align engineering efforts more closely with company objectives, ensuring that development work directly supports business success.

How to get started

For companies aiming to leverage the benefits of generative AI in their software development lifecycles, here are a few key considerations:

• Evaluate policy concerns, quality standards and associated risks
  
  
• Test generative AI tools within your development teams to gauge their impact, potential and advantages
  
  
• Establish a responsible AI framework that addresses transparency, accountability, quality and performance
  
  
• Develop plans for comprehensive tool deployment and gradual expansion of automation capabilities

With a well-thought-out approach, generative AI can enhance the software delivery process, offering a competitive edge. The time to start harnessing this transformation is now.