Accelerating Software Delivery: How AI Is Shaping the Future of Development

By Nate Berent-Spillson, Launch by NTT DATA

The demand for faster, better, and more cost-effective software delivery has reached unprecedented levels. While value continues to be measured by working code in users' hands, recent AI advances in language models, behavior, and reasoning have shattered previous productivity limitations. We're now experiencing the first wave of a dramatic acceleration in our ability to deliver higher-quality, more secure software at unprecedented speeds.

Organizations face a critical dilemma regarding the pace of this transformation. While some hesitate, waiting for the technology landscape to stabilize, others recognize that delaying adoption carries substantial opportunity costs. Many companies are discovering their existing infrastructure cannot fully leverage AI-powered development tools. Though the software delivery pipeline measures value through working code, common bottlenecks — pull request reviews, security vulnerability fixes, accessibility improvements, and test coverage enhancements — often impede feature delivery despite their importance.

AI-augmented development enables teams to overcome these constraints and unlock productivity throughout the software delivery value stream. By integrating generative AI (GenAI) tools into development workflows, organizations can fundamentally transform their build, test, and deployment processes.

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However, successful adoption requires more than simply purchasing tools or LLM subscriptions. Organizations must first assess their position on the technology adoption curve to understand their readiness and map their journey. Whether they identify as enthusiasts, visionaries, cautious adopters, or skeptics, knowing their starting point is crucial for progress.

Driving Value Through Barrier Resolution

Implementing AI-powered development acceleration demands strategic planning. To ensure successful implementation while addressing common misconceptions, organizations should prioritize:

• Model Selection: Choose reliable, vetted partners and opt for trusted, paid tools offering superior security, support, and accountability.

• Vendor Assessment: Demand transparency in data protection policies and proper licensing for open-source models.

• Data Protection: Block proprietary data from training to maintain confidentiality and prevent data leaks.

• Continuous Testing: Regularly audit GenAI systems throughout the software delivery lifecycle.

• Human Oversight: Maintain human-in-the-loop processes to catch errors, bias, and security concerns.

Related:Quick Reference Guide for Software Development

While safety is paramount, measuring impact is equally crucial. Traditional productivity metrics have evolved as GenAI transforms workload patterns. Success is now measured by quality and impact rather than volume. Research demonstrates that developers using GenAI tools achieve significantly faster completion times with higher quality outcomes. McKinsey reports efficiency gains including 45-50% reduction in documentation time, 35-45% faster code generation, and 20-30% improvement in refactoring speed.

Transforming Potential into Results

Successful implementation requires identifying specific areas where GenAI can eliminate friction and generate measurable business impact. Organizations must:

1. Pinpoint delays, inefficiencies, and bottlenecks in their software delivery value stream.

2. Align improvements with business objectives.

3. Maintain tool agnosticism to preserve flexibility.

4. Establish meaningful metrics (but don’t resort to metrics micro-management).

5. Gather qualitative feedback for continuous optimization

This comprehensive approach provides real-time insight into effectiveness while enabling ongoing adjustments as organizations evolve.

Internal Adoption and Change Management

Technical strategy alone cannot ensure success — organizational change management is equally vital. Developers, as the backbone of AI-powered development acceleration, require proper training, coaching, and shortened feedback loops. Teams need to understand the why, where, and how of these new tools and processes.

Change occurs gradually through unlearning old habits and developing new skills. Creating an environment where teams feel safe to experiment, make mistakes, and grow increases buy-in for organizational changes.

Implementation Strategy

Successful AI-powered development acceleration requires harmonious integration of multiple elements. Organizations must maintain clear alignment between business goals and improvements to their software delivery value stream and development team capabilities.

Provide space for the teams to absorb the change curve and modify behaviors. A Microsoft study found that it takes developers about 11 weeks to build new behavior patterns around AI.

While implementing GenAI into software delivery workflows presents challenges, success depends largely on approach. Partnering with experienced vendors who offer proven productivity improvements and can adapt to rapid change is invaluable, whether organizations are just beginning their journey or looking to scale existing initiatives.

About the author:

Nate Berent-Spillson is senior vice president of engineering at Launch by NTT DATA.