Developers Under Pressure: How AI Tools Are Shaping the Future of Software Delivery

How AI Tools Are Reshaping Software Delivery: Insights from Harness’s Report

The News: Harness’s latest report highlights the growing cognitive burden on developers, caused by expanding responsibilities and the prevalence of manual, repetitive tasks. The report examines how AI-powered tools are transforming the developer experience while introducing new challenges like debugging AI-generated code and managing security risks. To read more, visit the original report here.

Analyst Take

The Growing Complexity of Application Development

The role of software developers has fundamentally shifted. No longer focused solely on writing and maintaining code, today’s developers are expected to excel in security, infrastructure management, system optimization, and user experience. While this expanded scope can drive efficiencies, it has also significantly increased cognitive load which leaves developers juggling multiple priorities.

Outdated processes and manual workflows accentuate this problem. Developers report spending over 30% of their time on repetitive tasks like compliance writing, quality assurance testing, and error remediation. The financial impact of these inefficiencies is outrageous. For example, with an average developer salary of nearly $108,00, organizations with 250 developers are losing roughly $8 million annually in productivity. Beyond financial costs, burnout, overtime, and high stress levels are becoming increasingly common which creates a workforce sustainability issue.

AI Tools: A Mixed Blessing

AI-powered tools, especially AI code generation (CodeGen) tools, are being touted as solutions to alleviate developer pressure. Developers seem to agree that AI has the potential to reduce burnout. Unfortunately, these tools are not without challenges. While AI accelerates initial code production, it does have the potential to create additional work downstream.

Key challenges identified in Harness’s report include:

  • Increased debugging efforts: 67% of developers spend more time debugging AI-generated code.
  • Security risks: 68% report more vulnerabilities in AI-created code that require resolution.
  • Shadow IT risks: Unauthorized AI tool usage introduces compliance and intellectual property concerns.

AI tools can reduce some burdens but they also require time for validation, testing, and refining outputs which may offset the productivity gains.

What About Before AI?

Before the advent of AI tools, developers relied heavily on traditional approaches such as:

  1. DevOps practices: Breaking down silos between development and operations for smoother workflows.
  2. Manual QA and testing: Identifying and resolving issues in the development process.
  3. Specialized toolchains: Employing purpose-built tools to manage security, compliance, and deployments.

These methods have been effective but they still have their limitations. Fragmented workflows, heavy manual intervention, and lack of process automation have remained constant barriers to scaling. Organizations are now looking to AI for a solution but adoption needs governance.

What’s Next for Developers and AI?

Harness’s report cites that developers want AI tools to go beyond code generation. The most promising opportunities for AI adoption in the next 12 months include:

  • Continuous Integration/Delivery (CI/CD): Automating deployment pipelines (50%).
  • Performance optimization: Improving system efficiency (48%).
  • Security and compliance: Addressing vulnerabilities at scale (42%).

As developers transition to using AI tools across the entire software development lifecycle (SDLC), their roles are evolving. Increasingly, developers should be focused on:

  • AI prompt engineering: Creating effective instructions for AI tools.
  • Output validation: Ensuring quality, compliance, and relevance of AI-generated code.
  • Workflow integration: Embedding AI into DevOps pipelines for seamless operations.

These shifts highlight that AI’s greatest value lies not in replacing developers but in augmenting their abilities.

Looking Ahead

AI adoption is already redefining software development. Organizations will continue investing in AI solutions for CI/CD, performance optimization, and security, but success will depend on effective governance, policies, and measurement of impact. Developers will assume new responsibilities by focusing on creative problem-solving, architecture design, and aligning technical outputs with broader business goals.

Harness has an opportunity to expand its AI capabilities beyond code generation into areas like performance optimization, compliance, and error remediation. Organizations will need tools that integrate across the SDLC and support sustainable developer practices. The focus should shift to providing a balanced approach where automation handles repetitive tasks while freeing developers to focus on innovation and higher-value activities.

Author

  • Bringing more than a decade of varying experience crossing multiple sectors such as legal, financial, and tech, Sam Weston is an accomplished professional that excels in ensuring success across various industries. Currently, Sam serves as an Industry Analyst at Efficiently Connected where she collaborates closely in the areas of application modernization, DevOps, storage, and infrastructure. With a keen eye for research, Sam produces valuable insights and custom content to support strategic initiatives and enhance market understanding. Rooted in the fields of tech, law, finance operations and marketing, Sam provides a unique viewpoint to her position, fostering innovation and delivering impactful solutions within the industry. Sam holds a Bachelor of Science degree in Management Information Systems and Business Analytics from Colorado State University and is passionate about leveraging her diverse skill set to drive growth and empower clients to succeed.

    View all posts