Skip to main content

The next era of software development: Insights for businesses

share arrow printer bookmark flag

January 13, 2025

According to Gartner, Inc., by 2028, 75% of enterprise software engineers will rely on AI code assistants – a significant increase from less than 10% in early 2023.

This emerging trend isn’t about revolutionizing everything overnight – it’s about creating measurable benefits for businesses.

AI-augmented development needs to translate into tangible improvements in cost, quality and speed to delivery.

These tools provide opportunities to streamline development, enhance code reliability and reduce timelines, but their true value lies in how effectively they integrate into workflows to deliver results organizations can measure and build upon.

What is AI-augmented development?

AI-augmented development refers to the use of artificial intelligence technologies – including generative AI (GenAI) and machine learning – to enhance the tasks of software engineers. 

These tools are designed to assist with activities such as designing, coding and testing applications, directly boosting developer productivity.

By automating repetitive coding tasks, suggesting optimizations and even generating code snippets, AI-augmented development allows teams to meet the increasing demand for high-quality, business-critical software faster and more efficiently.

This approach empowers developers to focus on complex, value-driven aspects of their work while leveraging AI to handle routine or time-consuming tasks.

The implications are far reaching, impacting internal software development resources within businesses and third-party entities, such as professional service consultants, custom app development service providers and developer staffing services.

As these tools become ubiquitous, they’re poised to reshape the software development landscape and create new competitive pressures for service providers.

Understanding the dynamics of software development

At its core, software development involves designing data structures, implementing business logic and creating user interfaces to deliver seamless interactions between users and systems. 

Though innovation has been steady, software development still grapples with the interconnected challenges often described as the “iron triangle.”

This concept highlights three critical factors – cost, quality and speed to delivery – that are interdependent.

Improving one typically impacts the others, requiring teams to strike a balance that aligns with project priorities.

Each of these factors has seen incremental improvements over the past two to three decades:

Cost

Innovations like open-source libraries, third-party services and the commoditization of infrastructure have reduced expenses.

Access to low-cost labor markets and an increased supply of quality resources have also played significant roles.

For instance, GitHub provides free repositories, empowering developers worldwide to collaborate at minimal cost.

Quality

Agile methodologies, automated testing and improved communication between product and engineering teams have enhanced quality.

Practices like paired programming and upfront solution design ensure robust software.

For example, tools, like Selenium, streamline automated testing, identifying bugs before they reach production.

Speed to delivery

Agile methodologies and 24/7 development cycles have accelerated timelines, albeit with mixed results.

Developers now ship features individually when ready rather than waiting for full releases.

Tools like code generators and the enablement of parallel development streams further boost efficiency.

For example, frameworks, such as Ruby on Rails, provide templates that reduce setup time for new projects.

Some innovations, such as DevOps and continuous improvement practices, go beyond focusing on a single factor. These practices simultaneously reduce costs, improve quality and speed up delivery timelines by fostering better collaboration, automating repetitive tasks and enabling teams to adapt quickly to changes.

AI-augmented development impacts

AI-augmented development builds on these advancements by offering cross-cutting innovations that impact all three constraints of the iron triangle:

Cost

AI tools increase the supply of quality resources, offering affordable alternatives to traditional development

For instance, AI-based coding assistants, like GitHub Copilot, automate repetitive tasks, reducing project expenses.

Quality

AI enhances quality through tighter communication loops and quicker test generation.

Paired programming, where developers collaborate with AI tools, ensures consistent code quality.

For example, AI-driven testing frameworks can identify edge cases faster than manual efforts.

Speed to delivery

AI accelerates delivery by increasing developer capacity and automating code generation.

Tools, like ChatGPT and Github Copilot, can generate boilerplate code or provide functional templates, enabling developers to focus on high-value tasks.

By functioning as a cross-cutting innovation, AI-augmented development addresses all aspects of the software development lifecycle, ensuring efficiency, accuracy and scalability.

Rate of change, adoption

The adoption of AI-augmented development will vary across organizations, with noticeable shifts expected over the next five years:

  • This year: Early adopters will likely see inconsistent cost benefits, improved quality and slightly shorter delivery times. Though providers might retain the savings initially, end customers may not immediately benefit from these changes.
  • Two to three years: As tools and developer experience with these tools mature, faster development and higher quality become commoditized. Service providers emphasize efficiency, leading to higher competition and better pricing for clients.
  • Five-plus years: Development teams evolve into AI-enabled units, where developers act more as product designers. These teams iterate and validate multiple solutions with AI before delivering the best choice, fundamentally changing how software is conceptualized and delivered.

Rate of adoption by organization type

The pace of adoption depends on the organizational context:

  • Service providers and professional service departments: These groups adopt AI sooner due to their focus on cost savings and expertise.
  • Startups and smaller teams: Driven by agility and cost constraints, smaller teams and startups are often early adopters, leveraging AI to level the competitive playing field.
  • Enterprise IT teams: Larger organizations may adopt AI at a slower pace, often hindered by legacy systems and organizational inertia.

How to engage now

Businesses can prepare for the AI-driven future of software development by engaging proactively:

  • Ask questions: Engage with service providers or IT departments to understand how they’re introducing AI-augmented development in meaningful and measurable ways.
  • Understand the plan: Request long-term strategies that outline how value will be delivered to the business and its customers.
  • Encourage transparency: Seek clarity on metrics and include yourself in the conversation as a partner. Offering participation and feedback fosters a collaborative environment that encourages innovation.

By taking these steps, organizations position themselves to thrive in a landscape reshaped by AI-augmented development.

Conclusion

AI-augmented development represents a seismic shift in the world of custom software development and professional services.

As the adoption of these technologies accelerates, businesses must navigate the changes thoughtfully, ensuring they remain competitive while delivering value to their customers.

The journey starts with understanding, collaboration, and a willingness to innovate.

By embracing AI, organizations can unlock new possibilities, setting the stage for a future defined by efficiency, quality and creativity.

TBN
share arrow printer bookmark flag