AI Developer Workflows: 1M+ Devs Now Using GitHub Copilot

AI developer workflows help answer a pivotal question: What is the workflow of artificial intelligence?

The software market is set to eclipse almost $700 bn in revenue in 2024. With the dramatic surge in technical production over the past two decades, software developers have more to produce, tighter deadlines, and a neverending flow of work. The U.S. Bureau of Labor Statistics estimates that software developer roles grow at 25% per year, reflecting the heightened demand for effective developers. 

Over recent years, software engineers have increasingly used AI developer workflows to keep up with the rising demand for their skills. Recent research demonstrates that as many as 82.55% of programmers use AI developer workflows to write code, with nearly 50% using it to debug code. 

As the tech industry continues to skyrocket, it is becoming an integral tool for developer workflows. This article details the rise of AI in software development, demonstrating how AI developer workflows solve critical pain points and pave the way for a technologically streamlined future.

The Need for AI Developer Workflows

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The vast majority of current innovations are centered in the tech field. Breakthroughs in healthcare, financial operations, and customer experiences all rely on the increasing availability of technology that streamlines workflows and enhances efficiency. 

Behind this mounting litany of technological tools is an overworked and increasingly pressured workforce. Software developers bear the brunt of the technological revolution, as their skills produce the tools we need to continue to increase productivity.

The top challenges faced by software developers all closely relate to workplace stress. On one hand, nearly 20% of software developers feel that the deadlines they are given are unrealistic. Without large-scale teams of experts, this field is constantly overworked without the resources available to alleviate the pressure.

Another common complaint that over 40% of developers have is that reworks and changes to their code create delays, making timely delivery impossible. The vast majority of these reworks result from communication failures, with multiple revisions of code aiming to turn individualized portions of code into larger, holistic final product deliveries. 

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AI developer workflows is the key to overcoming these challenges. A recent McKinsey study suggests software developers can complete coding tasks twice as fast when leveraging AI. By doubling the efficiency of the average programmer, AI empowers them to reach deadlines, rapidly produce code, and mitigate errors.

McKinsey’s research demonstrates that artificial intelligence in developer workflows can streamline in the following ways:

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• Code Generation: Rapidly produce level code based on developer input.‍
• Code Testing: Generative AI tools can test code and automatically generate insights for potential improvements or issues.‍
• Personalized Development Environments: Streamlined communication in personalized developer environments will enhance productivity and create a better programmers' experience. 

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Pieces, an AI developer workflow co-pilot, specializes in developing this final benefit, offering a highly functional way of overcoming communication issues between developers. 

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Pieces: AI Developer Workflow Solutions for Common Pain Points

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Although a piece of software's final product is singular, the process of creating it is anything but individual. Collaboration is at the heart of software development, with developers working together to define requirements, update stakeholders, write code, and test its functionality. Redundancies in this process can reduce development speed, create roadblocks, and produce information gaps.

Pieces for Developers aims to act as a workflow copilot, using AI developer workflows to solve collaboration frustrations. It provides automatically generated AI context on code, increasing cohesion between developers and removing data silos. 

Tsavo Knott, Co-Founder and CEO of Pieces summarizes this as, “It’s at that moment where you’re looking at a dock or doing a code review, or looking at a web page, where we think, ‘Can you have a feed that is smart enough to know what you’ve been doing recently, what others have been doing recently? And then surface that document you’ve been talking about—or say, ‘Hey, Sam worked on something similar two days ago.”

By making developer work histories more accessible and visible, AI developer workflow platforms boost collaboration, streamline development, and remove redundancies. This approach streamlines the communication needed around development.

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Companies increasingly need effective teams of software developers to build and launch products. With AI developer workflow systems, the platform’s programmers become highly responsive, giving insight, suggestions, and transparent work histories to enhance efficiency.

With faster and more effective systems, software engineers everywhere can leverage AI developer workflows.

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Fidelity Investments—AI Developer Workflows and ROI

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Analysis by Gartner suggests that AI will advance 70% of all design and development workflows for web and mobile applications by 2026. At the forefront of this trend is the software development team at Fidelity—a global leader in investment services with over 50 million clients worldwide.

Paul Howard, Head of Compliance Analytics Architecture at Fidelity, saw AI developer workflows as an opportunity to reinforce the creative liberty of software engineers while driving productivity, “Our AI orchestration platform enables us to deliver robust, value-generating models at speed and keep them that way.”

Fidelity began using AI modeling in all software developer workflows, creating scalable deployments that streamline the developer lifecycle. Developers can use these workflows to complete lines of code, see more context while developing, and streamline communication.

As a result of implementing AI developer workflows, teams at Fidelity saw two significant profit-enhancing results. Firstly, the production speed of new apps and features doubled. Equally, the total time that developers took to identify production issues fell by 80%.

Together, these benefits synergize to dramatically enhance the development workflow, driving profit for the business and reducing the need for manual, time-consuming tasks for developers. 

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Practical Advice for Implementing AI Developer Workflows

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Even in a field on the cusp of technological innovation, many software developers still need to be exposed to AI technologies. Without experience leveraging AI developer workflows, integrating new technologies is simply not enough. Businesses need to take steps to streamline integration and provide developers with resources to understand how to use AI best for their particular needs.

Although the initial integration process may seem laborious, 33% of developers who have an AI-first workflow report productivity increases, while 25% report greater efficiency at work. These striking improvements offered by AI demonstrate its utility, leading to revenue increases and faster product production.

Based on research by GitHub, here is how software engineers implement AI developer workflows in over 500 companies across the USA:

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1. Identify Software Developer Needs: Different teams across the United States cite distinct ways that development teams spend their time. 32% primarily write code, 31% find and fix security vulnerabilities, and 30% get feedback from end users. By pinpointing where your company’s software programmers spend the most time, you’ll see where you can most critically implement AI developer workflows.

2. Choose the Right Tools: Not every AI tool will integrate similarly into a software developer workflow. One tool, like GitHub Copilot, may focus on code writing, debugging, and testing. Pieces, however, focus on integrating into developer workflows to enhance collaboration. Select the AI tools that align with the main area your developers need help with to achieve maximum returns. 

3. Train Developer Teams: For 87% of developers, AI developer workflows reduce the mental strain of developing code. Yet, to achieve this efficiency enhancement, businesses must ensure that their workforce understands how to use these technologies best. Offering educational modules demonstrating how to leverage AI for specific functions identified in Step 1 will streamline the implementation process. Even tech giants like Microsoft already offer specialized training for AI developer workflows.

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The specific implementation pathway for AI developer workflows will vary depending on the company’s current technical stack, the experience of its developers with AI, and its current security posture. By moving through the steps above, businesses are able to pinpoint how AI will most dramatically help their company thrive.

In their recent State of the Industry report, the American Society for Training and Development revealed that ongoing comprehensive levels of employee training can lead to a 21% increase in profit. Especially when training software developers—an already high-value role—in AI technologies, businesses are poised to experience a rapid increase in productivity, value creation, and revenue.

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AI in Action: the Microsoft Copilot Stack

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Back in 2023, Microsoft, OpenAI, and GitHub released the GitHub Copilot. Early data demonstrated that companies that used the code developer tool achieved production rates that were 55% faster than those without AI developer workflows. Developers across the globe have now begun using this tool, with over 1 million developers actively interacting with this AI-first platform. 

Since then, Microsoft has continued to develop its AI Copilot experiences for software developers. The GitHub Copilot Stack allows developers to write, test, and push code faster. Commenting on the possibility of these integrations, Scott Gutherie, Executive VP of the Cloud and AI group at Microsoft, states,

“Copilot dramatically accelerates developer productivity. In fact, 46% of all lines of code written by developers who are using GitHub Copilot are now fully AI-generated. Seventy-five percent of developers using Copilot feel that they can now focus on more satisfying work. And 96% of developers report being able to complete repetitive tasks a lot faster.”

With over 1,000,000 current developers on the platform working for the most prominent companies worldwide, Microsoft’s coding copilot demonstrates just how effective AI developer workflows can be in this field.

According to Microsoft’s data, businesses that turn to AI-enabled code platforms can:

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• Automate Repetitive Tasks:96% of software developers can complete repetitive tasks via automation, saving time and streamlining their workflows.‍
• Generate Base Code: 46% of the total code developers produce using the platform is directly taken from AI generation. This equates to a 46% increase in productivity, as developers no longer have to write nearly half the code they use.‍
• Accelerate Task Completion: GitHub examined the time it took 95 developers to complete the same task. 45 developers had access to AI development suites, while 50 did not. The 45 developers who leveraged AI averaged 1 hour and 11 minutes completion time, while the non-AI developers averaged 2 hours and 41 minutes. AI developer workflows can increase productivity by up to over 55% in routine coding tasks, saving your business time and money.

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Intel and AI Developer Workflows: Efficiency-First Strategies

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In an interview on the AI Today Podcast, Wei Li, VP/GM of AI Software Engineering at Intel, commented on the vital enhancement that AI makes to software developer workflows. According to Li, Intel has already implemented key AI developer workflows, finding new ways of driving efficiency.

First of all, Intel has trained a proprietary model on internal data. This deployment services as an in-house chatbot for developers. Whenever they have a question about Intel-specific guidance, code styles, or even the current status of other projects, they can turn to this AI. This form of AI reduces the need for extensive communication between developers and drives efficiency.

Intel also uses AI in “Boring but necessary” tasks, Li states. Software developers routinely spend extensive amounts of their day on triage and debugging. AI can enhance the speed of these tasks, making developers faster and more productive. This strategy also frees up more time for the developers to concentrate on value-producing tasks. 

Predictions of the economic savings of implementing these AI tools in companies like Intel reach upward of $4,915,883 per year. AI tools offer time savings, interoperable efficiencies, and hardware resource savings that create real-world value for businesses.

After their success, Intel plans to integrate Microsoft Copilot locally into future PCs. Their commitment to AI tools reflects the value they see in these products and the efficiency generation they provide. 

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Final Thoughts: How AI Developer Workflows Provided Industry-Defining Efficiency

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The demand for software developers has increased dramatically over the past decade. Advances in technology and a genuine need to produce have led companies worldwide to hire more developers and seek ways to enhance their efficiency.

Across the board, regarding speed advancements, productivity enhancements, collaboration improvements, and stress reductions, AI developer workflows are the ultimate solution to the rising demand. Leveraging AI allows teams to stay competitive in today’s digital landscape, drive revenue, and optimize the process of developing the fundamental software that keeps businesses thriving.

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