How companies are boosting productivity with generative AI

Is your company using generative AI yet?

While it’s still in its infancy, generative AI coding tools are already changing the way developers and companies build software. Generative AI can boost developer and business productivity by automating tasks, improving communication and collaboration, and providing insights that can inform better decision-making.

In this post, we’ll explore the full story of how companies are adopting generative AI to ship software faster, including:

• What generative AI is
• How generative AI coding tools are changing the developer experience
• How companies are using generative AI

What is generative AI?

Generative AI refers to a class of artificial intelligence (AI) systems designed to create new content similar to what humans produce. These systems are trained on large datasets of content that include text, images, audio, music, or code.

Generative AI is an extension of traditional machine learning, which trains models to predict or classify data based on existing patterns. But instead of simply predicting the outcome, generative AI models are designed to identify underlying patterns and structures of the data, and then use that knowledge to quickly generate new content. However, the main difference between the two is one of magnitude and the size of the prediction or generation. Machine learning typically predicts the next word. Generative AI can generate the next paragraph.

Generative AI tools have attracted particular interest in the business world. From marketing to software development, organizational leaders are increasingly curious about the benefits of the new generative AI applications and products.

“I do think that all companies will adopt generative AI tools in the near future, at least indirectly,” said Albert Ziegler, principal machine learning engineer at GitHub. “The bakery around the corner might have a logo that the designer made using a generative transformer. The neighbor selling knitted socks might have asked Bing where to buy a certain kind of wool. My taxi driver might do their taxes with a certain Excel plugin. This adoption will only increase over time.”

What are some business uses of generative AI tools? 💡

• Software development: generative AI tools can assist engineers with building, editing, and testing code.
• Content creation: writers can use generative AI tools to help personalize product descriptions and write ad copy.
• Design creation: from generating layouts to assisting with graphics, generative AI design tools can help designers create entirely new designs.
• Video creation: generative AI tools can help videographers with building, editing, or enhancing videos and images.
• Language translation: translators can use generative AI tools to create communications in different languages.
• Personalization: generative AI tools can assist businesses with personalizing products and services to meet the needs of individual customers.
• Operations: from supply chain management to pricing, generative AI tools can help operations professionals drive efficiency.

How generative AI coding tools are changing the developer experience

Generative AI has big implications for developers, as the tools can enable them to code and ship software faster.

Similar to how spell check and other automation tools can help writers build content more efficiently, generative AI coding tools can help developers produce cleaner work—and the models powering these tools are getting better by the month. Tools such as GitHub Copilot, for instance, can be used in many parts of the software development lifecycle, including in IDEs, code reviews, and testing.

The science backs this up. In 2022, we conducted research into how our generative AI tool, GitHub Copilot, helps developers. Here’s what we found:

Source: Research: quantifying GitHub Copilot’s impact on developer productivity and happiness

GitHub Copilot is only continuing to improve. When the tool was first launched for individuals in June 2022, more than 27% of developers’ code was generated by GitHub Copilot, on average. Today, that number is 46% across all programming languages—and in Java, that jumps to 61%.

How can generative AI tools help you build software? 🚀

These tools can help:

• Write boilerplate code for various programming languages and frameworks.
• Find information in documentation to understand what the code does.
• Identify security vulnerabilities and implement fixes.
• Streamline code reviews before merging new or edited code.

Using generative AI responsibly 🙏

Like all technologies, responsibility and ethics are important with generative AI.

In February 2023, a group of 10 companies including OpenAI, Adobe, the BBC, and others agreed upon a new set of recommendations on how to use generative AI content in a responsible way.

The recommendations were put together by the Partnership on AI (PAI), an AI research nonprofit, in consultation with more than 50 organizations. The guidelines call for creators and distributors of generative AI to be transparent about what the technology can and can’t do and disclose when users might be interacting with this type of content (by using watermarks, disclaimers, or traceable elements in an AI model’s training data).

Is generative AI accurate? 🔑

Businesses should be aware that while generative AI tools can speed up the creation of content, they should not be solely relied upon as a source of truth. A recent study suggests that people can identify whether AI-generated content is real or fake only 50% of the time. Here at GitHub, we named our generative AI tool “GitHub Copilot” to signify just this—the tool can help, but at the end of the day, it’s just a copilot. The developer needs to take responsibility for ensuring that the finished code is accurate and complete.

How companies are using generative AI

Even as generative AI models and tools continue to rapidly advance, businesses are already exploring how to incorporate these into their day-to-day operations.

This is particularly true for software development teams.

“Going forward, tech companies that don’t adopt generative AI tools will have a significant productivity disadvantage,” Ziegler said. “Given how much faster this technology can help developers build, organizations that don’t adopt these tools or create their own will have a harder time in the marketplace.”

3 primary generative AI business models for organizations 📈

Enterprises all over the world are using generative AI tools to transform how work gets done. Three of the business models organizations use include:

• Model as a Service (MaaS): Companies access generative AI models through the cloud and use them to create new content. OpenAI employs this model, which licenses its GPT-3 AI model, the platform behind ChatGPT. This option offers low-risk, low-cost access to generative AI, with limited upfront investment and high flexibility.
• Built-in apps: Companies build new—or existing—apps on top of generative AI models to create new experiences. GitHub Copilot uses this model, which relies on Codex to analyze the context of the code to provide intelligent suggestions on how to complete it. This option offers high customization and specialized solutions with scalability.
• Vertical integration: Vertical integration leverages existing systems to enhance the offerings. For instance, companies may use generative AI models to analyze large amounts of data and make predictions about prices or improve the accuracy of their services.

Duolingo, one of the largest language-learning apps in the world, is one company that recently adopted generative AI capabilities. They chose GitHub’s generative AI tool, GitHub Copilot, to help their developers write and ship code faster, while improving test coverage. Duolingo’s CTO Severin Hacker said GitHub Copilot delivered immediate benefits to the team, enabling them to code quickly and deliver their best work.

”[The tool] stops you from getting distracted when you’re doing deep work that requires a lot of your brain power,” Hacker noted. “You spend less time on routine work and more time on the hard stuff. With GitHub Copilot, our developers stay in the flow state and keep momentum instead of clawing through code libraries or documentation.”

After adopting GitHub Copilot and the GitHub platform, Duolingo saw a:

• 25% increase in developer speed for those who are new to working with a specific repository
• 10% increase in developer speed for those who are familiar with the respective codebase
• 67% decrease in median code review turnaround time

“I don’t know of anything available today that’s remotely close to what we can get with GitHub Copilot,” Hacker said.

Looking forward

Generative AI is changing the world of software development. And it’s just getting started. The technology is quickly improving and more use cases are being identified across the software development lifecycle. With the announcement of GitHub Copilot X, our vision for the future of AI-powered software development, we’re committed to installing AI capabilities into every step of the developer workflow. There’s no better time to get started with generative AI at your company.