Today, organizations face immense pressure to speed up their R&D processes to get innovative products to the market that much quicker. Competitive forces, higher customer expectations, and tough regulatory requirements are pushing many industries to rethink their approach to innovation. Additionally, attaching software to hardware products increases complexity and causes lead-time requirements.
According to McKinsey & Company, growth in R&D spending in both the automotive and industrial sectors has hit all-time highs in 2021. Global R&D investments have breached $1.6 trillion, with R&D spending representing at least 30% of earnings, on average. Meanwhile, time-to-market reduction for new products is the number-one priority for most companies, followed by opportunities to increase productivity, lower costs, and improve quality. Generative AI rapidly becomes one of the most disruptive technologies showing the potential for fast R&D acceleration and innovation.
Generative AI, best exemplified in generative tools like GitHub Copilot, is a game-changer toward improving R&D productivity and code quality. GitHub Copilot became commercially available in June 2021 and is now being used by more than 1.2M developers. In its research, it is proved that companies using GitHub Copilot at scale increased the productivity and satisfaction of developers manifold. For instance, General Motors has embedded GitHub Copilot in its development process, influencing the ways of working of over 400 developers. As an on-demand pair-programmer, GitHub Copilot understands the context of an application and then generates the code example to serve the needs of the developer. This integration has considerably increased GM’s productivity during the development process.
As generative AI technologies mature, its use cases go beyond code generation to various R&D processes. For example, McKinsey & Company research shows that many European automotive and manufacturing firms are experimenting with generative AI application domain areas. This research reveals that 75% of the companies investigate generative AI applications within their R&D centers, with massive investment to drive innovation.
Generative AI offers a lot of potential across the various product development lifecycle processes and maps across the ‘V’ model to provide end-to-end development stages in alignment. Common use cases of generative AI include requirement engineering, testing and validation of software, and product designing and optimization. Companies are leveraging generative AI in making regulatory compliance seamless, automating testing processes, and optimizing product design and engineering. For example, Synopsys Inc., which introduced Synopsys.ai Copilot, will ameliorate the skills gap in chip design and democratize access for complex applications to augment engineering talent.
In industries with specialties in Computer-Aided Manufacturing, generative AI assists in the aspect of automation, ensuring the quality of products and reducing cycle time. Generative AI for industrial automation is used by providers like Siemens and Rockwell Automation, who help in accelerating PLC code generation and equipment troubleshooting for high operational efficiency.
Microsoft has invested in generative AI so that the R&D ecosystem can collaborate to progressively gain the best use of AI capabilities with more software vendors tailor-made to their products. The Copilot Stack and Azure AI Studio tools enhance techniques for developing generative AI across various applications, thus ensuring continued innovation and efficiency. The impact of generative AI on R&D transformation is more than technological; it includes a change in organization and data governance. Successful AI implementation is about mature data capabilities and a culture of innovation, which helps companies to lead the change in their respective industries.
One of the most important areas to consider in implementing AI is the aspect of intellectual property rights. Generative AI solutions are large-scale data applications, and hence, questions regarding copyright infringement and third-party claims are raised here. To mitigate these risks, Microsoft offers a series of contractual commitments, including the Customer Copyright Commitment, to provide protection to customers from such claims emanating from generative AI outputs. Its recent expansion to the Azure OpenAI Service gives more proof of the commitment that Microsoft has to its customer base and ensures it offers protection to commercial customers leveraging AI technologies.
As AI is reimagining industries and speeding up innovation rapidly, the first leap to be taken by organizations must be in the direction of building responsible AI. As McKinsey & Company mentions, a full strategy is needed in technology, processes, data governance, and talent. Responsible AI for Microsoft goes beyond this technology frontier and aims to promote collaboration and innovation while mitigating harm in ethical AI use. Microsoft believes it can help deliver positive societal change and usher in a new era of innovative development if capabilities are built for organizations to leverage AI in ways that are responsible.
The only way to maximize the transformative potential of generative AI in a collaborative and responsible manner is by being responsive to ethical considerations. Nurturing open innovation, fostering principles of ethical AI, and uncovering information from a diversified set of data can create the best use of AI, crafting positive changes and shaping a more inclusive, collaborative future.
