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Integrating AI into Product Development and Engineering Processes

Less than 1 minute Minutes
by Ram Viswanathan, CTO, AI, Rackspace Technology

Astonishingly, nearly one-half (46%) of organizations are already using AI for new product development, according to our recent global AI survey, “The Impact of AI on Business: Trends, Challenges and Opportunities.” This statistic underscores a growing trend: companies are not just experimenting with AI, but are strategically integrating it to boost efficiency, reduce costs and enhance customer experiences.

As AI technologies continue to evolve and expand, we can expect an even greater surge in adoption, reflecting AI’s increasingly pivotal role in shaping business strategies.

In software product development, the automation of repetitive tasks such as code generation, enables engineers to concentrate on high-value activities that demand human creativity and problem-solving capabilities. As AI technologies advance and organizations grow more accustomed to them, we anticipate AI playing a larger role in product development and engineering, extending into more sophisticated areas, like predictive modeling and optimization.

In the meantime, organizations can proactively enhance their adoption in product management and engineering. To expedite this integration, consider these three essential actions:

  • Evaluate and use AI technologies to identify areas for immediate impact.
  • Provide comprehensive AI education and training to empower teams.
  • Secure senior management sponsorship to drive organizational commitment.

Our best practices suggestions on these three points include the following:

Leveraging AI in product development and engineering

AI has the potential to revolutionize predictive modeling and optimization in product engineering and development. By analyzing extensive data sets, AI-powered systems provide valuable insights that can guide decision-making. For instance, they can pinpoint product features that are most likely to meet customer needs, significantly influencing new products’ design and functionality.

AI can also impact automation and intelligent workflows, streamlining engineering processes and accelerating time-to-market. For example, AI-powered tools can assist in prototyping by optimizing the design and spotting risks early on to accelerate product development processes.

Innovative collaboration between AI systems and humans is an area where AI is expected to impact product development and engineering significantly. For example, AI-powered tools can enhance collaboration by providing real-time progress updates. This can help managers quickly identify issues that could negatively impact product development.

Accelerating AI integration in five steps

AI technologies offer product management and engineering teams capabilities that extend well beyond coding. Here are five steps your team can take to help accelerate adoption and start reaping benefits:

  1. Create a clear strategy: Identify key areas where AI can bring the most significant value, such as automating repetitive tasks, improving decision-making processes and enhancing product development cycles.
  2. Assess data readiness and quality: Conduct a broad assessment of your data infrastructure, governance practices, and quality assurance protocols to ensure AI algorithms have access to accurate and reliable data for optimal performance.
  3. Invest in AI talent and training: Build a strong foundation of AI expertise by investing in training programs to upskill existing employees on AI and recruit specialists who can lead AI projects effectively. Collaborate with data scientists and domain experts to ensure you’re taking a holistic approach to integrating AI into the overall product development processes.
  1. Pilot AI projects incrementally: Rather than attempting a large-scale AI implementation from the outset, start with smaller pilot projects to test AI applications. By starting small, you can evaluate AI performance, identify challenges and iterate on solutions before scaling AI initiatives across your organization. At FAIR™, we call this Ideate, Incubate and Industrialize.


2-4 weeks

We help you establish desirability, readiness, goals and guardrails for the adoption of AI through an interactive and collaborative ideation engagement.


8-12 weeks

We determine the feasibility of AI within your organization and work with you to co-create your first generative AI solution within your enterprise.


3-6 months

We ensure the viability of your solution through the systematic productization of AI in alignment with defined metrics and governance guardrails. 

  1. Monitor performance and iterate: Implement robust performance metrics to track the impact of AI on product development processes. Analyze key performance indicators to assess ROI and user feedback, allowing for iterative enhancements and refinements based on real-world insights.

Motivating product developers to use AI tools

A crucial step in successfully integrating AI into product development workflows is motivating your developers to embrace AI tools. There are several strategies IT decision-makers can employ to drive adoption and enthusiasm for AI:

  • Communicate the benefits: Highlight how AI can streamline repetitive tasks, enhance productivity and improve product quality. Emphasize that AI is not a replacement for developers, but a valuable tool to augment their skills and accelerate innovation.
  • Provide training and support: Help product developers understand how to effectively use AI tools in hands-on workshops, online courses or directly from AI experts. Show them you’re committed to supporting their AI learning journey.
  • Showcase success stories: Inspire product developers to embrace AI by illustrating how AI tools are successfully being integrated into product development processes. Highlight the positive outcomes, such as faster time-to-market, improved customer satisfaction and cost savings.
  • Foster collaboration: Facilitate cross-functional teamwork with knowledge sharing and brainstorming.
  • Recognize and reward adoption: Celebrate the achievements of product developers who embrace AI tools. Highlight the positive impact they’ve made through AI. This can motivate others to follow suit and further drive adoption across the organization.

Enlisting leadership support for AI adoption

Gaining support for AI adoption from leadership comes down to showcasing its value in multiple areas. For product development and engineering, AI can have a significant impact on removing manual tasks and introducing efficiencies and cost savings in these areas:

  • Automated testing and frameworks: AI can expedite test script creation to help ensure product quality and reliability.
  • Version control and continuous integration: AI tools can help developers manage code changes, automate build processes and integrate code changes from multiple developers seamlessly.
  • Requirement management: AI can help support product requirement capture, tracking and management, ensuring all requirements are met.
  • Release and deployment automation: AI can help package and deploy software applications consistently across different environments, reducing errors and accelerating release cycles.
  • Design and prototyping: AI can be used to automate 3D printers for product prototypes, enabling rapid iteration and design validation.

AI technologies are advancing quickly. It’s crucial for product development and engineering teams to explore these capabilities, fostering optimal AI use and securing leadership support. The more quickly you can integrate AI into your product development processes, the sooner you’ll establish a critical competitive advantage in what will likely become an AI-driven marketplace. 

Download The 2024 Global AI Report today.