AI in product development

AI is a technology that enables computers to learn, solve problems and decide like humans. Since its widespread integration into core business processes beginning in the 2010s, product designers and managers have been experimenting with how best to apply it to product development.

In just the last decade, AI-powered systems have transformed the way organizations develop products, streamlining workflows, automating repetitive tasks and helping enable a more data-driven approach to product development. Some of the largest and most successful companies in the world have incorporated AI into their processes, including Microsoft, Open AI (ChatGPT) and McKinsey.

Today, the most advanced AI product development tools help designers generate new product ideas that use complex algorithms and generative AI (gen AI) models and prototype and test them faster. This approach shortens the overall time-to-market.

AI is fundamentally changing the product development process, helping yield insights into customer behavior faster than in the past. It’s also transforming product ideation, prototyping, testing and deployment. Here’s a look at how AI is being integrated into the various processes of product development.

AI tools help improve the first stage of product development primarily by using gen AI systems. These systems are based on AI that can create original content like text, images and video to help teams conduct market research faster.

Gen AI tools can sift through large datasets, speeding insights into user behavior on social media platforms like LinkedIn, X and Facebook. Machine learning (ML) algorithms help product managers identify challenges and customer preferences that can lead to new opportunities for product development.

For example, some modern organizations are experimenting with AI assistants that are fully integrated into product management software. These advanced AI tools analyze customer feedback in real-time, helping product managers quickly spot areas for new product development.

Ideation or brainstorming is the second phase of the product development lifecycle and has been heavily influenced by AI in the last decade. This influence was evident especially in the two years since the launch of ChatGPT and other gen AI tools.

Gen AI tools can help product managers take the insights from research they’ve gathered in the first phase. The tools convert the insights into ideas for new products that are likely to have a market fit.

During this phase, AI-powered text and image generators help teams build AI-generated mockups of potential new products that stakeholders can review. While initially seen as a potential replacement for designers, gen AI has proven in the last three years that it can behave more like a partner. Gen AI can help with enhancing and speeding creativity by performing rote tasks.

During the design and prototyping phase, AI tools help product designers take a concept for a new product and turn it into a prototype faster than they could manually. AI-enhanced computer-aided design (CAD) systems can create complex designs faster and more efficiently than humans. Automating repetitive aspects of the design and prototyping process allows engineers and product designers to focus more on creative and strategic decision-making tasks.

AI-enhanced simulation tools also play a pivotal role at this stage by helping teams predict how their designs and prototypes will be used by customers. ML-powered simulations compare each product design thoroughly, helping product managers understand how their designs are likely to fare in the market.

After the design stage is complete, AI-driven tools automate aspects of the test and build phase, producing hundreds and even thousands of prototype variations that are tested against each other for performance.

When a prototype has been validated, gen AI is critical during the build phase, automating processes that used to be done manually and saving quality assurance (QA) teams hours of time.

As the test phase nears completion, product managers use AI to validate their choices, with metrics like performance, user experience (UX) and sustainability to evaluate a new product. AI technology is even used to test market fit and pricing before the final phase of the product development lifecycle, launch and iteration.

During the launch and iteration phase, AI enables products to undergo continuous improvement, gathering real-world data to give organizations a competitive advantage.

Advanced AI systems are designed to monitor customer experience and market adoption metrics. They link directly to a product development pipeline to inform new products and new product features.

ML algorithms are again key during this stage, highlighting any anomalies in the data so product managers can spot potential problems faster and detect meaningful shifts in user behavior and market trends.

AI’s integration into the product development lifecycle has enabled product managers and development teams to use AI capabilities to enhance processes, gain deeper insights into customer behavior and innovate faster. Here’s a look at the top enterprise benefits of using AI in product development.

The benefits of using AI in the product development lifecycle have led companies of all sizes to seek new ways to integrate it into their core processes and strategies. From design and prototyping to scouring volumes of customer feedback data for insights, here’s a look at the top enterprise use cases for AI in product development.

AI tools are being widely used during the product design stage to help teams brainstorm faster, experiment with different designs and test hypotheses. Gen AI and machine learning have been especially helpful in recent years, helping teams test product variations and even generate new designs based on pre-defined metrics.

AI-driven predictive analytics has transformed how companies approach new product development. This procedure involves the use of AI tools to analyze how a product will likely fare in the market based on social media, customer feedback and online reviews. AI-driven insights can reveal trends in customer preferences faster than manual research, allowing organizations to be more nimble and adaptive in their approaches.

Gen AI tools that can write and test code have changed how DevOps teams build and test new software products and services. AI agents developed by Microsoft, OpenAI and others automate many aspects of testing and deployment. These tools allow engineers to iterate faster and focus more on creative tasks. Agents are AI systems that can autonomously perform a task without human input.

AI has improved many aspects of modern supply chains, helping inventory managers optimize supply and demand of a product with advanced, AI-enhanced forecasting tools. AI systems analyze real-time data from suppliers, automating the manufacturing and shipping of products when supply is low.

AI is vastly improving sentiment analysis in product development, the process of analyzing large volumes of text to determine whether it expresses a positive or negative sentiment.

AI sentiment analysis tools comprehensively gather customer feedback from social media sites and other platforms and analyze it according to parameters set by product managers. ML algorithms even help suggest new product ideas or features based on the feedback they analyze.

AI’s introduction into product development workflows represents a switch in how organizations approach the discipline. From streamlining how designers brainstorm and prototype to automating time-consuming aspects of DevOps like writing and testing code, it is transforming the entire product development lifecycle.

As technology continues to advance, three areas appear likely to drive innovation:

• Agentic AI

• Low- and no-code platforms

• Responsible and ethical AI

AI’s integration into product development is trending less toward replacing human creativity and more toward enabling and enhancing it. Agentic AI, which represents AI systems that reason, use tools and solve problems like people do, is at the heart of this effort.

Agentic AI’s high level of sophistication makes it more goal-oriented than gen AI. It is also capable of taking on complex tasks like research and process management that can inform and improve the creative process.

Low-code and no-code software platforms allow individuals without a background in computer programming to create simple applications. These platforms are being significantly enhanced by AI.

Non-coders are using low-code and no-code platforms to build increasingly sophisticated applications at a reduced cost. According to a recent report, 70% of new enterprise applications will be built on low-code and no-code platforms this year, a 25% increase over the last 5 years.²

Headlines abound with AI fears, and the more the technology is integrated into tools businesses rely on every day, the more those fears seem to increase. Rather than running from the technology, modern enterprises are increasingly looking for ways to responsibly and ethically incorporate it into their processes.

Examples in product development include designers who are weaving fairness and transparency guardrails into gen AI tools and engineers who are building AI frameworks that reduce biases into their underlying algorithms.