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Transform New Product Development with AI

Preparing for AI Readiness

Understanding the new product development process and how AI can support that ecosystem is a window into just how much this new tool can help benefit the innovative product lifecycle. Optimizely, in its blog, “How to start using AI in product development today without breaking your existing process,” by David Carlile, Senior Director of Product Strategy, looks at several angles of new product development and how AI can help. Ultimately, AI can help best by saving time in the NPD process. Optimizely points out that “understanding potential users, getting deeper insights from customer data, and building prototypes,” all takes an extensive amount of time. AI can create efficiencies in these areas.

AI tools may help build better features and functions of a product, but human interaction, of course, is still necessary. In fact, Carlile points out that at least on the surface, the NPD process and practices may remain similar to what it was. “As a product team, you should evaluate your product development process for AI readiness. It means assessing the existing infrastructure, availability of quality data, and the technical capabilities of your team,” he says. Once an assessment is complete, Optimizely maps out that the team may be able to identify just where AI can help—with automation, such as tasks like basic product design, generative design, product plans; use a recommendation engine to personalize the user experience; and manage inventory, resource allocation and pricing based on data.

AI can also be used to augment customer research in myriad ways. “What you can do is use AI for data analysis and break down your research into a functional hypothesis,” says Carlile. “Here data will help your product development team learn what kind of products you can build for your target audience; which messaging and content will resonate with this audience even if you’re using chatbots; and test optimum price points that will be relevant to your target audience.” Leveraging AI in this way will streamline the new product development process, and its multitude of iterations and idea generation practices.

An Insights-Critical Mission

Ultimately, AI can help streamline and strengthen the insights process for innovation. In “Make Insights Critical to the Innovation Process,” All Things Innovation looked at the critical data driven processes necessary for innovation and product development. Increasingly, insights is tasked with supporting the product development and innovation team, giving innovators robust and meaningful research that can help them understand consumer sentiment. This, in turn, can help guide the innovation team’s efforts in building new products and services that aim to satisfy consumer preferences, and bypass any concerns or roadblocks they may have before purchasing.

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature the session, “AI-Powered Insights: How to Get Your NPD Process Down to a Science,” presented by Nik Pearmine, Chief Strategy Officer at Black Swan Data. We’ve entered a new era of product innovation. Instead of asking a handful of consumers survey questions, we can harness what millions of people are actively talking about online to scientifically answer the big and small questions along the innovation process. Join Pearmine as he delves into the transformative power of AI, and how it’s revolutionizing the speed, precision, and NPD success rates for industry leaders like PepsiCo, Heineken, and General Mills. Register for FEI 2024 here.

A Look at Tech’s Advantages

Using AI in the new product development process offers a range of benefits that can significantly enhance efficiency, effectiveness, and innovation. Some of the top benefits, according to ChatGPT, include:

  1. Faster Decision Making: AI-powered analytics can process vast amounts of data quickly, enabling faster decision-making based on market trends, consumer preferences, and competitor analysis.
  2. Improved Product Design: AI algorithms can analyze consumer feedback, market trends, and historical data to suggest product features, designs, and functionalities that are likely to resonate with target audiences.
  3. Enhanced Personalization: AI enables companies to personalize products and services based on individual customer preferences, behavior patterns, and demographic information, leading to higher customer satisfaction and loyalty.
  4. Cost Reduction: By automating repetitive tasks and streamlining processes, AI can help reduce production costs, optimize resource allocation, and minimize waste in the product development lifecycle.
  5. Predictive Maintenance: AI-powered predictive analytics can forecast equipment failures and maintenance needs, ensuring optimal performance and minimizing downtime in manufacturing processes.
  6. Market Prediction: AI algorithms can analyze market data, consumer behavior, and competitor strategies to forecast demand, identify emerging trends, and anticipate future market opportunities, helping companies stay ahead of the competition.
  7. Optimized Supply Chain Management: AI-driven supply chain optimization tools can improve inventory management, demand forecasting, and logistics planning, leading to more efficient production and distribution processes.
  8. Risk Mitigation: AI can analyze potential risks associated with new product development, such as regulatory compliance issues, market uncertainties, and production challenges, enabling companies to proactively mitigate these risks and make informed decisions.
  9. Iterative Improvement: AI-powered analytics can continuously monitor product performance, gather feedback from customers, and identify areas for improvement, facilitating iterative product development cycles and enhancing overall product quality.
  10. Competitive Advantage: Companies that effectively leverage AI in new product development gain a competitive edge by accelerating time-to-market, delivering innovative solutions, and meeting evolving customer demands more effectively than their competitors.

Ramping Up the Experiment

Overall, integrating AI into the new product development process can drive innovation, improve efficiency, and help companies deliver products that better meet the needs and preferences of their target audience.

For Optimizely’s Carlile, tying AI to experimentation is essential in the new product development process. A test-and-learn approach will also benefit how customer impact can be measured at every step. “Going forward, I see AI and experimentation aligning perfectly in terms of spirit and decision-making. Remember to test and learn as ultimately anything you can do to understand your user will help you build better products,” says Carlile.

Video courtesy of Design Theory

Contributor

  • Matt Kramer

    Matthew Kramer is the Digital Editor for All Things Insights & All Things Innovation. He has over 20 years of experience working in publishing and media companies, on a variety of business-to-business publications, websites and trade shows.

 
 

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