Product development is a resource-intensive process. You need to understand customer needs, create a product that meets (or, ideally, exceeds) customer expectations, and manufacture a product for a cost that allows you to turn a profit.
Artificial intelligence (AI) technology can help.
“All ecommerce businesses can benefit from integrating AI in a variety of ways—from improving the efficiency of daily tasks with large language models (LLMs) or other specific tools, to improving and personalizing product recommendations, to creating content and copy for marketing materials,” says Chris Wlezien, founder and product innovation strategist at mHUB.
“But ecommerce brands that create their own products can benefit the most by using tools to rapidly develop new products,” says Chris.
Here’s how businesses are using generative AI and machine learning technology in product development—and three tips to help ecommerce business owners integrate AI into the process.
Benefits of using AI in product development
Here are a few benefits of integrating AI in product development:
Reduced time to market
AI can increase efficiency throughout the entire product development process, resulting in a shorter lead time for new products. There are multiple places where AI can speed up the development process, from visualizing product designs to suggesting features and quickly identifying errors.
“We have seen initial concept development cycles reduce from weeks to days because we can generate high-quality detailed concepts in minutes using AI image generation tools at the hands of experienced designers,” says Chris. “These detailed and rapid concepts can align teams, making decisions easier and aiding in getting customer research and buy-in earlier than ever.”
Improved decision-making
AI algorithms can analyze vast amounts of data accurately and efficiently, generating valuable insights and enabling data-driven decision-making. AI technology also enables predictive data analysis, which looks at historical data to predict future trends, and prescriptive analysis, which runs multiple predictive scenarios and recommends strategies.
For example, a product development team might use predictive AI algorithms to analyze historical product data and customer trends. Then they could have the AI recommend product variations most likely to increase sales within a specific target audience.
Better products
AI tools can generate ideas, reduce human error, help validate product ideas, and test product designs under a range of theoretical conditions. These capabilities can all lead to higher-quality products.
Product development teams can also use AI systems to automate quality assurance processes and other repetitive tasks. This reduces the risk of human error in the development process and frees up time for higher-level creative tasks.
Risks of using AI in product development
Product development teams are already using AI to identify market opportunities, generate ideas, and test and refine products—but using any new technology introduces an element of risk. Potential risks include:
Overconfidence
AI empowers businesses to be bold—potentially too bold.
“A false sense of confidence can come with AI,” says Chris. “You run the risk of creating polished-looking concepts that cannot be produced or function as they are depicted.”
Successfully integrating AI into product development strategies requires product managers and developers who understand both the strengths and limitations of AI systems. These professionals can ensure accuracy and functionality by reviewing the AI’s output.
Biased or inaccurate outputs
AI systems can make mistakes or generate false or misleading information—outputs called hallucinations. They can also replicate biases in their training data sets, which can lead to assumptions about the customers a product serves and the needs the product should meet.
For example, an AI tool is trained on a data set that includes references only to male professors. It might recommend a smart board designed to optimize product performance for the average man, inadvertently limiting accessibility for a large percentage of female users.
Oversight challenges
The AI product development process requires management by human engineers, and AI oversight is notoriously tricky. To succeed, you’ll need product development engineers with a deep understanding of your customers as well as machine learning outputs. Plus, there needs to be a commitment to incorporating AI algorithms and human creativity in the development process.
“These tools can generate anything,” says Chris, “but it still takes a seasoned designer or engineer to look at the concepts and shape them so they are actually manufacturable and meet customer needs.”
Ways to use AI in product development
- Automate tasks
- Generate renders and concepts
- Test designs
- Conduct market research
- Create customer feedback loops
AI can increase efficiency and improve accuracy across the entire product development lifecycle. You’ll just need to determine the places where AI integration will work best for your team. Here’s an overview of how you can use AI in product development:
Automate tasks
You can use AI and machine learning to automate tasks like data entry, code formatting, test writing, and error documentation. Automating routine or repetitive tasksenhances operational efficiency and can reduce the risk of human error.
Generate renders and concepts
You can use AI image generators to help you draft product renderings and graphics about your product.
“AI image generation tools in the hands of experienced designers are particularly helpful for generating concept renders and photorealistic marketing materials,” says Chris.
Test designs
Generative AI can also speed up design testing by creating and running simulated product tests. AI can generate test cases and synthetic testing data, replicate real-world testing environments, and identify potential failure points.
Conduct market research
AI algorithms can help you validate your product ideas by parsing through market research, analyzing customer data, and examining market trends to extract valuable insights into customer preferences and market demand.
Create customer feedback loops
You can also use AI to automate customer feedback collection, process feedback, and extract valuable insights about customer preferences. AI algorithms can analyze user feedback to identify sentiment and themes. Prescriptive data analysis can recommend new product features or modifications to existing features that are likely to drive demand.
3 tips for using AI in your product development process
Integrating AI tools into your product development process can increase efficiency, improve your understanding of your target audience, and protect time for the creative process. This allows you to create unique and optimized products in response to identified market needs. Here are three tips for AI integration:
1. Have a plan
As you get started, be strategic about how and where you use AI for product development.
“Target the areas that are most critical to your business, or are your biggest challenges, or are areas you want to explore or grow into,” says Chris.
Chris suggests listing the areas you haven’t been able to explore because of limited resources or capabilities and looking for AI solutions to those challenges.
“There are so many AI tools that are somewhat hard to find unless you are looking deep in a specific service,” he says. "But once you find them they can dramatically improve some of the most critical aspects of your business or help you easily build out a new capability that traditionally would require too much effort."
Some popular AI tools for business include Otter.ai (transcription and note-taking), HubSpot (marketing), and Shopify Magic (copywriting and editing).
2. Experiment
There are many ways to integrate AI tools into the product development lifecycle—from automating repetitive tasks to brainstorming ideas to generating product documentation. Chris suggests experimenting with different technologies.
“Allow yourself the freedom and flexibility to try a variety of tools regularly, and give yourself the space to learn them and even sometimes fail,” he says.
You might find a new use for AI-powered tools or identify AI integration issues before they impact your product development processes.
“These AI tools are being created so rapidly and have so much power,” says Chris. “But they also have many shortcomings, which are hard to expect. Experimenting and allowing yourself to be curious and try a variety of tools is really the foundation to being successful in this rapidly growing landscape.”
3. Focus on the customer
Effective product development strategies meet customer needs, and integrating AI doesn’t change this. Chris encourages product development and product management teams to focus on consumer trends and expectations.
“I have seen AI generate so many concepts which look viable but would not be practical for a consumer and not manufacturable within any reasonable cost target,” he says, adding that losing sight of customer needs can result in letting down early consumers.
Problems arise when product development teams focus on leveraging AI’s capabilities to create groundbreaking products without considering customer expectations and market demands.
At the same time, AI can also help you prevent this outcome when used thoughtfully. You can use the technology to analyze market trends, identify customer needs, and predict demand for specific product features or functionalities at a given price point.
AI in product development FAQ
How does AI help in product development?
Artificial intelligence (AI) technology accelerates time to market by increasing efficiency and reducing errors throughout the product development and production process. Here are a few ways AI solutions can benefit your product development process:
- Automating repetitive tasks
- Generating ideas
- Analyzing customer data
- Identifying market trends
- Generating concept renders
- Simulating product testing
- Automating quality assurance processes
Can AI replace product design?
No. Effective product development relies on an experienced product development team. Your team can leverage AI algorithms as part of their product development strategy and use these tools to optimize product features and performance. Even the most advanced machine learning algorithms can’t replace a skilled product development team.
What AI tools are useful for product development?
Businesses can use several artificial intelligence (AI) tools as part of a product development strategy. Here are a few types:
- Generative AI tools built on large language models, like ChatGPT, can generate ideas and written content.
- Generative AI tools built on diffusion models, like Midjourney, can generate images and product renders.
- Machine learning tools, like Enzzo, can validate product designs and simulate testing environments.
AI-powered productivity tools like ClickUp can automate repetitive tasks.
