November 7, 2023
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Artificial intelligence (AI) is one technology that is used in almost every field, including software, education, retail, automotive, and healthcare. Therefore, an increasing number of industries continue to incorporate this technology into their day-to-day operations, and product design is no exception.
Businesses of all shapes and sizes today are leveraging AI and machine learning (ML) to launch new products or accelerate new product development.
To this end, we will learn more about AI in product development here, including its role, stages, examples, and applications in the field.
Role of AI in product development
The role of AI in product development is much more than just another way to speed up the processes. If used appropriately, AI technology can completely revolutionize the product development process and support many aspects of the product development life cycle.
Besides reducing bottlenecks, AI can automate tasks and make real-time data-driven decisions. Additionally, the technology can be used to:
- Perform detailed sentiment analysis, allowing product development teams to analyze customer emotions better.
- Enable product designers to fine-tune their product designs and correct any product flaws.
- Inspire product designers to generate new ideas and concepts.
- Simulate the behavior or performance of a product in real-world scenarios.
- Help brands and organizations gather strategic data on the latest market trends.
However, to reap the full benefits of AI in product development, businesses must first embrace a strategic, data-driven mindset.
The aim and focus here should be to ideate, brainstorm, and strategize how exactly AI solutions will help and support the product development aspect and the business as a whole.
It is important to remember that the role of AI in product development is essentially about leveraging tech advancements to stay updated and ahead of competitors by reducing time to market.
Why is AI needed in product development?
Data suggest that the AI development market is projected to grow from $86.9 billion in 2022 to a whopping $407 billion by 2027. This clearly reflects the need for companies to innovate and thrive amidst fierce competition and constant product launches.
Product teams must focus on efficient prototyping, designing, and development to stay competitive and cost-effective. If done right, AI and machine learning can emerge as game-changers in accelerating and improving new product development processes.
This can enable businesses to seize exciting and relevant market opportunities with unprecedented speed.
Where to integrate AI into the product development process
AI can bring multiple advantages to the product development process by improving efficiency, driving innovation, and reducing costs. However, the first question that product planners or managers have is where and how to integrate AI into product planning.
The first step in integrating AI into your product development processes is identifying areas where it can add value. Among these include tasks such as forecasting, data analysis, and decision-making.
For instance, you might use AI to analyze customer data during product development to identify trends and patterns and predict demand for your products.
Once you have identified specific areas where AI can bring value, develop a proper plan for incorporating it into your workflow.
Benefits of AI in product development
There are several examples of AI improving product development. In this section, we will focus on some of the key areas where AI is helping in developing a product.
Faster time-to-market
One of the key benefits of AI in product development is the ability to speed up the development process.
Since AI algorithms can analyze large amounts of data accurately and generate valuable insights informing product design decisions, companies can quickly create prototypes and work on designs faster.
This leads to faster time to market, streamlined by quick and accurate decision-making, thus allowing you to gain a competitive edge in the marketplace.
Cost savings
AI in product development is instrumental in helping companies save costs. AI algorithms can automate various time-consuming tasks, such as product testing and data analysis, which would otherwise require too much time and significant manual labor.
By reducing dependency and the need for human intervention in product development, companies can reduce costs associated with training new employees while improving overall efficiency at the same time.
Leads to process optimization in product development teams
Yet another advantage of AI in product development is process optimization, as AI can assist projects from start to finish. AI algorithms are designed to focus on identifying any issues that may impact the progress of a project negatively, followed by offering quick solutions to eliminate potential issues
Apart from this, AI also helps automate mundane, time-consuming processes and deliver error-free results for tasks that are otherwise prone to manual errors or mistakes.
Imagine a team building a computer program. AI can help by doing the following:
- Spots mistakes in the code and suggests how to fix them.
- Tests the program on its own to check if it works correctly.
- Makes suggestions to improve the code.
- Predicts when parts of the program might stop working and help fix them before they cause trouble.
Stages of AI product development
AI can be leveraged at various stages of the product development cycle, including ideation, conceptualization, manufacturing, and more. This has completely redefined the way businesses and companies innovate and bring their products to market.
Here are some of the most common stages of AI product development-
a. Identification of the business objective
This is the very first stage and the main driver of developing an AI product. It involves identifying a business problem that AI can systematically solve. The step also involves performing a detailed market search that requires the product teams to gather data on aspects such as target market, use cases, competition, and more.
To navigate the stage, there are several techniques you can use, such as scraping and crowdsourcing. Here, you also need to gain better visibility of your market’s needs and access the right market information to help you develop an actionable AI strategy and winning AI models.
b. Data preparation and exploration
The next step in the AI product development stage is to understand the collected data better and define the problem the AI product is trying to solve. The data here will help validate the need for either a new product or improve an existing one.
The most important part at this stage is to evaluate the need for complex tools like AI algorithms. These steps are important to build an AI model or prototype.
c. The design sprint
The design sprint is the stage where the product team comes up with a product that will meet the users' needs and requirements. The main goal of product managers at this stage is to visualize the product and find the exact type of products to be built, the type of data sets required, and more.
This is a very important stage of the product development process for brands and companies to ensure there is no wastage of resources on erroneous or faulty product concepts.
d. Deployment and maintenance stage
This is the last stage of product development, where the product can be deployed for operational use when proven to work as required.
This stage also requires you to monitor, review, and make changes to ensure that it delivers the desired results and adjust if it doesn't fulfill user requirements.
Use cases of AI for product development in different industries
This section explores some interesting use cases and examples of AI in product development across various industries.
a. Software technology
Requirements gathering is typically the first stage in the product life cycle and requires significant interaction between various project members. Functional requirements are gathered to give developers a better idea of product development tasks.
This process of manual requirement gathering is usually very time-consuming, and there is always a chance of missing out on key details.
Using the right AI tool helps automate the process, thus minimizing human effort while ensuring better data analysis. Statistics also suggest that companies using AI tools have reduced their requirements collection time by as high as 50%.
Watson AI, developed by IBM, is a great example here. The platform is useful for requirements management and effectively assists in other stages of the software development life cycle. The use of AI here eliminates or reduces the need to manually sift through time-consuming paper and digital documentation.
b. Consumer goods
AI not only empowers consumer goods companies to gain deeper insights into their customers, but also offers innovative solutions like AI image generators or AI Video Generators for conceptualizing products and product marketing.
Hyper-personalization, for instance, is one of the strategic themes in today’s consumer market. Every sports/apparel company or big retailer focuses on creating a relationship with the customer to get a fact-based understanding of their customers.
Data also suggest that the global sports analytics market is projected to reach a valuation of $22 billion by 2030, whereas the global artificial intelligence sports market is forecasted to reach $19.9 billion by 2030. This shows the use of AI in sports and its rapidly expanding global market.
Bayes Holding is an excellent example of a gaming company leveraging AI in product development and developing real-time coaching companion tools. The tool is designed to help aspiring players master titles such as League of Legends and Overwatch.
c. Hardware technology
AI-optimized hardware technology can significantly enhance the proficiency and speed of AI processes. AI models are equipped and designed to perform complex calculations much faster with the help of specialized hardware tools and other resources.
This allows faster training and inference times and improves real-time decision-making skills and overall AI system performance.
Among some of the examples of AI-optimized hardware are Tensor Processing Units (TPUs), Graphics Processing Units (GPUs), and Application-Specific Integrated Circuits (ASICs).
Tensor Processing Units, Google’s AI-optimized hardware, can accelerate AI workloads, especially those requiring tensor-based calculations, often utilized in deep learning.
In conclusion
With multiple applications, advantages, and use cases, AI has already made a breakthrough in product development. Since AI technology is continuously evolving, the future looks quite promising, with some interesting trends to watch out for in the AI product development space.
For instance, autonomous product design is one area that will allow companies to handle more complex product development processes without any human intervention.
Among some of the other interesting trends to look out for in the product development space is augmented reality (AR), where companies will be able to create immersive product experiences using AR, thus allowing users to visualize a product in a virtual environment.
Likewise, AI is going to completely revolutionize the overall product development process to help you design products that meet specific user requirements. For instance, you can easily analyze a user’s buying history using AI and recommend product features such as size, color, or type depending on their preferences.
If you are looking for an AI system to craft delightful user experiences, automate workflows across apps, and transform raw data into insights, contact OSlash to learn more.
