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How AI Development Is Improving New Products Design and Development

Artificial Intelligence and its subset Machine Learning are constantly pushing the boundaries to establish roots across industries, from automotive to pharmaceuticals, retail to education, manufacturing to energy and utility. Markets&Markets research has proven that the AI development market, which was valued at USD 21.46 billion in 2018 will hit $190 billion mark by the year 2025.


With the speed of technological changes happening faster than ever, companies must innovate to thrive in this cut-throat competition where everyday a new product is launched. In fact, prototyping, designing, and developing must be efficient to stay competitive and cost-effective. 

A Korean biotech company named Seegene, for example, built a COVID-19 testing kit using AI technology. The company reports that AI shortened the development time of test kit from months to weeks.

To get new products launched with the speed of a market opportunity, AI and machine learning are making the way open to accelerate and improve new product development process.

AI for New Product Development

New product development is devising a market ready product that answer an opportunity or a real-world challenge. In the current times, AI developmentcan accomplish a lot of actions accurately. One of the major advantages of AI superseding human experiences can be seen in launching higher quality products. Turning to AI software development can simulate and accelerate the development process, upgrade manufacturing lifecycle with better products, superior design, and enhanced performance standards.

AI can be grouped into General AI (GAI) and Applied AI (AAI). While General AI includes Machine Intelligence, Applied AI covers machine learning and predictive analysis. AI is tapping into every aspect of product development. Therefore, putting AI into an architecture is not mere a need but a mandate.

Time is a Crucial factor in New Product Development

Typically, it takes several years for an idea to go from conception stage to commercially valuable product. In the early phases of the development, potential product opportunities are created. Then, commitments are made for resource allocation. And there are chances that resources to those potential products may lead to potential future failures. The inability to foresight during this phase of product development is painful in terms of time spent. Time is a crucial factor when it is about development of new product. As it accounts for a large portion of a company’s profits and hold a strong competitive advantage, rolling out new product with speed is utmost important to a company.

Artificial Intelligence can be implied to accomplish digital testing and predictions of prototypes before a company allocates time and teams on running the physical product trials.

Top 5 Ways AI is Improving Product Development

With a tsunami-like power, AI and ML continue to transform the way we engage and navigate the world. Advances in AI-based apps, force the convergence of other revolutionary technology like IoT to support and improve new product development process. Let’s take a closer look at how AI helps in uplifting new product development.

Driving Efficiency Using Product Lifecycle Management

A research conducted by PwC found that digital product development is expected to uplift efficiency by 20%, reduce product launch time by 17%, and production costs by 13%.  Fast launch of new products needs an integrated partner ecosystem that incorporate a culture of co-creation where multiple stakeholders and partners work together. AI algorithms produce accurate results when they are fed with high-quality data.

Strategic partnerships are crucial in augmenting data quality, which further impacts prediction accuracy. For example, Amazon teamed up with a few clinics in the USA to train Alexa to understand different kinds of human sounds like coughing, hawking, sneezing, etc. Thus, digital product development teams that work on AL & ML achieve greater efficiency and speed gains in the product designing and development stages.

Improving Demand Forecasting Accuracy

Eliminating the roadblocks to release new products faster starts with implementing AI technology to improve demand forecast accuracy. For example, Honeywell, uses AI to reduce energy costs by tracking and analyzing product price elasticity. The company is leveraging AI and machine-learning algorithms into various operations including procurement, cost management, strategic sourcing to receive stable returns across the new product development process. Businesses are keeping the race on to predict future so that they can reverse engineer to adjust or re-imagine supporting functions.

Another excellent example is XpresSpa, world’s largest airport spa chain, that implemented AI analytics to eliminate uncertainty of footfalls in forecasting sales on any given day. The algorithm analyzes the relevant data points that directly and indirectly affect the throughput of spa customers and predicts the sales of a location in their new application.

Fueling up Cross-sell and Up-sell Opportunities 

It has become common amongst businesses to work on data-driven new product development using propensity models (used to predict the likelihood of certain actions performed towards purchasing a product). Such propensity models are based on imported data obtained from Microsoft Excel which is time-consuming. Putting AI into the setup can streamline creation of the propensity model and can fuel revenue contributions derived from up-sell and cross-sell strategies. The below figure shows a propensity model created using Microsoft Power BI.


Reducing Time-to-market and Improving Product Quality

Now it is possible to synchronize better suppliers, technical teams, Devops, product management, marketing, sales to achieve success for a new product in the marketplace. BMC’s Autonomous Digital Enterprise (ADE) is one of the excellent examples that delivers next-gen business models for forward-thinking organizations that are interested in reinventing their businesses with AI/ML capabilities. This ADE framework is flexible enough to respond to customer requirements than other competitive frameworks. BMC's ADE framework is actually the future of digitally driven business frameworks that can support scalability for new product development.

Providing Recommendations on Improving Product Usability

Generally, DevOps, engineering and product management teams run A/B tests and multivariate tests to find out usability features, workflows, app & service that customers enjoy. In fact, one of the most challenging aspects is designing an intuitive user experience that turns out to be a strong product with effective usability. AI algorithms like generative design will help identify design constraints and provide insights around product usability to make the experience engaging than ever before.

Final Thoughts

AI offers agile companies the decision competitive advantage for new product development to gain a strong foothold in the market. With so many benefits including lowering the cost of doing business, allowing automated testing of features, assisting with user experience and design, AI is reshaping the product development cycle. Companies turning to AI/ML capabilities are not just focusing on algorithms, programming, and data engineering, in fact, they are paying attention to underlying data models that make the entire development process possible and efficient. 

As one of the leading artificial intelligence companies, we at Icreon, help businesses get to market faster and improve competitive position while reducing manual labor & guesswork in their next product development journey.