In business, one of the hottest topics in recent years has been the potential of artificial intelligence to facilitate different areas of operations. There is a wide range of opinions about AI, from the enthusiasts who believe it will simplify absolutely every aspect of global commerce to the naysayers who fear the machines will rise and enslave humanity. We won’t attempt to predict the future of AI and humanity, however at Genimex, since we’re always looking for ways to improve our performance for our clients, we’re studying how AI might help us develop and manufacture amazing products. One key area is product development, that complex exercise of refining a concept until it’s ready for market. In this article, we’ll take a look at how AI might be able to assist companies that want to launch a new product.
The stages of new product development, assisted by AI
Companies launching a new product have to be excited about its potential rewards, but wary of the risks. For this reason, product development is a painstaking process covering many stages, as follows:
- Concept — The old business cliché that if you build a better mousetrap, the world will beat a path to your door still holds today. Thus, most new products are not radically new inventions, but improvements on existing products. Still, it’s hard to know what products might benefit from a little tweaking or a complete overhaul. AI can accelerate product discovery by helping companies examine market trends, conduct product research, and ascertain public opinion. If your company performs a SWOT analysis—Strengths, Weaknesses, Opportunities, Threats—AI can gather and analyze volumes of data quickly, helping you to discern where the greatest market opportunities lie.
- Research — Here, companies want to take a two-pronged approach, conducting market research to understand consumer sentiment in their sector, and competitor research to see what other companies are offering. Consumer surveys are an important aspect of market research, and AI can help you compose questions that elicit the critical information you need. In competitor research, AI can help craft user tests to see how consumers are using a product and identify usage gaps and areas for improvement. Then, of course, AI can rapidly analyze the responses to the surveys.
- Planning — Once you have a concept supported by research, you’re ready to plan how you will bring the concept to life and market the final product. Designers already use computer-automated design (CAD) to render 3-D images, and AI is already showing its ability to boost productivity. Tools such as Midjourney, Canva, DALL-E 2, and Adobe Firefly are currently helping designers create customized images, which also serve the company’s marketing efforts. And speaking of marketing, so much of its success depends on data analytics, which is AI’s forte.
- Prototyping — When it comes to building a prototype, AI-generated recommendations can suggest adjustments and corrections that improve the model, saving time and expense.
- Testing — Prototype testing enables the development team to scrutinize choices made in the design and material selections, and to uncover issues related to usability, aesthetics, durability, and other aspects of the customer experience. can support the building of a prototype with autogenerated design elements and animated tests that allow for virtual analysis before a physical product is even built. In the automotive industry, designers can simulate different driving scenarios and environmental conditions to optimize vehicle performance. Benefits include enhanced fuel efficiency, reduced emissions, and improved safety. Studies have shown that AI-enabled virtual prototyping could reduce product development costs by up to 30 percent. AI is also being used to minimize physical testing in the aerospace industry and refine designs of consumer electronics
AI can also design tests and speed up the analysis of data collected. AI also performs predictive analytics to map user interactions and expose areas of concern. In short, AI puts developers in the position to make reliable, data-driven design decisions early in the process, saving time and expense.
Design for manufacture — Once a company has a proven prototype, the question is whether that design is suitable for mass production at an appropriate price point. At this stage, can automate and speed up various tasks, reducing human error, and improving accuracy. Software engineers can integrate AI into CAD to reduce design iterations and improve accuracy. AI can also assess the CAD model for deviations from the design, which reduces the number of manual inspection cycles.
Where potential problems in manufacturing processes arise, AI can generate possible solutions, perform cost-benefit analyses of various alternatives, and adapt the design to new requirements.
Commercialization — Major retailers, such as Amazon, are investing heavily in AI marketing solutions. This software uses algorithms for segmenting and targeting consumers, as well as automating campaigns across various platforms. The software also collects and analyzes performance data, adjusting campaigns as needed to improve results. For marketers, generative AI is a powerful tool for creating advertising content including text, images, audio, and video. With all of these benefits, companies can optimize their marketing budget, getting the greatest possible bang for the buck.
Of course, even though AI is very useful for enhancing human knowledge, experience, and judgment, it is no substitute for these qualities. Anyone relying on AI has to be aware of its limitations. Chief among these shortcomings are:
- Reliance on yesterday’s data — AI draws on information that’s been uploaded to it. How long has it been since your AI program’s last knowledge refresher? In a fast-paced business environment, depending on old data can put you behind the curve. This is not some much an issue for design as it could be for marketing.
- Available data might be limited — The beauty of AI is its ability to analyze vast amounts of data quickly. But you won’t gain much of an advantage if there is sparse data available that’s directed toward your concerns.
- Algorithms might have blind spots — An AI program may not be designed to account for every concern your product development team must address. This can lead to frustration when you attempt to get answers on aspects of a complex product, such as safety, performance, aesthetics, and cost. Your team might have to try several different algorithms before getting satisfactory answers. AI might be an advanced computer program, but it’s still governed by the adage: garbage in, garbage out.
- AI is not entirely user-friendly — Getting answers from AI is a bit of science and a bit of art. Questions must be phrased in a way that the bot understands. This can require a great deal of. This situation is likely to improve as the technology matures. Many of us can remember the huge learning curve necessary to master personal computers before Apple developed a system most users could access right out of the box.
Perhaps the greatest concern with AI is that it will alter expectations, so companies will expect product development to magically happen overnight. Product development must go through its various phases, allowing time for reflection, thoughtful discussion, and prudent decision-making. If you want to bring a quality product to market, it’s still better to choose a team of experienced and conscientious humans than to rely on algorithms that are not dedicated to your success.