AI and Product development - Part 1.

 

This article focuses on the role of Artificial Intelligence in product development. Both in business and engineering, a new product development covers the complete process from concept to realization and introducing it to the market. There are many aspects and interdisciplinary endeavors to get a product and thereby a venture off the ground. A central aspect of this process is the product design, which involves various business considerations and is broadly described as the transformation of a market opportunity into a product available for sale. A product is meant to generate income and technological companies leverage innovation in a rapidly changing market. Cost, time, and quality are the main variables that drive customer needs. Business and technology professionals find the product-market fit as one of the most challenging aspects of starting a business and startups are often constrained to meet this long and expensive process. This is where Artificial Intelligence holds promises for startups and SMBs.

Since the product design involves predicting the right product to build and investing in prototypes, experimentation and testing, Artificial Intelligence can help us be smarter about navigating the product development course. Research studies cite that 35% of the SMBs and startups fail due to no market need. AI powered data analysis can help them to be more accurate with a well-rounded view of the quantitative and qualitative data to determine whether the product will meet customer needs or even whether the right audience has been selected in the first place. Collecting and analyzing are strengths of AI and in this case helps to connect with the customers at a deeper level. One such technique is often referred to as latent semantic analysis in AI which helps to articulate the real customers’ needs. Hidden matrix or latent semantic analysis or SoftMax classification was nearly unknown until 2013. The traditional way of creating software products, especially when it was technologically driven, attributed to the high failure rate. This is an opportunity to correct that.

Second, AI boosts the iteration and time to market cycles by plugging into the CI/CD pipelines and reports. Mockups and prototypes often take time in the range of a few weeks at the least as they overcome friction and unexplored territory. This is a fairly long period of time for all participants in the process to see the same outcome. The time and money spent to create and test a prototype could end up costing the initiative in the first place. If this period could be collapsed by virtue of better insights into what works and what doesn’t, reprioritizing efforts to realize the products, better aligning with a strategy that has more chance towards becoming successful, and avoiding avenues of waste or unsatisfactory returns, the net result is shorter and faster product innovation cycles.

One specific ability of AI is called to attention in this regard. The so-called Generative AI can create content from scratch with high speed and even accuracy. This ability is easily seen in the field of copywriting which can be considered a content production strategy. Only in copywriting, the goal is to convince the reader to take a specific action and achieve it with its persuasive character, using triggers to arouse readers’ interest, to generate conversations and sales. Copyrighting is also an essential part of digital marketing strategy with potential to increase brand awareness, generate higher-quality leads, and acquire new customers. Good copywriting articulates the brand’s messaging and image while tuning into the target audience. This is a process that has parallels to product development. AI has demonstrated the potential to generate content from scratch. The difference between content writing and copywriting remains with these product developers to fill.