AI Will Revolutionise Product Development
“How AI is Set to Transform Product Development, According to AWS’ Senior Advisor to Startups
- Enhanced Accuracy in Predicting Product-Market Fit Mishra’s extensive experience has shown that many startups falter due to inadequate product-market fit. This aligns with broader statistics, indicating that a staggering 35% of SMBs and startups fail because they fail to address market needs.
Fortunately, AI offers a solution to this challenge. AI-powered data analysis empowers startups to gather a more precise and comprehensive dataset, encompassing both quantitative and qualitative aspects. This data is crucial in determining whether a product genuinely caters to customer demands or if the initial target audience was misconceived.
Leveraging AI during data collection and analysis also enables teams to gain a deeper understanding of their customers. As Mishra aptly puts it, “AI simplifies the process of uncovering the genuine customer needs concealed beneath apparent issues. Often, engineers commence prototype development without a profound comprehension of the quantitative and qualitative aspects of customer requirements. Before the advent of generative AI, tools for such analysis were less capable.”
- Accelerated Iteration and Reduced Time to Market Creating prototypes and mockups for product testing represents one of the most time-consuming phases in the product development lifecycle. Crafting an electronic prototype typically spans four to twelve weeks, while a 3D printed mockup can take anywhere from one to four weeks.
As Mishra explains, generating a physical or even visual representation of a product involves intricate processes rooted in physics. Product managers, designers, and software engineers face a substantial challenge in transforming a concept into a three-dimensional model.
In simpler terms, the extensive time and resources invested in creating and testing prototypes could ultimately jeopardize the viability of your business.”
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Consider the potential of a world where AI can assist in generating mockups and prototypes within mere hours.
This rapidity extends beyond mere convenience; it could be a lifesaver for small and medium-sized businesses (SMBs) and startups that cannot afford to squander time and resources on product elements that may not deliver substantial returns.
For Mishra, this stands as one of the most thrilling avenues for innovation within the realm of product development.
In his words, “The capability to craft content from scratch at such lightning speed while achieving a higher level of precision is undeniably one of the most exhilarating aspects of this advancement.”
- AI is poised to revolutionise the process of gathering customer feedback. Once you have a prototype or even a minimum viable product, the journey of iteration doesn’t end there. It becomes essential to conduct testing with prospective or existing customers to uncover insights for further improvement.
Historically, the realm of product analytics has primarily focused on structured or numerical data. However, structured data has its inherent limitations.
Mishra pointed out, “A significant portion of enterprise information exists in unstructured forms, such as documents, emails, and social media conversations. I would estimate that less than 20% of a business’s data is structured data. So, neglecting the analysis of that remaining 70% to 80% of information incurs a substantial opportunity cost.”
In essence, there has been a scarcity of scalable solutions for collecting and analyzing quantitative data to gauge customer responses to your product.
Presently, many product teams resort to using focus groups to gather feedback. However, focus groups don’t consistently provide accurate reflections of customer sentiment, exposing your product development efforts to the risk of creating a product that doesn’t align with your customers’ needs.
Thankfully, as Mishra explains, “Generative AI has the capability to transform customer feedback into actionable data for your business. Imagine you receive substantial social media feedback, product usage comments, or discussions on customer forums. Now, you can convert this information into comprehensible charts and trend lines, subjecting it to the same analysis as you would structured data.”
He further elaborates, “Essentially, you can discern which product features customers are discussing most frequently or identify the emotional responses associated with specific product elements. This empowers you to ascertain product-market fit and make informed decisions about adding or removing features from your product.”
The potential impact of the ability to convert quantitative feedback into actionable data is monumental.
With AI’s assistance, your team can gain greater confidence in the investment of time and resources into the product features that hold the greatest significance for your customers.