From Apple Siri to Google Assistant, Artificial Intelligence has found its place in our daily lives in a seamless manner. It has brought some interesting and exciting changes to several business functions that were earlier heavily reliant on manual data processing and analysis.
Product development is one of them. According to Harvard Business School professor Clayton Christensen, each year more than 30,000 new consumer products are launched and 80% of them fail (Source).
McKinsey Global Institute, on the other hand, reports that for every 7 product ideas, only 1.5 launches, and only 1 succeeds (Source).
It is easy, to sum up, that product development is a tough nut to crack. However, not all hope is lost. Artificial Intelligence is offering some respite to the toughest challenges of all product developers and managers. It is bringing to the table some unique advantages that will positively influence product development.
Here are some possibilities that are on the horizon.
1. Significant improvement of bottom lines for digital services
There are two questions that every C-Suite member is pondering over:
- How do we make more money?
- How do we save more money?
Artificial intelligence offers answers to both questions. One, it tells where a significant amount of money is coming from. Of course, even the basic analytical tools or a quick spreadsheet analysis can tell that. However, AI can comb through millions of historical transactions and sketch a pattern in customer spending. It can give a forecast of when, how, and why customers spend on the products they spend money on.
For businesses, this is a gold mine of information. They can tweak their product strategy, create new pricing plans, move most-wanted features into higher plans, and make similar strategic moves. Ultimately, this will lead to a significant improvement of bottom lines for digital services.
2. Automated feature tests that accelerate product releases
Product development always faces a severe shortage of QA testers and analysts. The situation even forces some enterprises to outsource their QA requirements to third-party vendors. However, the quality is never at par with what an in-house quality testing team can offer.
However, a shortage of QA resources again forces the enterprise to consider other options. It is here that AI is positioned perfectly. AI’s capability to wring large volumes of data and squeeze accurate predictions out of it has a product development application.
Automated feature tests. With automated feature tests, product companies can accelerate their releases without being held back by the resource crunch in the QA department. An AI system can pore over thousands of user sessions and handpick bugs that even a human QA tester might overlook.
3. Optimizing product design through UX and design suggestions
Despite having several tenets existing around it. user experience is a subjective element. Even for the most experienced UI and interaction designers, creating a mobile or web application with the perfect UX is an uphill climb. It requires an in-depth understanding of the user’s pain points, expectations, aspirations, and ultimate objectives.
Even with a brief understanding of these points, it is necessary to have them validated so that a resultant UI can be created. Also, it is not feasible or is humanly possible for a UI designer to pore over the behavioral data of the end-users to validate the findings. But, Artificial Intelligence can.
An Artificial Intelligence system fed with data of past user interactions can run through multiple proposed user journeys and determine the one which has the maximum potential for a user completing the action. This helps in building better products without having to struggle through the multiple iterations that demand excess resources, and most importantly, time.
4. Improved product frameworks to reduce time-to-market
How do you replicate success? Actually, it is is difficult to replicate success. There are several variables that make a venture successful. However, in product development, these variables are usually predictable. They can be planned for and accounted for which makes it easier to replicate success in product development. In product engineering parlance, these are referred to as frameworks.
A framework is a basic structure underlying a system, concept, or text. It gives a direction as to how to do something the right way. From manufacturing to software development and even marketing with its countless moving parts, frameworks help bring a level of consistency to daily operations.
Even in product development, a number of frameworks are used to accelerate version releases. However, this pace can still be increased with the help of Artificial Intelligence. A good example of this is the AI tool developed by IBM for McCormick and Cerealto Siro Foods.
The tool enabled the food producer to create new flavors based on AI recommendations which sourced data from existing proprietary data and publicly available information on social media.
5. Demand and price elasticity forecasting
One of the advancements in AI is neural networks. Thnk of neural networks as similar to the human brain. They have countless connections and interconnections that make them intelligent at sharing information and decision-making. When fed with historical data, they can conduct a deep analysis of patterns and forecast fluctuations in demand and prices.
It will enable a product company to upscale or downscale production accordingly to boost profits or avoid losses. Similarly, AI systems can also help in making better operating cost decisions, identify cheaper vendors, and so on.
The growing influence of AI on product development
Artificial Intelligence, as we know it, is no longer an enigma. With eerie passing day, we are able to harness its power in a better way and with a better understanding of the implications if something goes wrong.
Product development, which has been for a very long a manual process-driven activity has also come under the positive influence of AI. AI brings to product development unique benefits that no product manager or business can turn a blind eye to. It is taking product development closer to a future where decisions are not made on impulses or guesswork but based on factual data that has been subject to deep analysis.