Artificial intelligence (AI) created loads of opportunities for market researchers working on new product development. We can now automate certain aspects of insights, streamline the innovation process, and interpret vast amounts of consumer data quickly. 

But just like with any new technology, we should always be wary of their risks. In this post, we will explore the benefits and drawbacks of using AI to develop new products and offer some guidance on how AI can help make your job much easier.

Here are the benefits and drawbacks we are going to cover:

Benefits: 

1. Faster and more efficient research
2. Access to more creative ideas
3. A better understanding of your target audience

Challenges:

1. Integration and adoption of AI technology
2. Data privacy and security concerns
3. Ethics and accountability of AI decision-making

Benefits of using AI in product development

1. Faster and more efficient research

AI has proven to be really useful for streamlining the research process. With the help of AI-powered algorithms, insights teams can now perform predictive analytics faster, automate repetitive tasks and summarize large chunks of data.

For example, Dig Insights, the market research consultancy that created Upsiide, uses tools like Canvs AI to analyze and summarize open-ended responses. With qualitative data often considered hard to examine (because it usually takes a long time if done manually), AI helps reduce the load for researchers so that they can focus instead on developing a better product concept.

2. Access to more creative ideas

Do you find yourself opening a Google doc and having no clue how to start brainstorming? This is the norm these days, and AI is your friend in this situation. 

A lot of AI engines we see today, such as Open-AI’s ChatGPT, were built with that in mind. They are perfect for the early stages of product development because they help you get through writer’s block, suggesting stuff you would never think of. 

Even if none of these ideas apply, they help you think more critically and creatively. But if you use the right prompts, you can even get ideas that can easily compete with yours. 

We recently ran a study where we tested AI-generated ideas against human-generated ones to see which would win out. We collected 20 dipping sauce flavor ideas from Chat-GPT and our team and tested them on Upsiide.

Even though the winning idea, Smoked Crispy Bacon Crunch, came from a human, AI showed some strong competition throughout the test. Maple-Bourbon, which was ChatGPT’s idea, came in second place. Curious? Find the full breakdown of the study here.

3. A better understanding of your target audience

Chatbots are perfect for getting a sense of how your customers feel about your new product. For example, when doing qual research, chatbots can ask respondents to give you more detailed answers or follow up on specific responses. 

Not only does it make it easier to conduct some aspects of qualitative interviews online, but it also allows your product development team to get precise answers from customers more quickly.

Challenges of using AI to develop new products

1. Integration and adoption of AI technology

Learning AI technology can be tricky for companies new to AI. If you have ever used ChatGPT or other AI alternatives, you know that it takes some practice to nail down your prompts. Sometimes, the tool is excellent at picking up complex requests. And sometimes, well… not so much. This might be because the system is still learning to process human language or just doesn’t have the latest data.

Plus, it takes some time for teams to change their habits and take AI seriously. When thinking of the ideation or brainstorming process, they might feel that it’s easier to come up with new product ideas themselves or turn to the product marketing team. 

And in general, you’d need to persuade your team to take some time reading up on resources to understand how AI can benefit their product development processes, identify use cases where AI can add value, and implement the technology effectively. 

Upsiide’s answer: Just like with any new technology, be ready to allocate some time and money to teaching your team how to make AI work for you.

Invest in education and resources to build internal AI expertise – maybe enrol them in a special AI adoption program for beginners. Offer to develop a pilot project to apply AI in a limited and controlled manner – this can allow skeptics to get used to AI and validate that it really can solve for or help with daily tasks (e.g. brainstorm ideas).

By educating stakeholders on how AI can benefit their specific business and use cases, you can persuade your team to use AI more often in their work. 

2. Data privacy and security concerns

Data privacy and security are also major concerns for AI systems that have access to sensitive customer data or proprietary company information. Companies must ensure that AI systems are secure, compliant with regulations, and do not compromise users’ privacy or intellectual property. This requires robust data governance and security practices.

Upsiide’s answer: Okay, this one’s kind of tricky and will depend on your internal systems when it comes to data privacy and security measures. As a rule, having some sort of governance around the responsible usage of AI is crucial. Those can include policies on data privacy, security, bias, and accountability. If possible, create an oversight committee that can review AI systems, address issues proactively, and determine if and when to scale the technology throughout the organization. 

3. Ethics and accountability of AI decision-making

As AI is integrated into critical business decisions, questions emerge around accountability and ethics. For example, who is responsible if an AI system produces harmful or unfair outcomes? How can companies address biases in AI algorithms and mitigate unfairness? These are complex issues with no easy answers and will require continual effort to manage responsibly.

Upsiide’s answer: The solution to the question of ethics in AI is still wishy-washy. So it’s pretty hard to give a definitive solution because there’s always a risk of bias. But if the team isn’t sure about whether AI is safe to use, we’d recommend limiting the usage to more simple tasks such as automating some aspects of product development, e.g. brainstorming ideas or summarizing survey data.

To conclude

While AI promises to make product development faster and more efficient, it also introduces new challenges that companies must consider seriously. By educating teams on how to leverage AI responsibly and establishing proper governance, these challenges can be addressed.

Though the solution to ethics in AI is pretty elusive, continual effort and reflection are key. When you aren’t sure about using AI for a particular task, maybe limiting its scope or conducting pilot projects can help build trust.

Overall, just bear in mind that AI should augment human capabilities, not replace them. With the right approach and administration, AI can enhance creativity, provide deeper customer insights, and streamline product development processes – all while keeping that crucial human touch.

The possibilities of AI for product development are exciting, but only if we are willing to have honest conversations about its risks and responsibilities. By being transparent about the pros and cons of AI, we can build a future with this technology that is ethical, inclusive, and beneficial for both businesses and our community.