AI is ushering in one of the most exciting periods ever in the innovation sector. In fact, you could argue that we’re seeing an unprecedented revolution related to AI in new product development. Product managers are using the technology to understand their customers better, create the most robust analytics for their products and improve iterations in product development.
For me, AI-related ‘a-ha’ moments occur almost daily. Many of those moments result from conversations with a diverse group of innovation experts who bring unique points of view. On the Innovation Talks podcast, I recently had the pleasure of chatting with William Mulholland, Senior AI Product Leader at the AI Accelerator Institute, an organization dedicated to connecting and empowering passionate AI infrastructure innovators. He is also a product leader member of the Product-Led Alliance.
William shared a number of insightful suggestions during our chat to help companies navigate AI in new product development. The following four tips stood out.
Maintain a culture of curiosity and adaptation
I love this quote because it gets to the heart of innovation. While AI can simplify many tasks, it cannot replace the innate human desire to discover something new, especially when external forces require us to pivot. AI can help us develop new ideas and undoubtedly make fast transitions less painful, but it cannot innovate. That requires humans.
Invest in a strong foundation of data and talent
This touches on the ‘garbage in, garbage out’ philosophy. You have to have real, not anecdotal, data about your customers, their challenges, your ability to solve them, portfolio needs, etc. Brilliant analytics + bad data = more bad data.
Employing an InnovationOps approach is one way to create conditions for accessible, accurate data because the philosophy encourages an infrastructure of collaboration and real-time data input. And as we mentioned previously, you need the combination of good data and talented people to ensure AI exercises yield the best ROI.
Align business goals with AI implementation
Regardless of the application, this insight should serve as connective tissue throughout an organization. Every product — and decision, for that matter — should support overarching goals and missions. Use of AI is no different. An ‘adopt first, learn later’ mentality is common when a new technology takes off and everyone (specifically competitors) is using it when you are not.
However, fear of losing the ‘arms race’ shouldn’t supersede the intelligent application of technology. Move forward with caution. If employing AI doesn’t align with the business objectives supporting your unique place as a market differentiator, go back to the drawing board. It may take more time to determine how to harness AI, but you’ll waste a lot less time and, in return, find the best use of technology as it relates to your organization.
Lead with empathy and ethics in AI product development processes
Business can be cut-throat and impersonal, but it doesn’t have to be. I’d argue that the best product development makes empathy a priority. As companies rush to understand how to use AI to improve innovation processes, they must also ensure that the data they collect and use does not compromise the privacy of the customers they’re trying to serve.
Even further, empathetic product design isn’t something AI understands. This input can only come from humans. As companies implement AI into their processes, it’s critical to incorporate ethical concerns for others continuously.
AI is just a tool. A powerful one, but a tool nonetheless. The organizations that find the best way to use it will do so by keeping customer challenges, business objectives and human curiosity at the forefront.
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