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Generative AI is quickly emerging into many product strategy agendas. Although the technology is imperfect, it has reached a stage of development and offers the potential for change. It’s reminiscent of the original iPhone — a product with plenty of room for improvement, but clearly on the way to revolutionizing human-machine interaction.
How should product strategy adapt to generative AI? Here are five key tasks to get you started:
1. Really dig in to finish your clients’ jobs
Twenty years ago, I worked on my first completed consulting project with Harvard’s Clay Christensen for a large technology company looking for ways to bring mobile electronics into enterprises. It ran the risk of becoming a classic case of technology-push, but the company wanted to do better. So, instead, we focused on what tasks mobile electronics can perform well, discussed a few customer types and use cases, and then dug deeper to find critical insights into where the technology can have the most impact in these contexts. .
Then, as now, grappling with transformational technology isn’t just about asking customers what they’re trying to do or breaking down activities into actions. Generative AI can raise new opportunities that customers haven’t considered, and completely reshape the way they do things. Be open-ended in your questions and very rigorous, using work to be done to get the details of how AI is reshaping generative long-term approaches.
For example, AI currently helps direct display advertising to the most appropriate digital media. That’s not new. Instead of focusing solely on how AI can help media planners better perform tasks like deciding how to split ad budgets between media properties like Facebook and Google, step back and use tasks to explore revolutionary opportunities as well. Can generative AI suggest ad creative that performs best across these different assets, sets an appropriate budget, and shapes the ad campaign’s ROI? It might not be easy, but maybe yes. That translates into unique, highly customized digital advertising creative content.
2. Understand how customer preferences are changing
In times of rapid change, planning products is risky. Consider how generative AI will change expectations, such as the ways people interact with machines. Do people want to see it in menus? Want to do searches in a software? Or do you tell the computer what you want and expect to get a customized answer?
Think about how changing preferences will affect your business. An analysis of the tasks to be performed can provide direction, as these tasks may not change as much as the solution set does. However, choices exist on a different plane and affect the picture as well. People quickly get used to some form of interaction with their software. It might be useful to check the comparisons and see what the leaders ahead of the curve are doing, such as how companies like Adobe and Shutterstock are integrating the experience of using their creative suite products.
3. Understand where the benefits of generative AI relate to your business
Here you need to see two sides of the coin. What generative AI can help you, and what you can do for generative AI. Let us explain.
Some of the benefits of generative AI are obvious: for example, it can be integrated, personalized, and well-engaged. You want to evaluate how these benefits change the user experience and core functionality of your product. But you can go further. Can generative AI suggest new actions that the user may not have considered? Can he give a preview of what might come of those actions? Push the envelope.
On the other side of the coin, determine how your systems can make your generative AI better. AI systems are fed by data, and if everyone is using the same data, competitive advantages will be fleeting. But enterprise-level generative AI systems, feeding on proprietary data, are coming in fast. How can you use your systems to find and create data that gives you an unfair advantage? For example, can you access proprietary data to better tailor practices or improve problem solving with more accurate information about the value of outcomes? The data wars are coming, and the companies with the best data can win.
4. Fundamentally rethink the customer journey and experience
Generative AI’s greatest potential lies not in improving customers’ interactions with software (although that may be an initial effect), but in transforming them. This is where you want to deploy knowledge in design thinking to get a fresh look. After you’ve extracted quick wins by optimizing your current experience, decide what could be revolutionary.
To get there, go back to your understanding of the tasks to be done. You can determine the detailed design parameters of the solution, not only about the tasks, but also using related factors such as triggers and barriers to adopting new solutions. How can generative AI provide truly innovative ways to perform key tasks? How can it offer different ways for customers to succeed on an emotional and practical level? Where can he offer happy moments?
5. Re-evaluate your competitive strategy
Proprietary data can help you stay competitive in an AI-enabled world, but it may not be enough. We can expect competition to heat up as AI also enables code to be written and debugged faster and cheaper than ever before. What does this mean for your product strategy?
Competitive strength comes from many sources. Consider all the possible creative vectors available. Can you provide AI-powered professional services to ensure customer success with your product and connect it to the customer’s way of doing business? Can you become the cornerstone of a complementary supply ecosystem that rivals can’t root for? Ask how AI will transform not only competitive strength, but sustainable advantage.
The advent of generative AI has been compared to the dawn of the Internet, but there is a crucial difference: this time, it moves even faster. As change rapidly takes root, you can use these five approaches to be on the front foot in your product strategy.
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