Generative AI has quickly appeared on the strategic agenda of many products. While far from perfect, the technology has achieved tangible breakthroughs, offering the potential for disruptive change. It even reminds people of the original iPhone in 2007 - although the product itself still has a lot of room for improvement, it marks the arrival of a new era of human-computer interaction.
So, how do technology products adapt to the explosive popularity of generative AI? The following five methods may be worth considering.
About 20 years ago, I worked with Clay Christensen of Harvard University on his Jobs to be Done consulting project. The main content was to help a company Technology giants introduce mobile electronics into business processes. The so-called "Jobs to be Done" is a set of query methods developed by Clay. The core is to help people figure out the difference between the jobs at hand and the jobs that must be done. The state of that technology company at the time was very typical: it was attracted by new technologies and wanted to take a risk. Clay's idea is to help the other party sort out the core motivations for change. So we started exploring where mobile electronics would perform better, ultimately identifying a handful of customer types and use cases, and then used Jobs to be Done to sort out how to make the most of the technology and what impact it would have on existing jobs.
But things are different now, and technology transformation is about more than understanding what customers want or splitting activities into tasks. Generative AI can even propose new possibilities that customers themselves have never thought of, thereby completely reshaping the shape of tasks. Therefore, we must maintain this open and rigorous thinking attitude and explore step by step the opportunities for AI to reshape the original business system.
For example, AI can currently help target advertising content to the most appropriate digital media. This is nothing new. Instead of focusing only on how AI can help media planners complete tasks efficiently (such as helping Facebook and Google allocate advertising budgets), it is better to take a step back and explore the possibility of change using the concept of Jobs to be Done. Can generative AI generate the best advertising creative based on different attributes, set appropriate budgets, and model the return on investment of advertising campaigns? This is certainly not simple, but it is feasible. What will be derived from this will be truly unique and highly customized creative digital advertising content.
In this rapidly changing new era, it is often very dangerous to base product planning on the present. Considering the changes that generative AI brings to user expectations, such as the subversion of the interaction between humans and machines, perhaps new opportunities lie within. Will future devices still offer menus? Are users willing to search manually in the software? Or will they get used to telling the computer what they want and then waiting for a tailor-made answer?
This change in preference will have a significant impact on the business. Although the degree of disruption is not as direct as pure solutions, the convergence of preferences from all aspects will also influence the future vision. People quickly become accustomed to new forms of software interaction, so it’s a good idea to observe and summarize what industry leaders are exploring. For example, how will companies like Adobe and Shutterstock incorporate generative AI into the experience of their own creative product suites? And what kind of changes in customer expectations will come from functions such as instructing AI through text to create customized images for content?
What we want to talk about here is actually a problem with two sides. Specifically, we need to consider both what generative AI can do for us and what we can do for generative AI.
Generative AI has a series of obvious advantages, such as excellent integration, personalization and engagement capabilities. We need to evaluate the impact of these advantages on user experience and even the core functions of the product, and use the power of AI to take it to the next level. For example, could generative AI suggest new actions for users to try that they haven’t done before? Can I preview the possible results of these operations?
On the other hand, we might as well think about how existing systems can help generative AI become better. AI systems rely on data. If everyone uses the same data, there will be no competitive advantage at all. On the contrary, after the introduction of proprietary data, enterprise-level generative AI for thousands of people is the general direction of the future. How can we use our own systems to collect and generate data that can help build competitive advantage? For example, can personalized experiences be better built with proprietary data, or solutions optimized with more precise value-based information? Can existing systems be used to label and classify data to help AI make better use of it? The data war is about to begin, and whoever has the best data will win.
The huge potential of generative AI is not limited to improving the interaction between customers and software (this is only the initial impact), but ultimately changing this everything. Therefore, we should uphold professional design thinking and be ready to update the original design plan at any time. After accumulating a certain number of existing experience optimization solutions, you can gradually figure out in which direction revolutionary disruption will occur.
To this end, we still have to return to the “must-do work” emphasized by Jobs to be Done. This includes not only the work content itself, but also factors such as motivations and obstacles to adopting new solutions, based on which detailed standard design is made. How can generative AI bring unprecedented capabilities to critical tasks? How can you provide customers with different paths to success on an emotional and functional level? Where can you create your highlight moments?
Although proprietary data can help us maintain a certain advantage in the AI competition, it cannot last long. Considering that AI may increase the efficiency of code writing and debugging to unprecedented levels, market competition is expected to continue to heat up. So what does all this mean for our product strategy?
Competitive pressure will come from all aspects. We need to carefully consider all feasible innovation vehicles, such as whether we can provide AI-assisted professional services to ensure that customers can succeed with our products and that the solutions can be closely integrated with the way customers do business. In addition, you should also consider how to build a complementary product ecosystem that is difficult for competitors to match. The addition of generative AI will not only change the intensity of market competition, but also change the specific face of sustainable business advantages.
The emergence of generative AI has accelerated the emergence of many people on the eve of the birth of the Internet. Yes, but the difference this time is that everything will change faster. As the AI revolution takes root rapidly, you may wish to make planning adjustments to your product strategy in advance through the above five methods.
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