Amid the rapid advancement of generative artificial intelligence technologies, many companies are wondering how to move this technology from mere experiments and ideas into effective solutions that enhance business performance and deliver true value. Despite the high expectations and optimism that accompanied generative AI upon its emergence, some analyses have shown that a significant percentage of projects relying on it are abandoned after the proof-of-concept phase, with one study predicting that at least 30% of projects in this field will be abandoned by 2025. This is attributed to what is known as the “trough of disillusionment” in the technology lifecycle, a phase where people feel that capabilities were exaggerated compared to results.
However, some companies have managed to overcome these challenges and proven that there are ways to translate this emerging technology into practical business value. By gaining a good understanding of the challenges generative AI can address, using the right technical infrastructure, and focusing on practical use cases that generate revenue, these ideas can be turned into successful and profitable projects. Here are four key steps to achieve this.
1. Identify the right challenge
getBounding the first step to achieving real value from generative AI experiments is identifying the appropriate problem that requires a solution. Successful projects in this field often begin with a precise understanding of a customer need or a common challenge that technology can solve in an innovative way. For example, travel companies can determine that trip planning represents a major challenge for many travelers. Using generative AI, tools can be developed to help users plan their trips easily and customize the experience to suit their specific needs.
A company’s inventory of customer data and available information can be leveraged to develop personalized and unique experiences. Data is analyzed using large language models (LLMs) and advanced recommendation tools, enabling users to receive tailored recommendations for tourist destinations, accommodations, and activities that match their personal preferences. By focusing on providing a satisfying and personalized experience for users, companies can attract a large number of customers and increase usage and engagement rates.
2. Use the right technology
Success in generative AI projects cannot be achieved without investing in the proper technical infrastructure. Running generative AI models requires efficient data collection and analysis, which calls for utilizing technologies such as cloud computing that allow data to be gathered in one place and analyzed centrally. For instance, dedicated cloud platforms can be used to analyze data in real time, granting the company full control over its data and enabling its effective use.
In addition, available commercial models like OpenAI’s GPT models can be combined with recommendation tools developed internally by the company to achieve a customized and effective recommendation experience. This blend of ready-made models and internal systems can contribute to delivering contextual recommendations tailored specifically to each user’s needs. This way, user data can be transformed into actionable insights that help significantly improve the customer experience.
3. Turn practical use cases into revenue streams
getBounding the ultimate goal of any tech project is to achieve tangible financial returns. When using generative AI to provide practical solutions, improving the customer experience leads to generating additional profits. For example, when customers receive personalized recommendations aligned with their interests, engagement rates increase, which extends the duration of customer visits to the site and opens opportunities to generate further revenue.
To illustrate this, consider the example of a vegan family of four looking for trip recommendations. Using the AI planning tool, the family will receive personalized recommendations for activities and restaurants that match their dietary needs and the children’s ages. This level of personalization increases user attachment to the service and raises the chances of them returning to use it again, which ultimately translates into financial returns for the company.
Generative technology enables higher levels of engagement by providing more accurate and personalized recommendations. Some studies have shown that AI-driven personalized recommendations can achieve up to three times the engagement of standard recommendations, helping companies secure greater profits by attracting and retaining users.
4. Plan for long-term innovation
Innovation in the field of generative AI should not stop at delivering current solutions; rather, there should be a long-term plan to explore new applications and maximize the technology’s benefits. For example, AI can be used to summarize long customer reviews left on travel sites, allowing users to quickly grasp the best highlights of each location and what to watch out for.
Furthermore, images available on the platform can be used to provide visual recommendations that align with user preferences. For instance, vegan users can be directed toward images of restaurants that serve meals fitting their needs, making recommendations even more tailored and relevant.
Investing in technologies like these is an important step toward enhancing engagement and user experience, and strategies such as image recommendations represent a fresh step in harnessing generative AI to achieve deeper and better user interaction. This forward-looking orientation ensures that companies stay ahead in the market by delivering innovative experiences aligned with customer needs.
in conclusion, generative AI experiments can be transformed into practical value by following clear steps that start with identifying the right challenges, utilizing appropriate technology and infrastructure, moving on to applying use cases in revenue-generating areas, and developing long-term innovation plans. These strategies ensure that companies make the most of this promising technology, turning it into an effective tool that boosts business value and delivers a better customer experience.
