How can Generative AI boost your product strategy - and your company's value?
Generative AI is a branch of artificial intelligence that can create new content, such as text, images, music, or code, based on existing data and models. It has the potential to transform the way we design, develop, and deliver products, by enabling product designers and leads to generate novel and diverse solutions, optimise existing features, and personalise user experiences.
Meaning, it will become (and already is for some) an integral value creation lever in your product strategy.
1. EMERGENCE OF GENERATIVE AI
Generative AI is not a new concept, but it has gained a lot of attention and momentum, thanks to the advances in deep learning, cloud computing, and data availability.
Examples of popular Generative AI include GPT-3, a natural language processing model that can generate coherent and diverse texts on any topic; StyleGAN, a computer vision model that can generate realistic and diverse images of faces, animals, landscapes, and more; and Jukebox, a music generation model that can create original songs in various genres and styles.
These models demonstrate the power and creativity of Generative AI, as well as its potential applications in various domains and industries.
2. PRODUCT DEVELOPMENT
Generative AI can help product teams across the product development cycle, from ideation to prototyping, testing to launch. For example, Generative AI can help with:
Ideation: generating new ideas for products or features, by synthesising existing data and knowledge, or by exploring novel combinations and variations. For example, create new names, logos, slogans, or taglines for a product or a brand.
Prototyping: creating realistic and interactive prototypes of products or features, by generating high-quality content or code. For example, help create mock-ups or wireframes of user interfaces, or generate code snippets or scripts for web or mobile applications.
Testing: helping to test the functionality and usability of products or features, by generating realistic and diverse scenarios or inputs. For example, generate test cases or data sets for software testing, or generate user feedback or reviews for user testing.
Launching: helping launch and market products or features, by generating engaging and personalised content or experiences. For example, create catchy headlines or descriptions for product announcements or advertisements, or generate customised recommendations or offers for users.
3. DATA SCIENCE NEEDS & CONCERNS
Generative AI relies on large amounts of data and complex models to produce high-quality outputs. This poses some challenges and risks for data science teams, such as:
Data Quality: Generative AI requires high-quality data to train and fine-tune the models. This means that the data should be relevant, accurate, complete, consistent, and unbiased. Data quality issues can affect the performance and reliability of the models, as well as the quality and ethics of the outputs.
Data Privacy: Feeding generative AI models involves collecting and processing potentially sensitive and personal data from users or customers. This means that the data should be protected from unauthorised access or use. Data privacy issues can affect the trust and reputation of the products and the companies, as well as the rights and safety of the users.
Data Governance: Generative AI requires clear and transparent policies and procedures for managing and monitoring the data and the models. This means that the data should be documented, audited, controlled, and updated. Data governance issues can affect the accountability and compliance of the products and the companies, as well as the fairness and responsibility of the outputs.
4. VALUE TO SHAREHOLDERS
Generative AI can create significant value for shareholders – particularly private equity investors – by improving the efficiency, effectiveness, and innovation of product teams. For example:
Efficiency: Generative AI can help reduce the time and cost of product development by automating or augmenting some of the tasks or processes. For example, speed up the generation of ideas or prototypes, or reduce the need for manual coding or testing.
Effectiveness: Generative AI can help increase the quality and satisfaction of product development by enhancing or optimising some of the outcomes or results. For example, help improve the accuracy or relevance of ideas or prototypes, or increase the functionality or usability of products or features.
Innovation: Generative AI can help foster new opportunities and growth for product development by creating or discovering some of the solutions or insights. For example, help uncover new trends or patterns in data, or generate new products or features that meet unmet needs or expectations.
Valuation: Generative AI will impact the ultimate value of a company – and never more so than during the acquisition or sale process. For example, companies and investors need to carefully position and assess the entire data ecosystem to make sure that the data quality, privacy, governance, team, sources, and infrastructure (to name just a few of the data interdependencies) is appropriate and scalable to meet their investment thesis and valuation expectations.
Generative AI is a powerful tool that can enable product teams to create better products faster. By leveraging Generative AI in their product strategy, product teams can generate more value for their users, their companies, and – ultimately – their shareholders.