Exploring ambiguous situations: AI crossroads
In addition to opportunities and limitations, AI also presents several ambiguous situations or "crossroads" that businesses need to consider. These crossroads involve areas where the implications of AI are not entirely clear, requiring careful navigation and strategic planning.
The future of innovation
GenAI will impact innovation differently in each sector. In a manufacturing context GenAI is applied differently than in a software engineering context. The traditional innovation processes are at a crossroad. There is general consensus that GenAI will deeply impact the future of innovation management as well as creativity processes at companies. How this impact on innovation processes will exactly take place is still uncertain and subject of discussion and research.
Examples & reflections:
- A pharmaceutical company uses GenAI to accelerate drug discovery. While this leads to faster development times and potentially life-saving treatments, it also raises questions about the changing role of human researchers and the ethical implications of AI-driven discoveries.
- A manufacturing company adopts GenAI to design and optimise new product prototypes. This accelerates the product development cycle but raises questions about the changing roles of human designers and the ethical implications of AI-generated designs in terms of ownership and originality.
- Organisations need to be ready and prepared for disruption in this field. Will it be extremely disruptive and will machines monopolise innovation? Or will the impact be an explosion of AI-based innovation tools to support creativity? Or will it be something in between?
Prompt Engineering is not the future
Prompt engineering is the practice of text describing what an AI system should produce. Although it currently receives significant attention, its importance might be temporary. "Problem formulation"—the ability to identify, analyse, and define problems—will be a more sustainable and adaptable skill. This skill helps in fully harnessing the potential of GenAI.
Examples & reflections
- A software engineering firm focuses on developing robust problem formulation skills among its employees, ensuring they can effectively leverage GenAI to address complex software development challenges.
- Dream school is an online platform that offers “standard” prompts for GenAI related questions in the domain of education. Teachers do not have to write prompts themselves, but start from the many examples provided on this platform.
A Sustainability Problem or a Sustainability Solution
The green transition will be digital, with the 'twin transition' referring to the integration of digital innovation and environmental sustainability. Digital technology, particularly AI, can help address sustainability challenges. However, digital solution builders face growing concerns about the environmental impact of their technology, such as energy and water consumption.
Examples & reflections:
- Builders of digital solutions are increasingly being questioned and criticised about the (hidden) environmental impact/cost of their solutions (energy consumption of digital technology, impact on ESGs, water consumption, …). For them this is a relatively new concern that lingers since a couple of years.
- The massive adoption of GenAI requires a colossal amount of energy and water consumption. Not only for training the models, but also for executing the user prompts: a typical ChatGPT prompt consumes between fifty and ninety times more energy per query than a conventional “Google” search.
- Adidas Futurecraft: Adidas used generative design to create the Futurecraft Loop, a 100% recyclable running shoe. The AI-generated design focuses on using a single material type and optimising the shoe structure for performance and recyclability, reducing waste and environmental impact.
- Generative engineering at Siemens: Siemens uses generative AI to design more efficient parts and manufacturing processes for industries such as aerospace and automotive. The AI-generated designs use less material and result in lighter, stronger components, reducing waste and energy consumption during production.
These ambiguous situations highlight the complex landscape businesses must navigate when integrating AI into their operations. The Sirris AI Insight Chart can help companies to understand and address these crossroads, providing guidance to make informed decisions and strategically plan their AI initiatives.
Define the GenAI gamefield in your company
OPPORTUNITIES
Generative AI (GenAI) will revolutionise the way businesses operate, by making advanced AI technologies more accessible and enabling transformative customer experiences and self-learning applications. Let's delve into the specific opportunities GenAI can bring to your company.
SHOW STOPPERS
While AI offers numerous opportunities, it is essential to recognize the challenges that you, as a business, must navigate. There are two significant constraints to consider.
