6 Generative AI Focus Areas We are Looking at in the Upcoming Foundry Cycle
Arttu Närhi
The possibilities of generative AI are virtually limitless at this point. We don’t want to rule out any ideas yet, so we seek solutions based on broad focus areas. Each one features numerous case examples provided by the participating companies, which we will publish on September 15th.
These areas have been chosen for their relevance to industrials, potential for development, and the specific needs of the corporations participating in the cycle. Check them out!
1. R&D and Product Development
“Machine learning”, “deep learning”, and other terms all have it in their name already – the very foundation of AI technology is based on research tools. Moreover, the research needed in product development, i.e., going through large amounts of test data that can come in various forms, is perfect for generative AI to handle.
Generative AI solutions can help at all stages of product development. From material discovery and generating efficient designs with the new solutions, corporations can streamline simulations and testing, ensuring thorough data collection and management. In the big picture, we envision solutions to further improve the efficiency and sustainability of these processes with generative AI.
2. Manufacturing and Production Optimization
Manufacturing has always been the domain of large industry incumbents. However, with the emergence of new technologies, including 3D printing, automatization, and augmented reality, many corporations turn to startups to get a head start utilizing new solutions and developing a competitive advantage. Generative AI is no different: it has the potential to propel 100-year-old corporations to new heights if they find the right partners to work with.
Optimizing production and supply chains are essential challenges for any global manufacturing business. Recent years have shown an increased need to sure up these processes to protect business interests. Generative AI can assist in improving efficiency, planning maintenance, and gathering expert knowledge to guarantee retaining decades of experience within the company.
3. Software Engineering
Coding is one of the best-known examples of practical generative AI applications. Writing code can be an arduous task, not to mention reviewing it. Moreover, for organizations not specializing in software, such tools can be a godsend, as all companies inevitably have to utilize software.
Recent research has shown generative AI to boost developer productivity, but the technology cannot replace humans in this field. Therefore, industrial generative AI solutions must work together with human experts to guarantee the best possible outcomes. The concept of an AI Co-pilot is already known among developers in a general sense; specialized co-pilots for specific industry needs are likely development goals for many corporations.
4. Sales and Marketing
Beyond coding, many of the latest generative AI products and services have direct marketing applications: content generators like ChatGPT and DALL·E have been most utilized by creative experts to speed up their own work and help with inspiration. However, like coding, generative AI needs a human counterpart to ensure content integrity and uniqueness.
What has been less covered is sales support with generative AI. Sales operations consist primarily of unstructured data related to lead generation that generative AI technology could hack through in moments, compared to human experts. Companies spend billions on lead generation annually, which is forecasted to keep growing. Deploying new technology will, without a doubt, inflate this growth further. Sales teams will also benefit from demand forecasting and customer insights that can be created with the help of generative AI.
5. Customer Success
The era of voice recognition customer service lines and simple chatbots might be coming to an end. Generative AI has opened up possibilities for much more lifelike and “smarter” customer service automation that can help B2C and B2B sectors alike.
However, customer success does not stop at reacting to new circumstances. Proactive troubleshooting and AI assistants for technicians in the field are lucrative areas for heavy industries. Along with optimized user interfaces and supporting predictive maintenance, the need for customers to call up service agents in the event of predictable breakdowns might shrink down entirely.
6. Internal Operations
While not strictly speaking a field in itself, internal operations still covers plenty of focus areas where generative AI has great potential. This can include HR, legal and compliance, communication, cybersecurity, data management, and documentation, to name a few examples. But in the big picture, anything that helps a large company run more smoothly is highly interesting.
We will publish our specific case examples and participating companies tomorrow, September 15th. Make sure to follow us on LinkedIn and Instagram to get the latest updates!