Design
Augmented Scientific Co-Pilot
In recent times Generative AI (GenAI) has been a big hit with the public, but it must be said that it’s largely been a novelty item. It’s in business that it’s likely to make its greatest impact, where it can be used to drive development of novel products and services and increase customer engagement.
To get there, organizations need to accelerate their pace, but not be taken in by the hype. They need to be pragmatic – to recognize that GenAI is a tool that has to be used correctly and in the right context. Get it right, and it can generate new knowledge and insights from corporate data. But if you get it wrong – if there is a flaw in the model or the data that it has been trained upon or that it’s working with, then the knowledge it generates cannot be trusted or used with confidence.
This is a vital point for engineering research and development (ER&D) applications, where mistakes come with significant impacts. Untrusted knowledge may be fine when generating a new ad for shoes, for example, because much of the content and indeed output may be generic – but it is not fine for when you are predicting something as specific as the failure points of a turbine blade in a newly designed jet engine. Design engineers need to know with certainty that their GenAI model is right, or, conversely, that it’s horribly wrong.
GenAI is closely linked to the use of large language models (LLMs), but these on their own are not enough. In the fields of science and ER&D, accuracy, trust, and repeatability are important corollaries. Indeed, they are critical for any new tool, platform or technology that is deployed to a business. A trusted GenAI platform must be able to reason logically, pro-actively recommend new approaches, and thoughtfully learn from the new data and people with which it is interacting.
Customized to need, designed to deliver
That’s why Capgemini Engineering has developed the Augmented Scientific Co-Pilot, a digital lab partner using Generative AI technologies. To do this, we partnered with one of the world's most respected computer, web, and cloud software companies to bring together our scientific, engineering and computation skills.
The key elements of the knowledge generation platform are shown in the graphic. It’s bespoke to organizations and to their specific research needs: the knowledge engine element makes use of their data, their algorithms, and their expertise, and is safely accessible to anyone who needs it.
The aim is to deliver a knowledge generation platform that will provide expert teams with the power of Generative AI using a flexible and intuitive natural language interface. Using Augmented Scientific Co-Pilot, organizations can uncover new knowledge from their enterprise data in seconds, rather than in months or years.
With the joint Augmented Scientific Co-Pilot solution as part of the research team, organizations can improve their delivery efficiencies, using the platform to find data rapidly, to execute models, and to automate creative workflows, bringing new levels of efficiency to the discovery process. They can also foster collaboration: by putting GenAI models in the hands of teams, barriers to collaboration are removed, and new avenues for creative thinking can be opened. What’s more, with active suggestions from a learning platform, teams can discover new ways of delivering innovation more rapidly.
Representative benefits
Let’s look at some typical benefits from this approach.
Product design can be accelerated by generating and iterating novel design options. These new product concepts can be designed and optimized based upon solid data, and not guesswork.
Time to market can be substantially reduced. Teams can explore multiple research directions simultaneously, identify which designs have the greatest chance of succeeding, and prioritize future investments to get to market faster than previously possible. Simultaneously, costs can be reduced: costly late-stage attrition can be minimized by avoiding investment in product designs that can be shown up-front to be unlikely to deliver on their promise.
Quality assessment and control can be improved. Using Generative AI in R&D and design can produce a higher quality product, and can also reduce the time and costs associated with the testing of complex systems.
There are sustainability implications, too. A GenAI-based approach removes dependency on physical prototyping and design iterations, and takes advantage of in-silico modelling in the design and development of new products.
Substantial and lasting benefits
The collection of which this story forms part demonstrates the application of GenAI to key stages of the product development lifecycle. Augmented Scientific Co-Pilot will accelerate the early phases, and in particular the identification and development of viable concepts. It’s a testament to the truth of the argument with which this article began – that applied in the right way and to specific circumstances, Generative AI can provide substantial and lasting dividends.
Augmented Scientific Co-Pilot – what’s involved:
Accelerated outcomes – new knowledge in seconds, not months or years
Scientific rigor – Custom AI models embedded with real-world scientific and engineering knowledge
Power of partnership – Capgemini Engineering in collaboration with a world leader in knowledge engine development
Part of the team – a collaborative GenAI model that knows your organization and talks your language
