Use of AI in Research and Development

Research and Development (R&D) is the growth engine for any business and has the onus of identifying new technologies, customizing them as per the business domain, and building a strategy around them that can be translated into technology or a product.

Though the Research and Development (R&D) teams are always under constant pressure to mine new areas of interest and innovate, high-pressure situations like pandemics, mergers, acquisitions, and new product development inflict an additional burden on expediting the innovation process.

• So, after all, what impacts Research and Development (R&D) productivity?
• Why has it been a troublesome area for so long?
• What resources are available that can help improve this parameter?
• Is existing data and knowledge being leveraged?
• Are NLP tools (such as XLSCOUT Ideacue) being leveraged?

The very first challenge seems to be the measurement of research and development (R&D) productivity. R&D, in its essence, is to brainstorm ideas, research, and innovate. This may seem straightforward, but it is more complex than it seems. Brainstorming ideas alone is something of a superlative task on its own. Take any R&D team within any business; they must be getting tons of ideas every month.

But which ones should I choose? Which ones should I nurture?

Answering these questions takes the most time and effort. And the next stages of research and innovation are dependent on this one variable. So can just the number of innovations provide a measurement of R&D productivity? Well, the debate is long.

Now, besides the much-debated issue of “measurement of R&D productivity,” there are a few more pressing issues that affect this space largely.

The Gap between Consumer Need and Innovation

Often, innovations churned out by the R&D departments are either too ahead of time or too late. They just miss grabbing the market nerve in real-time. This factor is the most common, yet it is the one that leads to huge losses. Not just money but time and effort too.

So what can be the possible aid to this problem?

Maybe enable the R&D to be in sync with the market. More interactions with teams on the ground can help. In short, anything that can help R&D gain insights on anything and everything related to consumer and market trends. So maybe surveys will not be taken just as a marketing function anymore. Even simple measures like real-time market alerts, competitor tracking, and similar measures can help. 

Data Analytics through Machine Learning

Use of data analytics and machine learning tools that include tools like XLSCOUT Ideacue to ensure there is an adequate data-driven innovation and machine learning-driven approach to innovation.

The IP AND R&D Dimension

IP and R&D are the two pillars of any business, and despite co-existing, the nature of the functions they perform is hugely independent. Both need IP data, but their relationship and understanding are crucial in the race to protect innovation.

R&D and IP need effective collaboration, but that’s easier said than done. There are limited mediums that can cater to the data needs of both and, at the same time, enable collaboration.

Missing Collaboration Across Various R&D Teams

Just now, we talked about the issues in R&D and IP collaboration. They were still different teams with different natures and functions. But collaboration gaps exist within different R&D teams as well. Bringing all R&D teams onto the same page is crucial but difficult. The use of the XLSCOUT corpus is important in such scenarios. 

So now to conclude, there are various factors that are affecting R&D productivity, and there will be many issues that are distinct to businesses.

However, the ones listed above seem to be the most common.

XLSCOUT supports and offers solutions that can assist in minimizing inefficiency. The platform supports tools for data aggregation, data visualization, and idea generation. XLSCOUT SDI is a collaborative tool that assists in collaboration between IP and R&D teams. It offers patent alerts and a limitless searchable repository that is secure and can be accessed via the cloud.