Boosting Efficiency with State-Of-The-Art Machine Learning

By Jeremy Brown | Spigit
October 10, 2018

In Spigit’s recent 2018 Fall Product release announcement, we shared that we developed a new machine learning technology that’s first of its kind in innovation management software.

The technology, which will continue to be developed and applied in new ways, brings on a whole new level of efficiency for innovation program managers and users alike.

When you look under the hood, the real value of machine learning is in the smarts it brings. When algorithms learn and improve themselves overtime by studying high volumes of data, they enable smarter, more informed decision making with less effort.

Imagine being able to power deeper insights into emerging concepts and trends without any manual work. Just let the algorithms crunch the data automatically and give you answers.

By incorporating machine learning into Spigit’s innovation management software, innovation program managers will have the ability to unleash a whole new level of smart innovation and efficiency.

Why does efficiency matter?

Helping large companies around the world create cultures of innovation has taught the Spigit team several important lessons. One in particular is this: in order for an enterprise to transform its culture into a culture of innovation, they need to scale their efforts – from launching more innovation challenges to ensuring all employees are participating – in the most efficient manner possible.

We’ve witnessed company after company scale their efforts only to find that not only is it easier to create an innovation culture, it’s easier to sustain it over a longer period of time.

Efficiency is extremely important. This is why we believe Spigit’s machine learning, and the many ways it can be applied, will play a critical role in speeding up the process of cultural transformation for customers.

Boosting efficiency with Machine Learning

With the ability to identify concepts through natural language understanding and processing techniques, Spigit’s new machine learning technology is being used to surface comparable ideas in the platform.

Why is surfacing comparable ideas useful? Two reasons:

  1. Team formation. It provides new intelligence for users, enabling them to connect with like-minded individuals to refine their own ideas or contribute to those that already exist.
  2. Program manager efficiency. A program manager is able to instantly see things such as: idea trends and opportunities to create teams. Plus, it makes sifting through ideas a breeze. If you’re looking at lots of ideas, this comes in handy.

From an efficiency perspective, both users and program managers will find that they’re able to see more ideas with less effort. For the latter, this means that they can focus more of their time on downstream activities such as advancing the best ideas.

Final thoughts

As programs scale up in both the number of users and ideas, the ability to identify and manage comparable ideas becomes essential.

Spigit’s industry-first machine learning capabilities produces a level of efficiency and intelligence that supports the scale companies need to turn their cultures into culture of innovations.

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