One of the key drivers of a collaborative digital workplace is idea-sharing. Modern intranets today feature an idea management module which enables organizations to capture, review and implement employees’ ideas. This decentralized approach to innovation is imperative for driving not only employee engagement but also revenue growth. In a study by Mckinsey, 84 percent of executives say innovation is extremely or very important to their companies’ growth strategy.
While traditional idea management systems have been quite successful, the incorporation of AI in them is unlocking new capabilities essential to drive innovation at scale.
In this article, we’ll discuss:
The need for established businesses to rapidly innovate and stay competitive in the market started a decade ago when there was a sudden emergence of agile and innovation-first startups.
This led to the emergence of technologies like idea management platforms that help generate innovation from employees – people who actually generate some of the best ideas to solve business problems.
Even though the concept of decentralized innovation existed in different forms, the heightened need for innovation led to the mainstream adoption of idea management platforms. These platforms provide a streamlined workflow to create, manage and nurture ideas while enabling discussion and review across multiple communities.
Some of the popular features of today’s idea management platforms include:
In the last few years, as innovation started gaining more importance among enterprises, the idea management technology has evolved even further. And the advancements in Artificial Intelligence have further fueled this evolution. Let’s explore the different ways AI is enabling a scalable and sustainable innovation programmer.
Organizations with a large workforce often face the challenge of an increasing volume of repetitive and similar ideas. This is primarily because contributors won’t be aware if their idea has already been submitted by someone else.
AI tackles this challenge by intimating about similar ideas (if they exist) to contributors while they are trying to enter their submission. When contributors type the details of their idea, AI matches the keywords they’re using with the existing pool of ideas and checks if there are similar ideas posted previously.
This way contributors will know if there is a similar idea that might already have been posted and withhold from submitting their idea. Instead they can choose to upvote or engage with the existing idea.
This prior intimation about similar ideas drastically reduces the amount of repetitive ideas and ensures only the unique ones remain in the database.
Whenever a business challenge is created, it’s possible that the post gets thousands of responses. And after intense review and scrutiny, only a handful of ideas might actually get implemented. A majority of the ideas might be rejected due to poor strategic fit, lack of resources, untimeliness etc. An idea that isn’t right at the moment might prove to be valuable for a different business challenge in different circumstances.
With AI and machine learning algorithms, an idea management system can keep building a memory of these unused ideas and learn to develop relationships among them. This means when people look for solutions, they will be notified about previously submitted ideas relevant to their needs.
Traditional systems provide a keyword-based search engine to find ideas. Given the enormous amount of data that gets stored in these platforms, a normal search capability can’t fetch relevant results. And some ideas stay hidden and don’t get retrieved.
AI technologies like Natural Language Processing (NLP) empower search engines to improve their relevance and accuracy of results. This’ll also ensure relevant hidden ideas are illuminated and make it easier for people to find the right ideas quickly and seamlessly.
As the number of ideas grows, it becomes next to impossible for reviewers to manually go through and understand the status of each idea – whether it’s approved or implemented or rejected. AI tools help overcome this problem by developing unexpected relationships between ideas and filter ideas based on their status.
The high volume and diversity of ideas makes it difficult to manually group similar ideas together and make connections between them. Machine learning and NLP tools can help create clusters of similar ideas. Each cluster will have its own supporting files, resources and documents which provide deeper insights into the crux of the idea. Anyone in the review team can access and make sense of ideas without any hassle.
Modern SharePoint intranets like Mesh 3.0 are acting as a central information hub for organizations. Employees can communicate, collaborate, share information, access everyday apps in a unified interface.
Given the rapid adoption of idea management platforms among organizations, intranet providers are incorporating an idea management module within the intranet – thereby eliminating the need for a specialized app.
Organizations that don’t constantly innovate will lose their competitive edge or even market share. An AI-driven idea management module is a must-have feature in modern SharePoint intranets. AI creates a scalable and sustainable innovation management. The workflow offered by a traditional idea management system is already streamlined – the incorporation of AI makes it more smarter to tackle the challenge of growing volume of ideas in enterprises.
If you’re evaluating intranets or looking to incorporate idea management into your intranet, you might be interested in exploring Mesh, World’s First Autonomous SharePoint Intranet by Acuvate.
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