Information management has changed from pure document management and archiving to a true business enabler. Today's intelligent information management solutions offer ways to automate time-consuming and often boring document-centric processes within a company. Artificial intelligence and machine learning play a crucial role here. But what specifically does AI and machine learning mean for information management in organisations?

One of the key drivers in automation is the use of Artificial Intelligence (AI) and machine learning. AI and machine learning have the ability to reason and discover meaning and learn from past experiences. Moreover, AI systems can easily move back and forth through a lot of information to recognise patterns and categories in data. That ability is being harnessed to enable new ways of searching, finding, using and managing information and adding automated workflows to document management processes.

But, AI and machine learning can have different meanings for different organisations and people. For us, it means that it really helps business operations while making it very easy for end users to benefit from it. Our goal is to enable customers to intelligently find and manage critical data they need to make smart decisions.

In that light, I want to discuss three defining issues that are important to make the best use of AI and machine learning in the field of information management.

AI should not be difficult for the user

We believe in providing out-of-the-box intelligent solutions, so you or someone in your organisation does not have to be a data scientist to apply it in your business.

What sets the Artificial Intelligence in M-Files apart from others is the use of machine learning behind the scenes. Users do not have to change their ways or take extra steps to train the system. By just observing what the users do, M-Files will automatically help them and the system will get smarter and smarter by itself.

 

Some products offer fantastic, powerful AI capabilities, but they require a deep understanding of how to carefully train, test, tune ....and repeat. Trying to implement this without the right skills can end up with high costs and no results. A better alternative are solutions that offer AI out-of-the-box, without heavy user-side requirements. M-Files is one such solution.

AI should work with your own data

There has been a recent rush of "intelligent" products and services on the market, but some of these products and services are not at all as intelligent as they claim to be. It is important for organisations to be able to see what is going on behind the scenes and understand how well an AI solution really works with their own data, not just demo data.

To test with real data before a purchase, the AI solution must be simple enough to implement. If it takes too much effort to make the AI work, it may be totally impossible to try it out with real data.

On the surface, an AI function can sometimes seem rather mundane or obvious to a user. All the clever algorithms and mechanisms are invisible to them and they simply see something happening automatically.

A great metaphor to explain this is ducks. Ducks swim around quietly, but a lot of impressive work happens below the surface of the water to make sure a duck ends up where it needs to go. In that light, some suppliers are now selling 'rubber ducks'. They look and quack like a real duck, but only if you squeeze them just right. In reality, they are hollow inside and nothing happens under the surface of the water.

Cloud leads the way, but on-premise AI offers big opportunity

Cloud services and SaaS have been around for some time, but certain customers and industries have yet to fully embrace them. As long as AI services are only available in the cloud, many dollars are still up for grabs. So even though it may seem a bit old-fashioned, there is a real opportunity for premium, fully on-premise AI backend solutions.

A related, emerging technology is so-called Edge AI, in which AI is performed directly on the device, so data stays where it is.

M-Files takes a cloud first approach, but almost all AI capabilities are also there for on-premise.

Intelligent services

In November, we will launch a new tool, M-Files Smart Classifier, as an add-on feature. M-Files Smart Classifier is based on the approach described above, where the product learns as it is used, and it provides suggestions for what type of document a particular document is.

Besides the new M-Files Smart Classifier, we also offer other smart services such as, for example, the M-Files Repository Sensor, which helps to search through large amounts of data and identify business-critical information, such as personally identifiable information, from the clutter.

These services are all part of offering more automation and improved efficiency for organisations.

Our goal is to move organisation towards intuitive and intelligent information management in three steps:

Step 1: Provide meaningful metadata suggestions

• Use AI to understand and analyse the content users manage

Step 2: Separating business-critical information from dark data

• Crawling through different storage locations.
• Find relevant data, make it visible and link it to business processes.
• Remove unnecessary information and content.

Step 3: Offer users data that is relevant to them now

• Understand who the user is and what his or her role is.
• Proactively offer information and content that is relevant now.
• More relevant search results for users.

Are you curious what this looks like in practice? Attend the M-Files online demonstration at and discover it with your own eyes.