By Alexandra Lilienthal, SER
In the past few years, technology scientists have made breakthroughs in AI and machine learning. AI now has the power to change societies and our world for the better. Using AI and big data could one day highlight trends that will help us solve climate change, for example. It will help to automate transport and take over dangerous and mundane jobs. But, in order for AI to achieve these and more it needs to continually be fed data. This is why it is critical for organizations to start managing their data properly now.
AI, once associated with science fiction, is now part of our world and becoming cleverer by the day. It’s now even capable of complex decision making. This has been made possible by the enormous proliferation of both computing and data power.
AI is better at handling multiple inputs than humans and can process gargantuan amounts of data at once to implement the best possible decisions. AI is also capable of spotting patterns that may not jump out during human analysis — for example, evaluating radiological images to help radiologists make a final diagnosis.
AI has the power to process enormous amounts of data at a scale and a scope way beyond the capacity of humans. This alone is invaluable when it comes to the speed and accuracy required in decision making in the digital business world.
AI comes into its own when the best solution or the best possible decision must be made grounded on enormous volumes of data and a high number of options. The technology has made headway recently in areas containing complex challenges; for example, in language control and processing.
But it is a waste of time for organizations to throw large investments at AI if they don’t have clean, quality data. Having accurate, verified data to feed the AI machine is crucial. Organizations must master data management before they think about mastering AI. The stark reality is that AI systems are only as good as the data they are fed. As AI and deep learning develop, so enterprise content management (ECM) will find itself having greater significance in the content management arena.
Nothing is achieved without data
Just to reiterate, AI feeds on quality data. The good news is it is unlikely to starve in the foreseeable future given the amount of data we are churning out.
We are creating more and more data via new technologies such as the connected world of the Internet of Things and Industry 4.0, which has been dubbed the fourth industrial revolution. The latter describes trends in automation and data exchange in manufacturing technologies in smart factories. By 2020, it is forecast that the global volume of data will increase tenfold, jumping from 4.4 zettabytes to a colossal 44 zettabytes — a trend that shows no sign of abating.
Basic ERP systems, however, just aren’t capable of processing these huge amounts of data coming down the pipe. The answer is straightforward: A context-sensitive software solution that can efficiently manage and store massive amounts of data, which can be horizontally scaled if required. This provides an opportunity to create smaller chunks that can be processed extremely quickly. This is without a doubt a strength of ECM.
Look back 20 years or so and 80 percent of data in a business context was unstructured and only 20 percent structured. The landscape looks pretty much the same today. In ECM repositories, however, this data, including documents, images and emails, can easily be found.
Cognitive services are a seat for AI
In the world of AI, information is finally being viewed as a production factor. Information logistics will become a central element to value creation. Information logistics looks after information flow, providing the right data to the right recipients as they need it and when they need it. ECM and business intelligence gleaned from data flow have one common aim – to create value from that information, be it at an analytical level or by way of transaction. Enterprises that are switched on to the value of their data and manage it by way of an ECM platform are already ahead in terms of current thinking.
In addition, all AI technologies of deep content analytics (CA), ontology and natural language processing (NLP) are covered in cognitive services. And yes, they are accessible on all ECM applications. Plus, there is the potential for these services to link to analytical methods such as statistical semantic modeling.
A simple question of integration
With the ever-increasing amounts of data pouring into organizations, information management is becoming a much bigger challenge. Business applications are being used to create data that is stored in separate structures and databases across departments. These so-called data silos that are being built are detrimental to business as potentially useful information remains unshared. As organizations have become more digitized, so the problem has grown.
AI needs access to all this information, however, to enable it to do its job as an accurate analytics and decision-making tool. The only way valued insight can be gleaned from this data is to prepare it properly. Thus the integration of these information silos spread out across organizations is strategically more important than ever for IT departments. ECM is a powerful platform here in linking all these disparate parts together.
Don’t take digitalization for granted
Digitalization is a prerequisite of AI. But we must not take digitalization as a given in organizations. It may be on CTOs’ agendas, but how much of it is actually practiced in reality? Do organizations really want to start remodeling their information management processes and strategies just to adopt AI? The truth is, organizations don’t feel under any pressure to start changing immediately. They are comfortable in their old content management surroundings. But, it may be something they come to regret as disruptive companies chasing their market share evolve through digitalization and know how to use it to their advantage.
Digital transformation is racing ahead and isn’t about to wait for anyone. Now is the time for organizations to move forward with digitalization and get ahead of the curve before AI starts maturing. In the future, without a well-ordered deep learning repository, AI will not have the data it needs to work properly. This is exactly why ECM platforms still need to be very much a part of organizations’ IT strategies. The importance of information in organizations has never been greater, and ECM is paramount in helping manage this changing business data priority.
Talking to our data
The many ways we interact with our data and devices is also changing dramatically. We no longer want to be tied to a mouse and keyboard, for example. We want our machines to respond instantly. This is why the potential for natural language processing in ECM is so vast. AI will alter our expectations of ECM platforms. We will see the arrival of voice-activated document retrieval. We will be able to converse with computers on the content of documents. Wearables will collect data and send it straight to ECM platforms. VR glasses will retrieve training manuals from ECM systems for engineers who need to prep for complex tasks. The possibilities are mindblowing.
AI: Plan now for the future
In the future, AI will change the human experience, alter our urban landscapes and shape our economies. It will help us rethink how to solve the world’s problems and alter our demographics.
AI is fast becoming a disruptive core technology that will revolutionize current software applications and our workspaces, thanks to its ability to self-learn. It is already becoming increasingly valuable in amplifying human capabilities.
Many organizations are underestimating the impact that AI will have on their businesses. CTOs need to act now if they are to harvest the incredible potential of AI. They need to shift into gear to become AI-enabled and prepare their data accordingly. The link between quality data and the power of AI can’t be stressed enough. For AI to be accurate and impactful, it needs to be fed clean data. It really is that simple!
The digital world is in continuous motion and businesses have to stay one step ahead. Organizations can’t afford to rest on past successes. AI is capable of disrupting any industry that stores data. It is going to allow organizations to process and interpret data like never before and is fast emerging as a business force to be reckoned with. Organizations that choose to ignore it will be quickly swallowed up by the ones that have taken the time to understand and deploy it.
Alexandra Lilienthal heads the international communication team of SER Group
This article originally appeared in the May 2018 issue of Workflow.