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A Case for Adopting Artificial Intelligence (AI) for Data & Document Management

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If your business relies on clear and accurate data right at your fingertips, then you already know how important it is to maintain a proper data management system. Error-riddled information and incomplete documents can cost organizations a lot of time and money. The sheer volume of a company’s data, be it within digital or physical documents, can overwhelm even the most efficient data management teams.

The financial, legal and real estate industries, in particular, operate within stiff regulatory guidelines, meaning that data errors can prove costly in more ways than one. As regulations tighten on high-profile, data-dependent sectors, a company’s legal standing and PR optics increasingly rely on its ability to play back trades, recall correct information and answer tough questions without hesitation. Technologies such as Artificial Intelligence (AI) negate data errors and improve efficiencies within these processes. AI tools can maintain structured data that is easily accessible and more importantly error-free. Better yet, AI works cheaper and more efficiently than even the most attentive human data technicians.

Human Errors Skew Data and Increases Work Load

Reports from various quarters suggest that human beings are more prone to errors than machines and it is reported that 9 out of 10 spreadsheets contain some form of errors that are human-made. Factors including fatigue, distraction, other external conditions and emotional state of one’s mind increases the probability of errors when reporting. While increased human skill and improved working conditions may reduce errors, nothing can fully eliminate them.

So, what does that mean for your company? Consider that most data passes through human hands many times before it’s finally stored. The potential for a single human to enter an incorrect keystroke increases with each pass through. Finding that error, correcting it, or uncovering the source of flawed information can take several weeks. Locating information in various repositories, verifying or correcting it, and finally rekeying it also ramps up costs. It is also worth noting that not all skewed data are mistakes. Some errors are a direct result of intentional malpractices. Since AI has not yet been trained to adopt the ethical identity of its programmer, companies making use of data management technologies can also put an end to the possibilities of corporate espionage and deliberate sabotage attempts.

AI Eliminates the Possibility Of Errors in Data Management

Unlike human beings, AI never gets tired. It does, however, require increased storage capacity, computing power and enhanced memory. AI’s neural networks are models of human brains, consisting of neurons that convert an input into an output. For human nerve cells the inputs and outputs are shots of electrical currents. In computer science these currents are simply modelled as smooth numbers, which capture the strength of the signal. Each neuron transmits its output to a selection of other neurons to potentially activate them. A single neuron cannot compute anything complicated. It is rather the collective activity of a many neurons that matters. Collectively, the neurons develop a form of intelligence, which emerges from the neurons’ interaction through the patterns of their connections. Currently, AI software’s based on deep learning algorithms are able to truly understand the languages of contract, leases and other documents.

AI can sort through this data, extract it for specific information, and organize the extracted data for review into a structured data repository with a speed unrealized in most businesses to date. Moreover, AI learns from the data it processes, thus better the quality of data the system absorbs, the more accurately it responds to specific requests. Reporting also becomes simpler with AI technologies that lets users work with the information from start to finish. It turns an array of loose documents into insightful, actionable, and structured data. Since the data is already organized in a searchable and accessible manner, most reports all but prepare themselves.

Error-Free Data Can Lead to Effective Decision-Making

Corporate executives know they should make the most important company decisions based on data. But they rarely do. Why is that the case? According to a recent survey, 50% say the essential information is not fully available, 40% say the quality of existing data is not good enough and 29% can’t find the information they need, when they need it. The problem, of course, is not the amount of data in storage but the quality of existing data and the ease of access to this data. Error-free, structured and accessible data gives decision-makers the power to make those decisions based on accurate information.

With AI, users can import data through simple drag-and-drop systems that automatically reviews the information, classifies it and stores it effectively. Users can then browse the system for the data they need, link it directly to a document or table, and import it to any target systems of their choice. Importantly, company executives can access the data, review it, and determine its viability with relative ease.

Data Fuels Transaction-Business Engines

Transaction-based industries find that structuring data provides a new level of transparency. Structured data allows companies to get quick, real‐time answers to important business questions. It also allows them look at other performance indicators that are key to making informed business decisions. Plus, it makes reporting one of the least stressful parts of the job. International players such as JLL realize the huge potential to optimizing their processes, and some of them have started rolling out AI platforms in lease abstraction at a global scale.

Historically, key data would be extracted out of retail leases such as co-tenancy, rents or renewal options and entered into excel files manually, keeping in mind the volume of such transactions globally pulling out key information would have led often to inability to focus on important transaction work. Customized data models fitting the key information needed automates and speeds the processes. Lease abstraction time can be reduced by over 60% while enabling accurate lease results and excel output reporting to visualize and compare data across the entire portfolio and ongoing lease abstraction management. This trend revolutionizes conducting of business and increases the performance of operations.

Companies adopting end-to-end solutions that turn unstructured documents into insightful and actionable structured data see the value of improving reporting efficiency and removing uncertainty from data management exercises.

Abhinav Somani, Chief Revenue Officer, LEVERTON Abhinav (Abe) is an experienced investment, financial, technology, business development, and operations strategist. He is currently the Managing Director of Leverton Intelligence for MRI Software. Abe worked in commercial real estate and finance and has a deep understanding of the real estate technology / CRETech / PropTech space. Abe is revolutionizing how corporations use artificial intelligence-based machine and deep learning algorithms for data extraction.

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