For the world’s largest and most regulated organizations, understanding each employee’s day-to-activities and behaviors is simply impossible and, oftentimes, unnecessary. But uncovering internal issues – such as operational inefficiencies or even criminal activities – that could result in wasted time, lost money or damaged reputations, is critical. Therefore, it is important that businesses invest in tools to effectively identify and help correct these problems, and in turn drive sizable ROI.
Artificial intelligence is one technology that many enterprises have implemented to solve these internal challenges. In fact, IDC forecasts worldwide spending on cognitive and artificial intelligence (AI) systems to reach $57.6 billion in 202. But, which AI applications are actually helping enterprises, and how are their investments driving returns?
Forget Big Brother: How AI Surveillance Helps Enterprises “Know Your Employee”
There’s a general consensus among consumers that AI technology will create an Orwellian world; but the fact of the matter is that this technology has the potential to do a lot of good for the modern-day enterprise as well as their workforces from a surveillance standpoint.
Oftentimes, enterprises leave employee communications untapped. There simply aren’t enough hours in the day to monitor every message someone sends via email or business chat – nor is this a necessary practice as the majority of communications are benign. But within every e-communication lies unique insights that could lead businesses to uncover some harsh truths about employee activity.
One industry that has quickly adopted AI technology for this exact reason is financial services. Today, smart machines capable of understanding the true meaning behind human communications are augmenting the work of human analysts at most of the world’s major investment banks. The technology extracts messages that indicate misconduct with unparalleled precision, while entity resolution and knowledge mapping helps analysts identify sources of human risk and hidden networks of collusion. Among the compliance organizations of leading investment banks, widespread adoption of AI-enabled analytics has taken place in less than 3 years – it’s no exaggeration to say that, for these organizations, regulatory compliance would be impossible without the amplifying effects of AI.
The innovations happening in financial services herald a paradigm shift in enterprise surveillance that will inevitably encompass other sectors. Gartner estimates that up to 80% of enterprise data is unstructured and the majority of this is made up of communications such as emails, chat messages, phone calls, and other documents. AI has the distinct ability to make sense of this information for the betterment of a business. Whereas banks were effectively forced to analyze this data due to ever-changing regulations, the clear results and benefits of AI technology translate nicely into use cases for businesses across industries – uncovering insights to unlock new business opportunities while hastening the resolution of problems.
Imagine an airline being able to consolidate insights captured in tweets, at a call center, or in emails into a heatmap of problems and opportunities. It could quickly see issues with the quality of meals emanating from a particular supplier. It could see trends in requests for unserved destinations and early insight into the likely popularity of a new route. Anything from confusion about security procedures, praise for great service, or ignorance of company policies, would be surfaced. Such an airline would have turned the inputs of its employees and customers into a valuable asset for management.
The Real Value of AI: Both Financial and Operational
So what’s the measurable impact of AI for surveillance? AI can bridge the gap between the subject matter expert and the software in the quickest way by transferring the knowledge into the software system. AI can further reduce the human glue by having an easy-to-learn system empowered by the large computational power of today’s machines. Thus AI-enabled software provides that effectiveness (covering more hits or true positives) and efficiency (reducing the false positives and time to learn) tool for all and realizes the ROI in quickest way. Independent assessments have shown that an AI-enabled approach to communication analytics delivers a marked improvement in results, bringing false positive alerts down by at least half, even as monitoring of employees expands from 5% to 100% coverage.
The good news for enterprises looking to get started on their AI journeys is that all the research is out there. Instead of commissioning AI experiments, today’s enterprise leaders can use the lessons learned in financial services to invest in solutions with proven results and a quantifiable ROI. Today, any enterprise can augment its processes and amplify employee productivity by using AI to turn the 80% of data that is currently overlooked into actionable insights.
AI is entering the enterprise at a fast rate – and with more reputable brand names adopting the technology and new applications being announced, the potential business impacts are becoming all the more real and concrete. The experience of financial services is instructive. We know that more productive, profitable, and better-behaved enterprises produce a dividend for society as well as shareholders. And as businesses compete to win customers and attract the best staff, those with an effective enterprise surveillance capability will be best placed to outperform their cohorts.
by Uday Kamath, Chief Analytics Officer at Digital Reasoning
Uday has spent more than two decades developing analytics products and combines this experience with learning in statistics, optimization, machine learning, bioinformatics, and biochemistry. Senior roles, including that of Chief Data Scientist for BAE Systems Applied Intelligence, have seen him apply analytics to challenges in compliance, cybersecurity, banking fraud, anti-money laundering, and insurance. Uday has contributed to many journals, conferences, and books, is the author of Mastering Java Machine Learning, and has a Ph.D. in Big Data Machine Learning and Automated Feature Generation. He likes to volunteer, teach math, and is an avowed foodie – balancing his enthusiasm for cooking with long distance running. When he has the time, he indulges his passions for poetry and Indian classical music.