Artificial intelligence can be loosely defined as the science of mimicking human behavior. Machine learning is the specific subset of AI that trains a machine to learn. The concept emerged from pattern recognition and the theory that computers can learn without being programmed to complete certain tasks. Things like cheaper, more powerful computational processing, the growing volumes of data, and affordable storage has taken deep learning from research papers and labs to real life applications. However, all the media and hype surrounding AI, has made it extremely difficult to separate exciting futuristic predictions from pragmatic real-world enterprise applications. In order to avoid begin caught up in the hype of technical implementation, CIOs and other tech decision makers have to build a conceptual lens and look at the various areas of their company that can be improved by applying machine learning. This article explored some of the practical use cases of machine learning in the enterprise.
1. Process Automation
Intelligent process automation (IPA) combines artificial intelligence and automation. It involves the diverse use of machine learning. From automating manual data entry, to more complex use cases like automating insurance risk assessments. ML is suited for any scenario where human decision is used, but within set constraints, boundaries or patterns. Thanks to cognitive technology like natural language processing, machine vision and deep learning, machines can augment traditional rule-based automation and overtime learn to do them better as it adapts to change. Most IPA solutions already utilize ML-powered capabilities beyond simple rule based automation. The business benefits are much more extensive than cost saving and include better use of costly equipment or highly skilled employees, faster decisions and actions, service and product innovations, and overall better outcomes. By taking over rote tasks, machine learning in the enterprise frees up human worker to focus on product innovation and service improvement; allowing the company to transcend conventional performance trade-offs and achieve unparalleled levels of quality and efficiency.
2. Sales Optimization
Sales typically generates a lot of unstructured data that can ideally be used to train machine learning algorithms. This comes as good news to enterprises that have been saving consumer data for years, because it is also the place with the most potential for immediate financial impact from implementing machine learning. Enterprises eager to gain a competitive edge are applying ML to both marketing and sales challenges in order to accomplish strategic goals. Some popular marketing techniques that rely on machine learning models include intelligent content and ad placement or predictive lead scoring. By adopting machine learning in the enterprise, companies can rapidly evolve and personalize content to meet the ever changing needs of prospective customers. ML models are also being used for customer sentiment analysis, sales forecasting analysis, and customer churn predictions. With these solutions, sales managers are alerted in advance to specific deals or customers that are risk.
3. Customer Service
Chatbots and virtual digital assistants are taking over the world of customer service. Due to the high volume of customer interactions, the massive amounts of data captured and analyzed is the ideal teaching material required to fine tune ML algorithms. Artificial intelligence agents are now capable of recognizing a customer query and suggesting the appropriate article for a swift resolution. Freeing up human agents to focus on more complex issues, subsequently improving the efficiency and speed of decisions. Adopting machine learning in the enterprise cloud have an infallible impact when it comes to customer service-related routine tasks. Juniper research maintains that chatbots will create an annual $8 billion cost savings by 2022. According to a 2017 PWC report, 31 percent of enterprise decision makers believe that virtual personal assistants will significantly impact their business, more than any other AI powered solution. The same report found that 34 percent of executives say that the time saved as a result of using virtual assistants allowed them to channel their focus towards deep thinking and creativity.
Machine learning can help enterprises improve their threat analysis and how they respond to attacks and security incidents. ABI research analysts estimate that machine learning in data security will increase spending in analytics, big data and artificial intelligence to $96 billion by 2021. Predictive analytics enables the early detection of infections and threats, while behavioral analytics ensures that any anomalies within the system does not go unnoticed. ML also makes it easy to monitor millions of data logs from mobile and other IoT capable devices and generate profiles for varying behavioral patterns with your IoT ecosystem. This way, previously stretched out security teams can now easily detect the slightest irregularities. Organizations that embrace a risk-aware mind-set are better positioned to capture a leading position in their industry, better navigate regulatory requirements, and disrupt their industries through innovation.
The key to getting the most out of machine learning in the enterprise lies in tapping into the capabilities of both machine learning and human intelligence. ML-enhanced collaboration tools have the potential to boost efficiency, quicken the discovery of new ideas and lead to improved outcomes for teams that collaborate from disparate locations. Nemertes’ 2018 UC and collaboration concluded that about 41 percent of enterprises plan to use AI in their unified communications and collaboration applications. Some uses cases in the collaboration space include:
• Video intelligence, audio intelligence and image intelligence can add context to content being shared, making it simpler for customers to find the files they require. Image intelligence coupled with object detection, text and handwriting recognition helps improve meta data indexing for enhance search.
• Real time language translation, facilitates communication and collaboration between global workgroups in their native languages.
• Integrating chatbots into team applications enables native language capabilities, like alerting team members or polling them for status updates.
That is just the tip of the iceberg, machine learning offers significant potential benefits for companies adopting it as part of their communications strategy to enhance data access, collaboration and control of communication endpoints.