6 ways how enterprises use powerful AI

Increasingly, management boards of companies need to account for artificial intelligence (AI) systems as part of their annual goals. No company wants to lose the digital race, especially in this era of tight margins and increasing competitiveness. It is worth understanding how AI technologies have been successfully used in dealing with clients or business processes worldwide. 

AI technologies tempt management boards, but also create challenges.

Companies supporting mass customer traffic (B2C), such as Facebook, UBER or AirBnB, easily launch new AI-based products or services, implementing similar technologies in the B2B model, as  in banks, insurance companies and similar large organizations, encounter many legal, organizational or procedural obstacles to solutions based on AI. Not only do companies fear the risk of unsuccessful investments, but also the  damage to the company’s reputation from the misuse of artificial intelligence.

The potential for long-term benefits from using AI outweigh the fears. More and more senior managers (the so-called C-level; management boards and executives) have annual goals of implementation of new AI-based solutions. Mainly, this is to increase the competitiveness of products or the margins themselves. There are already methods to minimize the risk – for example, the introduction of additional supervision over the data used to train artificial intelligence algorithms.

However, the biggest problem today is the multitude of AI-based solutions and the fact that managers and leaders simply do not have sufficient knowledge in what areas to implement them or what ROI (return on investment) to expect. So let’s check the best cases of global applications. 

4 Main AI technologies Used In Enterprises Today

When analyzing the application of AI in enterprises, it is necessary to understand what technologies are the dominant players.   

Technology # 1 Machine Learning 

(ML) is a basic AI technology that relies on statistical modelling to develop skills and improve its results, including its accuracy, without having to follow clearly programmed instructions. 

Technology # 2 Deep Learning

It is a complex form of ML involving neural networks, that better themselves over time with less instruction from a programmer. Deep learning models are excellent for image and speech recognition, but are difficult or often impossible to interpret by humans. 

Technology # 3 Conversational AI

CA, including Natural Language Processing, is AI that can extract or generate the meaning of a question or statement in a readable, stylistically and grammatically correct form, as well as conducting virtual conversations between the user and the machine.

Technology # 4 Computer Vision

CV is AI that can extract meaning from complex visual elements, e.g. words (in the case of digitizing documents), and content categorization, for example  in images such as faces, objects, scenes and actions.

Each of these technologies solve different problems, and each gives significantly different ROI and benefits. When incorrectly used, they will not just be useless for automation, but can increase actual workloads. Below are examples of the most commonly used of these technologies – Conversational AI. 

6 examples how AI is used in corporations

Conversational AI is one of the leading AI technologies for companies. It enables a new, conversational way of contacting clients through a virtual conversation, usingChatbots and Voicebots to send messages through a messaging platform (e.g. Facebook Messenger). It allows you to settle problems for your clients immediately and 24/7, without the participation of an employee. Ideally, conversations feel  that same as if talking to a real salesman or consultant. The aim is to automate communication but retain a personalized customer experience. 

Managers can expect cost savings, increased conversions and customer satisfaction (higher NPS – Net Promoter Score). What’s more, AI allows you to monitor in real-time the desires of customers users via the tone of their voice.

Application  # 1: Automating repetitive, mundane business processes

AI transforms costly, traditionally operated Call Center service processes into almost completely independently-run conversations. Up till now, dealing with complaints, applications or forms were supported by telephone consultants (in 60-80% of cases *). This is not only expensive for companies, but also burdensome for clients. AI Chatbots listen to questions and understand their meaning through a virtual dialogue that allows mapping and digitizing existing processes.

Applications for process automation:

  • transfer of support for entire processes to Chatbot, e.g. processing damages / claims in the insurance industry,
  • support for existing processes by Chatbot e.g. extension of payment deadlines in the financial industry,
  • enabling changes in ongoing contracts thanks to dialogue with Chatbot, e.g. changes in telecommunications or energy plans.

Application # 2: Shopping assistance, including choosing and  finalizing the cart / basket.

Total automation of the sales process has always been a visionary idea. Technology supports customers throughout the entire purchasing process. Chatbot can act as a sales representative; it can answer product questions and guide you through transactions. Customers always need support in the decision-making and purchasing process, for instance with explanations regarding products or services and their features. The inability to quickly obtain information often results with a purchase losing its momentum and the cart being abandoned before checkout The solution is Conversational AI technology, which can now help in contextual support of the purchasing process. With proper programming, there is the possibility of full integration into a product catalog.

Applications for sales:

  • implementing Chatbot into already existing communication channels, e.g. chat on a website, a solution willingly used by service companies
  • to enable contact via mobile applications, e.g. Messenger, often implemented by clothing brands that want to be in constant contact with users,
  • sales integration Voicebots, e.g. by using Google Assistant.

Application # 3: Complementing traditional Call Centers with self-service digital channels, including chat and messenger communication

By adding automatic chat or speech recognition to IVR or Call Center, the company can reduce the number of telephone calls to the hotline by up to 30% *. Well-known IVR systems can be effective, but they are not accepted by customers who have to spend a few or several minutes on the handset to learn how to solve their problem. As a result, numerous, repetitive connections flow into the Call Center. AI-based solutions, including Voicebots or Chatbots, which use speech recognition or virtual dialogs in their operation, can be added to almost any Call Center and support key KPIs (FTR, Hold Time or NPS).

Applications for Call Center:

  • using AI to minimize phone calls and optimizing time and costs, e.g. large Call Centers can handle repetitive queries automatically, delegating consultants only to more complex matters.

Application # 4: Exploiting the Potential of Voice Assistants Voice

Chatbots and Home Assistants are new resellers and customer service agents. The brands are trying to benefit from the development of Voice Assistants, which will soon become # 1 in human-machine relations. However, companies are not ready to share their users’ data or data from internal IT systems with Amazon, Google or Baidu. That is why they decide on their own solutions – the Virtual Assistant from InteliWISE is 100% owned by the company and is controlled by the company and communicates with the voice interface of Google, Amazon or another supplier.

Applications for Voice Assistants: 

  •  The implementation of Voice Assistants to minimize customer involvement in solving a problem / achieving a goal, e.g. companies enable customers to place instant orders while performing other activities that require attention, e.g. on the road to work.

Application # 5: In-app support – supporting the conversion of low-margin B2C services supported via mobile applications 

The average cost of handling a single task or problem by a Call Center is about 1$ to 1.5$. If the mobile application is designed to sell or support a widely-used service with a margin lower than this amount, the entire project may not be profitable. In such a situation, it is an automated response system that is the most cost-effective way for a company to support a mobile transaction. Reservations or small purchases (e.g. sharing services) through applications have too low a margin to justify the cost of a consultant if a problem should arise. The ideal solution is a Chatbot supported by AI, that provides immediate answers to questions, or – in the case of more complex questions – InteliWISE Hybrid Chat – which has dual-support with a consultant. This solution reduces the single cost of handling an incident or problem to even less than 3 cents – very  competitive compared to the cost of handling by a Call Center.

Applications for In-app support:

  • providing low-cost, highly-automated support for mobile applications focused on mass transactions, e.g. matters related to payment card support in the financial industry, reservations, etc.

Application # 6: Intuitive HR Department and Helpdesk support for employees

Acquiring employees in todays jobs market is becoming more expensive. Employees are also increasingly using new channels, such as Facebook, to deal with HR matters. The recruitment process, as well as employee communication, can be supported by Chatbots – especially in areas that generate repetitive questions from employees / job candidates related to pay, holidays / time-off or completing required forms. Normal telephone and information support is costly and ineffective, especially for young, internet-savvy employees expecting an immediate response. Therefore, companies willingly invest in Recruitment Chatbots for quick selection of candidates or Virtual HR Assistants and Helpdesk that can be applied to internal messengers (e.g. Slack).

Applications for human resources:

  • Market giants have embraced this for recruitment support, where Recruitment Chatbots allow for immediate responses to job applicants’ queries,
  • facilitating internal communication between personnel through Chatbots or supporting IT departments through integration with company IT systems.

 

The six examples above from InteliWISE do not cover every application for AI, but show those in which CEOs, directors or managers can obtain proven investment benefits (ROI). The use of AI can be a relatively fast way to automate and increase business efficiency. It is worth starting a larger transformation project using AI in an enterprise, starting with a pre-implementation analysis. In doing so, our experts will propose options based on previously-implemented projects and globally-proven best practices. This will guarantee you the most optimized adaptation of solutions and technologies to your company’s specifications.

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