Supply chain management is sometimes seen as an additional business function rather than a practice that applies to all aspects of a business. In reality, supply chain management ensures a smooth workflow and efficient operations across an entire organization. Even companies that rely heavily on logistics operations sometimes concentrate on only one or two aspects–which is the primary reason why supply chain hazards have long been overlooked.
According to a McKinsey report, while global supply chain operations have surpassed the $10 trillion mark in the previous two decades, linked supply networks have gotten riskier. Supply chain interruptions cost at least one in every five businesses more than $100 million a year. While this only equals 5%, how can you assure that your organization is not among the few?
Conversational AI brings a new set of opportunities for reducing supply chain&logistics risks. If you look closely, many supply chain procedures are redundant and can be automated, and this is the type of issue that conversational AI intends to address.
Nearly every company today is looking to implement conversational AI to solve myriad issues. Cutting-edge conversational AI assistants enable expedited engagement, extended support for remote working, self-service, and autonomy in request resolution, and drastically increase productivity while saving money. Furthermore, conversational AI provides unprecedented customer data insights for educated sales leads, upselling and cross-selling, and fast response to developing trends. An intelligent virtual assistant may save you millions of dollars, increase customer happiness, and handle more complicated use cases. In other words, everyone benefits.
With the growing popularity of conversational AI come many business options. There are plenty of options for virtual AI assistants on the market right now, and choosing the best one may greatly influence your investment's advantages and value. At the same time, you want to invest in conversational AI capabilities that support your markets, use cases, and target audiences without introducing unnecessary features. If you've decided to add a conversational AI to your business, let's have a look at the six "must-have" qualities of your conversational AI solution:
The best conversational AI is built on advanced Natural Language Processing (NLP) and Natural Language Understanding. Consumer-oriented, data-driven, predictive chatbots such as Apple's Siri and Amazon's Alexa are examples. They are sophisticated, advanced, intuitive, competent, and individualized. A virtual AI assistant must be contextually aware to be as human-like as possible, which can only be accomplished with Natural Language Processing (NLP). Machine learning (ML) is also critical to your virtual AI assistant’s capacity to learn new things while operating. The right conversational AI solution should also use predictive intelligence and analytics to tailor responses based on user profiles; it recalls a user's preferences and gives solutions and suggestions, or it makes educated estimates about a customer's future requirements.
The best conversation AI solution ensures that every user's previous interactions with the virtual AI assistant are remembered across all channels, whether online, through SMS, website, or phone. It uses information from a user's profile, order history, prior transactions, and other data to conduct accurate, relevant, and enjoyable discussions. This allows your conversational AI to fix user problems in seconds.
People nowadays have access to almost infinite channels for carrying out their professional and personal demands. Because of this, they often switch between channels if their questions aren’t answered promptly. Your ideal virtual AI assistant should be able to connect with the user via whichever channel they desire and do so quickly. As a result, an omnichannel messaging platform is critical to delivering a great user experience and enabling speedy self-service resolution of customer, agent, and staff support concerns.
Because omnichannel is expanding, the virtual AI assistant must be able to function throughout the entire range of channels. This covers workplace messengers such as Skype and Microsoft Teams, social messengers such as Twitter and Facebook Messenger, voice help such as Amazon Alexa and Google Assistant, email, and web/mobile apps. At the core of the omnichannel are NLU and NLP, which ensure that your conversational AI correctly understands and interprets messages and reacts to discussions, regardless of the topic.
Training your conversational AI agent should be simple, intuitive, and painless. That also means no code. To execute this, look for a visual flow builder that adapts to your needs and supports zero-code bot creation, allowing you to design and tweak your bot directly on the platform without any technical knowledge.
Without advanced dev skills, you can now train a sophisticated virtual AI assistant that begins producing benefits immediately. Your visual flow designer enables you to easily view, configure, and update conversational AI flows, add notes, provide context and troubleshoot existing issues. This enables a boost in productivity, faster time to production, better relevance, and ultimately provides a better end-user experience.
Machine Learning can also be used by your conversational AI platform to continually enhance performance and alter your virtual AI assistant’s processes. The virtual AI assistant may be tweaked and customized to respond to new business trends, initiatives, and consumer feedback.
In some situations, human intervention is required to settle an issue. As a result, the best conversational AI should enable smooth human transfer at the appropriate time. Your virtual AI assistant should be designed to handle two situations: 1) when the query is too complex and 2) when the user wants to communicate with a live representative.
Virtual AI assistants require specific intelligence to assess a developing feeling of urgency or intricacy when participating in a discussion. This functionality protects the assistant’s value by notifying it when to end the interaction and give it over to a human. When a virtual AI assistant processing an online order, for example, is unable to understand or carry out the request, it may quickly transfer the interaction to a person, preventing aggravation and preserving a pleasant conclusion.
NLP technology enables conversational AI to assess whatever attitude a user is speaking and detect displeasure. The call or other channel may seamlessly link to a live agent for tailored, hands-on assistance and interaction. Even after the agent has engaged, certain virtual AI assistants can continue to assist the process by transmitting background information on the caller's location (even down to the street or ZIP code!). The virtual AI assistant can also inform the agent of previous transactions and other relevant user data. As a result, conversational AI may continue to function even when not explicitly requested, assisting the agent and the caller in building a happy, successful experience.
Sentiment analysis is one of the most recent and fascinating conversational AI functions. When consumers are typically short on time and temper, a virtual AI assistant’s ability to discern the purpose behind a user's inquiry, recognize sentiment from the tone of voice, and answer accordingly is an immensely important talent. Even if the phrase structure, spelling, or grammar are inconsistent, confusing, or informal, such as jargon or slang, the virtual AI assistant can intuit the meaning and improve the user experience.
Conversational AI solutions, like humans, learn rapidly and preserve that information for later use. The virtual AI assistant grows more intelligent, perceptive, and useful with each engagement. Previously, a standard chatbot could just regurgitate its replies; the capacity to detect client sentiment was speculative at best. However, using today's story mapping technology, conversational AI can identify essential words and assign them a relative value: positive, neutral, or negative. This influences the virtual AI assistant’s "understanding" of an interaction's mood and tone. The solution can then find out how to reply to the user and have a meaningful conversation.
The most advanced conversational AI systems typically include dashboards that give human analysts a bird's-eye view of the logistical process. While this may appear to be convenient, it is frequently insufficient.
Analysts and managers can spend less time analyzing criteria throughout the supply chain and more time smoothing them out. This is only achievable if the supply chain is analyzed and faults are flagged by a conversational AI system.
A human agent in charge of assessing supply chain bottlenecks will only be able to focus on those that are trending or on a significant scale. Minor concerns frequently go ignored. Conversational AI systems may interface with numerous ERP systems on the backend and even extract actionable data from third-party suppliers such as fleet owners, drivers, or vendors. As a result, a conversational AI system follows a consistent approach to examining each variable and reporting any instances of deviation.
Even throughout the settlement process, conversational AI may give updates to all relevant parties over the channel. This contributes to more transparency in communication. You will often find that if you notify your clients in advance, they will not mind a delayed delivery.
Integrating AI-powered agents at strategic locations can help you better understand your supply chain and conduct an automated procedure to optimize it. While the advantages are numerous, you must guarantee that your conversational AI system includes the following features:
Conversational AI offers several advantages for businesses of all sectors. With DRUID AI, your logistics team may benefit from conversational AI in its supply chain operations, customer-focused activities, and more. Integrating conversational AI may effortlessly place orders, issue delivery warnings, and answer frequent queries, all while guaranteeing the efficient operation of your warehouse.
Interested to explore a real-life customer story of a successful implementation of conversational AI in logistics?