Four ways artificial intelligence can streamline business processes
From the Internet of Things to sales and marketing, artificial intelligence is having an impact on the way companies do business. Here are 4 ways artificial intelligence is revolutionizing businesses right now.
Today, artificial intelligence (AI) is everywhere. While the technology is still in its infancy, there is no doubt that AI will soon play a central role in almost all areas of business. Evidence of this fact is the presence of artificial intelligence in several applications, although its current capabilities only scratch the surface of everything it can do.
From the Internet of Things to sales and marketing, artificial intelligence is having an impact on the way businesses do business. Here are 4 ways artificial intelligence is revolutionizing businesses right now.
1. Stable development of the Internet of Things
Heavy industry is one of the earliest industries to adopt Internet of Things technology. From tracking parts lifecycle to quality control, IoT plays a central role in manufacturing and supply chains. Typically IoT devices transmit usage data to a control center, which ingests these data sets for further analysis.
While this sounds great, there are some limitations. First, collaboration is difficult when a control center has access to multiple data sets. For example, IoT devices connected to industrial pumps will generate data sets measuring flow output and component quality. These data sets are monitored by different teams, making setting alerts for usage thresholds challenging.
Artificial intelligence changes this by allowing businesses to create custom alerts for different teams within the organization. It also solves the problems surrounding large amounts of data. The human eye takes hours to verify and parse these data sets. Artificial intelligence can process data instantly and quickly alert operators to improper use or potential risks.
Startups like Sternum are increasing this observability by using artificial intelligence to simplify the work of those building IoT.
Sternum has developed an AI-based learning engine that uses user-defined trace data to create profiles of desired device behavior and highlight important and anomalous patterns. Once the device is connected, the system begins collecting data and, after a brief learning period, begins acting as a second set of eyes, providing alerts about unusual activity that could take a human operator hours, or even days. To discover.
Thanks to these advancements, enterprises can unleash more of their IoT devices to collect larger data sets and better apply lessons learned from analytics.
The result is a safe operating environment that produces results with optimal efficiency.
2. Simplify the B2B SDR process
B2B sales are indispensable to the success of enterprises in these fields. However, B2B representatives face significant challenges. First, the buying cycle is long and involves multiple stakeholders. Interpreting purchase intent can be challenging as sales terms can change from product demo requests to callbacks. For example, a competitor may release new features that cause more problems.
While businesses can’t change the length of the customer buying cycle, they can give sales reps more firepower during the sales process. AI-assisted sales is now a game-changer for B2B sales, and SDR is better suited for it.
Predictive AI can now provide sales reps with buyer intent predictions based on their previous behavior. By measuring engagement on marketing materials and conversations, AI platforms can guide sales reps in determining the challenge level of closing a sale.
Complementing predictive artificial intelligence is prescriptive artificial intelligence. The former provides sales reps with action items based on what happened, while the latter processes data in real time to provide sales reps with a way forward. It provides sales reps with a way to close deals.
Platforms like Demand Science can track prospect behavior and identify gaps in a business’s current sales process. The result is a smooth customer experience and more opportunities to convert into sales. In some cases, AI platforms even use natural language processing to engage potential customers when sales reps are not around.
So prospects stay engaged and sales reps can follow up with additional information to help close the sale faster.
3. Support more customer self-service
Chatbots represent artificial intelligence in the customer service field for some time. However, recent developments have pushed AI further up the customer service chain, helping businesses reduce less important customer calls and helping service representatives prioritize important customer calls.
Artificial intelligence can now interact with customers through multiple channels. The humble chatbot has become more powerful, answering complex customer questions than ever before. For example, an AI chatbot powered by Dialpad can retrieve data from previous conversations, customer order data, and dispute conversations to provide insights into status and more.
The platform also interacts with customers through voice channels. For example, customers can dial a number and have common queries resolved by entering information that the AI processes and delivers through voice. The result is lower call volume and more efficient customer service.
AI is also good at detecting when a customer wants to talk to a human, rather than interact with a bot. Often, customers have difficulty retrieving a phone number or email that allows them to contact a person. Artificial intelligence can quickly provide this number as an answer to a simple question.
4. Reduce the complexity of accountants
Accounting is a very mysterious field, and the smallest mistake can complicate the problem. Big companies are afraid to risk restating financial results for fear of brand damage and other repercussions, including plummeting stock prices.
Currently, artificial intelligence embedded in accounting platforms can automate bookkeeping and clerical tasks, such as accounts payable matching. For example, once a payment clears, AI will classify it based on the correct journal entry and match payment receipts with invoices and purchase orders.
So accountants have all the information they need at their fingertips. More complex platforms, such as the one being developed by Vic.ai. AI will go one step further and automate accounting entries. The result is less paperwork for accountants and more time to analyze financial performance.
Artificial intelligence also makes reporting easier. CFOs looking for financial insights into their performance can request data in natural language and receive a customized report that digs deeper into the data.
Artificial Intelligence has just begun
The artificial intelligence revolution has begun, and we are witnessing the biggest step forward in technology. While time will tell where business will go, there is no doubt that AI is here to stay and bring efficiencies to everyday workflows.
The above is the detailed content of Four ways artificial intelligence can streamline business processes. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1386
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
