Technology peripherals
AI
Five reasons why your enterprise's ERP software needs artificial intelligence assistance
Five reasons why your enterprise's ERP software needs artificial intelligence assistance
ERP software increases productivity by automating core business processes and managing them for optimal performance. ERP software has been around since the 1990s, and they have evolved from traditional implementations to more adaptable client-server architectures. Artificial intelligence has far-reaching applications in many fields. Technological advancements are developing rapidly, and artificial intelligence is also changing ERP software. Artificial Intelligence is a powerful tool, so one needs to understand how it can change ERP software and the importance of AI assistance.
Why is AI assistance needed?
For years, core aspects of ERP software have remained stagnant. Data management and enterprise data processing are improved with automated input, enhanced communications, sophisticated reporting, and future visualization tools. Artificial intelligence can fundamentally change ERP software by reducing human coding intervention and precise transaction input to complete operations. Strategic use of artificial intelligence can ensure better integration, scalability, hyper-automation and enhanced security of ERP solutions.
Due to the COVID-19 outbreak, some ERP initiatives were either not started or not completed. In the new normal, hybrid working environments have become a reality and are becoming increasingly popular. As a result, delivering remote ERP programs is now a standard working practice. According to a study by Gartner, by 2023, 65% of large enterprise ERP deployments will be remote deployments rather than on-site implementations.
Industry-leading companies are interested in a combined ERP approach. Cloud-based products are more popular than those offered by incumbent vendors because they offer true business portfolio capabilities. Another study by Gartner predicts that by 2024, 60% of enterprises will deploy cloud ERP as an ecosystem of applications and technology platforms through multiple vendors.
Artificial intelligence capabilities have increasingly been built into their packaged application platforms by top ERP companies. Simple use cases like AI-powered invoice matching are easier to crack. However, complex solutions like AI-powered supply chain routing will be solved in the coming years. By the end of 2022, 65% of CIOs are likely to incorporate AI into their ERP strategy to gain a competitive advantage.
The five main reasons why an enterprise’s ERP software needs artificial intelligence assistance are:
(1) Conversational artificial intelligence robot for CRM
Chat robot is an artificial intelligence robot used by enterprises A prime example of intelligence. Conversational AI reshapes customer relationship management through front-end applications or business processes. Chatbots and virtual agents are now widely accepted as interfaces for customers of a variety of different businesses.
(2) Analyze complex data patterns to gain better insights
Data is the new oil. However, the insights must be worth realizing quickly. Processing data by analyzing large amounts of information is very time-consuming, and artificial intelligence can split data into more manageable chunks. Enterprises can drive growth at scale with real-time insights and responsive decision-making capabilities.
(3) Best Inventory Management
Artificial intelligence is known for reducing costs and eliminating human errors, it can also manage inventory better. Sufficient inventory can be maintained while maintaining cash reserves. Inventory management problems can be minimized and production scale optimized.
(4)Innovative marketing solutions
Target positioning, segmentation and penetration are the keys to successfully planning marketing activities. With personalized messaging and greater engagement, AI can take businesses’ marketing solutions to the next level.
(5) Improvement of core functions
Enterprises are using technology to improve existing processes. They are looking for future AI-infused ERP solutions to stay at the forefront of the industry. Working seamlessly across departments is just the tip of the iceberg. Artificial intelligence can optimize multiple processes and improve performance with minimal modifications. The benefits of AI integrated ERP can be seen in various processes:
- Robust predictive model
- Warehouse management
- Financial planning
- Cross Functional Process
- Production
- Human Asset Management
- Sales Process Automation
Will artificial intelligence affect the future of ERP software?
ERP vendors introduce “intelligent applications” into SaaS products, bypassing the need to develop artificial intelligence solutions for enterprises. Consistent data management and reliance on high-quality data have led to faster adoption of artificial intelligence and predictive analytics. Better business results are the end result supported by improved business process execution efficiency. Core ERP functionality is being enhanced through investments in enabling technologies. Businesses can outcompete the competition and let AI shape the future of their ERP software.
The above is the detailed content of Five reasons why your enterprise's ERP software needs artificial intelligence assistance. 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
AI Hentai Generator
Generate AI Hentai for free.
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
1378
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
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
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
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
