Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions -- all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.

AI is assisting the sector in the planning and optimization of operations. COVID-19 has proved the importance of planning forward and anticipating supply chain challenges. Artificial intelligence can be used to evaluate supply chain risks and identify possible bottlenecks. Supply chains have become significantly more challenging to manage in recent years. Physical flows are becoming longer and more interconnected as product portfolios become more sophisticated.

Orange Business Services launched a network tool employing AI to help IT personnel proactivity monitor their network services.

Cybersecurity best practices are greatly aided by using Artificial Intelligence (AI) and Machine Learning (ML) technology, as shown by this sector's growth. According to one study, the market for artificial intelligence in cybersecurity is expected to reach $46.3 billion by 2027. AI drastically improves a business's cybersecurity posture by applying the technology to help identify, isolate, or remediate potential cyber threats from penetrating a business's network. Read more: AI vs Machine Learning: What Are Their Differences & Impacts? The technology gets better over time: As AI/ML learns a business network's behavior and recognizes patterns on the network over time, it becomes more difficult for hackers to penetrate a business's network.

The goal of artificial intelligence (AI) is to mimic the same intelligence that humans display. While it has many uses, it could be a highly effective way to improve cybersecurity. If AI systems are used in the right way, they could potentially identify new threats, send alerts, and ensure that sensitize data is properly protected. A report from TechRepublic found that it's normal for a midsized company to receive approximately 200,000 cybersecurity alerts a day. This makes it difficult for security teams to effectively manage all threats.

With today's technology, there's no excuse for not innovating and envisioning how to make things better. Innovation knows many different drivers. However, where technology meets innovation is the birthplace of new ideas. As machine intelligence capabilities continue to move into mainstream for business processes, all companies need to focus on how to innovate at a faster pace to ensure they stay ahead of competition and establish a long lasting competitive advantage. To create lasting technology-driven business innovation, companies need to empower their core with data and technology.

Building and managing business network in the digital economy is a challenging task. With multiple communication channels, both conventional (e.g., phones) and new (e.g., social media), your contacts can become a real mess. As a result, both your productivity and your ability to build strong business relationships and leverage your network's value suffer. Fortunately, new AI-powered tools for social media, contact management and networking solve most of these problems. One option to help you stay on top of your business relationships in the digital age is to use AI-enabled contact managers like Etch.

A business network is far more than the business itself; it's a collection of many connected things--customers, suppliers, partners, and more. The transferring of assets to build value for business networks is happening continuously with participants, transactions, and contracts. There is always a need for a shared ledger across the business network to operate efficiently with broader participation to reduce cost, reduce risk and fraud, and increase trust. Blockchain technology provides a distributed database of records that have been executed and shared among participating parties. Blockchain can operate in two modes--private and public.

Information technologies are accelerating at an exponential rate, ushering in the fourth industrial revolution. This is a digital revolution and the pace of change is unprecedented. This revolution incorporates machine learning (think parallel processing and neural networks) and the concept of self-assembly or self-programmability. As technologies continue to advance, they accelerate the progress of other technologies, and so on, and so on. Thanks to parallel processing, big data, cloud technology, and advanced algorithms, Artificial Intelligence (AI) and machine learning are becoming more powerful.

"As digitization and machine intelligence continues to proliferate and get pervasive, the need for customer intimacy, transparency, and security of data are becoming essential to next generation business networks," said Nij Chawla, Chief Product Officer of CognitiveScale. "CognitiveScale is one of the first companies to combine Blockchain, big data, and machine learning for industry-specific outcomes that power the next-generation Internet of Trust." CognitiveScale provides two products called ENGAGE and AMPLIFY that use machine intelligence to transform customer experience at the edge of the business and deploy self-learning, self-assuring business processes at the core. The company's product portfolio has been enhanced in the past year to address an evolving market, where trust, relevance, and assurance are becoming increasingly integrated and business critical. CognitiveScale will use Blockchain technology to underpin its products to put additional "smarts" in Blockchain smart contracts for multiple industries and processes, including financial services, healthcare, and procurement.