By: A Staff Writer
Updated on: Jul 05, 2023
Top Emerging Technologies
The following are the Top Emerging Technologies that enterprises must consider investing in and building capabilities and competencies.
Definition: Quantum Computing uses quantum bits, or qubits, which can represent and store a large amount of data. It’s a revolutionary computing model designed to solve complex problems much faster than classical computers.
Enterprise Use Cases: Quantum computing can be used in logistics for route optimization, in finance for portfolio optimization, in drug discovery for simulating molecular interactions, and in artificial intelligence for improved machine learning models.
Potential Rewards: It can solve problems exponentially faster than classical computers, providing a significant competitive advantage in fields like drug discovery, finance, logistics, and AI.
Risks: As quantum computers could potentially crack traditional encryption methods, this poses a risk to existing data security methods.
Best Practices for Implementation: Invest in quantum-safe cryptography methods. Stay updated with the latest advancements in quantum computing and start considering potential use cases for your business.
Definition: XR is a term that includes Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR). These technologies change the way users interact with the digital world.
Enterprise Use Cases: VR can be used for training in environments that are too dangerous or expensive to replicate. AR can assist in tasks like maintenance or surgery by providing real-time information. MR allows for better remote collaboration.
Potential Rewards: These technologies can improve efficiency, reduce errors, provide immersive customer experiences, and open new avenues for products and services.
Risks: Privacy and data security concerns arise as these technologies collect more user data. There can also be physical risks if the technology is not used correctly.
Best Practices for Implementation: Understand the specific needs and context of your organization before choosing between AR, VR, or MR. Address privacy and safety concerns upfront. Consider partnering with experienced XR solution providers.
Definition: IoB is an extension of IoT, where data collected from various sources is used to influence or modify human behavior.
Enterprise Use Cases: IoB can be used in marketing to understand and influence consumer behavior, in healthcare for patient monitoring and encouraging healthier habits, and in workplaces to improve productivity and employee well-being.
Potential Rewards: Businesses can gain deeper insights into customer behavior, improve marketing strategies, and enhance user experiences.
Risks: There are significant privacy and ethical implications related to tracking and influencing behavior. Businesses need to ensure they are transparent and comply with all relevant data protection regulations.
Best Practices for Implementation: Prioritize transparency and consent in all IoB initiatives. Use data responsibly and ensure robust data security practices.
Definition: Data Fabric simplifies and integrates data management across different locations and formats. It enables efficient data access and operations.
Enterprise Use Cases: It can be used in any industry that needs to manage large amounts of data from various sources, including finance, healthcare, e-commerce, and logistics.
Potential Rewards: It can significantly improve data accessibility, quality, and security, leading to better decision-making.
Risks: Implementation can be complex and requires careful planning. There may also be issues around data privacy and security.
Best Practices for Implementation: Start with a clear data strategy. Use data fabric tools that are compatible with existing systems and ensure that all data is securely encrypted.
Definition: Biotechnology uses living organisms to make products or solve problems. Genomics is a branch of biotechnology that focuses on the structure, function, and editing of genomes (the complete set of genes or genetic material in a cell or organism).
Enterprise Use Cases: These technologies can be used in healthcare for personalized medicine, in agriculture for genetically modified crops, and in environmental science for biofuels and bio-plastics.
Potential Rewards: They hold the promise of breakthroughs in medicine, agriculture, and environmental sustainability.
Risks: There are ethical and regulatory concerns related to genetic modification. There’s also a risk of biotech products affecting ecosystems in unpredictable ways.
Best Practices for Implementation: Maintain strict ethical guidelines and stay informed about relevant regulations. Safety testing and monitoring should be thorough and transparent.
Definition: Cybersecurity mesh is a flexible and scalable architectural approach that decentralizes policy enforcement from a single location to individual devices, data, users, and workloads.
Enterprise Use Cases: It is useful in protecting complex digital ecosystems, including remote work environments and IoT networks.
Potential Rewards: It offers improved security as it can be customized for each device or user, and it’s scalable to growing and evolving digital ecosystems.
Risks: Implementation can be complex, and poorly configured security policies can leave vulnerabilities.
Best Practices for Implementation: Develop a comprehensive understanding of the network and its vulnerabilities. Use professional security services if needed.
Definition: This is a form of IT infrastructure that abstracts hardware resources into software services and automatically manages those resources based on the needs of applications.
Enterprise Use Cases: It can be used in any industry that requires IT infrastructure, providing flexibility, scalability, and faster deployment of resources.
Potential Rewards: It can significantly reduce time and effort in managing IT resources, allowing for faster response to business needs.
Risks: The main risk is the complexity of implementation and the need for significant changes in IT operations.
Best Practices for Implementation: Start with a clear understanding of current and future IT needs. Use a phased approach to implementation, and provide training for IT staff.
Definition: AI Engineering is a discipline that brings together various practices such as DevOps, ModelOps, and DataOps to make AI projects more robust, scalable, and maintainable.
Enterprise Use Cases: It can be used in any industry implementing AI to ensure the long-term success and reliability of AI systems.
Potential Rewards: It improves the robustness and reliability of AI systems, reducing the risk of failures and the associated costs.
Risks: Implementing AI Engineering practices requires significant organizational change, including new skills, roles, and responsibilities.
Best Practices for Implementation: Incorporate AI Engineering principles from the start of AI projects. Invest in training and upskilling for staff.
Definition: This refers to various techniques that allow data to be used and shared for analytics and machine learning while preserving privacy.
Enterprise Use Cases: It can be used in any industry that uses sensitive data, like healthcare for research, finance for fraud detection, and advertising for personalized marketing.
Potential Rewards: It allows businesses to gain insights from data while maintaining privacy, enabling collaboration and data sharing without violating regulations.
Risks: These techniques can be complex to implement and may require significant computational resources.
Best Practices for Implementation: Collaborate with privacy experts and consider using privacy-enhancing computation tools. Always ensure compliance with data protection laws.
Definition: Hyperautomation involves the use of advanced technologies like AI and machine learning to automate tasks that were previously done by humans, including decision-making tasks.
Enterprise Use Cases: It can be used in industries like manufacturing for automating production lines, in customer service for chatbots, and in HR for recruitment and onboarding processes.
Potential Rewards: It can significantly improve efficiency, reduce errors, and free up human employees for more complex tasks.
Risks: There’s a risk of job losses, and over-reliance on automation could lead to vulnerabilities if systems fail.
Best Practices for Implementation: Start with tasks that are time-consuming but straightforward to automate. Ensure there’s a human oversight of automated systems.
Definition: Smart spaces are environments where humans and technological systems collaborate in increasingly open, connected, coordinated, and intelligent ecosystems.
Enterprise Use Cases: They can be used for smart offices that improve productivity and well-being, retail spaces that enhance customer experiences, and cities that optimize infrastructure and services.
Potential Rewards: They can provide enhanced experiences, improved efficiency, and new insights from the collected data.
Risks: There are significant privacy and security considerations, as well as the complexity of coordinating various systems.
Best Practices for Implementation: Prioritize user needs and experiences in the design of smart spaces. Ensure robust data privacy and security measures.
Definition: Neuromorphic computing refers to systems that attempt to mimic the human brain’s architecture to perform complex computations faster and more efficiently.
Enterprise Use Cases: It can be used for complex simulations, AI model training, and data analysis tasks in fields like research, healthcare, and finance.
Potential Rewards: It promises faster and more efficient computation, especially for tasks involving pattern recognition and decision making.
Risks: The technology is still in the early stages, and its full potential and limitations are not yet fully understood.
Best Practices for Implementation: Stay updated with the latest advancements and start considering potential use cases. It may also be worth partnering with academic institutions or tech companies that are pioneering in this field.
Definition: Generative AI refers to the use of AI algorithms to create content. This can range from text and images to music and video.
Enterprise Use Cases: Generative AI can be used for content creation in marketing, personalized advertising, automatic report generation, product design, and even drug discovery.
Potential Rewards: It can greatly enhance creativity, improve productivity, and personalize customer experiences.
Risks: The technology can be used to create deepfakes or generate misleading information, which raises ethical and legal issues.
Best Practices for Implementation: Use with a clear understanding of the potential ethical implications. Incorporate adequate safeguards to prevent misuse.
Definition: Machine learning is a type of AI that enables software the computers the ability to learn from data, and patterns, and make decisions without being explicitly programmed.
Enterprise Use Cases: Machine learning can be used for predictive analytics, customer segmentation, fraud detection, recommendation engines, and more.
Potential Rewards: It can help businesses gain insights from data, improve decision-making, and automate tasks.
Risks: Models can be biased or inaccurate if not properly trained, and the use of data raises privacy concerns.
Best Practices for Implementation: Start with clear business objectives. Use quality data and validate models regularly.
Definition: Distributed cloud is a model where cloud services are distributed to different physical locations, but the operation, governance, and evolution remain the responsibility of the public cloud provider.
Enterprise Use Cases: Distributed cloud can be used for low-latency applications, data sovereignty requirements, and edge computing.
Potential Rewards: It offers the benefits of cloud computing with the added advantage of geographical distribution, reducing latency and increasing resilience.
Risks: There may be issues with data security, and managing distributed cloud services can be complex.
Best Practices for Implementation: Choose a reliable cloud provider and ensure clear service level agreements (SLAs). Plan for security and compliance.
Definition: MLOps, or Machine Learning Operations, is collaboration and communication between data scientists, AI researchers, and operations professionals to help manage production ML lifecycle.
Enterprise Use Cases: MLOps can be used to manage machine learning models in production, ensure their reliability, and update them as necessary.
Potential Rewards: It can increase efficiency, improve the reliability of models, and enable faster deployment of new models.
Risks: Implementing MLOps requires significant organizational change, including new skills, roles, and responsibilities.
Best Practices for Implementation: Incorporate MLOps principles from the start of ML projects. Invest in training and upskilling for staff.
Definition: SASE is a security framework that integrates network security and wide area networking capabilities into a single cloud-based service.
Enterprise Use Cases: SASE can be used to secure remote workforces, cloud applications, and mobile users.
Potential Rewards: It can simplify security management, reduce complexity, and improve security for distributed workforces.
Risks: Implementing SASE may require significant changes to existing network and security infrastructure.
Best Practices for Implementation: Start with a clear understanding of the security needs of your organization. Choose a reliable SASE provider that fits these needs.
Definition: Multiexperience refers to the various permutations of modalities (touch, speech, and gesture), devices and apps that users interact and transact across digital work.
Enterprise Use Cases: Multiexperience can be used in retail to provide seamless shopping experiences, in customer service to provide omnichannel support, and in any sector that involves user interactions across multiple touchpoints.
Potential Rewards: It can lead to improved user experiences, higher customer satisfaction, and increased customer retention.
Risks: It requires significant investment and expertise to design and manage consistent experiences across multiple platforms and devices. There can also be challenges in maintaining data consistency across multiple touchpoints.
Best Practices for Implementation: Focus on user needs and the user journey. Implement strong data management practices to ensure consistency across channels.
Definition: Development platforms are environments that assist developers in creating apps, services, and systems. They can include tools for coding, testing, collaboration, and deployment.
Enterprise Use Cases: Development platforms are essential for any industry that develops software, whether for internal use or as a product.
Potential Rewards: They can significantly increase productivity, improve code quality, and shorten time-to-market for software products.
Risks: Choosing the wrong platform can limit flexibility and scalability. There’s also a risk of vendor lock-in.
Best Practices for Implementation: Consider current and future needs when choosing a platform. Look for platforms that offer flexibility, strong community support, and robust security features.
Definition: Responsible AI refers to practices that ensure AI systems are transparent, explainable, fair, and accountable.
Enterprise Use Cases: Responsible AI should be a consideration in any industry that uses AI. This includes everything from financial services using AI for credit decisions to healthcare providers using AI for diagnosis.
Potential Rewards: Implementing responsible AI can build trust with customers, reduce regulatory risks, and improve decision-making.
Risks: There can be significant penalties for unethical or unfair AI practices. It may also require significant resources to ensure AI systems meet responsible AI principles.
Best Practices for Implementation: Involve stakeholders from diverse backgrounds in the AI development process. Regularly audit AI systems for fairness, transparency, and accountability.
Definition: Blockchain is a decentralized and distributed digital ledger that records transactions across multiple computers in such a way that any involved record cannot be changed retroactively, without altering of all subsequent blocks.
Enterprise Use Cases: Blockchain can be used for supply chain management to improve traceability, in finance for secure and efficient transactions, and in healthcare for patient data management.
Potential Rewards: Blockchain provides enhanced security, transparency, and efficiency, which can drive cost savings and improved trust among participants in a network.
Risks: Blockchain technology is complex to understand and implement, and it may not be necessary or cost-effective for all use cases. There can also be regulatory uncertainties.
Best Practices for Implementation: Start with a clear understanding of how blockchain can address a specific business problem. Test the technology with a pilot project before full-scale implementation.
Definition: Edge computing refers to processing data near the edge of any network, where the data is generated, instead of in a faraway centralized data-processing warehouse.
Enterprise Use Cases: Edge computing can be used in manufacturing for real-time processing on IoT devices, in retail for personalized marketing, and in telecommunications for low-latency services.
Potential Rewards: Edge computing can reduce latency, save bandwidth, and improve privacy and security by processing data locally.
Risks: Managing and securing edge devices can be challenging due to their distributed nature. There can also be higher upfront costs for edge infrastructure.
Best Practices for Implementation: Identify specific use cases that benefit from low latency or local data processing. Implement strong security measures for edge devices.
Definition: 5G is the fifth generation of wireless technology for digital cellular networks, designed to increase speed, reduce latency, and improve flexibility of wireless services.
Enterprise Use Cases: 5G can be used for remote work, IoT devices, autonomous vehicles, telemedicine, and any applications that require high-speed, low-latency connections.
Potential Rewards: 5G can enable new business models, improve productivity, and enhance user experiences.
Risks: Implementation requires significant investment in infrastructure. There can also be issues with coverage and compatibility with existing technologies.
Best Practices for Implementation: Plan for a phased rollout and prioritize areas that will benefit most from 5G. Work with telecom providers to ensure adequate coverage and service.
Definition: A digital twin is a visual virtual representation of a process, product, or service that allows for simulation and analysis before actual devices or systems are built and deployed.
Enterprise Use Cases: Digital twins can be used in manufacturing for design and simulation, in smart cities for urban planning, and in healthcare for personalized medicine.
Potential Rewards: They can improve understanding of systems, enable simulation and testing, and improve decision-making.
Risks: Creating and maintaining accurate digital twins can be complex and resource-intensive. There can also be issues with data privacy and security.
Best Practices for Implementation: Start with clear objectives for what you want to achieve with a digital twin. Ensure data accuracy and implement strong data security measures.
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