������

Adopt Most organizations should act on this trend

• 1. AI as a service

  “Ready-to-go” AI solutions offered as a service on cloud platforms. They often don't require specialized AI or ML skills to be used.
• 2. AI/ML on edge

  The ability to run AI and machine learning algorithms at the edge of a network, often on resource-constrained devices.
• 3. Automated compliance

  The use of technology to make all the data required to satisfy compliance reports, checks and balances readily available. In many cases, the automation simplifies reporting by sifting through data; however, AI is now beginning to replace manual decision making.
• 4. Code of ethics for software

  A set of guidelines organizations can use to manage risk and mitigate the potential negative consequences of given technologies (such as AI bias).
• 5. Collaboration ecosystems

  When individuals or organizations share common goals, they will probably want to work together. To do so, they need a set of tools and resources they can use to unlock value effectively — a good example is a remote environment for development teams. It allows people to solve problems together.
• 6. Data mesh

  A data platform organized around business domains where data is treated as a product, with each data product owned by a team. To enable speed and drive standardization, infrastructure teams provide tools that allow data product teams to self-serve.
• 7. Digital ecosystems

  Disparate participants, systems and even organizations that cooperate, collaborate and compete to create an emergent ecosystem where the whole is greater than the sum of the parts. Examples include the travel industry, online marketplaces and new “super apps” such as Gojek and WeChat.
• 8. Digital twin

  A virtual model of a process, product or service that allows both simulation and data analysis. 3D visualization can be used together with live data, so you can understand what is happening to pieces of equipment you can’t actually see.
• 9. Edge computing

  Bringing data storage and processing closer to the devices where it is stored, rather than relying on a central location that may be thousands of miles away. Benefits include reduced latency for real-time systems and improved data privacy.
• 10. Ethical frameworks

  Decision-making frameworks that attempt to bring transparency and clarity into the way decisions are made, especially around the use of AI and potential bias in data.
• 11. Explainable AI

  A set of tools and approaches to understand the rationale used by an ML model to reach a conclusion. These tools generally apply to models that are otherwise opaque in their reasoning.
• 12. FinOps

  The practice of bringing financial accountability to the variable spending model of cloud computing. It involves a collaborative approach among teams such as finance, operations and development to manage and optimize cloud costs effectively.
• 13. Green cloud

  Data centers fed by renewable energy, running software and systems designed and optimized for efficient processing while also minimizing energy consumption.
• 14. Integrated data and AI platforms

  Platforms designed specifically for machine learning, providing end-to-end capabilities such as data management, feature engineering, model training, model evaluation, model governance, explainability, AutoML, model versioning, promotion between environments, model serving, model deployment and model monitoring.
• 15. Knowledge graphs

  A way to represent knowledge and semantic relationships between entities using a graph data structure.
• 16. MLOps

  A movement to bring DevOps practices to the field of machine learning. MLOps fosters a culture where people, regardless of title or background, work together to imagine, develop, deploy, operate, monitor and improve machine learning systems in a continuous way. Continuous Delivery for Machine Learning (CD4ML) is ������' approach to implement MLOps end-to-end.
• 17. Online machine learning

  A technique where algorithms continuously learn based on the sequential arrival of data, and can explore a problem space in real time. Contrasts with traditional machine learning where model training uses only historical data and cannot respond to dynamic or previously-unseen situations.
• 18. Platforms as products

  A way of creating and supporting platforms with a focus on providing customer (user) value instead of treating platform building as a time-boxed project.
• 19. Privacy first

  Privacy first is a significant shift in business, organization and product strategy, where privacy operates as a core business value and offering. This shift moves away from the prior movement where "users are the product", into a new realm, where building trust and transparency comes first.
• 20. Privacy-enhancing technologies (PETs)

  A collection of technologies and techniques for preserving user privacy, such as anonymization, encrypted computing and differential privacy.
• 21. RAG (Retrieval Augmented Generation)

  A method in artificial intelligence where the system enhances its response generation by fetching relevant information from a large database or knowledge source. This approach combines the creative aspects of generative AI models with the precision of data retrieval, enabling more accurate and contextually relevant responses in various business applications.
• 22. Secure software delivery

  Security applied to the entire process of software creation, which in modern architectures includes the delivery pipeline used to build, test and deploy applications and infrastructure.
• 23. Smart homes

  Featuring smart hubs, homes are now becoming 'smart', allowing people to control almost all household systems. Analytics can even guide or manage heat and energy supply and learn from individual habits or those in a neighbourhood.
• 24. Smart systems and ecosystems

  Networks of networks that use AI and ML to enhance a system to become more than the sum of its parts. For example, in a smart city, networks of cars and roadside sensors help speed the flow and safety of traffic.
• 25. Vector databases

  Specialized storage systems designed to efficiently handle and index high-dimensional data vectors, commonly used in machine learning and AI applications.

Analyze Decide the value to your specific circumstances and adopt if appropriate

• 26. AI, IoT and XR combined solutions

  A new breed of solutions in which multiple technologies are combined and act together. Drones, robotics and autonomous vehicles are all examples of devices that require machine learning, processing streams of data and layers of intelligence to solve problems.
• 27. AutoML

  An approach to partly automate the work of data scientists and machine learning engineers by automatically selecting and training machine learning models for specific tasks.
• 28. Autonomous robots

  Smaller and cheaper than their industrial counterparts, robots with on-board AI are able to sense their environment, navigate, learn to complete tasks and even fix themselves and other things.
• 29. Autonomous vehicles

  Self-driving cars, trucks and public transport. While the headline focus may be on self-driving cars, autonomous vehicles also have high potential for specialized industrial and business applications such as mining and factory floors.
• 30. Data clean room

  Secure environments for organizations to share and combine data with each other without having to physically share their own data
• 31. Data marketplaces

  A system that enables the finding, buying, sharing and selling of data within and outside an organization.
• 32. Decentralized data platforms

  Use of multiple data stores instead of singular, monolithic centralized stores. A good example is data mesh (see above).
• 33. Differential privacy

  A privacy technique that introduces noise in a dataset in such a way as to provide individual privacy while still allowing insights to be drawn or machine learning models to be built on top of the data.
• 34. Encrypted computation

  The ability to perform calculations on encrypted data, without first decrypting it. Useful to maintain data privacy while allowing data storage and manipulation to be outsourced. This includes technologies like secure multi-party computation and homomorphic encryption.
• 35. Federated learning

  An approach that downloads a machine learning model and then computes or trains a specific, modified model using local data on another device. The approach helps multiple organizations to collaborate on model creation without explicitly exchanging protected data.
• 36. Increased regulation

  The steady increase of regulation, especially around data, privacy, security and greenhouse gas emissions.
• 37. Personal information economy

  A business model that aims to extract business value from the possession and use of large amounts of personal information. Examples range from the primitive use of cookies to the targeted profiling of people via their online behavior. This has historically been the domain of companies or intermediate ad-based services trying to retain and target customers, but, since GDPR and similar privacy laws, we are seeing a shift towards people controlling what data they wish to expose in exchange for a service.
• 38. Privacy-aware communication

  Communications software that directly advertises its security stance and features, such as end-to-end encryption.
• 39. Privacy-respecting computation

  New techniques that allow stronger guarantees for privacy, even when personal data is used in computations. Part of the broader category of privacy-enhancing technologies (PETs).
• 40. Smart cities

  An urban area that uses different types of IoT sensors to collect data coupled with platforms to integrate and act on the data, advising or commanding digitally enabled systems to perform some response. Insights gained from the data are used to manage assets, resources and services efficiently; in return, that data is used to improve the operations across the city.
• 41. Zero knowledge proofs

  A method that allows one party to prove to another that a statement is true without revealing how it knows it is true.

Anticipate Monitor this trend and consider how and when to react as it evolves