What You Need to Know About Emerging Technologies in 2024 and Beyond – Part 2

Table of contents

In part 1 of our article, we discussed the transformative power of emerging technologies in reshaping industries and enhancing our interactions with the world—from generative AI to sensor data and digital twins. This part examines the themes of privacy, transparency, and human-centered approaches in AI—ensuring that technological advancements drive innovation while fostering trust, fairness, and security.

Why do we focus on these topics? Privacy and transparency are paramount as AI becomes more integrated into various sectors. According to the AI Index Report 2024, there has been a significant focus on responsible AI development, with attention to privacy and data governance as critical areas for ensuring ethical AI use. 51% of organizations recognize privacy and data governance-related risks as relevant to their AI adoption strategy.

Additionally, we will explore the critical enablers that drive technological advancements and revolutionize existing experiences. These enablers, ranging from new computing paradigms to advanced security measures, provide the foundation for innovative solutions, ensuring that progress is rapid but also sustainable. The list is based on Gartner’s Emerging Technologies Impact Radar.

Privacy and Transparency

This theme is all about creating AI systems that are transparent, fair, and secure. These systems should help boost human abilities while tackling ethical challenges. By working together across industries and governments, we can tap into AI’s potential to create positive outcomes for society’s benefit.

Human-Centered AI (HCAi)

Human-centered AI (HCAI) focuses on enhancing human capabilities rather than replacing them, keeping humans in control of AI models. Supported by major research entities, HCAI aims to use AI as a force for good, addressing various societal and ethical challenges. Unlike machine autonomy, it promotes technologies that increase people’s control over automation.

While traditional AI promotes task automation for efficiency, HCAI values real-world experiences and user engagement, improving technology design by studying human performance and needs.

It aims to ensure AI is fair, unbiased, secure, and transparent. Policymakers must understand these approaches to address AI’s ethical challenges, and cooperation at organizational and governmental levels is essential for implementing HCAI principles. The goal, after all, is for the technology to benefit everyone.

Behavior Analytics

By understanding behaviors like mouse movements, clicks, and scrolling patterns, companies can make informed improvements to their sites and apps. Unlike standard web analytics, which only offers metrics like pageviews, behavioral analytics digs deeper to understand why users behave as they do.

Interestingly, it involves tracking user interactions to build trust models that distinguish between fraudsters, trusted users, and bots. This extends beyond on-page tracking, combining social media info and customer service interactions for a comprehensive view, leading to more personalized yet non-intrusive user experiences.

Specific examples include retail for product recommendations and cybersecurity for threat detection. The main cross-industry focus, however, is on advancing this technology to create better user experiences, improve engagement, and drive business growth through informed decisions.

Responsible AI

Responsible AI is about making ethical and sound business choices when adopting AI. It addresses challenges like privacy concerns, bias, and lack of transparency and requires cross-industry collaboration to reduce these risks, with some partnerships already in place.

For example, Microsoft has endorsed voluntary commitments to ensure that advanced AI systems are developed and used responsibly. By supporting these guidelines and committing to additional measures, the company underlines the need for industry-wide cooperation.

Such initiatives include red-team testing (identifying vulnerabilities), publishing transparency reports (keeping AI processes clear), and adopting frameworks like the NIST AI Risk Management Framework.

Decentralized Identity (DCI)

Decentralized identity (DCI) or self-sovereign identity (SSI) systems address privacy and transparency issues in traditional identity systems. These digital credentials are fully controlled by their owners and can be verified using blockchain or other public ledgers.

DCI makes credentials hard to fake, reducing identity theft risks and benefiting people without paper documents, like refugees. Unlike traditional systems, where central authorities manage user data, DCI allows individuals to own and control their digital identities, preventing misuse and unauthorized access.

DCI benefits organizations by instantly verifying credentials and streamlining processes like hiring and certification. This reduces the risk of fraud and speeds up validation. DCI also enhances privacy and security by minimizing stored user data and using cryptographic keys for secure authentication.

Privacy-Enhancing Technologies (PETs)

Privacy-enhancing technologies (PETs) are designed to process information while protecting personal data. They address the growing privacy challenges as the internet integrates into every aspect of our lives. PETs use encryption and data obfuscation to ensure data anonymity and security.

Although beneficial, PETs are complex and costly. Additionally, because PETs provide robust privacy protections, there is a risk that users may develop a false sense of security, believing their data is entirely safe. This can lead to misuse, where organizations might over-rely on PETs without fully understanding their limitations or implementing additional necessary security measures. Hence, ongoing research and development are needed.

Despite these challenges, PETs are essential for balancing data utility and privacy, especially in healthcare, finance, and public safety, where secure data analysis and sharing are crucial.

Emerging technologies in 2024 - AI robot sitting at the computer.

Critical Enablers

In this part, we’ll focus on the emerging applications and technologies that promise to unlock new use cases and enhance existing experiences. By understanding these critical enablers, organizations can determine where to invest and which technologies to evaluate, ensuring they stay ahead in an increasingly competitive landscape.

Neuromorphic Computing

Neuromorphic computing aims to replicate how the human brain works to improve the efficiency of computers, especially those used for AI. Traditional computers have separate units for memory (where data is stored) and processing (where data is analyzed and manipulated). Moving data between these units uses a lot of energy. Neuromorphic computing minimizes this separation, allowing data to be processed where it is stored, thus saving energy.

Additionally, neuromorphic systems process information in parallel, much like the brain does, and only use power when they detect a change or event that needs processing. This event-driven approach further reduces energy consumption compared to traditional computers.


Neuromorphic computing is particularly suited for edge computing, where devices process data locally rather than relying on the cloud. This is crucial for AI-powered devices like autonomous vehicles, which must process data in real-time without external data centers. Additionally, neuromorphic computing can be used in smart vision systems, autonomous robots, and IoT devices, providing efficient low-power AI solutions. Its market is expected to grow from $139 million in 2024 to $672 million by 2028.


Tokenization means converting sensitive data into a secure, tokenized form that can be safely stored and transmitted. Instead of using actual data (like a credit card number), a token – a unique string of characters – represents it. This token has no exploitable value if breached because it doesn’t reveal the original data.


Tokenization protects sensitive information like passwords and payment data, ensuring that the actual data remains secure even if a system is breached. It’s particularly crucial in sectors like healthcare and finance, where protecting sensitive information is paramount. In 2022, over 90% of North American payment volume used digital tokens.

In the context of artificial intelligence and machine learning, tokenization can also refer to the process of breaking down text into smaller units called tokens (e.g., words, characters, or subwords). This type of tokenization is used to prepare text data for processing by machine learning models, enabling AI systems to understand and learn from textual data without compromising privacy.

Hyperscale Edge Computing (HEC)

Hyperscale edge computing (HEC) is a solution where cloud computing services extend to the network’s edge and are primarily managed by major cloud providers like AWS, Microsoft, and Google. This approach leverages distributed cloud platforms to bring computing power closer to end users, optimizing costs and reducing the carbon footprint of digital resources. Edge computing decreases the need for extensive data transfer, thus saving energy and storage.


HEC is crucial for scenarios that require fast data processing. For instance, autonomous vehicles use edge computing to quickly process data and make decisions without relying on distant cloud data centers. Similarly, Augmented Reality and Virtual Reality (AR/VR) applications also need low-latency processing, which edge computing can provide. Additionally, edge computing is used in smart cities to manage traffic, utilities, and emergency services efficiently.


Blockchain technology uses a secure, decentralized system to store and manage data. It focuses on distributing data across many computers, making it hard to alter, and uses cryptographic techniques to keep information safe. Each block in the chain contains a unique code (hash), the previous block’s hash, a timestamp, and the transaction data, which makes the system secure and unchangeable.


Blockchain is widely used in finance, from money transfers to identity identification, smart contracts, and lending. In supply chains, blockchain helps track the journey of products from production to sale, ensuring transparency and authenticity. In healthcare, it securely manages patient records, ensuring data is accurate and accessible only to authorized people. For real estate, blockchain provides a safe way to record property ownership and transactions, reducing fraud. This technology’s popularity is ever-growing, and by 2030, it’s expected to add $1.76 trillion to the global economy.

Knowledge Graphs (KGs)

A knowledge graph represents a network of real-world entities (objects, events, or concepts) and their interconnections. These entities are visualized as nodes, and their relationships are shown as links. Advanced technologies like NLP and machine learning allow knowledge graphs to integrate and analyze data from various sources, allowing for a deeper understanding and discoveries.


Knowledge graphs are widely used to gain deeper insights from diverse data sources. In consumer behavior analysis, they help companies understand customer preferences by connecting purchase history with social media activity. In medical research, they assist in linking genetic data with clinical trial results to identify new treatment approaches. They also improve decision-making in areas like supply chain verification by combining production data with transportation and sales information to optimize logistics and reduce costs.

LEO (Low Earth Orbit) Satellite Mega-Constellations

LEO (low Earth orbit) satellite mega-constellations are networks of satellites that orbit relatively close to Earth, between 300 and 2,000 kilometers above the surface. Because they orbit closer to Earth, they can offer real-time connectivity for video calls, online gaming, and guiding autonomous vehicles.


LEO satellites are instrumental in areas where wired broadband isn’t available, in difficult-to-reach locations, or during emergencies. In aviation and maritime industries, they provide reliable connectivity over oceans and remote flight paths, improving safety and communication. In agriculture, these satellites help monitor environmental changes and crop health, supporting more efficient farming practices. Military and defense sectors use LEO satellites for real-time data and secure communications.

Private 5G

Private 5G is a network technology tailored for individual businesses, meaning it offers a dedicated and secure network just for one organization. Unlike public networks, private 5G provides optimized services and enhanced security. It has a broader range and more reliable connectivity than Wi-Fi, making it perfect for advanced industrial applications.


Private 5G networks are beneficial in places like industrial plants and vehicle factories. They support automation, real-time monitoring, and remote machinery control, all with secure, seamless communication across large areas. In healthcare, private 5G ensures reliable, high-speed connections for telemedicine and remote surgeries. For logistics, it enhances the tracking and management of goods. Even smart cities benefit from private 5G, which enables secure and efficient infrastructure and services management.


Navigating the rapidly evolving tech landscape requires balancing ethical considerations and understanding critical enablers. From ensuring privacy and transparency in AI to leveraging innovative technologies like neuromorphic computing and private 5G, these topics are crucial for companies shaping the future.

We encourage you to reach out to discuss these themes further and explore their potential impact on your organization. Stay tuned for more updates as we continue to monitor emerging tech trends. Let’s work together to ensure technology benefits everyone and fosters a more connected world.

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