Technology trends to watch in 2024
Welcome to our Tech Trends series. We tell by blending expertise with an engineering mindset. As another year in technology unfolds, enjoy our tech trends with a dash of engineering approach. It’s an exciting time as breakthrough technologies, especially in artificial intelligence, transform business models and the digital world faster than ever.
Last year, when anticipating what would happen in the tech landscape in 2023, we noticed several noteworthy patterns:
- the no-code/low-code movement continued to democratise software development,
- layoffs in Big Tech prompted discussions about efficiency and the essential workforce,
- AI and machine learning continued to expand.
Fast forward to the end of 2023. Let's look at what else we got right in the previous tech trends blog post.
Today, we’ll explore what we anticipate to be technology trends for 2024. And yes, it screams AI.
Have a great read, and Happy 2024! 🥳
Technology trends 2024: key takeaways
- AI is commercialised in various industries and requires a thoughtful approach to ensure ethical use.
- AI-powered tools are helping companies optimise decision-making and be data-driven and smart.
- Platform Engineering is the next cultural evolution in the IT industry, making Cloud software a well-oiled machine.
- Again, focus on performance: developers build better serverless apps with tools like WASM, Rust, or Zig.
- There are emerging threats in cybersecurity, including, yes, you guessed correctly, AI attacks.
For a few years now, Artificial Intelligence (AI) has been the most exciting technology trend, making waves across various industries. Software such as OpenAI Dall-E 3, ChatGPT 4, and Github-Copilot give a glimpse of what we will see in 2024.
Can AI be even more exciting? Indeed, it can. As we are writing this blog post, a strange story is unfolding. Sam Altman, CEO of OpenAI (the company behind ChatGPT), publicly hinted at significant AI advances. Shortly afterward, the board decided to terminate Altman's position as CEO.
The information was a surprise to everyone, including people working at OpenAI. The reason was that Altman “was not consistently candid in his communications”.
Days after his ousting, Sam Altman has returned as CEO of OpenAI. Sounds like a corporate drama, but it is way bigger. We can only speculate what happened; some questions about AI ethics have arisen, and many hinted at the dawn of Artificial General Intelligence (AGI). The plot thickens.
Challenges and opportunities
Emerging technologies in AI show up at tremendous speed. Every day, we hear about the new software tools and technology solutions that help many do their job better. When some are practicing the art of writing the best possible prompts in GPT-4, Google, just days ago, released its rival. According to Google data, the algorithm beats its predecessor on every level. Gemini AI, Google's latest LLM, has been designed to be more powerful and capable. It is built for multimodality that reasons seamlessly across text, images, video, audio, and code.
As AI is increasingly prevalent, it brings both opportunities and challenges. Businesses can leverage AI technology for various purposes, such as content creation, product innovation, and customer service, to automate business processes and improve efficiency.
With ongoing advancements comes responsibility, as the ethical implications and potential misuse of such technologies must be carefully considered. In 2023, AI and machine learning continued their march from buzzwords to essential business tools. In 2024, we expect these technologies to become even more deeply integrated into everyday business processes.
Discover what to expect in the realm of AI commercialization in AI trends 2024 by Kamil Rzechowski, a Senior ML Engineer from ReasonField Lab.
AI-powered development is revolutionizing software development processes. By leveraging AI-augmented tools, developers can:
- Complete tasks faster
- Reduce errors
- Streamline workflows
- Increase productivity
- Create better software products
AI-powered development allows businesses to refine IT operations and remain competitive in an increasingly digital environment.
AI-augmented tools transform software development by automating and optimizing various tasks, such as writing code with code completion, documentation research, problem analysis, and optimization. These tools, such as GitHub Copilot, Copilot Chat, and JetBrains AI Assistant, enable developers to work more efficiently, complete tasks faster, and deliver high-quality software.
The above may improve developers' job satisfaction and retention, benefiting both the individual and the organization.
Impact on developers
The rise of AI-powered development tools is reshaping the role of developers, requiring them to adapt and upskill to stay relevant in the industry. With AI-augmented tools automating mundane tasks and providing intelligent code suggestions, developers can focus more on intricate and creative tasks, leading to a more fulfilling and productive work experience.
However, integrating AI into their work also brings challenges, such as inadequate AI suggestions and scarcity of AI expertise. By continuously learning and embracing new technologies, developers can stay ahead in the ever-changing landscape of software development.
Data-driven: making smart decisions
Leveraging advanced AI techniques to extract insights and knowledge from large and complex datasets is what becomes important for companies on a strategic level. More and more CxOs recognize the importance of AI and will want to adopt this technology in their organizations. The combination of intuition and AI-powered analytics holds great potential for expanding companies' knowledge and making better decisions. As users can interact with data more efficiently, they can comprehensively track, identify, understand, and act on what is most important.
As we move forward in 2024, AI and automated insights will become more common among businesses. This trend will simplify the process of decision-making. We will see more and more enterprise applications with the element of AI built into them. This year, we saw it in Atlassian but also in a lot of Microsoft products ranging from Windows 11 to Microsoft Teams. The AI transformation will go from tools and processes to strategic improvements over time.
More on a trend where AI tools simplify extracting information from datasets even for non-technical users from our Adam Kaczmarek, ML Engineer from ReasonField Lab.
As we move forward in 2024, the integration of natural language processing (NLP) and automated insights will enable people to interact with data.
- Zero-shot Information Extraction: LLMs allow the extraction of personal information allowing identification (such as name, SSN, e-mail, phone number, date of birth) from unstructured text data into a structured form. Possible use cases are either better internal organization of data from textual sources, e.g., email, or the anonymization of various documents.
- Invoice Extraction: extracting information from documents, especially invoices and forms with OCR + LLMs combination or even without the OCR. Usually, there are three subtasks that can be treated separately:
- Document Layout Analysis: dividing or segmenting the document into consistent parts containing, e.g., whole multiline addresses, multi-paragraph clauses, tables, or table items.
- Span Classification:identification of certain classes of text - addresses, amounts, phone numbers, company names, dates, etc.
- Relation Extraction:the most promising future approach is zero-shot semantic relation extraction, which allows the recognition of relationships between text spans within a document without requiring additional fine-tuning.
- CV Parsing: extracting structured information from candidate resumes. While having varying structures, shapes, colours, and content, resumes usually have a standard set of values that can be more easily extracted with the help of LLMs and properly formulated prompts.
- Retrieval Augmented Generation: one of the most reasonable use cases for LLMs and one that can be developed for privacy-requiring on-premise solutions is Retrieval Augmented Generation. This is probably one of the cases that would be popular in the short term as it allows for LLMs to act based on internal knowledge bases, minimising the so-called "hallucinations" while keeping the benefits of a human-friendly interface.
Organisations find it challenging to have a tab on data reliability, in other words, managing and maintaining data is a new ball game altogether. With data observability, it is easy for them to monitor, track, and ensure the quality, reliability, and performance of data throughout its lifecycle. With 85% of organisations relying on data-driven decision-making and analytics, it is essential to have the right data at hand.
Some of the key aspects of data observability for organisations include Data Quality Monitoring, Data Lineage and Traceability, Data Security and Compliance, Data Auditing and Logging. Having said this, it is evident that observability maintains data quality, security, and performance while ensuring compliance with regulations and supporting data-driven decision-making. By implementing robust data observability practices, organisations can get reliable insights and make better-informed decisions.
This trend isn’t about reinventing the wheel but building well-organized teams of Cloud engineers that can be managed as a product and bring greater efficiency to Developer Teams. Platform teams work in a way that all operational services are integrated into one internal platform that enables continuous delivery while its users, developers, DevOps engineers, and SRE engineers can operate at their speed and cadence.
More companies will switch to the Platform Engineering model to provide the infrastructure and tools necessary for the development, deployment, and management of applications on cloud platforms. This includes:
- Constructing and sustaining an Internal Developer Platform (IDP)
- Formulating CI/CD pipelines
- Developing self-service capabilities
- Maintaining an engineering platform
Platform Engineers, by maintaining the seamless functioning of the cloud platform, will play a significant role in underpinning the efficient and secure operations of digital businesses.
Additionally, DevOps and Platform Teams are adopting automated solutions powered by AI to streamline tasks in the creation, deployment, management, and testing of infrastructure and software. This shift not only accelerates project completion with fewer errors but also ensures improved product quality.
MLOps: the future of AI and Machine Learning
Machine Learning Operations (MLOps) is the paradigm of streamlining and automating the deployment, monitoring, and management of machine learning models. Implementing MLOps principles allows organisations to effectively roll out and manage AI and machine learning solutions, utilising these cutting-edge technologies to innovate and refine business processes.
MLOps offers numerous benefits, such as faster development and deployment, scalability, and reduced operational costs. However, it also presents challenges in terms of infrastructure, processes, and security.
Read more about MLops in this blog post
Sustainable technology and its impact
Sustainable technology, an emerging trend, emphasizes the development and implementation of eco-friendly solutions aimed at minimizing environmental impact and fostering sustainable growth. With escalating concerns regarding climate change and resource management, a World Economic Forum report identifies the technologies expected to have the most significant global impact over the next three to five years. One is in particular close to our engineering hearts.
In previous years, all top cloud providers introduced tools and features allowing you to calculate your cloud infrastructure’s CO2 impact and choose, e.g., low CO2 regions. Those capabilities are still being improved. Microsoft Cloud for Sustainability brings together a range of environmental, social, and governance (ESG) capabilities. Organizations are able to better track and manage their environmental footprint.
Using sustainable technology helps businesses save resources. In 2024, we will see more products leading to growing green technology adoption.
Quantum Computing technology
Quantum computing is a groundbreaking technology that has the potential to revolutionise traditional computing by offering immense computing power and speed. Leveraging quantum mechanics, quantum computers can solve complex problems and perform calculations previously impossible for classical computers.
The continual advancement of quantum computing will shape the future of technology as its applications and implications take form.
Quantum computing capabilities
Quantum computing capabilities include solving complex optimization problems, simulating quantum systems, and enhancing cryptography. By leveraging the unique properties of quantum mechanics, quantum computers can explore multiple solutions simultaneously and find the optimal solution more efficiently than classical computers.
These capabilities have the potential to transform industries, such as finance, healthcare, and logistics, by enabling faster and more efficient problem-solving and data processing.
Industries such as finance, healthcare, and logistics can benefit from quantum computing. However, hardware development challenges remain. IBM plans to release its new chip, Condor, offering 1121 qubits still in 2023, while just in 2021, we just saw the first systems with 100 qubits.
With the proper investment in research and development, quantum computing can reshape the way we process and analyze data, driving innovation and creating new opportunities for businesses and professionals alike.
It may sound like quantum computing is still a distant topic, but various systems have already started preparations. Quantum computers introduce threats to cybersecurity. Signal (secure internet messaging platform) introduced this year changes in its protocol to guarantee Quantum Resistance. GitHub announced tools aimed to help with post-quantum cryptography. We expect to see new developments in cryptography and new algorithm applications so that the internet will be safe once the new powerful quantum computers are created.
Cybersecurity in the digital age
Cybersecurity is becoming increasingly important as new types of threats emerge every year. With the increasing usage of digital technology and the growing volume of data, businesses and professionals must stay vigilant and proactive in addressing cybersecurity risks.
Emerging threats and solutions
New cybersecurity threats include AI-driven attacks, privacy leaks, and impersonation. Deepfakes can generate fake videos or the voice of any person, creating a completely new attack vector. Various AI tools are tempting, but sometimes it is unclear what will happen with the provided data. Companies may start leveraging private instances or enterprise packages offering additional privacy-related features.
Cybersecurity intersects with various areas mentioned in this blog. It is related to AI (e.g., in terms of data privacy), MLops, Platform Engineering (can't forget about good security practices), and Quantum Computing (new types of threats). Security remains a top concern in cloud computing, as businesses must protect their digital assets and their customers' data privacy. We said that already in previous years, but we expect to grow security importance in IT companies even further in the next few years.
Wrap up of our tech trends
This year brought us the most innovations in AI, and we expect to see that trend continue in 2024. Other areas may be familiar but are still gaining importance from year to year.
We're curious what your engineering eye has spotted and what, in your opinion, we will see more of in the upcoming year. Share your trends in the comments!
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The past decade technology trends
Yes, we summarised the advanced technologies and strategic technology trends that occurred in the past decade. Check it out to get a broader perspective on the tech world.