The world of technology is undergoing some big changes, and artificial intelligence (AI) is at the heart of it all. As more and more companies start using AI and other new technology, there is a growing need for people who know how to use it to make things better and grow the business.
If you want to build a successful career in computer science, you need to understand the key trends shaping the future of AI and how they might affect the job market. In this article, we will name the main ones and explain how they work.
If you want to build a successful career in computer science, you need to understand the key trends shaping the future of AI and how they might affect the job market. In this article, we will name the main ones and explain how they work.
1. Sustainable Technology
We believe sustainable technology plays an important role in solving the world’s environmental and social challenges. Digital solutions will be integrated into various aspects of business and society to promote long-term ecological balance, social equity, and ethical governance. Key areas include:
Organizations need to get on board with sustainable technology to drive ecological balance, social equity, and ethical governance. Only then will we create a more sustainable future for all.
- Environmental Innovation. Efficient renewable energy sources and smart grids
- Social Impact. Digital inclusion, healthcare accessibility, and education technology
- Governance and Transparency. Blockchain, data privacy, and digital governance platforms
- Human Rights and Ethics. Ethical AI, supply chain traceability, and fair algorithms
Organizations need to get on board with sustainable technology to drive ecological balance, social equity, and ethical governance. Only then will we create a more sustainable future for all.
2. AI Augmented Development
AI-augmented development is a trend that combines generative AI and machine learning to enhance the software development lifecycle. This approach improves the efficiency, speed, and quality of software engineering processes. Here are the key elements:
Code Generation
AI can create code based on developer specifications, speeding up the coding process.
Natural Language Processing (NLP)
AI models with NLP capabilities can understand natural language descriptions, which makes it easier for developers to express their intentions in a more readable format.
Automated Testing
ML algorithms can create intelligent test automation tools that learn from past tests, making the testing process faster.
Predictive Analytics
ML models can predict areas of code that are likely to have defects. This helps developers focus their testing efforts on the most important parts of the application.
Automated Code Review
AI tools can analyze code for quality.
Gartner says that by 2028, 75% of software engineers will be using AI coding assistants. As AI-augmented development keeps on evolving, it will play a big role in shaping the future of software development.
Code Generation
AI can create code based on developer specifications, speeding up the coding process.
Natural Language Processing (NLP)
AI models with NLP capabilities can understand natural language descriptions, which makes it easier for developers to express their intentions in a more readable format.
Automated Testing
ML algorithms can create intelligent test automation tools that learn from past tests, making the testing process faster.
Predictive Analytics
ML models can predict areas of code that are likely to have defects. This helps developers focus their testing efforts on the most important parts of the application.
Automated Code Review
AI tools can analyze code for quality.
Gartner says that by 2028, 75% of software engineers will be using AI coding assistants. As AI-augmented development keeps on evolving, it will play a big role in shaping the future of software development.
3. Platform engineering
Platform engineering is the creation of robust infrastructure and services that help developers to build, deploy, and manage applications efficiently. There are several key aspects to this process:
Self-Service
Providing tools for development teams to manage their applications.
Layered Architecture
Modular design with layers to make things more flexible and scalable.
Integration and Scalability
Seamless integration with CI/CD pipelines, monitoring tools, and horizontal scalability.
User-Centric Approach
Prioritizing user experience, documentation, and training to ensure intuitive platform usage.
By adopting platform engineering with self-service internal platforms, organizations can accelerate development, improve collaboration, and foster innovation. According to Gartner, by 2026, approximately 80% of software engineering teams will be platform-based.
Self-Service
Providing tools for development teams to manage their applications.
Layered Architecture
Modular design with layers to make things more flexible and scalable.
Integration and Scalability
Seamless integration with CI/CD pipelines, monitoring tools, and horizontal scalability.
User-Centric Approach
Prioritizing user experience, documentation, and training to ensure intuitive platform usage.
By adopting platform engineering with self-service internal platforms, organizations can accelerate development, improve collaboration, and foster innovation. According to Gartner, by 2026, approximately 80% of software engineering teams will be platform-based.
4. CTEM — A Proactive Approach to Cybersecurity
In 2024, Continuous Threat Exposure Management (CTEM) will change how we protect our data. This approach helps organizations understand their security and respond to threats quickly.
CTEM includes vulnerability assessments, threat intelligence, risk management, monitoring, patching, automation, and compliance. Leveraging these components, organizations can move away from traditional security assessments and establish a dynamic security posture.
Companies that use CTEM can cut security breaches by 66%. This approach is important in today’s fast-changing cybersecurity world, where new problems and threats emerge often.
CTEM includes vulnerability assessments, threat intelligence, risk management, monitoring, patching, automation, and compliance. Leveraging these components, organizations can move away from traditional security assessments and establish a dynamic security posture.
Companies that use CTEM can cut security breaches by 66%. This approach is important in today’s fast-changing cybersecurity world, where new problems and threats emerge often.
5. Industry Cloud Platforms
The emergence of industry cloud platforms is one of the most significant developments in the tech world. These specialized cloud computing solutions know their sector inside out. The platform is designed to help organizations meet regulatory requirements. By 2027, over half of enterprises will use industry cloud platforms. This shows how important they are in the tech landscape.
Industry cloud platforms go beyond generic cloud services by integrating industry-specific Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) components. This architecture allows for seamless integration, flexible scaling, and a pay-as-you-go model, maximizing efficiency and reducing costs.
Industry cloud platforms go beyond generic cloud services by integrating industry-specific Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) components. This architecture allows for seamless integration, flexible scaling, and a pay-as-you-go model, maximizing efficiency and reducing costs.
6. AI TRiSM
As AI changes our lives, we must think about how reliable, trustworthy, and secure it is. The AI Trust, Risk and Security Management (AI TRiSM) framework helps companies develop trust in AI applications.
The AI TRiSM framework has three key elements:
Deploying and managing AI models is a crucial aspect. This framework helps organizations deal with AI challenges.
AI and related technologies are transforming industries across various sectors. AI will introduce innovative capabilities, enhance efficiency, and foster new solutions in fields like automated creativity, generative design, and cybersecurity.
The job market will also be affected, with a shortage of AI expertise leading to more opportunities for skilled professionals. However, automation will take over roles that can be done more easily with AI.
The AI TRiSM framework has three key elements:
- AI Trust is about making it easy to understand how AI models achieve their results.
- AI Risk — the framework manages AI risks throughout the development and process stages, ensuring model integrity and compliance.
- AI Security Management — to keep the system secure by addressing any problems and preventing potential attacks.
Deploying and managing AI models is a crucial aspect. This framework helps organizations deal with AI challenges.
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AI and related technologies are transforming industries across various sectors. AI will introduce innovative capabilities, enhance efficiency, and foster new solutions in fields like automated creativity, generative design, and cybersecurity.
The job market will also be affected, with a shortage of AI expertise leading to more opportunities for skilled professionals. However, automation will take over roles that can be done more easily with AI.