2025 Trends for the Software Engineering Industry

Software engineering doesn’t sit still, but that’s not necessarily a bad thing. It keeps us on our toes and means that there are near limitless possibilities in this exciting and key sector. Whether it’s incorporating artificial intelligence and machine learning, utilising low-code and no-code development platforms, or making the most of cloud-native applications, there are a host of emerging technologies in software engineering. 

Our guide explores the key trends that look set to shape the future of software engineering. Join us as we take a look at them and their impact! 

Adoption of Artificial Intelligence and Machine Learning 

Never out of the headlines, artificial Intelligence and machine learning are shaking up software engineering, making development faster, smarter, and more efficient. These technologies are streamlining workflows, improving code quality, and enhancing overall productivity. But what does AI and ML integration actually bring to the table? 

Let’s break it down.

• Development lifecycles are sped up, resulting in a reduced time-to-market, ensuring software products and businesses alike remain competitive. 

• The quality and consistency of code are improved. 

• Repetitive tasks are automated, meaning software development teams can concentrate on creating in-demand applications. 

• Enhanced accuracy with project plans and resource allocation. 

• It allows for improved user experiences, hyper-personalisation, and adaptive software. 

Let's explore the latest software engineering trends driven by AI and ML:

Natural Language Processing (NLP)

Driven by AI and ML development acceleration, NLP is set to reshape the software industry, particularly in how companies engage with data and customers. Demonstrating its progress - and potential - the global NLP market was valued at $27.65 billion in 2024 and is forecast to reach $35.11 billion in 2025. Representing a CAGR of 27%, by 2033, the market could be valued at $237.63 billion by 2033. 

It’s clear that the NLP market is on an upward trajectory, but what’s driving this growth? Here’s a closer look at some key factors behind its rise.

Improved Customer Interactions

NLP-powered chatbots are revolutionising customer service by handling real-time interactions and significantly improving response times. As this technology advances, it could drastically reduce the need for human intervention.

Backing this up, a study by Juniper Research predicts that chatbots will manage 75% of customer interactions, helping businesses cut customer service costs by 30%.

Enhanced Data Analysis

With NLP allowing computers to understand and generate human language, data analysis has taken a big leap forward. It enables businesses to extract valuable insights from unstructured text, making data-driven decisions more effective. Here are some key techniques that make this possible:

• Sentiment Analysis: NLP algorithms can assess public opinion, customer feedback, and brand perception by examining social media posts, customer reviews, and survey responses. Companies incorporate sentiment analysis to learn if customer reactions to product launches are positive, negative, or neutral. 

• Text Classification: This approach automatically classifies text documents into predefined categories, refining the organisation and gathering of textual data. It’s useful for spam detection, news categorisation, and content filtering tasks. 

• Named Entity Recognition (NER): NLP can identify and extract specific entities from text, such as people, organisations, locations, and dates. This capability is valuable for uncovering key information and supporting knowledge graph development.

• Topic Modelling: Latent Dirichlet Allocation (LDA): uncovers thematic structures that may be overlooked within large text, aiding in the organisation and analysis of extensive textual data. 

Multilingual Capabilities 

Advanced NLP models are breaking language barriers, enabling machines to understand and generate human language across different languages and contexts. Here are some key developments shaping multilingual NLP:

• Pre-Trained Models: Multilingual models like BERT (Bidirectional Encoder Representations from Transformers) and its variant, mBERT, are pre-trained on text data from multiple languages. For example, mBERT is trained on Wikipedia articles in 104 languages, meaning it can execute NLP tasks without frequent finetuning. 

• Cross-Lingual Transfer Learning: This technique enables models to transfer knowledge from one language to several others, improving performance in languages with restricted training data. For instance, a model trained in English can be refined to perform effectively in other languages without extensive training. 

• Machine Translation: Sophisticated NLP models have improved machine translation accuracy and fluidity. Models like MarianMT can translate text between multiple language pairs in real time, allowing cross-lingual communication. 

Personalisation 

Personalisation is at the heart of many brands' messaging, and NLP is taking it to the next level. By understanding context and user intent, this technology allows systems to engage with human language in a more natural and meaningful way. Here’s how:

• Content Personalisation: NLP studies user-created text data to create bespoke content for individuals. e-Commerce platforms leverage NLP to analyse customer reviews, pinpointing relevant product features in personalised product descriptions.  

• Chatbots and Voice Assistants: NLP supports conversational AI to deliver tailored personalisations. This could be chatbots recommending products or voice assistants altering accents and tones according to users’ demographics. 

Want to ensure you have the talent so your business can maximise on these trends and not get left behind? Check out our dedicated software engineering page to see how we can support your growth ambitions. 

Computer Vision 

Another exciting development in software engineering is computer vision. While AI and ML have already transformed how we analyse text, they’re now revolutionising how machines interpret visual data. This innovation is driving progress across industries, opening up new possibilities for both businesses and consumers.

One standout example is AI-powered assistants with visual context. Tools like Microsoft’s Copilot Vision allow AI to "see" a user's browser content, creating more interactive and personalised browsing experiences.

But the impact of computer vision doesn’t stop there, it’s shaping multiple industries in ways you might not expect. Here’s how:

Industrial Automation and Manufacturing 

Computer vision is making a big impact when it comes to quality control and predictive maintenance across industries. In manufacturing, it automates inspections, spots defects, and ensures consistency, cutting down on human error. Take pharmaceutical production, for example, computer vision can detect defective tablets, making sure only high-quality products make it to consumers.

Beyond quality control, this technology is also great for predictive maintenance. Continuously monitoring equipment can identify potential failures before they happen, reducing downtime and keeping operations running smoothly.

Retail and e-Commerce 

In retail, computer vision is elevating the shopping experience, making it much smoother and more personalised. Autonomous checkout systems are becoming more common, allowing customers to walk in, grab what they need, and leave without waiting in line.

Beyond that, visual search and recommendation systems are taking e-commerce to the next level. By analysing images, computer vision can suggest products tailored to individual preferences, boosting customer satisfaction and engagement. It’s all about making shopping more intuitive and seamless.

Security and Surveillance 

In smart cities, AI-powered surveillance is ensuring greater public safety. These systems can predict and redirect traffic. Cities like Singapore and Dubai are already testing these systems, and by 2025, these measures will expand globally, saving governments an estimated $5 billion annually in efforts to improve efficiency. 

Low-Code and No-Code Development 

According to Gartner, it’s expected that in 2025, 70% of new enterprise applications being developed will use low-code or no-code technologies, which is an increase of 25% in 2020. These platforms accelerate development, making it 4.6 times faster and 4.8 times easier than traditional coding methods. Here’s an overview of its impact: 

• Rise of Citizen Developers: As low-code and no-code development becomes more prominent, so too do ‘citizen developers,’ which are non-technical users who create software applications. By 2025, it’s forecast that citizen developers will outnumber traditional developers four to one in large organisations.

• Increased Optimisation: Low-code and no-code platforms have reduced the need for coding expertise and development costs. Illustrating this, these platforms are decreasing app development time by up to 90% - meaning coding time is going from months to weeks. 

• Cost Efficiency: Businesses save around 40% on maintenance costs using these platforms. Because of this, the low-code development market is expected to reach up to $44.5 billion by 2026. 

These platforms are already having a significant influence, as demonstrated by the following examples: 

• Outsite: This website was created without traditional coding and allows workers to view and review locations for remote working. 

• My Nursery: An app designed for teachers to manage nursery work and build lesson plans. 

• Simini: This app facilitates communication between surgeons and owners. The result? A 40% reduction in response time for post-surgical plans. 

Cloud-Native Applications 

Cloud-native applications are going to impact and redefine the software industry in 2025. They will deliver unprecedented scalability, resilience, and agility. These apps, specifically designed for cloud environments, use microservices, containers, and DevOps principles to optimise resource utilisation. 

According to industry reports, by 2025, 80% of enterprise applications will be cloud-based or transitioning to cloud-native. What’s causing this seismic shift? We’ll explore below: 

Faster Development and Deployment

Cloud-native development methods allow faster development and cycle times. Developers can focus on digestible, independent components, implement regular updates, and create and release new features. According to an IBM survey, this practice has led to a 73% improvement in development and roll-out speeds.  

Cost-Effectiveness and Scalability

Cloud-native applications are built using microservices and containerised environments, which allow for unparalleled scaling. Companies have the ability to adjust resources in response in real-time, optimising cost and performance. An example is Netflix utilising Kubernetes to manage microservices. Even during global traffic spikes, smooth streaming is assured.

Integration of Artificial Intelligence and Machine Learning (AI and ML)

AI and ML have seamlessly integrated apps thanks to cloud-native environments. Notably, GE Healthcare’s Edison AI platform leverages cloud-native architecture to help doctors make faster and accurate diagnoses with real-time data from medical devices.

Resilience and Fault Tolerance

High availability and resilience are key considerations when designing cloud-native applications. Its distributed architectures and self-healing capabilities allow apps to be operational despite localised failures. Furthermore, they implement the latest security measures, like zero-trust security models, to protect against sophisticated cyber threats.

Wrapping Up the Trends for the Software Engineering Industry  

AI, ML, and NLP are driving a wave of change in software engineering, reshaping the industry with faster development cycles, greater efficiency, and highly personalised user experiences. NLP is transforming how humans interact with machines, while low-code and no-code platforms are making app development more accessible than ever.

To keep up, businesses need to embrace these advancements, integrating the latest tech to stay competitive. These innovations drive efficiency, help meet increasingly specific user needs, and unlock new possibilities. With so much happening, 2025 and beyond are set to be an exciting time for software engineering. 

Our question to you is - do you have the right team in place to maximise your growth in 2025? 

Specialist Software Engineering Recruitment Solutions 

Well, we can help you tackle 2025 and beyond head on. In the sea of software recruitment agencies, why should you trust Propel? Our consultants share your deep passion with the software engineering industry. We get the challenges faced by scaleups and startups, whether it be skills gaps, budget restrictions, or trying to compete in crowded and competitive markets. We are ready to find for you the talent you need to overcome your pain points and drive growth in 2025 and beyond. 

What are you waiting for? Visit our software engineering recruitment page or contact us today to see how we can support you.