Technology is changing faster than organisations can keep up. Just as one project gets the green light, a newer and smarter technology trend emerges that affects how businesses operate and compete. The challenge for business leaders is knowing where to place smart investment bets before the competition does. That means spotting what’s coming and moving quickly with the right tools, strategy and people.
Here are 10 technology trends that will shape the next three years.
Global investment in AI is projected to reach $500 billion by 2027 as adoption accelerates across industries. AI-driven applications powered by machine learning, natural language processing and computer vision are reshaping data analytics, automating decisions and streamlining workflows. In IT functions, intelligent chatbots are reducing ticket resolution times by 30%, while supply chain management is becoming automated from end to end.
In software development, generative AI is already changing how code is written, tested and architected. NashTech research reveals a 30% increase in greenfield code productivity when GenAI is combined with strong quality controls. This capability will extend into brownfield development, helping organisations modernise legacy systems that have held back progress for years. (link to modernisation content once published)
Unlike traditional AI systems that rely on prompts, Agentic AI operates with greater autonomy and can make decisions, initiate actions and complete complex tasks with minimal human involvement.
In IT, for example, an AI service desk agent could detect incidents, begin resolution steps automatically and escalate to human teams only when necessary.
This technology trend is set to increase efficiency across key functions including customer service, IT support and supply chain management. By 2029, Gartner predicts agentic AI will autonomously resolve 80% of customer service issues, reducing operating costs by 30%.
AI tools will quickly outpace the coding skills of junior developers, challenging the traditional developer hierarchy. Team structures are expected to become flatter, with senior engineers skilled in AI taking leadership roles and fewer junior developers being hired. Skills like adaptability, creativity and leadership will play a greater role in hiring moving forward. Demand is also rising for specialised roles in prompt engineering, MLOps and AI security, making upskilling essential for the future of software development.
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The rise in AI adoption is raising questions about governance, transparency and ethics. New regulations like the EU AI Act and the US AI Bill of Rights require companies to demonstrate that their AI systems are fair, accountable and compliant. Failure to meet these standards can lead to fines of up to €35 million.
AI-driven governance platforms will become essential business tools, particularly for organisations developing their own AI models. These platforms monitor model performance, detect and reduce biases and keep up with changing regulations. AI-driven governance platforms also overcome the "black box" challenge by providing explainability tools, along with secure access controls, version management and comprehensive audit trails. Together, these features give organisations deeper insight into AI decision-making and improve overall transparency.
Hyperautomation (automating every possible process in the business) will gain momentum, enabled by the combination of AI, robotic process automation (RPA) and advanced analytics. While early adoption of automation focused on streamlining individual tasks, hyperautomation will transform entire business workflows and functions. For example, in logistics, hyperautomation can optimise inventory management and automate maintenance and repair processes. By removing repetitive, manual work, employees can shift their focus to higher-value, strategic work.
Insurance group MarkerStudy Distribution reduced regression testing time by 90%, from 20 days to 2, with NashTech’s automation. Similarly, Tradetech, a logistics technology provider, also used NashTech’s RPA solution to cut data processing time by 57% and reduce quality control checks from 12 minutes to 4. Imagine the potential when hyperautomation is applied across entire organisations, transforming productivity, agility and innovation at scale.
Low-code and no-code platforms are transforming software development, reducing build times by 90% and cutting costs by 70%. Adoption is increasing as organisations face pressure to automate processes, modernise legacy systems and overcome talent shortages.
Low-code and no-code platforms enable non-technical users (like marketers and analysts) to design and deploy applications using intuitive drag-and-drop interfaces, reusable templates, prebuilt components and automate workflows. Teams can build and launch simple solutions on their own, freeing up developers to focus on more complex projects. Investment in training citizen developers is expected to increase to maximise the benefits of these platforms.
The integration of AI, machine learning and IoT will make low-code and no-code platforms even more intelligent and efficient. Features like automated debugging and smart design recommendations will speed up innovation and significantly shorten time to market.
Data-driven decision-making means using real behaviours, trends and patterns to shape business strategy. AI and advanced analytics will play a crucial role in processing data and unlocking its full value.
AI-powered decision-making enables organisations to forecast demand, personalise customer experiences and identify new revenue opportunities, such as launching products that are precisely tailored to customer needs.
Many organisations are moving in this direction. Netflix uses viewer data to decide what original content to create, directly influencing customer loyalty and revenue. Similarly, DBS Bank leverages AI-powered insights to deliver hyper-personalised financial advice and product recommendations, which helps increase customer engagement and satisfaction.
“There are two kinds of companies: those that see themselves as data companies, no matter what they sell, and those that don’t. The ones who understand that data is central to their strategy will win, while those who don’t will slip away.” - George Lynch, Director of Technology and Solutions
Where data is processed directly impacts how quickly insights can be acted upon. The increasing amount of data (IoT) routed through centralised cloud infrastructure results in latency and bandwidth limitations, which can delay critical operations.
Edge computing solves this by bringing processing power closer to the data source. This delivers faster, more reliable insights with reduced dependency on constant cloud connectivity. In fact, 80% of organisations deploying edge solutions for automation report significantly improved decision-making speed.
Edge computing will become essential in industries where real-time feedback is critical. In logistics, it enables predictive maintenance and live equipment monitoring, cutting downtime by 50%. In insurance, real-time driving behaviour analysis allows dynamic pricing, instant policy adjustments and faster claims resolution, enhancing the customer experience.
With data centres consuming 3% of global electricity (set to reach 4% by 2030) and large AI models emitting as much carbon as five cars, there’s growing concern over how compute resources are used.
Today, few executives fully consider the energy costs of AI. That is about to change. Technology leaders will soon be judged on how their infrastructure aligns with climate change goals. Organisations will need to determine where and how to deploy AI, focusing on use cases with significant impact and minimising energy-intensive, low-value tasks. Always-on AI models might face increased scrutiny, pushing a move toward more efficient and purpose-built solutions.
Green coding and carbon-neutral cloud services will also see wider adoption. Google for example, is committed to running its data centres with carbon-free energy 24/7 by 2030.
Data will be key to decarbonisation efforts. Real-time emissions tracking, AI-driven resource optimisation and automated sustainability reporting will become standard tools for organisations striving to meet regulatory requirements and ESG targets.
Cloud engineering will become even more critical for delivering the speed and flexibility needed to stay competitive. Organisations will rely on advanced cloud engineering practices to rapidly build, deploy and scale applications, enabling them to respond instantly to shifting market demands and customer needs.
Organisations will likely begin shifting away from “lift-and-shift” migrations, which in some cases are not cost-effective and can limit scalability. Instead, they will prioritise the development of cloud-native systems that offer greater flexibility, built-in security, and more effective use of the cloud’s full potential.
Research from Forbes shows that by 2025, 85% of large enterprises will adopt multi-cloud strategies to access the benefits of multiple providers. However, Gartner warns that by 2029, more than 50% of organisations won’t achieve the expected outcomes from their multi-cloud investments, highlighting the need for skilled cloud engineering teams.
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Development teams face increasing pressure to deliver faster, but fragmented tools and manual processes often create roadblocks. By 2026, 80% of large software engineering organisations will have dedicated platform engineering teams. These teams build Internal Developer Platforms (IDPs), which are centralised, self-service environments that provide developers with everything they need to build, test and deploy efficiently. This speeds up release cycles and lets developers spend more time coding and less time managing complex systems.
Spotify uses platform engineering to streamline their extensive development workflows, enabling continuous delivery and rapid innovation.
The adoption of IDPs will continue to grow as organisations scale software delivery, improve developer productivity and respond faster to market demands.
Markets will keep evolving and customer expectations will only grow higher. Organisations will need to deliver products that provide exceptional user experiences and measurable business impact. This demands smarter, faster approaches to product development and delivery.
Product engineering will reshape the entire product lifecycle by applying advanced engineering practices, from initial design through testing and optimisation. This will accelerate time-to-market and ensure products stay closely aligned with real user needs, driving innovation and customer satisfaction.
For example, a retail organisation might use product engineering to improve its e-commerce platform, leveraging real-time feedback and automation to solve problems faster, roll out new features and increase sales.
Whether you need platform engineering experts, AI engineers, or a global partner to help scale your tech initiatives, NashTech has the talent and experience to help. We can help you bring your technology vision to life faster, smarter and sustainably. Get in touch.