TechCrunch reports that European and Israeli companies that use AI have generated significantly more revenue. They got about $0.66 for every $1 invested in U.S. cloud/AI applications in 2025. Venture graphs show that EU/IL funding is closing the gap with the U.S., which is an example of the new “AI app layer” trend. GitHub’s Octoverse also shows that developers are committing code at a breakneck pace: a year ago, there were nearly a billion commits. In real life, this means that teams now use a lot of automated testing, CI/CD pipelines, and feature flags to ensure the quality remains high when they quickly release new versions. The Belitsoft application development outsourcing company analyzes how AI-powered tools and cloud-native methods are rapidly changing the methods of building and running business applications.
Funding for cloud and AI application startups around the world has skyrocketed. TechCrunch cites a report that says that in 2025, EU and Israeli companies will raise about 66 cents for every dollar raised by U.S. companies. This shows that the global ecosystem of AI-focused platforms is growing. A year ago, teams created more than 230 repositories per minute and pushed close to 1 billion commits, according to data on developer workflow. To handle this scale, organizations are putting in place fully automated pipelines – every code push starts testing, builds, and deployment in real time – and making extensive use of feature flags to guarantee that even incomplete work can safely reach production.
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Business Insider describes how AI-driven coding startups are exploding: Replit’s CEO projects $1 billion in revenue by 2026 (four times current revenues) as its “vibe coding” platform helps non‑experts build apps with AI. He even notes Replit is “replacing a lot of the no-code, low-code tools” that “never worked very well” with its generative-AI coding agent. In a similar vein, VentureBeat discovers that vibe coding – AI-generated code from simple English prompts – can significantly speed up prototyping, but if used carelessly, it can lead to unmanageable technical debt, insecure code, and secret leaks. There, experts say to handle UI and front-end tasks (“green zone”) differently from core business logic (“red zone”). They also say to add enterprise-grade tools for governance and security to AI assistants.
Analysts also see businesses shifting from “digital-first” to “AI-first” strategies. Entrepreneur says that 67% of businesses already spend at least 10% of their digital budgets on AI, and they think this number will rise quickly in 2025 as more companies switch to AI-first operating models. In fact, 27% of firms already have AI agents in production or at scale, and another 61% are piloting them. Because of this, spending on AI agents is expected to “increase three to four times” in 2025, which will often mean putting other projects on hold. In short, business leaders increasingly treat AI as mission-critical: 81% expect higher digital spending (especially on AI) than the prior year.
When you put all of these news stories and reports together, they make it clear that AI-powered tools (like code assistants and AI agents), flexible cloud delivery, and new team structures are all coming together to shape 2026. The sections below examine each major trend in detail, with a look at outsourcing trends as a key strategic shift for enterprises.
Artificial Intelligence and Automation
AI and machine learning have gone from being used in experiments to being used in everyday development. Gartner and others call this “AI-native development,” which means putting AI into every part of the software development lifecycle. Gartner says that by 2028, 90% of enterprise software engineers will use AI code assistants. This is a big jump from less than 14% a year ago. More and more developers are using tools like GitHub Copilot, ChatGPT, and generative engines that are made for a specific purpose. Gartner says that developers’ jobs are changing from “implementation to orchestration,” with a focus on system design and quality while smart tools take care of the boring coding.
AI is changing the apps themselves, just like coding is. Gartner says that about 40% of business apps will have AI agents that can do some things in the next year. At this point, this number is almost zero. Some people call these agents “AI copilots.” They can do hard work and follow complicated instructions on their own. An AI-powered threat-response agent, for instance, could look through logs, find a cyberattack, and start a countermeasure all by itself. Gartner says that this change in agency will turn apps into platforms for smooth, self-directed teamwork and managing workflows in real time.
People who closely watch the industry say that future AI strategies will use more than one model and be a mix of different types. VentureBeat says that businesses are no longer putting all their eggs in one LLM provider’s basket. Instead, they are using multiple models (both open and proprietary) across the stack. IBM and Zoom, for instance, discuss “multi-cloud, multi-model” approaches. In these approaches, a lightweight model on the device takes care of everyday tasks to save time and money, and larger cloud models take care of heavy reasoning when it’s needed. The costs of this hybrid local/cloud AI architecture are easy to predict (“running inference locally avoids unpredictable cloud billing”), the latency is always the same, and data governance is better.
Another priority is domain-specific AI models. Gartner says that by 2028, more than half of the GenAI models used by businesses will be specific to certain industries or functions, rather than general ones. In real life, this means that businesses are either fine-tuning LLMs or using open-source models on their own data to make sure they are up-to-date and follow the rules. For instance, banks could demonstrate a legal-domain model on how to read compliance documents, and manufacturers could show a quality-control model how to read sensor data from their factories. These specialized models combine the power of big AI with the accuracy that mission-critical apps require.
There is also more focus on AI security and governance. Companies are aware that “AI security” and data provenance are important issues for them. AI security platforms and digital provenance are two of Gartner’s top trends. Digital provenance is the concept that every AI-generated output or software component should have metadata (an “attestation”) that shows its origin, the data it used, and how it was modified. This will manifest in app development as security scans of AI code suggestions, SBOMs for AI models, and protocols that ensure AI-driven features comply with company rules.
According to Gartner analysts, “AI-enabled tools and technologies are changing the way software is made and delivered in a big way.” AI-assisted integrated development environments (IDEs), automated testing, and constant monitoring will help development teams come up with new ideas faster. This will make the software development lifecycle (SDLC) a smart, adaptable pipeline.
Citizen Development and Low-Code
Low-code and no-code platforms are changing who makes software, just like AI. Companies are letting “citizen developers” who don’t work in IT make apps for their businesses. Forrester and Gartner have been warning about this change for a long time. According to a recent Gartner report, by 2026, at least 80% of the people who use low-code development tools will be developers who do not work in IT. This is up from about 60% in 2021. This means that not just IT engineers will use low-code platforms; business analysts, product owners, and department heads will also use them.
Gartner also stated that by 2025-2026, approximately 70% of new apps will be created or developed using technologies that require minimal code or none at all. This indicates that enterprise software is beginning to resemble building blocks rather than code written by hand. Business teams can quickly create prototypes of solutions by dragging and dropping interfaces, workflows, and data connectors. Most of the time, these platforms feature new capabilities, such as AI services, mobile UI frameworks, and the ability to connect to the cloud.
It’s easy to see the benefits: speed and flexibility. Companies can make customer portals, automate their internal processes, or create data dashboards without having to wait months for custom development. During the COVID-19 pandemic’s digital push, many companies used low-code to quickly digitize their processes. OutSystems, Mendix, Microsoft Power Apps, and other companies say that businesses are using their products more and more. People in the U.S. and Europe are especially eager for quick fixes due to digital transformation mandates.
But analysts say that low-code is not a magic bullet. A VentureBeat article states that low-code can handle front-end and simple integration tasks, but it should not be used for core business logic without proper supervision. Just as with AI coding tools, indiscriminate use of citizen-developed modules can generate “spaghetti code” and hidden security gaps. For instance, a citizen-built workflow might hard-code credentials or skip tuning for better performance. Experts in the field say that the best way to manage low-code projects is to use enterprise standards, such as built-in audit logs, security scanning, and IT review.
Low-code and no-code are coming together with AI in a big way. AI assistants and code suggestion engines are built into modern platforms to help business users. Gartner’s 2025 Software Engineering trends include “GenAI Platform Engineering,” which is when companies create internal developer platforms that let people use GenAI features through self-service portals. In real life, this could mean that a low-code platform has an “AI copilot” widget or lets a developer use a company-tuned LLM to plan out workflows. Because of this, low-code in 2026 will look less like separate tools and more like composable stacks, where business users assemble apps from AI-powered microservices that work together.
In summary, low-code/no-code will continue to democratize app building. Gartner’s research makes it clear that citizen development is unstoppable and will be the main way to make apps by the middle of the decade. Companies that follow this trend can come up with new ideas faster, but to avoid shadow IT issues, they need to invest in governance, training, and hybrid teams (combining citizen developers with IT oversight). CTOs have a clear job: low-code is now mission-critical because it enables a larger team to build digital solutions. But to be successful, they need to treat these platforms with the same care as traditional development.
Cloud-Native and Microservices Architectures
In the near future, all business apps will run on cloud-based infrastructure. Many experts agree that we are now living in the “cloud-first” age. Gartner said that by 2025, 95% of new digital workloads will be run on cloud-native platforms. Four years ago, this was only 30%. Most new business applications will not work on the company’s own servers. Instead, they will run in containers or serverless environments on public clouds like AWS, Azure, and GCP. Companies have figured out that if they want to be flexible and reach people all over the world, they need to design apps for the cloud from scratch.
Microservices are what make up cloud-native apps. There are small, loosely connected services in each app that talk to each other using APIs. A number of well-known companies adopt this form of modular architecture these days. You can grow services independently (for example, you can scale just the payments microservice when sales are strong) and make updates faster (you can reload one service without affecting others).
Serverless computing is another cloud-native trend. Most businesses will utilize serverless functions (such as AWS Lambda, Azure Functions, Google Cloud Run, and others) for asynchronous or event-driven workloads by 2026. Serverless handles the provisioning of servers, so teams only have to pay for the time they utilize. This is great for tasks that happen in bursts, such as processing images, taking in IoT data, and doing real-time analytics. Statista says that by the middle of the decade, most businesses will run at least some of their infrastructure without servers. The best thing about this is that development teams do not have to worry about fixing infrastructure or planning for capacity anymore; they can just focus on writing code.
This shift is also cultural. Cloud-native businesses use DevOps and platform engineering. Companies no longer see IT as a “fixed infrastructure” function. Instead, they form teams that focus on products. Gartner says that companies will organize development around goods or services, which will lead to the creation of jobs like site reliability engineers (SREs) and platform managers to manage shared infrastructure. For example, SREs connect dev and ops by making sure that cloud services achieve their dependability goals. Platform teams provide internal developer portals (Platform Engineering) that offer standardized CI/CD pipelines, container registries, and infrastructure-as-code templates. As indicated above, these platforms generally have AI capabilities built in, so developers can easily check for compliance, maintain secrets, and access model hubs on the corporate cloud.
Multi-cloud and hybrid-cloud techniques are also very popular. VentureBeat recently reported that enterprise CIOs are aiming to use AI in “multi-model, multi-cloud” ways. The reason is to avoid being locked into a vendor and to get the best prices. Companies run small models on edge devices or on-premises for features that demand low latency. After that, they move big batch processing to GPU clusters in the cloud. Google’s purchases of companies like Firefly (which creates AI for devices) and deals for on-device LLMs (Gemini Nano) show how this mixed strategy works. Apps will be able to run on public clouds, private clouds, and edge networks by 2026. Container orchestration and service meshes will take care of all of this.
Last but not least, security and compliance are very important when building on the cloud. Cloud providers now offer strong identification platforms, tools for identifying weaknesses, and tools for keeping track of configurations. Companies are trying to make designs that are “Secure by Design.” For example, they are employing Kubernetes security contexts, policy-as-code (OPA/Gatekeeper), and security pipelines that run continuously. A number of app teams now employ automatic SBOM generation in real life to keep track of components, ensure that multi-cloud encryption requirements are met, and set up zero-trust networking from the start.
In short, the move toward cloud-native will be complete by 2026. New business apps will be built using microservices or serverless architectures and will be available in clouds all over the world. Analysts argue that businesses that cannot swiftly upgrade their cloud software may become obsolete, therefore this architecture is no longer optional. CTOs need to understand more about cloud databases, container security, and cloud platforms like Kubernetes. They should also work on making cross-functional teams that think of infrastructure as code. People who do this will be able to save money and have more options: McKinsey argues that organizations that move to the cloud can come up with new ideas up to three times faster and get more out of their digital investments.
DevOps Evolution
These days, most projects choose continuous delivery. Developers do smaller pushes more regularly, and the rest is taken care of by automated processes. Feature flags are now popular, so it’s okay to put functionality that isn’t done yet behind a switch. Automated unit, integration, and end-to-end testing help uncover problems early on. GitHub states that CI usage has increased by 35% (11.5 billion GitHub Actions minutes), as companies struggle to keep up with the rapid push rates. In short, “iteration is the new default state”: teams are continually working on something instead of waiting for a “done” milestone.
To keep up with this new velocity, businesses and cultures need to shift. Scrum stand-ups can be shorter or take place at different times, status tracking might move to issue boards, and engineers should work on all aspects of a project, not just coding. Leaders want a lot of skill in one place: Gartner says that high-performing engineering teams are made up of highly qualified experts who can ship swiftly. Modern project teams are frequently small and have people from different fields, rather than having a lot of people. According to VentureBeat, AI and platform teams are made up of small “squads” of 3 to 4 people. These teams have engineers, data scientists, and product owners who can quickly create prototypes and make changes to them. In fact, some analysts say, “everyone is a builder” – even team leads learn to orchestrate AI tools as part of development. The emphasis is on speed and communication: developers now need clear writing and collaboration skills to work with autonomous tools and global teams.
DevSecOps and SRE are also very important. Testing in different rooms isn’t useful anymore. This means that the pipeline already has sections that make sure it is safe and respects the laws. Automated vulnerability scans, “shift-left” security checks, and self-healing tests make sure that short releases meet the requirements. The leading companies also have SRE and Ops teams who view code and infrastructure reliability as a product. They utilize AIOps (machine learning for IT operations), predictive monitoring, and chaotic engineering to stop problems before they happen. Gartner states that more and more companies are using DevSecOps frameworks that operate well. They believe that by 2026, development teams would use AI all the time to check code and make sure it follows the rules.
Platform engineering is becoming more popular. Today, many internal developer portals offer common libraries, compliance checks, and lists of allowed services, such as middleware and AI models. Gartner says that by 2027, 70% of companies with platform teams would have GenAI technologies integrated into their platforms. In real life, a developer might click a button in the portal to automatically create a unit test or obtain an endpoint for a pre-trained model. These systems mask the hard tasks (such as keeping cloud APIs, containers, and credentials up to date) so that developers can focus on business logic. They also set up guardrails, such as only allowing infrastructure upgrades that pass policy-as-code.
Finally, the overall procedures become even lighter. Some businesses try out “GitOps,” which involves keeping Kubernetes configurations and infrastructure definitions under version control and are always in sync. Some people utilize low-code for DevOps within their own company. For instance, they might use a drag-and-drop pipeline builder to plan out deployment flows. The main point is to keep manual steps to a minimum. Shortly, almost all firms will use end-to-end automation to construct applications. Code review bots, automated documentation, and AI assistants will be involved in almost every step, from planning to monitoring after deployment.
Author: Dmitry Baraishuk is a partner and Chief Innovation Officer at a software development company Belitsoft (a Noventiq company). He has been leading a department specializing in custom software development for 20 years. The department has hundreds of successful projects in AI software development, healthcare and finance IT consulting, application modernization, cloud migration, data analytics implementation, and more for startups and enterprises in the US, UK, and Canada.
