IT Trends to Watch in 2026

Get ready for our sixth annual deep dive into the future of tech.

2026 won’t be about "if" AI touches your stack, but about how smartly you put it to work. AI is everywhere, and it’s not hitting the brakes. The winners won’t just adopt tools, they’ll orchestrate them, amplifying reliability and velocity, while designing systems that scale with latitude and lock down security by default.

We also discuss some security, cloud-native, and software engineering patterns that we believe will be especially relevant in 2026. Enjoy the read.

And because we keep score, you can also see how our 2021, 2022, 2023, 2024, and 2025 predictions aged.

Evolution of delivering software: AI agents, tools and productivity

AI adoption is unlikely to ease off next year. With the evolution of ChatGPT, GitHub Copilot, and other LLM assistants, AI has become a necessity, available both as an added feature in various products (e.g., Google Workspace) and as tools leveraged not only by software engineers, but also by UX Designers, Project Owners, and managers.

2025 StackOverflow Developer Survey shows that 84% of respondents are using AI; 66% say AI solutions are "almost" right, and 69% report increased productivity. At the same time, positive sentiment has decreased compared with the previous year.

AI is used for architecture, planning, coding, analysis, testing, and documentation generation. New models are released regularly, so people constantly test them, compare different variants, and assess which are best for their use cases.

The general way of working has undergone a significant change. For developers, AI can handle the tedious, repetitive tasks they never liked.

Everybody's wondering how to use AI best? Where to set the boundaries so that AI increases efficiency without introducing tech debt and maintains the overall maintainability of the systems created? How can we train people to use AI while also ensuring their self-development, so that we don't end up with a skills gap in a couple of years?

They’re also learning how to approach tasks more efficiently, getting the best results without exhausting credits and with minimal manual correction. AI still hallucinates. Productivity gains are sometimes questioned because outputs must be corrected, reviewed, validated, and tested.

In our view, the coming year will bring stabilization rather than major breakthroughs, with a focus on assessing AI-based workflow standards and polishing productivity. We have witnessed a moment where every day brought a new change in the AI world; now the changes have slowed down. Among the top-most often used solutions, we can find Claude Code and Cursor.

Moreover, with the Model Context Protocol, we can transition from simple "copilot" usage to working with an orchestrated group of agents that interact with various tools and systems. Definitely, we will see more agentic workflows allowing us to interact with different applications, including web browsers.

We’ll also see small models working locally on devices and tools, allowing us to integrate various AI-related technologies with other systems, such as n8n, which has recently emerged as one of the leading AI automation platforms.

On the other hand, there are many unknowns ahead of us. Will the AI bubble burst? Will AI prices rise to account for power consumption, or will new, smaller but more effective models emerge? Who knows?

Why does it matter?

• In 2026, AI-assisted coding isn’t an innovation, it’s a standard. And the challenge is who ships better with it, delivering code faster and safer.

Responsible Artificial Intelligence with AI governance

As AI is moving to production, the roles associated with AI governance are popping up. Simply because AI starts influencing customers and brand image, governance will be a necessity from a business perspective. We will see more companies actively taking care of safety, compliance, and transparency in the parts of their business enhanced by AI. There are some legal signals that such changes are supported.

The U.S. has a widely adopted National Institute of Standards and Technology (NIST) AI Risk Management Framework and is pushing formal accountability across government, while the EU has the world’s first comprehensive AI Act.

In 2025, 14% of organizations report having a CAIO, with many reporting directly to the CEO; this is evidence that AI leadership is maturing beyond ad-hoc task forces.

AI governance is the pragmatist’s move for 2026. As AI adoption matures, organisations treat governance as a velocity enabler, not a bureaucratic burden.

Why does it matter?

• Get ahead of the Act, not buried by it. Prove responsible AI, ship sooner, win trust.
• Day-one standards for building and documenting AI = lower compliance costs.

Cybersecurity of digital infrastructure

Cybersecurity’s importance grows every year. Last year, we discussed it in the context of AI, which not only increased the scale of cyberattacks but also their precision and complexity. This year, we see DevSecOps and shift-left security remain key trends, but AI is further changing the landscape.

It introduces new threat types, including deepfake video calls and impersonation scams, as well as hiring fraud where candidates alter their appearance and use AI tools during interviews. AI also creates risks at the code level when developer assistants suggest insecure or harmful patterns. Across the industry, security is shifting from a reactive model to proactive mitigation, a concept introduced by Gartner in 2023 as Continuous Threat Exposure Management (CTEM).

One of the loudest threats in the IT industry are supply chain attacks. The npm ecosystem saw several major incidents last year - for example, issues related to the debug and chalk packages.

New tools are emerging to harden package security, such as, e.g. Aikido Safe-Chain. In our view, preventing supply-chain attacks will be a major focus in the coming year.

Another notable development in the cybersecurity field is the growing momentum of post-quantum cryptography. Apple and Signal have led the way, and this trend is likely to continue. Two-factor authentication (2FA) and passkeys are also becoming increasingly standard across various services.

Why does it matter?

• A growing scale of AI-powered and other cybersecurity attacks can cause a single security incident to freeze your operations and shatter trust.
• Shift left, go DevSecOps, and make security part of the build, not the afterthought.

Competitive advantage with Developer Experience

Developer productivity has become a priority. Organizations want to move fast, smartly, and efficiently.

Gartner estimates that by 2026, 80% of organizations will have dedicated platform teams maintaining internal tools to streamline the development process. These platforms are viewed as internal products designed to shorten delivery times and minimize operational errors. One of the more useful concepts is ephemeral environments, which developers can create on demand to test features.

The term AIOps is emerging as a blend of DevOps and observability tooling with AI assistance, enabling easier automation and, for example, anomaly detection across logs and metrics. These developments will enhance the maintainability of platforms and promote greater stability.

When discussing developer experience and productivity, FinOps naturally enters the conversation. Primarily, FinOps is about reducing cloud costs, as well as reorganizing software teams and architecture to facilitate swift responses to changes. We expect the cost-optimization trend to continue, utilizing FinOps practices to leverage company resources most efficiently and thoughtfully.

Why does it matter?

• DevOps often determine how fast the team can ship.
• Most companies try to limit their spending, efficient DevEx enables that.

See our blog series: Developer experience done right.

Architectural patterns for modular monoliths are back

Microservices remain mainstream, but many teams are pivoting toward modular monoliths to cut complexity and cost.

Implementing a perfect microservices-based system is not an easy task. Many teams have learned the hard way that "microservices everywhere" can mean, for example, higher operational and logical complexity, despite sometimes being promised otherwise, higher infrastructure bills, and debatable gains in delivery speed.

When helping clients, we have seen microservices architectures implemented poorly and without making context-aware architectural decisions. Such unnecessary complexity definitely limits the possibilities of introducing the Continuous Delivery process, causes downtime, and restricts the time of reaction.

In this light, the modular monolith trend becomes a pragmatic shift, as in specific environments, well-crafted monolithic systems can respond to change more effectively, sometimes with significantly lower overhead and complexity.

This approach involves a single-deployable unit split into clear business modules, maintaining microservices-style ownership, and can offer swift delivery and agility of change when needed, which is particularly important in newly born systems, where we’re still in the discovery phase and boundaries are only starting to emerge. This also means less infrastructure and operational overhead, as well as lower costs, which is an essential factor, especially these days.

This isn’t "microservices vs. monolith" so much as modularity + encapsulation + minimal APIs. Boundaries are the win regardless of deployment shape. In the AI-augmented era, those boundaries matter even more: humans still review and operate the system, so scoping change to a module/package is the pragmatic path.

For deeper context, see Michał Ostruszka’s post on code modularity and clear boundaries.

Modular monoliths' growing popularity (Spring survey, InfoQ] is also influenced by modern DevOps practices, including data Observability.

Some platforms that offer modern DevOps tools implement a shift "left" to enable tech teams to maintain a single, cohesive codebase while providing clean, open, and analytics-ready interfaces to the rest of the organization. This includes Confluent Kafka and its recent Tableflow.

Interested in taking this further? As an official Confluent Partner, we can review your architecture and tailor a roadmap to your use case. Prefer to meet in person? Join us at the Confluent Meetups. See the recap from our last session and join our newsletter to get to know the new dates.

Why does it matter?

• Good design is still valuable in software architecture.
• Start modular, earn your microservices.

Wrap up

2026 will be about how smartly you put AI to work. Orchestrate agentic AI and find out where to put the boundaries, so that AI is increasing efficiency, while developers are leveling up.

The smart players will also adopt a proactive approach to security and DevOps, as well as take a pragmatic path to building their next-gen platforms.

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