What Will Change with Generative AI by 2027? A Guide for IT

After three years of experimentation, generative AI is moving from pilot project to standard IT infrastructure. According to BCG, generative AI budgets are expected to grow 60% between 2025 and 2027, rising from roughly 4.7% to 7.6% of total IT budgets (Computer Weekly). For technology leaders, the question is no longer “whether” to invest, but how to prepare for the changes ahead. This guide covers the key trends for 2027, the risks worth watching, and a practical checklist to put your IT team ahead of the curve.

Why 2027 Is a Decisive Year for Generative AI in Business

Between 2022 and 2025, most companies went through a cycle of experimentation: isolated pilots, proofs of concept, individual use of tools like ChatGPT and Copilot. Starting in 2026, that picture changes. Reports from global consultancies converge on the same diagnosis: generative AI is no longer a competitive differentiator; it’s becoming basic operating infrastructure.

IDC projects that global investment in generative AI will surpass US$143 billion by 2027, growing at an average of 73% per year between 2023 and 2027 (AMCOM). That volume of investment is no longer concentrated in proofs of concept; it’s flowing into production, legacy system integration, and automation of critical processes. In short: 2027 is the year the investment bill comes due in results, not promises.

The Key Changes Generative AI Will Bring to IT by 2027

1. From Generative AI to Agentic AI: IT Gets Autonomous “Coworkers”

The main shift over the next few years is the move from models that respond to systems that execute. Instead of just generating text or answering questions, AI agents perceive context, make decisions, and carry out complete tasks, with human oversight at the points that matter most.

The numbers confirm this acceleration. IDC estimates that half of companies worldwide will be using AI agents by 2027 to optimize processes and redefine the relationship between humans and machines (Daniel Nunes). Deloitte, for its part, projected that agent adoption among companies already using generative AI would jump from 25% in 2025 to 50% by 2027 (FIA); and Gartner estimates that by 2028, at least 15% of work decisions will originate from agentic AI systems, up from virtually 0% in 2024 (ISC Brasil).

In practice, this means systems capable of identifying operational bottlenecks, accessing internal data, and executing end-to-end tasks under human control. This is exactly the territory of AI Agents: solutions that integrate with ERPs, CRMs, and internal systems and learn continuously from operations, freeing up teams from repetitive tasks so they can focus on strategic work.

2. Specialized Models (DSLMs) Gain Ground Over Generic LLMs

Another significant shift for 2027 is the rise of domain-specific language models, or DSLMs. Unlike generalist LLMs, these are trained on the data, processes, and vocabulary of a specific sector or function: legal, financial, healthcare, customer service, and others.

Gartner projects that by 2027, more than 50% of the generative AI models used by companies will be industry- or function-specific, up from approximately 1% in 2023 (ISC Brasil). The same firm estimates that by 2027, companies will use three times more small, task-specific models than generic LLMs (Softdesign). The reason is simple: domain-specific models tend to make fewer errors, cost less to operate at scale, and deliver responses more closely aligned with business context, which matters when AI is handling sensitive decisions.

3. Multimodal AI Stops Being a Differentiator and Becomes the Standard

By 2027, Gartner estimates that 40% of generative AI offerings will be multimodal (capable of processing text, image, audio, and video together), up from just 1% in 2023 (TI Inside). This significantly broadens the field of application for AI within IT: analyzing visual documents, reading operational videos, cross-referencing data from different sources to support more complete decisions. Areas like technical support, infrastructure monitoring, and systems documentation are likely to feel this impact first, in very practical ways.

4. AI-Assisted Software Engineering Changes the Developer’s Role

The 2027 developer is likely to act less as an isolated code executor and more as a systems orchestrator, quality evaluator, and bridge between software and business strategy. Generative tools already help teams write, review, and document code, cutting down time spent on mechanical tasks.

Technology teams are spending less energy on operational activities (writing and reviewing code, monitoring infrastructure, generating documentation) and more time on work that requires human judgment: architecture, prioritization, business decisions. This is the kind of transition NextAge already delivers for clients through AI Agent orchestration applied to Technology teams, automating code writing and review, monitoring, and documentation.

5. AI Governance Stops Being Optional and Becomes a Business Requirement

If there’s one theme that runs through nearly every forecast for 2027, it’s governance. Gartner estimates that up to 40% of ongoing AI agent projects will be discontinued by 2027, precisely due to a lack of operational control (Medium). That figure becomes even more telling when cross-referenced with another one: independent analyses by EY Brazil, Gartner, and Deloitte, conducted between April and May 2026, found that only 7% to 8% of companies show real maturity in AI agent governance (Daniel Nunes).

This is exactly where most initiatives stumble: autonomous agents running without a clear audit trail, without well-defined limits on autonomy, and without a human accountable for the final decision. That’s why every AI Agent implementation at NextAge builds in monitoring and governance from the architecture stage, not as an add-on layer once the project is already live, but as part of the solution’s initial design.

6. Security Becomes Fully Part of the AI Equation

Generative AI is also reshaping cybersecurity, on both sides of the equation. It strengthens defense (anomaly detection, automated incident response), but it also expands attack capabilities. Gartner projects that 17% of all cyberattacks and data breaches by 2027 will involve generative AI, which is expected to drive a 15% increase in security software spending by next year (Stefanini Cyber). For IT, this means building in protection for data in processing (not just at rest or in transit) as a core part of any AI solution’s design.

The Risks Every IT Leader Should Watch by 2027

It’s worth summarizing, directly, the points of concern that come up most often in projections for the coming years:

• Lack of governance: autonomous agents operating without a clear audit trail, without defined limits on autonomy, and without a human accountable for the final decision.
• Automating broken processes: implementing AI on top of a poorly designed process simply speeds up the error; Deloitte had already identified this pattern in earlier research on agents in production.
• Shadow AI: uncontrolled use of generative AI tools by employees, outside the company’s formal governance, creating exposure of sensitive data.
• Vendor dependency and infrastructure costs: scaling AI across multi-cloud environments with GPU workloads requires financial planning (FinOps) that’s just as careful as the technical planning.
• Project discontinuation: as mentioned above, nearly 40% of AI agent projects are at risk of being abandoned by 2027 due to structural failures, not limitations of the technology itself.

How to Prepare Your IT Team for Generative AI in 2027 (Practical Checklist)

1. Map processes with the greatest automation potential, prioritizing by ROI. Not every process deserves an AI agent; start with the ones with the highest volume, the most repetition, and well-defined rules.
2. Design the architecture with governance built in from the start, factoring in privacy, data protection regulations, and full audit trails for every automated decision.
3. Choose the right model for each use case: a generalist LLM, RAG (connecting the model to internal knowledge bases), or a specialized domain model, depending on the complexity and sensitivity of the task.
4. Run controlled pilots before scaling. Validating in a restricted environment prevents the company from simply automating, at greater speed, a process that was already inefficient.
5. Train your team to act as orchestrators and reviewers of AI, not just users of ready-made tools; this is the skill that will matter most for IT professionals starting in 2027.
6. Measure results with clear indicators: hours saved, error reduction, cycle time, and cost per process, tracked throughout the operation, not just at launch.

This is, broadly speaking, the same path NextAge follows in AI Agent projects: ROI-driven bottleneck identification, defined scope and expected return for each agent, secure architecture with human control built in from the start, controlled deployment in a restricted environment, ongoing performance monitoring, and only then, planned scaling for the agents that have proven their return.

2027 Is the Year to Move from Pilot to Production

The trends pointing toward 2027 converge on a single idea: generative AI is no longer an experiment; it’s becoming a structural part of IT operations, with autonomous agents, specialized models, and multimodal AI becoming market standards. The biggest risk, however, isn’t the technology itself, but the lack of governance, architecture, and method when implementing it. The companies that will stand out by 2027 aren’t necessarily the ones that adopted AI first; they’re the ones that integrated it with clear purpose, security, and control.

Your company already knows AI is the future; the question is usually where to start. NextAge helps identify the IT processes with the greatest automation potential and designs an AI Agent plan tailored to your technical and operational context, with no cost and no commitment at the initial diagnostic stage. Talk to a specialist →

Frequently Asked Questions

What is agentic AI, and how is it different from generative AI?

Generative AI creates content (text, images, code) from a prompt. Agentic AI goes further: it perceives its environment, makes decisions, and executes complete tasks autonomously, triggering systems and tools along the way, with human oversight at defined checkpoints.

How much will companies invest in generative AI by 2027?

According to IDC, global investment is expected to surpass US$143 billion by 2027, growing at an average of 73% per year between 2023 and 2027 (AMCOM). BCG also estimates that generative AI budgets within IT will grow 60% between 2025 and 2027 (Computer Weekly).

What’s the difference between an AI agent and RPA?

RPA follows fixed rules and predefined scripts, with no capacity to adapt. AI agents reason, adjust their behavior based on context, and handle exceptions, which makes them far more effective in complex, unstructured processes.

Why do AI agent projects fail?

In most cases, due to a lack of governance: no audit trail, poorly defined autonomy limits, or automating a process that was already inefficient before AI was introduced. Gartner estimates that up to 40% of ongoing AI agent projects will be discontinued by 2027 for this reason (Medium).

Will AI agents replace IT jobs?

The trend pointed to by the research is transformation, not direct replacement: IT professionals shift into a role of orchestrating and reviewing AI systems, focusing on architecture, prioritization, and strategic decisions, while operational and repetitive tasks are taken on by agents.

How does AI agent governance work?

It involves clearly defined permissions, scope of action, measurable goals, audit trails, and continuous error monitoring, with a designated human accountable for every sensitive automated decision. It’s a component that needs to be designed alongside the agent’s architecture, not bolted on after implementation.