Five Trends Set to Shape Tech Strategy in 2026

As in 2025, AI will continue to play a dominant role in the coming year. The key difference, however, is that we’re now moving away from experimentation and toward adoption. While some 88% of organizations report regular AI use, only a small fraction can scale AI across multiple processes. As a result, digital transformation success rates are stagnant at around 35%. Worse still, only 2% of organizations can be described as well prepared for AI adoption.

The message is clear: The technology is in place – but operating models, governance and infrastructure are lagging behind. And high adoption rates don’t necessarily equate to readiness. In this, my final blog of 2025, I want to present what I see as the five most significant tech trends for 2026, their benefits and implications, and their current adoption stage.

1: Multiagent Systems – from Individual Copilots to AI Teams

As the name suggests, multiagent systems are collections of AI agents, orchestrated to collaborate in complex workflows. A system of this type could incorporate multiple specialized agents focusing, for example, on data retrieval, reasoning, and task execution respectively.

This approach has the major benefit of enabling automation of multi-step business processes at scale. Systems of this kind take AI out the chat sidebar, transforming it into a virtual operations layer capable of tackling not just isolated tasks, but entire process segments.

Streamlined Customer Service

In customer service, for example, multiagent systems can rapidly classify customers’ requests, retrieve relevant context from various systems, draft resolutions, log outcomes, and schedule follow-ups – often faster than a human would be able to read the initial request.

Currently, these systems are at the early adoption stage. They’re on organizations’ strategic radar and, in some cases, are already being piloted. However, most businesses are still in proof-of-concept mode rather than broad deployment.

2: Domain-Specific Language Models – Context as the New Moat

In 2026, we’re likely to see an increase in the number of large language models (LLMs) trained using data from specific industries, functions, or processes. In fact, Gartner predicts that by 2028, more than 50% of the GenAI models used by enterprises will be domain-specific.

This trend offers several advantages. Compared to generic LLMs, these models provide superior contextual understanding, resulting in greater accuracy and fewer instances of hallucinations. Their specialization also simplifies compliance with regulations.

Assessing Domain-Specific LLMs for Challenging Tasks

In energy-grid operations, for example, studies on domain-targeted large language models have explored these LLMs’ ability assist with operational adjustment, monitoring, and decision-making for dispatch planning and grid coordination under real-world conditions.

Today, the adoption of such LLMs is on the rise and has already reached the serious-development stage in highly regulated and high-value domains.

3: AI Governance – Sovereign, Hybrid, and Auditable by Design

Effective AI governance is essential for successful AI adoption. Governance approaches are now evolving to address not only model risk and bias, but also infrastructure and jurisdiction. Here, the key questions are as follows: Where do models run? Who owns the hardware? Which laws apply to data and logs?

Answering these questions necessitates hybrid AI setups, combining public cloud, private cloud, on-premise, and edge solutions to address cost, latency, regulatory, and sovereignty constraints.

Safeguarding Sensitive Models and Data

For instance, telecom players are now building sovereign platforms to ensure that sensitive models and data remain within national borders and infrastructure.

This trend is now in the early mainstream phase of adoption, with most large organizations in the planning or pilot stages. 2026 will continue to be a build-out year. And governance and infrastructure are rapidly transitioning from back-office concerns to board-level priorities.

4: Pre-emptive Cybersecurity – AI vs. AI

With AI increasingly being used to scale cyberattacks, security efforts must shift toward proactive defense, rather than merely detecting and mitigating threats. This involves implementing AI-enhanced monitoring, automated response playbooks, and continuous system hardening.

Consequently, defense must move to an earlier position in the kill chain. According to a recent survey, 87% of security leaders believe that AI is already leading to more frequent and more sophisticated attacks, yet only 29% feel adequately prepared to counter this threat.

AI-Powered Anomaly Detection

One example of this trend in action is the increasing use of AI-driven anomaly detection by financial institutions to analyze identity and transaction data. This enables compromised accounts to be quickly isolated, triggering automatic containment workflows and preventing further damage.

Currently, adoption is at the mid-stage: While many organizations have started using AI in security operations, only a few have developed mature, preemptive strategies.

5: Platformization Shift – Morphing from Consulting Services to Software Provision

Platformization refers to the transition from one-off projects and services to reusable software-centric platforms. The goal here is to share knowledge and working systems by implementing reusable solutions and applications (platforms) and offering these to multiple clients – eliminating the need to repeatedly solve similar problems in different projects.

From Hourly Rates to Software Subscriptions

This radical shift enables value delivery through workflow engines, data platforms, and AI-powered services. For consultancies and service providers. This marks a significant move away from selling hours billed and toward monetizing software, data, and AI capabilities at scale. For some examples of this approach in action, see my most recent blog post.

Platformization is now in the early mainstream phase of adoption. While many enterprises are talking about platforms and achieving small-scale successes – particularly with data or API platforms –, end-to-end platformization with AI at its core remains confined to digital frontrunners.

The Key to AI Success: Readiness

As AI adoption accelerates, readiness will become the key differentiator. While many organizations already include the above trends in their roadmaps for 2026, readiness to implement them remains a challenge.

The message for tech leaders is clear: In 2026, the focus must shift from experimenting with isolated tools to building environments that allow technology to scale.

Season’s Greetings

As another year draws to a close, I’d like to thank you, the readers of this blog, for all your feedback over the course of 2025. I wish you, your family, and your friends a relaxing and peaceful festive season and all the very best for 2026.