Unisys has released its latest report, identifying 10 trends that will define the next era of enterprise technology. Based on extensive conversations with industry experts and company leaders, the report offers insights into how AI applications can achieve breakthroughs in repeatable, high-ROI deployments, the impact of technology on the workforce, and the implications for cybersecurity.
“In 2025 we saw a lot of AI hype, with conflicting reports about what the technology can deliver, leaving business leaders asking, ‘what’s next?’ and ‘when will we start seeing results?’ In 2026, we are going to see more functional deployments of AI, a focus on quality rather than cost-cutting, and the emergence of AI applications that will deliver repeatable, ROI-driven results,” says Mike Thomson, CEO and president of Unisys.
The key insights from the report include the following.
- Focused AI deployments will outpace transformation projects. The majority of AI deployments will not be large-scale initiatives. Instead, they will be smaller, task-based integrations that fold into existing processes. These deployments will use smaller data sets that are easier to clean require lower investment thresholds, enable smoother change management, and deliver quicker results.
- Three AI applications will break through at repeatable, high-ROI deployments. After years of experimentation, enterprises are converging on a handful of repeatable, high-ROI applications. Chatbots for employees and clients, AI coding agents, and AI-driven service assistants will become packaged, measurable, and quick to deploy. This will change how organizations evaluate and choose to invest in AI.
- AI investments will shift from cost reduction to quality improvement. Early AI programs focused primarily on cost reductions because they were easy to model and sell. In 2026, AI will deliver greater quality—from measuring how AI improves decision confidence, reduces variance, and elevates outcomes. With quality output, industry leaders say organizations will see increased revenue and improved margins.
- Organizations will train AI models on small, task-specific data sets rather than pursuing scale. The focus will not be on generalists and large language models trained on massive data sets. Instead, specialists and small language models will be trained on cleaner, more simplified data sets. In practice, enterprises will benefit from more finely tuned industry-specific models, which will lead to more accurate and higher-quality outputs that are more cost efficient to operate and implement.
- Mass layoffs from AI automation won’t occur, though entry-level coding positions will shrink. Despite the rise of automation, widespread AI-driven layoffs are not expected to materialize in 2026. Organizations know that blunt headcount cuts undermine transformation. Rather, leaders will redirect productivity gains to backlog reduction, customer experience, and modernization. AI agents, however, will automate routine coding, further reducing “junior coder” roles.
- AI will accelerate both cyberattacks and defenses. Organizations will see both attackers and defenders deploy AI to their advantage. For attackers, this will include accelerated phishing personalization, deepfakes, and voice spoofing. For defenders, AI will augment pattern recognition and anomaly detection, as well as threat hunting with natural-language interfaces and automated responses. The goal for the next year is not perfect prevention, but rather fast containment, credible forensics, and resilient recovery.
