7 Tech Predictions Enterprise Leaders Are Watching in 2026
Scale is the theme carrying technology into 2026. AI systems are doing more, making more decisions, and operating with less direct oversight as they spread across industries.
As 2026 comes into view, executives and industry leaders across security, infrastructure, and AI are beginning to align on what that scale will change. Their forecasts, which they shared in exclusive interviews with TechRepublic, sketch a year defined by autonomy, specialization, and the need for tighter control.
1. AI flattens technical skill barriers
As AI takes on more of the repetitive, technical work inside organizations, the advantage once held by deep specialists will continue to narrow. Tasks that require years of training, including large portions of software development, will be increasingly handled by systems that assist or automate execution.
Matthias Steiner, senior director of Global Business Innovation at Syntax, said he expects that change to accelerate in 2026. As AI “levels the coding field,” he notes, competitive advantage will no longer hinge on coding skill alone, but on teams that can master the full software lifecycle, from strategy and domain-driven decisions to execution and ongoing oversight.
2. AI’s most reliable wins won’t be flashy
AI’s biggest gains in 2026 are expected to come from work that rarely draws attention. Value is consolidating around tasks that consume time, cost, and human effort.
Hanno Basse, chief technical officer at Stability AI, said that the strongest near-term returns will come from automating “necessary, but repetitive grunt work.” He points to content production tasks like wire removal in visual effects — a painstaking post-production process traditionally done pixel by pixel — as examples where generative AI can dramatically speed output without altering creative intent or decision-making.
3. The era of one-size-fits-all tech is ending
The idea that a single, general-purpose system can meet most enterprise needs is losing credibility. As AI and data-driven workloads move into core operations, the limits of generic platforms are becoming harder to ignore.
Udo Sglavo, vice president of Applied AI and Modeling (R&D) at SAS, predicts the belief that “one large, general-purpose language model will replace most enterprise software” will not hold up. Organizations, he notes, rely on tightly controlled systems that must be reliable, explainable, and compliant, making it unrealistic to entrust critical operations to a single opaque model. Instead, he expects smaller, specialized AI components governed by clear business rules and continuously monitored in production.
That same pressure for precision is reshaping the infrastructure beneath those systems.
Barry Baker, COO and General Manager of IBM Infrastructure, says “the era of generic AI infrastructure will come to an end in 2026,” as companies move away from identical servers as universal solutions. He said hardware and software co-designed for specific workloads will be essential to meeting real-world demands around latency, cost, reliability, and energy efficiency.
The shift is also reaching the user layer. Shawn Yen, SVP of Product Planning at ASUS, expects AI experiences to move away from generic chat-based interfaces toward tools designed around specific users and workflows. Rather than one-size-fits-all assistants, he sees AI being embedded directly into how SMBs manage productivity and how creators ideate, generate, and organize content; purpose-built systems optimized for what people are actually trying to do.
4. Autonomy replaces lock-in
After years of cost hikes and inflexible terms, more teams will be moving to cloud environments that give them the flexibility to choose, adapt, and move without being boxed in.
James Lucas, CEO of CirrusHQ, predicts that autonomy will become a defining priority as organizations turn to cloud marketplaces and modular services that offer greater freedom. But with that freedom comes risk. Without automated oversight, Lucas warns, shadow IT can undermine compliance and data sovereignty, leaving cloud environments harder to manage just as they become more independent.
5. Autonomous AI agents create a new attack surface
As organizations deploy AI agents that operate more independently, security teams are confronting risks that don’t resemble traditional threats. Unlike scripted automation, these agents can interact with systems, data, and third parties with minimal human oversight, often faster than existing controls can track.
Jessica Hetrick, vice president of Federal Services at Optiv + ClearShark, warns that autonomous AI agents will enable more sophisticated attacks that are harder to trace and attribute. Because agents can act on behalf of users and systems, she says, they expand the attack surface in ways legacy security models were never designed to monitor.
6. Observability becomes non-negotiable
Experts expect observability to become a baseline requirement for running complex technology at scale, especially when systems are making decisions and taking actions with limited human input.
Maryam Ashoori, vice president of Product and Engineering at watsonx.gov, says enterprises will operate dozens, or even hundreds, of AI agents in parallel, often built by different teams and running across multiple platforms.
At that scale, she adds, organizations will be forced to prioritize observability, evaluation, and policy enforcement to understand how agentic systems behave in real-world conditions and to keep autonomous workflows under control.
7. The first AI-agent breach reshapes cyber training
The next major change in cybersecurity will be driven by a failure that forces organizations to rethink how people work with autonomous systems.
Tiffany Shogren, director of Services Enablement and Cybersecurity Education at Optiv, forecasts that a major AI-agent-driven incident will redefine cyber training standards. She says organizations will be pushed to introduce formal “AI oversight” and human-in-the-loop modules, mandating employees to understand when and how to question, intervene, and override automated behavior, not just monitor it passively.
What scale ultimately demands
By 2026, technology won’t get the benefit of the doubt anymore. Systems will be evaluated in production, under load, across teams, regulators, and budgets, with little patience for fixes that arrive after the fact. What survives will be what was designed to operate continuously, not just impress early.
The predictions here describe a narrowing field. As technology advances, tolerance drops, and expectations harden.
The next year belongs to platforms, workflows, and controls that were built with scale in mind from the start — because by the time scale arrives, there’s rarely room left to retrofit discipline.
Find out how one cybersecurity insider expects AI-driven threats, predictive SOCs, and cracks in trust and identity to pressure security teams in 2026.
