2026 will not be defined by bigger models or louder headlines. It will be remembered as the year AI became operational, according to Roy Verboeket, VP Sales Engineering, Extreme Networks
What will be the biggest AI breakthrough we can expect in 2026 — technology, adoption, or regulation?
In 2026, the biggest breakthrough will be adoption, not raw technology. The models will continue to improve, but the real shift will come from organizations finally trusting AI enough to use it in everyday operations. We’ve reached a point where AI is no longer a curiosity or a side experiment. It’s becoming a dependable assistant that can turn large volumes of operational data into clear, actionable decisions.
In areas like networking and IT operations, we already see this transition happening. Customers are moving from asking, “What can AI show me?” to saying, “Let AI handle this task for me.” That mindset change is significant.
Regulation will also play a role, particularly around privacy, data residency, and critical infrastructure. But rather than slowing adoption, clearer rules will likely accelerate it by giving organizations the confidence to move forward. The real breakthrough is when AI becomes normal not magic or hype, just something businesses use every day to operate more effectively.
Is 2026 the year AI moves from pilots to full production? What forces will accelerate or slow that shift?
Yes, 2026 will be the year many organizations move AI from isolated pilots into full production. The demand is already there. Business leaders are under pressure to make faster decisions, reduce operational costs, and automate repetitive work across IT, security, customer service, and supply chain operations.
Several forces will accelerate this shift. First, trust in platforms matters. Organizations want AI embedded into secure, unified systems rather than deployed as standalone tools. When AI is built directly into the infrastructure teams already rely on, adoption becomes far easier, more reliable and far more scalable.
Second, there’s intense pressure to do more with fewer resources. AI is increasingly seen as a force multiplier, enhancing human productivity and decision-making rather than replacing people. By handling routine tasks, analyzing large datasets, and surfacing insights, AI frees teams to focus on higher-value work that requires judgment and creativity.
Third, data quality is improving as organizations modernize their environments. Cleaner, more complete, and better-organized data allows AI to generate more accurate insights and drive smarter outcomes.
The main barriers are cultural rather than technical: risk aversion, skills gaps, and fear of losing control. However, these concerns are fading as governance frameworks mature and organizations see tangible results from production deployments.
Which industries do you predict will experience the fastest AI-driven transformation next year, and why?
The fastest-moving industries will be those with high operational complexity, high costs of downtime, and large volumes of data. In these environments, the return on AI investment is clear and measurable.
Retail will move quickly, using AI to optimize supply chains, pricing, and customer experiences. With thin margins, even small efficiency gains matter. Healthcare will also accelerate adoption as hospitals look to reduce administrative burden, improve triage, and optimize operations without compromising care quality.
Manufacturing and industrial sectors are another strong candidate. AI can predict equipment failures, optimize energy usage, and improve safety, all of which directly impact productivity and cost. Finally, the public sector and smart cities, particularly in the Middle East, will continue to move fast due to strong government backing for digital infrastructure, citizen services, and security initiatives.
Will agentic AI become mainstream? How will autonomous workflows reshape enterprise operations in 2026?
Agentic AI will start to become mainstream in 2026, but not in a science-fiction sense. What we will see is the rise of task-focused agents that can run specific workflows end-to-end, with clear human oversight and control. Humans decide the objectives and boundaries, and agents execute within those parameters. Automation will increase gradually, with checkpoints in place as confidence builds. Over time, as AI proves reliable, the need for constant human intervention will decrease, speeding execution while reshaping how people interact with systems. The result won’t be the removal of humans, but a fundamentally changed human experience as routine work gives way to higher-value decision-making.
In networking, this shift is already visible. AI will not just identify an issue, but propose a fix, apply it, verify the outcome, and document the action. This reduces operational noise, speeds up response times, and frees IT teams to focus on more strategic work.
Across the enterprise, autonomous workflows will reduce manual errors, speed up repetitive processes, and make operations more predictable. Agentic AI will enable IT to move from reactive firefighting to proactive prevention, continuously optimizing performance, resilience, and security. Much of this will happen quietly, embedded beneath everyday business processes, but its impact will be felt in smoother operations, faster outcomes, and greater confidence across the organization.
What role will sovereign AI play — and how are governments likely to regulate local language models and data control?
Sovereign AI will grow rapidly in 2026 as governments focus on data control and cultural relevance. Global models are powerful, but they don’t always align with national policies, local languages, or regional privacy expectations.
Many governments, including those in the Middle East, are already moving toward clearer requirements around local data processing, data residency, certification frameworks, and approved AI systems for public-sector use. This reflects a broader desire to ensure AI aligns with national values and regulatory standards.
For enterprises, this means infrastructure needs to support flexible deployment models. Organizations want to choose where their data lives and where AI runs. We’re likely to see more hybrid approaches, combining global models with sovereign or sector-specific ones. This is a positive development because it builds trust and enables responsible adoption.
How will enterprise AI evolve in 2026 — from PoCs to measurable business value? What will success look like?
In 2026, enterprise AI will be evaluated the same way any other technology is evaluated: by measurable business outcomes, not demonstrations. Success will be reflected in shorter resolution times, fewer manual tickets, improved customer experiences, lower operational costs, higher reliability, and faster access to insights.
Organizations will stop asking, “Can AI do this?” and start asking, “What did AI improve this month?” That shift in mindset is critical.
In networking and IT operations, this means AI will increasingly prevent problems before users notice them, automate workflows that once took hours into minutes, and give leaders real-time visibility across their environments. This will help reduce blind spots and give AI the context it needs to detect and respond to issues more effectively. Over time, AI will move from reactive analysis toward predictive insight, spotting potential problems before they even occur.
When AI delivers value consistently without forcing people to radically change how they work, it becomes part of the organization’s operating rhythm. That is the moment when AI stops being a project and becomes truly strategic, reducing complexity, improving resilience, and unlocking new growth opportunities. The outcome is faster decision-making, more confident operations, and a clear advantage for organizations that fully leverage AI across their business.
