Operations | Monitoring | ITSM | DevOps | Cloud

CIOs are under pressure from every direction. Budgets remain tight, geopolitical uncertainty is forcing organizations to rethink resilience, and workforce expectations continue to evolve. At the same time, AI is accelerating a broader shift across enterprise IT – changing not only how organizations operate, but also the skills and roles they will increasingly depend on. The question is not whether AI will reshape IT teams, but how quickly organizations can adapt to these new ways of working.

Technology companies are being pushed to deliver faster outcomes while justifying growing investment in AI, SaaS, and digital infrastructure. But productivity does not improve just because new tools are deployed. It improves when employees can use those tools without the constant drag of slow devices, unstable applications, and fixes that do not fully solve the problem. That is the productivity tax of digital friction.

Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues, reducing operational costs by 30%. That shift points toward a zero-friction workplace, where employees do not have to navigate support just to get back to work.

In pharma, reliability becomes an operational priority because research and trial work depend on systems performing consistently across different teams, locations, and conditions. Much of that work sits inside scientific workflows, remote sessions, and compute-heavy environments where behaviour can shift with configuration or load. When that consistency starts to break down, teams keep moving, but time is lost in small increments across the day.

In 2026, I think it’s safe to say that most mobile devices in enterprise organizations aren’t purchased just for their ability to make calls. And for millions of employees, especially frontline workers, their primary device isn’t even a laptop anymore - it’s a smartphone or tablet. Yet, mobile device insights have largely remained a blind spot for IT.

Inside hospitals and health systems, the performance of clinical technology underpins nearly every care workflow and directly influences the timeliness and quality of patient care. Electronic health records sit at the center of admissions, discharge, imaging, lab coordination, and prescribing, so even minor technology friction can become a patient safety and operational risk. At scale, reliability becomes a prerequisite for consistent care.

For many EUC and Digital Workplace leaders, the challenge with digital employee experience (DEX) isn’t the technology, it’s building alignment. You can see the data. You know where friction exists. You can quantify disruption, productivity loss, and inefficiencies. But you struggle to achieve your targets, because you need buy in from other teams, and right now, they don’t want to hear anything about DEX. Security has different priorities. Application owners are focused on releases.

Generative AI has the potential to transform workplace productivity – but do organizations know how to deliver on that promise? New research shows that employees who use generative AI tools engage with them up to ten times per day, spending over three hours per week interacting with AI at work. And yet within the same organizations, large groups of employees have never meaningfully engaged with these tools at all.

Most IT organizations want to improve the digital employee experience. But good intentions alone rarely move the needle. The real shift happens when organizations evolve how IT operates. Traditional IT operations are built around reacting to incidents. But ticket-based operations, or operations based on poor data, lack the ability to create truly predictive ways of working.

Digital employee experience doesn’t fail because of technology. It fails because of operating models. Many digital workplace leaders invest in visibility tools, dashboards, automation capabilities, and sentiment platforms. And yet, months later, they’re still stuck in reactive mode. Tickets are down slightly. Reporting is better. But the organization hasn’t fundamentally shifted.