At a time when the pace of technological change has never been greater, leaders are under increasing pressure to deliver technology strategies that translate to growth. This is challenging when everywhere you turn, there’s a new capability, a new use case, or a new vendor coming on the scene. In what can feel like a blitz of buzzwords, it’s never been more important to not lose the forest for the trees.

In the coming year it will be key for leaders to focus on their broader tech journeys, rather than scrambling to get to their desired destination. Why? The question isn’t whether organizations are experimenting with AI – because most are. The real question is whether they’re successfully moving from experimentation to impact.

The gap between those two states is where competitive advantage gets won or lost (and fast). Too many organizations are stuck endlessly chasing pilots, looking for quick wins that just might justify the investment. The leaders pulling ahead? They are tackling their biggest problems from the jump. They’re not doing tech for tech sake – they’re redesigning operations with AI in mind and creating solutions with humans, not just for them.

For 17 years, Deloitte’s Tech Trends report has explored emerging technologies poised to reshape business in the next 18 to 24 months. In nearly two decades of tracking what works and what doesn’t, staying close to organizations actually doing this work has been key to gaining broad perspective. This year we saw last year’s observations come to life: AI has moved from focus to foundation, becoming akin to electricity. We predict, through five key trends, that leaders in 2026 are working on the necessity of scale, and offer advice for getting there based on what we’ve observed.

AI goes physical

Intelligence is no longer confined to just screens – it’s gone physical. Cities are experimenting with tools like drones to inspect bridges, and autonomous shuttles to transport people. What once felt like science fiction is happening here and now, and leaders across all industries are looking at how physical robots that perceive, learn, and adapt in dynamic settings can benefit their workforce, customers and even broader society. Physical AI solves real problems where safety, precision, or accessibility matter most; but to get to broad adoption, hardware providers, regulatory bodies, and enterprises need to be in lock step.

Prepare for a silicon-based workforce

Start thinking about agents as your silicon-based workforce. We’re already seeing several organizations treating agents as personnel costs. That’s the future. The challenge here isn’t the technology. It’s trying to automate existing processes designed for humans in mind, rather than exploring the art of the possible and redesigning for AI-first operations. Consider this: 93% of AI investments are going toward the tech, and just 7% are going to talent. Leaders should evolve that ratio, and getting this balance right requires not only investing in learning, culture, and growth for the human workforce, but also setting the foundation for the HR equivalent for advanced agents, robots, and more. Those leading the way in this space are reimagining what work means, and leaning into the different skill sets both AI agents and human workers bring to the table.

The AI-first infrastructure reckoning

While AI processing costs have plummeted, usage is growing far faster than costs are falling, and systems are built on top of aging infrastructure made for a different world. Organizations are hitting a tipping point where cloud services become cost-prohibitive for high-volume workloads. The culprit? Continuous inference from agentic AI can send token costs spiraling, with some enterprises already seeing monthly bills in the tens of millions. Leading organizations are implementing three-tier hybrid architectures: cloud for elasticity, on-premises for consistency, and edge for immediacy. This isn’t a cloud or an on-premises problem. It’s a strategy opportunity. Cost is only one piece of this puzzle. Organizations also need to rethink what this all means for data sovereignty, latency, resilience, and IP protection.

Modern tech organizations are being rebuilt

In a world where tech organizations were charged with keeping the lights on, we’re seeing them now be asked to lead the way. From architecture to delivery, AI is rewiring the ways IT organizations operate. As CIOs evolve from tech strategists to AI evangelists and orchestrators, there’s an opportunity to make sure each AI initiative is anchored in measurable business outcomes, designed with modular architectures, and redefining talent strategies in a world where we’ll continue to be humans with machines.

AI creates a cybersecurity paradox

AI is transforming enterprise cybersecurity: the same technology that can deliver competitive advantage and new business opportunities is also introducing new cybersecurity vulnerabilities and widening attack surfaces. Shadow AI deployments, adversarial attacks, and intrinsic system weaknesses are expanding attack surfaces. The opportunity is using AI defensively, including red teaming with AI agents, adversarial training, and automated threat detection operating at machine speed. By adopting an “AI for cybersecurity” and “cybersecurity for AI” approach with security blueprints at its core, leaders can proactively address emerging risks and drive tangible business outcomes.

Proactive tech leadership moves business forward

Here’s the reality: as AI reshapes everything from computing hardware to physical robotics, no corner of enterprise technology goes untouched.

The key to competitive differentiation will be using AI to drive automation, innovation, and acceleration. The path from experimentation to impact is clear. The organizations willing to walk it will shape what comes next.