The central narrative threading through Deloitte’s 17th annual Tech Trends 2026 Report shows a fundamental mindset shift underway across enterprises from “What can we do with AI?” to “How do we move from experimentation to impact?”

While the 2025 report focused on building proof-of-concept projects and exploring the art of the possible, the year ahead will fundamentally be about taking those pilots designed to prove possibilities to operationalizing AI-driven processes at scale, and translating those AI efforts into business value that can be measured. 

This, Deloitte contends, reflects the maturation of enterprise AI adoption, as they recognize that competitive differentiation will come from using AI to drive automation, innovation, and acceleration. 

And to drive that transformation, Deloitte identified five core trends:

AI Goes Physical: The Convergence of AI and Robotics

Physical AI represents artificial intelligence systems that enable machines to autonomously perceive, understand, reason about, and interact with the physical world in real time—moving beyond pre-programmed instructions to adaptive, learning systems. The implications are substantial: Amazon deployed its millionth robot, with DeepFleet AI optimizing fleet efficiency by 10%; BMW’s factories feature cars that drive themselves through production routes; and applications are expanding across healthcare, restaurants, utilities, and municipal services.

For CIOs, trend signals that robotics is no longer confined to manufacturing. The technology is expanding into operational areas where precision, safety, and accessibility are critical. According to Deloitte, current implementation challenges include simulation-to-reality gaps, safety and trustworthiness concerns, regulatory fragmentation, cybersecurity vulnerabilities, and data management complexity. “This level of physical cyber convergence is something that, unless you’ve been working in operational technologies, most CIOs are not used to,” says Bill Briggs, chief technology officer at Deloitte. 

UBS projects 2 million workplace humanoid robots by 2035, reaching a $30–50 billion market. The message for technology leaders: prepare infrastructure, security architectures, and fleet orchestration systems now, as physical AI adoption will accelerate dramatically. Briggs warns that the move to robotics will increase an already expanding attack surface. 

“We’ve seen across industries, this convergence of IT and OT and robotic technologies over the past five years is absolutely the future. But the risk surface certainly increases, especially when you have physical form with agentic systems that take actions, not just providing recommendations,” says Briggs. 

The Agentic Reality Check: Silicon-Based Workforce Readiness

Despite aggressive hype around agentic AI, only 11% of surveyed organizations have deployed agentic systems in production, despite 38% piloting them. Gartner predicts that 40% of agentic AI projects will fail by 2027—not because the technology doesn’t work, but because organizations are automating broken processes rather than reimagining operations.

According to Deloitte, the critical insight from successful organizations is that value comes from process redesign, not process automation. “If you just take your existing workflow and try to apply advanced AI to it, you’re going to weaponize inefficiency,” says Briggs.

For CIOs, the implications are transformative. According to Deloitte, agentic AI requires architectural modernization (microservices, APIs, modular design), new governance models that enable speed while maintaining oversight, and a fundamental rethink of what “work” means in hybrid human-digital environments. Organizations must treat agents as a silicon-based workforce, with specialized management frameworks for onboarding, performance tracking, and FinOps cost management. Multi-agent orchestration using emerging protocols (MCP, A2A, ACP) becomes essential infrastructure. 

The competitive advantage lies with organizations that redesign end-to-end processes to enhance agent capabilities, not those that layer agents onto legacy workflows.

The AI Infrastructure Reckoning: Inference Economics and Hybrid Architecture

Token costs have dropped 280-fold over two years, yet some enterprises are seeing monthly bills in the tens of millions of dollars. The mathematics is stark: usage growth has outpaced cost reduction dramatically. Cloud-based AI services, suitable for experimentation, become prohibitively expensive when they scale into production, particularly for agentic AI workloads that require continuous inference.

According to Deloitte, smart enterprises are shifting from binary cloud-versus-on-premises thinking to strategic three-tier hybrid architectures: cloud for elasticity and variable training workloads, on-premises for consistent production inference at predictable costs, and edge for latency-critical applications requiring split-second decision-making. “If you don’t have that foundation in place, cloud costs could become runaway,” says Briggs. ​

For CIOs, this signals the need for immediate infrastructure modernization. Deloitte says organizations should evaluate the tipping point where cloud costs reach 60–70% of equivalent hardware costs, at which point on-premises investment becomes economically rational; design purpose-built AI data centers (not retrofits of legacy infrastructure) featuring GPU-optimized hardware, advanced networking, and specialized cooling; implement observability architectures enabling holistic system management across heterogeneous platforms; address data sovereignty, latency sensitivity, resilience requirements, and IP protection as core infrastructure drivers; and finally plan for workforce reskilling around AI infrastructure management and AI agents managing infrastructure itself

Traditional data center architectures designed for general-purpose computing cannot efficiently run AI workloads. Greenfield AI factories explicitly designed for AI processing will outperform retrofitted brownfield environments.

The Great Rebuild: Restructuring Technology Organizations

AI is fundamentally restructuring technology organizations beyond simple automation. With 64% of organizations increasing AI investments, priorities are shifting from infrastructure maintenance toward strategic leadership. Deloitte found that only 1% of IT leaders report that their organizations don’t have any significant operating model changes underway.

Successful organizations are anchoring AI initiatives to measurable business outcomes, designing modular architectures for flexibility, and redefining talent strategies to focus on human-machine collaboration. New roles are emerging—AI collaboration designers, edge AI engineers, prompt engineers—while CIOs evolve from tech strategists to AI evangelists and orchestrators. “Enterprises need to have the CEO-level saying that the organization is going to reimagine everything. This is a moment in time for us to really rewire the nervous system of the entire organization,” says Briggs. 

“You need all of that executive leadership team to come together and have a shared vision on what that means, because if that’s out of harmony, do not pass go,” says Briggs.

For CIOs, that rewiring has changed their mandate, with 70% of CIOs telling Deloitte that their primary role is either implementing generative AI across the enterprise or serving as evangelists showing teams the possibilities. This requires them to shift from keeping the “lights on” to “light the way forward” and to position technology as a strategic revenue driver. 

The AI Advantage Dilemma: Security Risks and Defensive Opportunities

AI creates a cybersecurity paradox: the same capabilities driving innovation introduce new vulnerabilities. Organizations face threats from “shadow AI” deployments, adversarial attacks, and inherent weaknesses in AI systems across data, AI models, applications, and infrastructure. However, AI also enables powerful new defensive capabilities.

For CIOs, that means security becomes both a constraint and an enabler. Deloitte advises enterprises to adapt their existing practices to address AI-specific risks, including robust access controls, model isolation, secure deployment architectures, leveraging AI defensively through red teaming with AI agents, adversarial training, and automated threat detection at machine speed.

Security should also be considered from the very inception of AI initiatives and treated as a safe enabler rather than a constraint. 

The fundamental takeaways: organizations that are built for sequential improvement cannot compete with those operating in continuous improvement. And success requires organizations to have the courage to redesign processes rather than simply mindlessly automate existing ones, the discipline to connect investments to business outcomes, and the velocity to execute before relevance windows close.

Deloitte’s 17th annual Tech Trends 2026 Report is available here.