Abstract
CES 2026 marked a decisive transition in artificial intelligence adoption, from experimentation and bolt-on capabilities to AI that is architected as a foundational system layer. Across software, infrastructure, industrial systems, and semiconductors, organizations emphasized that transparency, governance, and trust must be designed in from the outset rather than retrofitted. At the same time, AI is rapidly becoming the primary interface through which users experience products and services, effectively redefining customer experience (CX). Interviews and announcements spanning navigation, silicon, enterprise consulting, industrial manufacturing, quantum computing, and edge AI platforms reveal a consistent conclusion: AI that cannot be trusted cannot scale, and AI that does not materially improve user experience will fail to differentiate. The next phase of AI adoption will be defined by systems that embed trust at the silicon, platform, and operating-model layers while positioning AI as the front door to digital and physical products.
The Shift from Bolt-On AI to Built-In Trust
A dominant theme at CES 2026 was that AI governance must begin far earlier in the technology stack, often at the semiconductor and software architecture level, rather than being addressed solely through policy or post-deployment controls.
TomTom illustrated how grounding AI agents in authoritative, domain-specific data improves explainability and reduces the risk of hallucination. By anchoring AI decisions to verified mapping and traffic intelligence and exposing that capability through controlled integration points, TomTom treats trust as an architectural property rather than a compliance exercise.
From a silicon and platform perspective, Arm reinforced that transparency and governance increasingly depend on how AI workloads are executed across heterogeneous compute environments. As inference shifts toward the edge, understanding where decisions are made, how power and memory are consumed, and how developers program CPUs, GPUs, and NPUs becomes essential to governing AI behavior, particularly in latency- and safety-sensitive use cases.
This theme was further reinforced by Hailo, whose CES 2026 demonstrations underscored that trust in AI is inseparable from where intelligence runs. By enabling advanced vision and generative AI workloads to execute locally on low-power, purpose-built edge processors, Hailo reduces reliance on opaque cloud pipelines while improving determinism, privacy, and availability. On-device execution inherently limits data exposure, improves the explainability of system behavior, and enables organizations to govern AI outcomes through hardware-level constraints rather than relying solely on policy overlays.
From an enterprise operating-model standpoint, EY Consulting emphasized that governance failures are often structural rather than technical. Enterprises attempting to layer AI onto existing processes replicate inefficiencies and risk at scale. EY Consulting’s advisory perspective highlighted that trust emerges when operating models are redesigned around AI from day one, embedding human oversight, exception handling, and accountability directly into workflows.
In safety-critical physical environments, Oshkosh demonstrated that trust must be earned incrementally. Clean training data, supervised autonomy, staged deployment, and privacy-preserving design choices, such as facial blurring, form a multi-tier trust model. Governance in these environments is experiential, validated through consistent system behavior rather than abstract assurances.
Even in emerging compute domains, QCI highlighted that transparency comes from clarity of intent. By focusing on application-specific quantum-AI systems, QCI reduces ambiguity around what problems the technology solves and how results are generated, accelerating trust through predictability and measurable outcomes.
From an architectural standpoint, the discussion with AWS reinforced that AI transparency and governance increasingly emerge from hybrid, multi-layer system design rather than centralized control alone. AWS’s approach to industrial and automotive AI demonstrates how trust is built by explicitly separating data collection, model training, simulation, deployment, and orchestration across cloud and edge environments. By validating models in simulation, deploying them back to edge and embedded systems, and using agent frameworks to coordinate multiple models and control systems, AWS reduces operational opacity while maintaining human oversight. This layered approach aligns governance with system behavior, ensuring that AI decisions remain traceable, auditable, and bounded by the physical and operational context in which they operate, rather than relying on abstract policy enforcement after deployment.
AI Becomes the New Customer Experience
A second major takeaway from CES 2026 is that AI is no longer an enhancement to CX, it is rapidly becoming CX itself. Across industries, AI is replacing traditional interfaces with intelligent, context-aware interaction models.
TomTom’s AI agents exemplify this shift by moving beyond turn-by-turn navigation to proactive orchestration. AI anticipates driver needs, coordinates tasks, and delivers context-aware recommendations without forcing users to change platforms or behaviors. The experience becomes conversational, adaptive, and largely invisible, an indicator of mature AI-driven CX.
Arm framed this transition as a generational shift comparable to the adoption of touch interfaces. Users increasingly expect products to understand intent, adapt in real time, and respond intelligently. AI becomes the experiential layer that mediates interaction across phones, wearables, entertainment systems, and emerging neural interfaces.
Hailo extended this CX narrative into the physical and edge domains, where AI-driven experiences are defined by immediacy, privacy, and autonomy. By enabling vision-language models, generative AI, and agentic behaviors to run directly on-device, Hailo enables new classes of consumer and commercial experiences—from intelligent cameras and retail systems to robotics and in-cabin automotive assistants. In these scenarios, CX is shaped by responsiveness and trust rather than connectivity to distant cloud services.
EY Consulting broadened the CX lens to include employees and partners, arguing that AI increasingly defines how work gets done inside the enterprise. When AI integrates siloed data and orchestrates workflows, CX evolves into continuous, personalized engagement, measured by confidence, speed, and relevance rather than interface design alone.
In industrial settings, Oshkosh reinforced that AI-driven CX is outcome-oriented. Safety, reliability, and productivity improvements delivered through AI-augmented vehicles define the experience for frontline workers. In these environments, the most successful AI experiences are often the least visible.
From a platform-ecosystem perspective, NVIDIA reinforced the idea that AI will increasingly serve as the universal interface for applications. Agentic AI systems are positioned to mediate interaction across industries, making AI, not screens or apps, the primary experiential layer.
In the automotive and industrial domains, the conversation with AWS illustrated how AI is rapidly becoming the primary experiential layer between humans and machines. The evolution of in-vehicle assistants, from command-based interactions to proactive, conversational systems, signals a broader CX shift in which AI anticipates intent, coordinates actions across systems, and reduces users’ cognitive load. Whether guiding a driver to a service appointment based on sensor data or orchestrating robots and quality inspection systems on the factory floor, AI is no longer a feature users engage with explicitly; it is the interface through which work and mobility occur. This reframes CX as outcome-driven and context-aware, where the quality of the experience is defined by relevance, timing, and trust, not by screens, buttons, or commands.
So What?
CES 2026 makes clear that AI strategy is no longer about models or experimentation; it is about system design, developer enablement, and execution at scale. Trust, transparency, and experience are now architectural requirements, not optional features. In order to achieve the right CX, you need to factor this in to get the ROI of AI!
Implications for CTOs
For CTOs, the shift to AI-native systems fundamentally changes technology leadership priorities:
- Trust must be engineered, not left to govern later. CTOs must ensure transparency, explainability, and privacy are embedded from silicon through software, rather than relying on downstream controls or compliance reviews. Architectural choices—such as on-device inference, domain-specific models, and deterministic data sources—are becoming primary mechanisms for trust.
- Edge and hybrid architectures are strategic, not tactical. As AI becomes the customer and employee interface, decisions about where inference runs (cloud, edge, or device) directly affect latency, reliability, cost, privacy, and user trust. CTOs must balance centralized scale with distributed intelligence, often using purpose-built silicon to enforce predictable behavior.
- AI is now a CX platform. AI increasingly defines how users interact with products, services, and internal systems. CTOs must collaborate more closely with product, design, and business leaders to ensure AI experiences are intuitive, proactive, and aligned with brand expectations, especially as agentic systems take on greater autonomy.
- Operating models must evolve alongside technology. Scaling AI requires rethinking workflows, accountability, and human-in-the-loop design. CTOs should prioritize platforms and architectures that make oversight, exception handling, and auditability inherent rather than external.
Implications for Developers
For developers, CES 2026 signals a material shift in how AI systems are built, deployed, and maintained:
- Developers are now responsible for trust by design. AI development is no longer just about accuracy or performance. Developers must understand data provenance, model behavior, inference boundaries, and system constraints, particularly as AI moves closer to users and physical environments.
- AI development is becoming full-stack and hardware-aware. With AI executing across CPUs, GPUs, NPUs, and edge accelerators, developers must design for heterogeneous compute environments. This includes optimizing for power, latency, and determinism, not just throughput.
- Agentic systems change how applications are written. Developers are increasingly orchestrating behaviors rather than coding linear workflows. This requires new patterns for supervision, guardrails, and recovery when AI systems encounter uncertainty or exceptions.
Local and on-device AI changes the developer experience. Running AI at the edge reduces dependency on cloud APIs but increases responsibility for optimization, lifecycle management, and update strategies. Developers who can build secure, offline-capable, privacy-preserving AI systems will be at a competitive advantage.
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