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CES 2025’s central AI claim was persuasive in a limited, useful sense: AI was already entering products and workflows, but that did not make every AI-branded demonstration mature or valuable. The Consumer Technology Association (CTA) used “digital coexistence” to describe technology working alongside people across homes, workplaces, vehicles, and health settings. It was a trend label, not a technical standard—and CES’s examples ranged from embedded sensing to speculative humanoid robots.
What “digital coexistence” meant at CES 2025
In a January 7, 2025, report on the CTA’s CES trends briefing, EE Times attributed the phrase “digital coexistence” to Brian Comiskey, CTA’s senior director of innovation and trends. The idea was that connected technology should operate with people, making systems more responsive and useful, rather than simply replacing human judgment or interaction. EE Times’ CES 2025 report presents this as a strategic framing, not a defined protocol or engineering architecture.
That framing reaches beyond chatbots. It includes AI embedded in devices, agents that may act on a user’s behalf, digital twins that represent physical systems, and robots or autonomous machines sharing human environments. It also reflects the increasingly blurred boundaries among smart homes, health technology, mobility, and workplaces. The practical question is whether those connections help people accomplish a specific task safely and reliably—not whether a product carries an AI label.
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The four themes behind CTA’s CES outlook
CTA organized its CES 2025 trend framing around four themes. They describe a broad direction for technology, not a ranking of product readiness.
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- Digital coexistence: Connected systems and AI working alongside people in everyday settings.
- Human security: Technology’s relationship to people’s safety and security as systems become more connected and autonomous.
- Community: Technology serving people in shared settings, rather than only as an individual convenience.
- Longevity: Applications such as remote care, wearables, precision medicine, and AI-assisted health technology.
The themes overlap. A connected health device, for example, can be part of a person’s home environment and a wider care process. That convergence may be useful, but it does not by itself establish privacy protections, clinical validation, or improved patient outcomes.
What the “AI is real” figures show—and what they do not
EE Times reported that CTA’s CES presentation cited 93% of U.S. adults as familiar with generative AI and said 61% of U.S. adults used AI tools at work, knowingly or unknowingly. Those are figures attributed to CTA, not independently verified findings in the article. It does not provide the survey’s sample size, field dates, question wording, confidence intervals, or definition of “use AI tools,” so they indicate the briefing’s account of awareness and workplace exposure rather than a precise measure of adoption.
The same report said CTA described 60% of U.S. Gen Z consumers as early technology adopters. It defined Gen Z as people born from 1997 through 2012 and reported that the group represented 32% of the global population. These figures should not be read as proof that all younger consumers want AI-mediated products.
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Where AI looked most tangible
A useful way to assess a CES example is to ask what task it addresses, what data and hardware it needs, and how a user or operator could measure success. On that basis, several areas had clearer practical routes than the broadest promises of general-purpose AI.
Edge AI and sensor fusion
AI at the edge processes data near the device or sensor instead of sending every decision to a remote service. That can help when low latency, offline operation, or local data handling matters. The trade-off is constrained power, memory, and thermal capacity, along with more complex software updates. Related EDN CES 2025 coverage highlighted edge AI, sensor fusion, and hardware acceleration; Institution of Electronics CES coverage discussed the combination of sensors and machine learning for applications such as perception and industrial inspection.
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The value is not “AI” in isolation. It may be more accurate object detection, 3D perception, inspection, navigation, or real-time decisions under power constraints. A buyer or engineering team still needs to check performance in the intended environment, energy use, latency, and what the system does when a sensor reading is ambiguous.
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Industrial automation and digital twins
Industrial inspection and automation offer comparatively concrete tasks: detect a defect, monitor equipment, or coordinate a process. But the evidence that matters is deployment performance—such as uptime, error rates, maintenance needs, and operating cost—not a polished demonstration.
A digital twin can mean different things. A static 3D model is not the same as a sensor-fed dashboard; neither necessarily constitutes a simulation model or a continuously updated representation used to make operational decisions. CES coverage connected digital twins with industrial and automotive uses, but the EE Times report did not quantify deployment results or payback periods. Ask what data updates the model, how often it is refreshed, and whether decisions made from it have been validated against the physical system.
Automotive systems and software-defined vehicles
Related CES coverage pointed to zonal vehicle architectures, centralized or function-agnostic processing, sensor fusion, edge machine learning, over-the-air updates, and software-defined vehicle platforms. These developments make a vehicle more like a connected computing system: it interprets its environment and may receive software changes after sale.
That can enable new features, but it also makes update policy, cybersecurity, safety validation, and support duration important. A claim that a vehicle is software-defined says little by itself about the reliability or availability of any particular AI capability.
Health, longevity, and pharmaceutical research
CTA’s longevity theme encompassed remote care, wearables, precision medicine, and AI-assisted health technology. EE Times cited Netri as an example of organ-on-chip work using stem cells and AI to help pharmaceutical companies characterize products. That is a specialized life-sciences application, not evidence that consumer health gadgets have achieved broad clinical adoption.
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For any health claim, distinguish assistance or monitoring from diagnosis and treatment. A device’s use of AI does not establish clinical validation, regulatory clearance, or better patient outcomes. Those require evidence for the specific product and intended use.
Smart-home coordination
The report described televisions evolving toward control centers for health integration, energy management, and connected-home functions, while noting overlap between smart-home and smart-health products. Coordination could reduce friction if devices share useful information and work together. It is less compelling when users must juggle incompatible ecosystems or multiple apps.
Before relying on a connected-home feature, check which devices and platforms it supports, what data leaves the home, whether essential functions work during an internet outage, and whether core AI features require a subscription.
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Where CES’s AI story remained unproven
AI agents
“Agent” can describe several different things: a chatbot that responds to prompts, software that plans and executes multiple steps, a device feature that controls apps or equipment, or an enterprise system operating under permissions and policies. The CES report named agents but did not document a specific production agent or benchmark. A credible claim needs evidence about reliability, authorization, audit logs, interoperability, and recovery when actions go wrong—especially when software can execute rather than merely recommend.
Humanoid and mobile robots
Humanoids are a visible symbol of the AI trend, but the report did not establish that general-purpose humanoids were commercially mature in 2025. A demonstration does not show whether a robot can manipulate objects consistently, run long enough on a battery, work safely around people, or justify its ownership and maintenance costs. Compare it with specialized automation for the actual task; a less flexible machine may be more economical and easier to validate.
Vague AI claims and curated demonstrations
A feature may be ordinary automation marketed as AI. Even when machine learning is involved, a trade-show demonstration does not establish shipping status, paying customers, failure rates, hidden human supervision, cloud costs, or performance outside a controlled setup. Ask what the system does, how it is measured, and whether independent or customer evidence exists.
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A five-part test for practical AI
Use these questions to separate a promising demonstration from a system with a stronger case for operational value:
- Specific task: Is there a clearly defined problem, such as detecting a defect or reducing a particular workflow step?
- Technical necessity: Does machine learning improve the task, or would a simpler rule-based system work as well?
- Operational evidence: Is there a shipping product, customer deployment, reproducible benchmark, or documented production commitment?
- Economic value: Does measured evidence show lower costs, higher output, better safety, or revenue sufficient to justify operating and integration costs?
- Failure containment: Can the system detect uncertainty, limit risky actions, provide a human override, and recover safely from errors?
A product with a defined task and plausible technical fit may still be only a demonstration if it lacks deployment evidence, a business case, or safeguards. The more consequential the action—especially in a vehicle, factory, or health setting—the stronger the evidence and controls should be.
Questions to ask before adopting a CES-style AI product
- What does the AI actually do, and how is performance measured?
- Does processing happen on-device, in the cloud, or in both places—and what happens offline?
- What personal, behavioral, location, household, or health data is collected, and who controls it?
- What is the full ongoing cost, including subscriptions, connectivity, storage, and support?
- Does the product interoperate with the systems already in use, or does it require a closed ecosystem?
- What happens when the model is wrong, unavailable, or changed by an update?
- Can a person review, override, or reverse consequential actions?
- For a health feature, what evidence supports the specific claim and intended use?
These checks also expose less visible costs. A device may depend on remote operators, manual exception handling, model updates, or a subscription for its most useful functions. More capable models can also require additional computing, electricity, and cooling.
What CES 2025’s claim amounts to
CTA’s “AI is real” message is best understood as a claim about AI becoming operational in selected products and processes—not proof that AI was universally mature or automatically valuable. Edge perception, industrial automation, vehicle software, and specialized research applications offer clearer ways to define and measure a task. Agents, humanoids, and broad claims about health or home integration demand more evidence about performance, safety, costs, and failure handling.
CES named a direction and showcased examples; the figures and demonstrations reported by EE Times do not establish market-wide readiness or product-level return on investment. The practical test remains whether a system solves a real problem, works under real operating conditions, and does so with acceptable cost and controlled failure.
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