The most bizarre tech announced so far at CES 2026

CES 2026’s Weirdest AI-Powered Gadgets, Explained Through AI Automation

CES is always a parade of “AI-powered” announcements, but the interesting part is rarely the gadget itself. It’s the AI automation underneath: sensors turning the real world into events, AI agents deciding what matters, and workflows taking action across systems without humans babysitting every step.

Below are the most bizarre products we’ve seen so far at CES 2026, and what each one teaches teams building AI automation tools and AI automation software in the real world.

1) The holographic anime desk companion: an “agentic AI” interface with a trust problem

Razer’s latest version of Project AVA turns an AI assistant into a desk-sized holographic avatar designed to help with gaming, productivity, and organization. The notable detail is how it works: audio + camera + screen context, plus a model-driven assistant responding in real time.

AI automation takeaway:
This is the consumer-facing version of what businesses are racing toward: agentic AI that can perceive context and take action. But it also highlights the core enterprise constraint: automation without guardrails becomes surveillance or chaos.

How this maps to Midpoint-style automation:

• Use AI agents to interpret intent, but keep approvals and auditability for high-stakes actions.
• Design workflows so “observe → propose → execute” is explicit, not implicit.
• Treat context as a permissioned input (what the agent can “see”), not an unlimited feed.

2) The cuddly AI panda for older adults: emotional AI plus workflow routing

Mind With Heart Robotics’ An’An is an AI companion aimed at elder care, with touch sensors, long-term personalization, and reminders that can loop in caregivers.

AI automation takeaway:
This is a reminder that “AI” isn’t just chat. It’s automation and machine learning working together: detect signals, classify risk, route updates, and trigger the right intervention.

If you’re building “AI-powered [Industry/Task]” experiences (e.g., AI-powered customer support or AI-powered operations), the pattern is the same:

• Ingest signals (messages, sensor data, events)
• Summarize and classify (LLMs / generative AI where appropriate)
• Trigger actions (alerts, tasks, notifications, CRM updates, scheduling)

This is exactly why modern AI automation can’t be “just a model.” It has to be an orchestration layer.

3) The $500 AI ice maker: predictive automation hiding in plain sight

GoveeLife’s Smart Nugget Ice Maker Pro uses “AI NoiseGuard” to reduce noise by preventing freeze-up conditions before they happen.

AI automation takeaway:
Some of the most valuable automation is boring on the surface: anomaly detection, predictive intervention, and silent self-correction.

In business systems, this looks like:

• Flag pacing anomalies before spend runs away
• Catch invoice mismatches before month-end surprises
• Detect duplicate records before pipeline reporting breaks

Auto-create “exceptions” queues for human review

In other words: the highest ROI AI automation use cases often live in exception handling, not flashy demos.

4) The rest of the weirdness: “AI-powered” as a wrapper for workflows

The CES list also includes an ultrasonic vibrating chef’s knife, a bone-conduction music lollipop, a home-patrolling robot, and an egg-shaped hormone tracker. They’re wildly different products, but they share a common architecture: event → decision → action (and usually an app + notifications layer).

AI automation takeaway:
Whether it’s a kitchen tool or a health monitor, value comes from what happens after data is captured:

• Where does the insight go?
• Who gets notified?
• What system updates?
• What action is triggered?

That’s workflow automation, not magic.

What this means for Midpoint: AI automation that actually ships

CES 2026 is a great reminder that Generative AI and LLMs are becoming the interface, but AI automation is the system that makes outcomes happen.

Midpoint’s “AI Automation Engineer” positioning should stay anchored on a simple truth:

The winners won’t be the teams that can chat with AI. They’ll be the teams that can operationalize AI—across Gmail, Slack/Teams, Sheets, CRMs, finance systems, and internal workflows—with reliability and human approval where needed.

FAQs

What is AI automation?

AI automation is the use of AI (often machine learning and/or LLMs) to trigger, route, and execute workflow steps automatically—especially when decisions require classification, extraction, summarization, or prioritization.

What are AI agents and agentic AI?

AI agents are systems that can plan and act toward goals (not just respond). Agentic AI typically refers to more autonomous behavior: the system observes context, decides next steps, and executes actions—ideally with guardrails and approvals.

How do AI algorithms work in automation?

At a practical level: algorithms score, classify, or predict; LLMs transform unstructured inputs (emails, tickets, notes) into structured actions; the workflow engine executes integrations, retries, logging, and routing.

What are common AI automation use cases?

Lead enrichment + routing, sales inbox triage, support triage/translation, reconciliation digests, exception queues, pipeline briefings, and cross-tool record syncing—anywhere latency and coordination overhead are the bottleneck