Tracking AI-Driven Product Features: What to Measure for 'Smart' Devices from CES

Hook: CES 2026 put AI in every socket — but product teams still struggle to know what to measure

CES 2026 showcased a wave of AI-powered devices — from smart toothbrushes that coach your brushing to refrigerators that “know” your grocery habits and mirrors that analyze your skin. If you own or build these products, your real challenge isn’t adding an LLM to a sensor. It’s turning those streams of telemetry into clear, actionable KPIs while keeping users’ privacy and bandwidth costs under control.

In this guide I’ll walk you through the exact telemetry, engagement, and privacy metrics you should instrument for AI-enabled appliances, with practical tagging strategy and event design you can implement in 2026. I’ll use examples — toothbrushes, fridges, and mirrors — to show what to collect, why it matters, and how to protect users and your analytics budget.

The 2026 context: why tracking AI devices is different now

Two things changed at scale in late 2025 and early 2026 that affect product telemetry:

• AI compute costs and memory constraints: Chip and memory demand driven by AI workloads raised device and cloud costs. That means devices increasingly push compute to the edge and batch telemetry to save bandwidth.
• AI for everything at CES 2026: The show proved that many features are AI-wrapped UX improvements — some useful, some marketing. Teams need metrics that separate novelty from long-term value.

Those trends force trade-offs: What data stays on-device vs. what you send to the cloud? How do you measure an AI model’s real-world value without leaking sensitive signals? Below are the telemetry patterns and metrics that will be essential for 2026 and beyond.

High-level taxonomy: what to instrument (and why)

When you design telemetry for AI-enabled appliances, split events into three families:

1. System & health telemetry — device state, firmware, connectivity, memory/CPU, model versions.
2. Usage & engagement metrics — how people interact, frequency, sessions, feature adoption.
3. AI-specific signals & privacy controls — model inputs/outputs, inference confidence, latency, consent states, PII flags.

This taxonomy maps directly to teams: reliability and SRE own system metrics; product/analytics own engagement; ML and privacy engineers own AI signals and compliance.

Core telemetry & tagging strategy (universal fields)

Start with a small, consistent schema across devices. Use an event schema registry (Avro/Protobuf/JSON Schema) and these universal properties for every event:

• device_id (hashed): persistent, non-PII identifier.
• timestamp_utc: ISO 8601; use device-local clock + server-received timestamp.
• firmware_version / model_version: essential for regression tracking.
• event_name & event_id: human-friendly name + UUID for dedupe.
• seq_num: incremental sequence per boot to detect lost messages.
• transport_meta: connection type (Wi‑Fi/cellular/edge), signal strength.

Keep the payload small and typed. Use enums for common values and numeric fields for durations and counts. This reduces storage cost and keeps queries fast.

Example event: minimal JSON payload

{
  "event_name": "brushing_session.started",
  "event_id": "uuid-1234",
  "device_id_hash": "sha256:...",
  "timestamp_utc": "2026-01-17T12:34:56Z",
  "firmware_version": "1.4.2",
  "seq_num": 42,
  "transport_meta": {"conn":"wifi","rssi":-57}
}

Device-specific telemetry: toothbrush

AI toothbrushes demonstrate the full stack: on-device sensors (accelerometer, pressure), local classification (stroke detection), and cloud personalization (habit coaching). Metrics should reflect behavior, model performance, and safety.

Essential telemetry

• Session metrics: session_start, session_end, session_duration_ms, frequency_per_day, days_active_last_7.
• Brushing quality: percent_coverage_by_quadrant, avg_pressure_pa, brushing_speed_mm_per_s.
• AI outputs: detected_technique (enum), confidence_score (0-1), correction_suggestions_shown (count).
• Model performance: inference_latency_ms, on_device_memory_mb, model_version.
• Safety & reliability: overpressure_event_count, battery_temp_c, firmware_crash_count.

Engagement metrics & KPIs

• DAU/MAU for brush users and week-over-week retention.
• % of sessions with full_coverage >= 90% (product success metric).
• Time to first personalized tip after onboarding (activation velocity).

Privacy notes

Sensor streams can reveal sensitive behaviors. Instrument consent_state, consent_timestamp, and data_retention_tier for each user. Use on-device aggregation for raw accelerometer traces — send only derived features (coverage, pressure summary) unless the user explicitly opts into research mode with hashed identifiers and clear retention windows. For local-first architectures and privacy-friendly sync, review field notes on local-first sync appliances.

Device-specific telemetry: AI refrigerator

Smart fridges blend inventory vision, shopping suggestions, and food-safety alerts. Their telemetry must measure economic value (reducing waste / increasing grocery purchases), model accuracy, and privacy risk (camera usage).

Essential telemetry

• Inventory events: item_added, item_removed, item_count_by_shelf, expiry_prediction_days.
• Shopping actions: suggested_item_accepted, list_shared, online_order_created (yes/no).
• Model signals: vision_confidence, label_suggested, model_version, false_positive_rate_est.
• Energy & uptime: compressor_cycles, avg_power_w, door_open_count_per_hour.

Engagement metrics & KPIs

• Adoption rate of shopping suggestions (% of suggestions accepted).
• Reduction in predicted_food_waste_kg per household month-over-month.
• Average order value uplift for online grocery referrals.

Privacy & compliance

Cameras are high risk. Always record camera_state (on/off), last_user_consent_ts, face_detection_enabled (bool). Prefer on-device inference with only labels (item_id) and confidence scores sent to cloud. For any images sent to cloud, record image_hash + retention_policy_id and log explicit opt-in timestamps. Consider applying on-device local differential privacy (LDP) to counts before uploading when aggregate insights are sufficient.

Device-specific telemetry: AI mirror

Mirrors that analyze skin or posture will deliver sensitive biometric signals and subjective model outputs (age, skin condition). Instrument to track model fairness, accuracy, and whether outputs lead to user actions.

Essential telemetry

• analysis_session.start/end, session_duration_ms
• features_extracted: [hydration_score, wrinkle_index, redness_index] (numeric)
• recommendations_shown, recommendation_type, clickthrough_rate
• model_bias_metrics: per-demographic accuracy estimates (if you collect demographics explicitly and with consent)

Engagement & safety KPIs

• % of users who follow a recommended routine within 7 days (behavior change).
• User-reported satisfaction score vs. model confidence (A/B test grouping).
• False_positive_alert_rate (cosmetic concerns flagged incorrectly).

Privacy guardrails

Store only derived metrics unless users opt in to storing images. Tag all outputs with privacy_tier and mask any PII. Provide transparent logs: users should be able to request their analysis records and delete them. In many jurisdictions (and increasingly in 2026), this is mandatory.

AI-specific metrics you must track (across devices)

These are the highest-value ML signals that product and ML teams need across appliance categories:

• Inference latency: median & 95th percentile (ms). Slow responses break UX and adoption.
• Confidence distribution: track per-class and overall. Use buckets (0-0.2, 0.2-0.5, 0.5-0.8, 0.8-1.0).
• Model drift indicators: input feature distribution shifts, performance by cohort, and data distribution divergence (KL divergence).
• Edge vs cloud fallbacks: percent_inferences_on_device, percent_failed_offline_inferences. For guidance on running local inference nodes and pocket inference architectures, see running local LLMs on small devices.
• Feedback loop: user_accepted_suggestion, user_rejected_suggestion, manual_correction_events.

Instrumenting these allows you to run experiments, detect regressions after firmware/model updates, and compute ROI for model improvements.

Designing events: naming, payloads, and volumes

Follow a simple event naming convention: domain.action (e.g., brushing.session_started, fridge.item_suggested). Keep payloads focused: avoid raw camera/microphone dumps unless essential and consented.

Event design checklist

1. Define the business question each event answers (activation, retention, revenue).
2. Map events to owners (product, ML, privacy).
3. Set retention policies per event type (e.g., raw inference traces 7 days, aggregates 2 years).
4. Quantify storage cost and set sampling for high-volume streams (e.g., sample 1% of raw sensor traces). Use edge storage and aggregation patterns to control costs (edge storage for small SaaS).
5. Use schema registry and validate on ingest to reduce bad data — pairing schema validation with provenance tools (audit-ready text pipelines) helps with compliance and reproducibility.

Example: event family for an AI suggestion

{
  "event_name": "suggestion.shown",
  "event_id": "uuid",
  "device_id_hash": "sha256...",
  "timestamp_utc":"2026-01-17T13:00:00Z",
  "model_version":"v2.1.0",
  "suggestion_type":"shopping_tip",
  "confidence":0.83,
  "user_consented_sharing":true,
  "context_hash":"ctx-12345"
}

Sampling, aggregation, and cost control

Telemetry costs escalate quickly with high-frequency sensors and camera streams. Use these tactics to control costs while keeping the signal:

• Edge aggregation: compute summaries on-device (means, histograms, counts) and send only aggregates — a classic offline-first approach used in field apps and edge devices (see offline-first field service patterns).
• Adaptive sampling: sample raw traces only when anomalies occur or when users permit research mode.
• Strategic retention: keep raw data short-term and only for labeled studies; keep derived metrics long-term for product KPIs.
• Dynamic fidelity: degrade sampling or fidelity when bandwidth is low; pair with robust transport and low-latency testbeds to measure UX impact (hosted tunnels & low-latency testbeds).

Privacy-first instrumentation: practical measures

Privacy is not a checkbox — it’s core to your tagging strategy. Here are practical controls you must implement in 2026:

• Consent-first flows: capture consent_state and consent_granularity (analytics, research, images) before emitting any PII-linked events. For voice and on-device audio flows, review voice-first and asynchronous voice playbooks (voice-first listening workflows, reinventing asynchronous voice).
• On-device anonymization: hash identifiers with per-device salts, and add noise for aggregated metrics where precise counts aren’t needed.
• Encryption & secure transport: TLS 1.3, forward secrecy, and device keys pinned to a hardware root of trust. For transport choices and low-latency overlay strategies, see guidance on interactive live overlays.
• Data subject requests: log deletion tokens and maintain an audit trail for compliance with global regulations evolving in 2025–2026 — provenance tooling in audit-ready pipelines helps here (audit-ready text pipelines).
• Privacy budget & LDP: consider a privacy budget for features that aggregate across users (e.g., LDP for counts from fridges).

“Measure what matters — but measure with boundaries.”

From telemetry to action: measurement recipes (3 practical use cases)

Below are concise recipes mapping events to analyses and action items.

1) Increase adoption of AI coaching on the toothbrush

1. Instrument: brushing.session.started, coaching_tip.shown, coaching_tip.accepted, session_quality_score.
2. Analysis: funnel conversion (start → tip shown → accepted → quality improvement). Segment by firmware_version and first_7_days cohort.
3. Action: A/B test first-run coaching timing; push firmware with reduced inference latency if model lag correlates with drop-off. Consider on-device inference improvements — see pocket inference nodes and run-local examples (run local LLMs).

2) Reduce food waste and demonstrate ROI for fridge subscription

1. Instrument: inventory.item_expiry_predicted, suggested_recipe_shown, suggested_recipe_cooked, predicted_waste_kg.
2. Analysis: cohort of households using recipe suggestions vs. control; measure reduction in predicted_waste_kg and grocery spend changes.
3. Action: tune suggestion model confidence threshold; move low-confidence classes to local-only suggestions to reduce false positives.

3) Validate mirror recommendations don’t unfairly target demographics

1. Instrument: analysis_session, recommendation_shown, user_feedback, and optional demographics (consented).
2. Analysis: compute accuracy and satisfaction by demographic cohorts; monitor for disparity in false_positive_rate.
3. Action: retrain or add calibration layers for underperforming cohorts and rerun A/B tests until parity improves.

Operational checklist: shipping instrumentation in 8 steps

1. Define 3–5 product KPIs tied to business outcomes (activation, retention, revenue, waste reduction).
2. Create an event schema registry and enforce schema validation at ingest.
3. Implement universal properties on every event (device_id_hash, firmware, timestamps).
4. Build on-device aggregations and adaptive sampling logic.
5. Track model_version and inference metrics in every inference event.
6. Log consent and privacy_tier for every user; implement deletion workflows.
7. Plan retention & aggregation strategy to control costs; implement monitoring alerts for telemetry volume spikes.
8. Run a data quality and privacy audit before and after the first 10k device installs.

Tooling & architecture recommendations (2026)

In 2026 the best practice stack for smart devices looks like this:

• Edge: lightweight inference (TensorFlow Lite/ONNX) + local aggregation library. If you're optimizing for cost and privacy, evaluate edge storage patterns and local-first sync appliances (edge storage, local-first sync).
• Transport: MQTT or gRPC with TLS; binary payloads in Protobuf to shrink bandwidth. For low-latency overlay patterns and transport choices, see interactive live overlay guidance (interactive live overlays).
• Ingest: Server-side schema registry + streaming layer (Kafka or cloud-managed equivalent) to handle bursts.
• Storage: time-series DB (Timescale/Influx) for high-frequency telemetry + data lake for aggregates and ML retraining.
• Analytics: BI + ML observability (Prometheus/Grafana for infra; custom dashboards for product ML KPIs).

Pick tooling that supports schema evolution and model-aware metadata out of the box — model_version tagging is non-negotiable in 2026 when memory-driven SKU changes can impact behavior.

Common pitfalls and how to avoid them

• Collecting everything: leads to cost and privacy issues. Instrument for decisions — not curiosity.
• No model versioning: you’ll struggle to diagnose regressions after updates. Tag every inference with model_version.
• Ignoring on-device aggregation: bandwidth and compute will bite you. Aggregate first, then send. Offline-first patterns in field apps are instructive (offline-first field service).
• Poor consent tracking: leads to regulatory and trust failures. Treat consent like a first-class data field and follow voice/consent playbooks (voice-first).
• Buying cheap, unvetted hardware: procurement strategy matters — consider sustainable procurement and verified device sources when scaling device fleets (refurbished device procurement guidance).

Final checklist: must-have metrics before launch

• System: uptime %, firmware_crash_rate, avg_power_w
• Usage: DAU/MAU, session_length_ms, feature_adoption_rate
• AI: inference_latency_p50/p95, confidence_buckets, suggestion_accept_rate
• Privacy: consent_rate, data_retention_policy_id, deletion_requests_processed

Closing: the measurement mindset for post-CES 2026

CES 2026 made one thing clear: AI is now a UI modality as much as an algorithm. But features without measurement are just buzz. The difference between a gimmick and a product is rigorous telemetry coupled with privacy-first design.

If you implement the schema and metrics above, you’ll be able to answer the hard questions executives ask six months after launch: Is the AI delivering retention? Is it making users’ lives measurably better? Is it safe and compliant?

Actionable takeaways — do these next:

1. Run a 90‑minute instrumentation audit: map existing events to the taxonomy above and identify gaps. Pair this with audit-ready provenance tooling (audit-ready text pipelines).
2. Enforce a schema registry and tag every inference with model_version.
3. Implement on-device aggregation and a consent-first UX before broad rollouts. For local-first device patterns, see field reviews and storage guidance (local-first appliances, edge storage).

Call to action

Ready to audit your AI-device telemetry? Download our free 20-point instrumentation checklist and tag templates for toothbrushes, fridges, and mirrors — built for 2026. Or schedule a 30‑minute consultation with our analytics team to map your telemetry to business KPIs and privacy controls. Visit analyses.info/ai-iot-checklist to get started.

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