Engineers Push AI Into Products as Edge Takes Off and Data Quality Drags

Engineers Are Shipping AI at Scale - But Data and Tooling Still Slow Teams Down

AI has moved from pilot to production for a big slice of engineering teams. In Avnet's latest Insights survey, 77% see improving market conditions and 56% say they're already shipping AI-embedded products - a 33% jump over last year. That's real traction, not hype.

If you lead product development, this is the moment to turn scattered AI wins into a repeatable system. The bottlenecks are clear, the use cases are proven, and the team skills you need are consistent across the board.

At a Glance

• 56% of engineers now ship AI-embedded products (up 33% YoY)
• Top uses: process automation (42%), predictive maintenance (28%), anomaly/fault detection (28%)
• Edge AI + ML: prioritized equally by 57% for functionality and value
• Top design hurdles: data quality (46%), tool integration (38%), high costs (37%)
• Ops hurdles: continuous learning/maintenance (54%), sustainability (43%)

What This Means for Product Development

AI is no longer a separate track - it's a feature set inside your roadmap. Fewer engineers are "working on adding AI" this year (33% vs. 40% last year) because it's already in the product. Your job is shifting from "prove it works" to "ship it reliably, again and again."

Think in systems: a clean data pipeline, a stable MLOps toolchain, and clear owners for model lifecycle and post-launch performance. If it's not repeatable, it won't scale.

Where AI Is Landing in Shipped Products

• Process automation (42%): Reduce human-in-the-loop steps, cut cycle time, standardize quality.
• Predictive maintenance (28%): Extend uptime, lower service costs, improve customer SLAs.
• Anomaly/fault detection (28%): Catch issues earlier, trigger safe modes, flag warranty risks.

These use cases map cleanly to ROI. If you need an entry point, start here. Scope tight, measure outcomes, then templatize.

Edge AI Is Now a Default Consideration

Over half of respondents (57%) prioritize Edge AI and ML models equally. For many products, that means inference on-device for latency, privacy, and reliability - with cloud handling training, fleet updates, and analytics.

Decision framework: put time-critical inference at the edge, push heavy training to the cloud, and standardize your deployment targets to cut integration churn.

The Chokepoints: Data, Tools, Cost

• Data quality (46%): Treat data like a product. Define sources, owners, schema contracts, and acceptance tests. Log data drift and close the loop with labeling improvements.
• Tool integration (38%): Pick a core toolchain (data versioning, model registry, CI/CD for models) and stick to it. Reduce "tool sprawl" to speed onboarding and incident response.
• High costs (37%): Move inference to the edge where feasible, quantize and prune models, and cache results. Tie every model to a unit economics line item.

Operational Reality: Continuous Learning and Sustainability

Over half (54%) cite continuous learning and maintenance as an operating challenge. Models drift, customers change their usage patterns, and new data keeps coming. Plan for it from day one.

Build model update cadences, A/B safety checks, rollbacks, and monitoring into your definition of done. For governance and risk, align with frameworks like the NIST AI Risk Management Framework.

LLMs in the Engineering Workflow

Engineers are already using LLMs for technical questions: ChatGPT (69%), Google Gemini (57%), and Microsoft Copilot (50%). But only 16% prefer public LLMs for this - nearly half (47%) want models trained by engineers outside their organization, hinting at a gap in domain-specific tools.

If you ship complex hardware or embedded systems, consider a domain-tuned LLM with retrieval from your own docs, specs, and tickets. Add guardrails, evals, and human review for anything safety-critical.

Skills Your Team Needs in 2026

• Model optimization (17%): Compression, quantization, and on-device trade-offs.
• Data analysis and interpretation (16%): Spot bias, skew, and drift before they tank outcomes.
• Understanding AI/ML algorithms (14%): Enough depth to debug models and make architecture calls.

If you're building a training plan, prioritize these three areas. Curate short courses, hands-on labs, and internal playbooks. For structured upskilling by skill area, see Complete AI Training: Courses by Skill.

Quick Checklist for Your Next AI Product Sprint

• Define the user-facing outcome and the single metric you'll move.
• Map data sources, owners, quality checks, and drift alerts.
• Choose edge vs. cloud for each model step based on latency, privacy, and cost.
• Standardize your model packaging, registry, and deployment targets.
• Instrument post-launch monitoring: accuracy, uptime, latency, cost per event.
• Set an update cadence with rollback plans and offline eval benchmarks.
• Document failure modes and escalate pathways for support and field teams.
• Close the loop: feed real-world outcomes back into labeling and training.

Where Adoption Is Trending Next

• Natural language interpretation: up to 26% (from 21%).
• Biometrics: 20% (down from 24%).
• Augmented reality: 20% (down from 23%).
• Text-to-speech: 20% (down from 24%).

Language interfaces are climbing. If your product has complex settings or lengthy manuals, this is a clear path to lower cognitive load and faster onboarding.

Bottom Line

AI is now a standard feature in shipped products. The teams that win treat data as a product, toolchains as a platform, and model updates as routine. Get those pieces right, and you'll ship faster, cheaper, and with fewer post-launch surprises.