AI adoption accelerates as EMEA engineers move from pilots to shipped products
Engineers across EMEA are now embedding AI into products that are leaving the factory, not just labs. New data from the Avnet Insights 2026 Survey shows a clear shift from experiments to production.
Globally, 56% of engineers say they are shipping AI-enabled products-a 33% jump year over year. In EMEA, pace varies by country and sector. Germany is more selective, with 41% reporting shipped AI products, reflecting industry-specific priorities and regulatory focus rather than technical limitations.
By the numbers
- 56% of engineers worldwide are shipping products with AI (up 33% vs. last year).
- EMEA adoption varies; Germany reports 41% shipping AI products.
- 96% expect AI tools to influence product design and development in 3-5 years.
Top obstacles to scaling
- Data quality: 46% cite it as a key concern.
- Integration with existing tools and workflows: 38%.
- Cost constraints: 37%.
- Operational load: over half highlight ongoing learning and maintenance needs in production.
- Sustainability factors: raised by 43%.
Engineers remain cautiously optimistic despite macro uncertainty. Shorter production cycles and steady demand are keeping AI investments moving, even as teams work through data, integration, and lifecycle challenges.
"AI has clearly moved beyond proof-of-concept for engineers across EMEA regions," said Dan Ford, vice-president of sales and services for EMEA at Farnell. "What we're seeing now is a focus on scaling AI responsibly - balancing innovation with data integrity, system integration and sustainability as AI-enabled products move into production."
Edge + multi-modal momentum
The survey points to growing use of multi-modal approaches and Edge AI. Teams are combining on-device inference with cloud training to hit latency, privacy, and cost targets. Expect more hybrid designs: small, efficient models at the edge; heavier training and monitoring in the cloud.
What this means for product development leaders
If you own delivery timelines, BOM targets, and product risk, make AI a first-class citizen in your development system-not an add-on. Here's a practical playbook to keep launches on track:
- Treat data as a product: define sources, quality checks, versioning, and ownership from day one.
- Standardize MLOps: automate training, testing, deployment, and rollback across environments.
- Integrate with existing toolchains: CI/CD, PLM, test automation, and observability must include ML components.
- Plan total cost early: model serving, inference hardware, labeling, monitoring, and retraining cycles.
- Build production guardrails: model performance thresholds, drift alerts, human-in-the-loop, and kill-switches.
- Design for sustainability: quantify energy use, choose efficient models/hardware, and enforce usage policies.
- Clarify edge vs. cloud split: decide what runs where based on latency, privacy, and unit economics.
- Allocate ownership: give PM, engineering, data, and QA clear responsibilities for AI features and lifecycle.
- Measure outcomes: tie AI features to specific KPIs (conversion, uptime, scrap rate, cycle time, warranty claims).
- Stay compliant: map features to risk classes and document decisions for audits and customer assurance.
Compliance and documentation
Regulatory pressure is rising across EMEA. Treat model cards, data lineage, and evaluation reports as part of your technical file. For context on upcoming rules, see the European Commission's AI Act overview here.
Why this matters now
The shift is underway: AI is becoming a standard component in shipped products, with EMEA teams tuning adoption to local industries and regulation. The winners in 2026 will be the teams that make AI reliable, measurable, and economical in production-without slowing delivery.
Next steps
- Pick one live product and close the loop: data → model → deploy → monitor → retrain, with clear SLOs.
- Run a design review focused only on AI failure modes and lifecycle costs; remove anything you can't operate.
- Upskill the team where gaps block delivery (MLOps, prompt engineering, edge deployment).
If skills are the bottleneck, explore curated learning paths by job role here.
Your membership also unlocks:
