Agentic AI and unified commerce are set to shape ecommerce in 2026 because the foundations are now in place: consumers are increasingly comfortable using AI tools, and retailers are under pressure to operate seamlessly across channels.
On consumer behavior, Eurostat reports that 32.7% of people aged 16–74 in the EU used generative AI tools in 2025 (for personal, work, or education use). That’s already a meaningful base, and it’s growing quickly.
Digital commerce executive and General Manager for EMEA & APAC at VTEX.
On the enterprise side, the market is moving from “AI chat” to “AI that executes.” Gartner predicts that up to 40% of enterprise applications will include task-specific AI agents by 2026 (up from under 5% in 2025). That said, unified commerce matters because AI is only as good as the operational data it can access.
When inventory, orders, pricing, and customer context live in disconnected systems, both humans and AI struggle to deliver consistent experiences. When those systems are unified, retailers can enable more reliable automation, better availability promises, and more resilient fulfillment, especially at peak.
Bridging the gap between online and offline
In Europe, the challenge is less about whether ecommerce matters and more about whether retailers are operationally ready to deliver seamless experiences across online and offline.
Online shopping is now mainstream: Eurostat reports that 77% of EU internet users bought online in 2024, up from 59% in 2014. At the same time, physical retail remains a major part of how consumers discover, compare, and receive products, especially with services like click-and-collect and store-based fulfillment.
Where many retailers struggle is behind the scenes: inventory visibility differs by channel, fulfillment networks aren’t synchronized, and pricing and promotions may not be consistent across touchpoints. In that environment, even advanced AI can’t “act intelligently,” because it’s working from incomplete or conflicting data.
The practical bridge between online and offline is unified operational data: a retailer needs the same real-time truth for stock, orders, pricing, and customer context, regardless of where the transaction starts.
Why unified commerce platforms matter
Unified commerce platforms matter because they provide a single operational framework for inventory, orders, pricing, and customer context. That coordination is increasingly critical as more interactions become automated or AI-assisted.
On the demand side, Europe continues to see strong adoption of online purchasing. In 2024, 77% of EU internet users bought online, confirming that digital commerce is a default behavior for a large majority of consumers.
On the supply side, the variability across EU markets is significant: Eurostat data shows that the share of turnover from e-sales varies widely by country, which reflects different levels of maturity and operational integration.
That unevenness is exactly why unified operations become a competitive advantage, retailers need the ability to deliver consistent experiences across channels and geographies.
Rather than claiming universal “conversion multipliers,” the more accurate point is: structured, accessible, real-time operational data improves a retailer’s ability to make reliable promises (availability, delivery, returns), run consistent promotions, and scale automation without losing control.
The operational and economic impact of agentic AI
Agentic AI’s impact in 2026 will be less about “AI generating content” and more about AI executing tasks within business systems with governance.
We can already see growing adoption of generative AI by individuals in the EU: 32.7% of people aged 16–74 used generative AI tools in 2025, and a meaningful portion use them for work. In parallel, adoption inside companies is rising: 20% of EU enterprises (10+ employees) used AI technologies in 2025, up from 13.5% in 2024.
The shift toward “agentic” happens when AI can safely take actions, like resolving a customer service step, updating a product feed, or proposing a replenishment recommendation, based on reliable data and explicit rules. That’s why unified commerce matters: it reduces the risk of automation acting on partial truth.
Because ROI varies dramatically by category, maturity, and data quality, it’s safer to avoid generic percentage claims. The defensible message is: companies that pair AI with clean operational data and clear governance will unlock automation faster and with fewer reputational risks.
Preparing for peak performance in 2026
With 2026 underway, the best approach is a phased roadmap that prioritizes data foundations and governance before ambitious automation.
– Audit & unify operational data (first 90 days): Create a single source of truth for inventory, orders, pricing, and customer context across channels.
– Start with controlled automation (months 3–6): Pilot narrow, high-confidence use cases where humans remain accountable (e.g., customer service triage, content enrichment, basic replenishment recommendations).
– Scale with governance (months 6–12): Introduce multi-step workflows and approvals, define escalation rules, and ensure auditability, especially in customer-facing scenarios.
– Stress test for peak (ongoing): Simulate peak events (Black Friday, seasonal peaks), validate operational resilience, and ensure monitoring and fallback processes are in place.
The reason this matters is that both consumers and businesses in Europe are moving into AI-enabled behavior quickly. Eurostat shows both rising consumer adoption of generative AI tools (2025) and rising enterprise AI adoption (2024→2025). A roadmap ensures the shift is sustainable, safe, and measurable.
Ultimately, success in 2026 will not be defined by how many AI features a retailer deploys, but by how well their systems can interpret context, act reliably, and scale under pressure.
As AI adoption accelerates across Europe, the retailers that win will be those that treat unified data, operational governance, and resilience as strategic priorities, not afterthoughts. Agentic AI amplifies what already exists in an organization: strong foundations lead to meaningful automation, while fragmented systems amplify risk.
The decisions made today around data structure and platform architecture will determine whether AI becomes a growth driver or a source of complexity in the years ahead.
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