Most enterprise commerce brands have already bought AI tools. The problem is not access. It is the gap between having an AI strategy and running one.
P3 Media launched its Forward Deployed Engineering practice on July 1, 2026, a new service area built around a simple premise: senior AI-native commerce engineers embed directly inside client organizations, joining standups, working inside client systems, and building production-ready AI workflows alongside the teams who will run them. Not a retainer. Not a consulting deck. A senior engineer in your standup on Monday.
The timing reflects a broader industry shift. AWS just backed their own Forward Deployed Engineering program with a $1 billion investment. ServiceNow and Accenture launched a similar FDE program in May 2026. The model of embedded engineers who build inside client environments and leave behind working systems, not slide decks, is becoming the standard approach for serious AI deployment at scale. P3 Media is bringing that same model specifically to commerce.
The problem this practice is built to solve
Enterprise organizations are not struggling with AI because they lack ambition. They are struggling because AI has to fit into real systems, real governance, real data, and real operating teams.
Commerce is especially complex in this regard. Every AI decision touches customer experience, inventory, pricing, fulfillment, merchandising, and brand trust simultaneously. Isolated AI tools (chatbots, content generators, recommendation widgets) produce isolated results. What most brands actually need is integrated AI systems that operate across the full commerce workflow. That is the gap the Forward Deployed Engineering practice is built to close.
What forward deployed engineering means in practice
Unlike a traditional agency retainer or consulting engagement, P3 Forward Deployed Engineers work as embedded members of the client's team. The model is built around four core service areas:
- AI Commerce Engineering: Senior Shopify and commerce engineers building AI-native features and workflows directly inside production commerce environments, including personalization, content operations, merchandising automation, internal tooling, and agentic commerce use cases.
- AI Team Enablement: Hands-on training and capability transfer delivered inside real sprint work, helping client teams develop practical AI fluency across prompt design, tool selection, workflow automation, and agent-assisted operating models.
- AI Infrastructure for Commerce: Technical architecture, data workflows, model routing, observability, and reliability planning designed to make AI systems and commerce agents usable in enterprise environments.
- AI Efficiency Audits: Structured diagnostics that identify where AI and agentic workflows can reduce manual overhead across catalog operations, merchandising, customer service, fulfillment, marketing, and internal decision-making.
The emphasis on capability transfer is intentional. P3 is not building systems that require the agency indefinitely. The goal is for client teams to be able to maintain, extend, and scale what gets built after the engagement ends.
Why agentic commerce changes the equation
The launch is timed around a specific shift in how AI is being used in commerce. The first phase of AI adoption: content generation, product recommendations, chatbots: produced isolated productivity gains. The next phase is agentic: AI agents that assist with and execute workflows across merchandising, customer service, catalog operations, personalization, marketing, and analytics.
As agentic commerce matures, brands will need more than standalone AI tools. They will need integrated systems, clean data architecture, strong governance frameworks, and internal teams capable of deploying AI safely inside real commerce operations. The brands that build this infrastructure now will operate with a structural advantage that late movers cannot quickly close.
Who the practice is designed for
P3 designed the Forward Deployed Engineering practice for organizations that need to move faster than a traditional agency model allows. The right fit is typically a brand with one or more of the following:
- Stretched internal engineering teams that have an AI roadmap but not the bandwidth to execute it alongside ongoing commerce operations
- Complex Shopify or commerce architectures where AI workflows need to be built around existing platform configurations, ERP integrations, or POS deployments
- Major platform initiatives like a migration, unified commerce launch, or B2B buildout where AI acceleration can compress the delivery timeline
- Operational workflows ready for automation across catalog, merchandising, customer service, or fulfillment where manual overhead is measurable and addressable
Engagements can begin with a single embedded senior engineer and scale into a larger cross-functional P3 pod as needs expand. Each engagement is structured around specific business outcomes rather than billable hours: faster feature delivery, reduced manual workflows, improved data visibility, stronger internal AI capability, and production-ready agentic workflow deployment.
Frequently asked questions
What is forward deployed engineering?
Forward deployed engineering (FDE) is a model where senior engineers embed directly inside a client's team rather than working as an external agency. They join standups, work inside client systems, and build alongside internal teams. The goal is to ship production-ready AI systems that the client can maintain and extend independently after the engagement ends. AWS, ServiceNow, and Accenture have all launched major FDE programs in 2026, reflecting the growing recognition that AI adoption requires embedded technical expertise, not just tools or advice.
How is this different from a standard agency retainer?
A standard retainer typically involves P3 building deliverables and handing them over. Forward Deployed Engineering is different: P3 engineers become part of the client's operating team during the engagement. They participate in the client's sprint cycles, work inside the client's production systems, and focus on capability transfer so the client team can build on what gets created. The output is not just a working system: it is a client team that knows how to use and extend it.
What kind of AI workflows does this practice build?
The practice covers the full range of AI use cases in commerce: personalization engines, content generation pipelines, merchandising automation, AI-powered customer service workflows, catalog operations tooling, internal decision-support systems, and agentic commerce implementations. P3 also builds the underlying infrastructure: data architecture, model routing, observability, governance: that makes these systems reliable in enterprise environments.
How quickly can an engagement get started?
Engagements can begin with a single embedded senior engineer and scale into a larger cross-functional pod as needs expand. P3's AI-assisted development workflow, which delivered a 5x acceleration on the Invicta Stores unified commerce launch, means complex builds can move materially faster than traditional timelines. Specific timelines depend on scope, but the practice is designed for organizations that need to move faster than a traditional agency model allows.
