Agentforce and SFCC: From AI Momentum to Commerce Execution

AI in commerce is no longer experimental. It is already influencing how customers discover and buy. Salesforce data shows AI-driven traffic has grown by more than 100% year on year, and industry projections suggest AI agents could influence over 20% of global ecommerce orders during peak trading periods. This is not a marginal shift. It is a structural change in how demand is captured and converted.

Salesforce is positioning Agentorce as a platform for deploying what it calls digital labour across commerce, service and operations. The shift is from assistive AI to agentic AI, where systems do not just support decisions but execute them. For organisations running Salesforce Commerce Cloud, this creates a new layer of capability across merchandising, service and trading. It also introduces a new level of complexity that many businesses are not yet prepared for.

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What Agentforce Means for Salesforce Commerce Cloud

Agentforce is not a feature sitting alongside SFCC. It is an operational layer that changes how work gets done inside the platform.

In practical terms, this means AI agents can now support and increasingly automate areas such as product discovery, merchandising execution and customer service interactions. Salesforce is already positioning Agentforce Commerce as enabling AI-led shopping journeys across external channels, including conversational interfaces, where discovery is no longer limited to traditional onsite search.

At the same time, Salesforce has embedded Agentforce across its ecosystem, and reported thousands of enterprise deals already in motion, with a significant proportion coming from existing customers expanding their usage. This signals that adoption is not theoretical. It is already being commercialised.

However, within SFCC environments, the challenge is not access to capability. It is how to implement it in a way that delivers measurable outcomes.

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The Execution Gap inside SFCC Environments

Across the market, the same issues are appearing consistently.

Many SFCC environments still operate with fragmented data across PIM, OMS and CRM layers, which limits the effectiveness of AI-driven decision making. Agentforce relies on structured, connected data, and without that foundation, outputs lack consistency and relevance.

Operationally, introducing AI into merchandising and service workflows without clear governance often increases complexity rather than reducing it. Teams end up managing new layers of tooling without simplifying the underlying processes.

Most importantly, many organisations are exploring Agentforce without a clear commercial prioritisation. Use cases are being tested, but not always tied to conversion, revenue growth or cost reduction. This is where momentum slows and ROI becomes difficult to prove.

This is the gap between capability and execution.

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TCTG Experience: SFCC at Scale, not Theory

Our work at TCTG is grounded in delivering Salesforce Commerce Cloud in complex, high-scale environments. We work with organisations managing multi-site architectures, large product catalogues and high transaction volumes, where performance, integration and governance directly impact revenue.

This experience changes how we approach Agentforce.

We understand how SFCC actually operates in practice. We understand the constraints around data models, integrations and operational workflows. More importantly, we understand where inefficiencies sit and where AI can realistically create impact.

In large catalogue environments, for example, product data enrichment and merchandising execution are often bottlenecks. Agentforce can accelerate these areas, but only when aligned with existing PIM structures and governance models. In customer service, AI agents only add value when they are integrated with order management and customer data, allowing for full context rather than isolated interactions.

Our role is not to introduce Agentforce as a new layer of innovation. It is to make it work inside the realities of SFCC environments.

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TCTG Strategy: How we Implement Agentforce in SFCC

Our strategy is built around one principle. AI must deliver measurable commercial outcomes, not just capability.

We start by identifying high-impact use cases where Agentforce can directly influence conversion, average order value or operational efficiency. This typically includes areas such as AI-driven merchandising, service automation and real-time trading decisions.

Implementation is then approached in a controlled and iterative way. Rather than broad deployment, we prioritise targeted use cases where impact can be measured quickly and refined before scaling. This reduces risk and ensures that adoption is grounded in performance, not experimentation.

Finally, we support ongoing optimisation. Agentforce is not static. Its value increases over time as models improve and teams adapt how they use it. Building that operational capability is critical to long-term success.

This is what turns Agentforce from a concept into a scalable commerce capability.

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From AI Capability to Commercial Advantage

For organisations running Salesforce Commerce Cloud, the opportunity is significant. Agentforce enables a shift towards AI-driven commerce operations, where decisions and execution are increasingly automated and optimised in real time.

The difference between success and complexity will come down to execution. That requires a clear strategy, a deep understanding of SFCC architecture, and a focus on outcomes rather than features.

At TCTG, that is exactly where we operate.

If you are looking at Agentforce implementation, SFCC AI integration or a broader commerce transformation strategy, connect with us on LinkedIn or reach out directly at info@thecommerceteam.com to continue the conversation

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