Everyone’s talking about Agentforce. Few are asking the right question:
“Is your data actually ready to support an AI agent?”
I’ve seen this play out before—technology gets ahead of process, and pilots get stuck in a holding pattern. What looks good in a demo rarely makes it past the first workflow if your underlying data and business logic aren’t aligned.
If you’re working across Salesforce and AWS, that complexity is an opportunity to create deeper integration and smarter automation—if the data strategy is sound.
At AllCloud, we take a measured approach to Agentforce, grounded in real customer conversations. We’ve seen what happens when it’s rushed or handed off without a clear data foundation. That’s why we’re doubling down on the part most often overlooked: the data strategy behind your AI agent.
It’s Not About the Agent. It’s About the Inputs.
Agentforce is powerful, but it doesn’t create your customer data story—it reflects it. If your underlying Salesforce instance is cluttered, disconnected, or missing key insights from your marketing and product systems, the agent has nothing reliable to work with.
Here are the patterns we’re seeing across early AI Agent adopters:
- Disconnected context: CRM may capture the account, but vital usage data lives separately in AWS or a data warehouse.
- Inconsistent taxonomy: Support, success, and sales teams all define customer health differently, leading to conflicting AI responses.
- No retrieval plan: GenAI agents need orchestration logic to call and retrieve relevant, external data, not just respond based on standard Salesforce fields.
When these problems show up mid-pilot, the agent stalls, the conversation breaks, and confidence in the entire program drops.
What a Successful Agentforce Pilot Looks Like
To illustrate a successful pilot, consider our AllPaws agent demo—a fictional pet wellness brand designed to showcase the potential of intelligent customer service.
The Situation: Olivia, a longtime AllPaws customer, reaches out via chat. She’s upset because Milo, her cat, had a bad reaction to the latest wet food she received. She wants answers immediately.
Image 1: Customer profile with chat interaction between Olivia and AllPaws agent.
The Response: Instead of starting from scratch, the AllPaws AI agent orchestrates a seamless experience:
- Instantly pulls Olivia’s Customer 360 profile, including order history and Milo’s preferences (via Salesforce).
- Cross-checks the batch and fulfillment data for that specific order (via AWS data services).
- Flags the issue for QA and proactively initiates a refund.
- Schedules a replacement delivery, including a free sample of fish treats (based on Milo’s preferences).
- Escalates a potential product recall workflow—all without Olivia needing to repeat herself.
This seamless interaction is powered by AllCloud’s integration of:
- Salesforce Service Cloud, providing a comprehensive view of Olivia’s customer profile and purchase history.
- AWS data services, enabling real-time tracking of inventory and logistics.
- Custom AI workflows, orchestrating the interaction and ensuring a personalized, efficient response.
The Outcome: What Olivia experiences is fast and thoughtful, reinforcing her trust in AllPaws. The company not only addresses a customer concern swiftly but also gains critical product insights. This seamless interaction is powered by Contextualization at Scale, where AI decisions are informed by a full customer data story, not just a single transaction.
This is the kind of GenAI pilot that builds confidence across teams and paves the way to scale.
Image 2: Agent Resolution Update to Customer Profile in Real Time.
Scaling GenAI: The Operational Rhythm
If you’re experimenting with Gen AI for customer service, the technology isn’t the hardest part. The hard part is ensuring what you build continues to work after the pilot and after the roadmap shifts.
Agentforce is a living layer in your customer experience. It needs data that makes sense, logic that adapts, and feedback loops your teams actually trust. The only way to get outsized results and gain trust is to invest in the inputs first: clean, connected, contextual data.
Our team of award-winning Data & AI experts can help you carry this forward with the right structure, guardrails, and operational rhythm from day one. Our services include:
- No-Cost Data & AI Assessments
- AI Advising Services
- Data Architectures Planning
- Funded Workshops
Ready to Get Going? Here’s the Agentforce Pilot Playbook.
When customers ask where to begin with Agentforce, we always say the same thing: don’t start with the tech. Start with the moments that matter.
Here’s the playbook we use to launch a pilot that builds real momentum:
- Spot the friction: Where are your teams bogged down? Where are your customers repeating themselves? Those pain points are prime territory for an AI agent to shine.
- Gut check your data: If your systems aren’t connected, your agent won’t have much to work with. A quick readiness review helps us figure out what’s solid and what needs attention for your Salesforce data strategy.
- Pilot where the stakes are low: Some of the most effective Agentforce pilots start internally. Think of it as an AI Assistant Buddy for your team, tackling repetitive tasks so your people can focus on higher-value work.
- Build with the long game in mind: A demo is great, but we help teams think beyond the first win. That means designing for scale, feedback loops, and measurable impact.
If that sounds like where you are, let’s map the path from pilot to production. With the right foundation and a clear plan, Agentforce can do some pretty amazing things. Contact us to learn more.