A national B2B payments provider serving credit unions across Canada sits at the center of its partners’ debit card programs. As partner engagement grew, leadership examined how support workflows could scale within Salesforce.
Service Cloud, Experience Cloud, and Knowledge formed the architectural backbone of the partner experience. Credit unions accessed support and resources through a Digital Experience site, while internal teams managed case workflows and maintained governed knowledge content. The model was stable but increasingly reliant on manual case handling for inquiries of varying complexity.
Leadership identified an opportunity to introduce an AI-driven messaging layer to absorb appropriate inquiries before they reached the case queue. The organization entered the engagement with clear KPI expectations, including a target case deflection rate. Delivering impact required not only technical implementation but also a rigorous assessment of system boundaries, case composition, and what automation could realistically resolve within the existing ecosystem.
When Manual Workflows and KPI Targets Collide
Partner support workflows were under review as the organization sought to improve efficiency for credit union partners. While the client was well-informed about Agentforce capabilities, they entered the project with a 25 percent target case deflection rate.
At the same time, the composition of cases in scope revealed operational limits. Nearly 80 percent of inquiries required interaction with external systems that the AI agent could not access, making the original deflection target unattainable.
The objective was to deploy a customer-facing AI agent via the Digital Experience site to manage partner inquiries. The agent would provide Knowledge-based answers and create Cases for escalation when necessary. Success required automating what could be automated while maintaining operational control for the remainder.
The challenge was operational rather than technical: aligning ambition with the agent’s capabilities, the structure of case types, and internal processes. Meaningful outcomes demanded careful calibration of expectations from the outset.
What AI Adoption Revealed About System Boundaries and Expectations
Even sophisticated organizations face challenges when introducing AI agents into structured support workflows. Key lessons emerged from this engagement:
- Align KPIs with scope: Roughly 80 percent of inquiries required manual interaction with external systems. Achieving the original 25 percent deflection target was unrealistic. Midway through the project, candid conversations recalibrated expectations once case composition and Knowledge base limitations became clear.
- Guidance is essential, even for informed clients: Understanding Agentforce capabilities does not automatically translate to realistic internal standards for agentic projects. Even sophisticated clients benefit from proactive guidance on defining what success looks like in structured support workflows.
- Consultative engagement matters: Delivering exactly what was specified does not guarantee business value. A technically excellent agent that does not advance KPIs is a missed opportunity to advise on the most impactful approach. Aligning implementation decisions with operational objectives protects both client and delivery team.
The overarching insight: success comes not from deploying an AI agent, but from aligning objectives, scope, and system boundaries from the outset.
Deploying an AI Agent That Delivers Measurable Impact
Lane Four delivered a customer-facing AI agent built on Agentforce, leveraging the client’s Knowledge base and creating Cases for inquiries requiring human intervention. The technical implementation was straightforward. The primary challenges were non-technical: aligning scope and KPIs with the agent’s capabilities to drive meaningful business outcomes.
The solution automated inquiries the agent could handle while providing a seamless escalation path for cases requiring manual intervention. Pairing technical delivery with consultative guidance ensured the AI agent advanced operational objectives and met realistic success metrics.
The agent launched successfully and remains in production. Support workflows are now more efficient, Knowledge-driven responses enhance the partner experience, and case deflection metrics reflect achievable operational potential. Addressing expectation gaps early reduced risk and reinforced the value of combining technical execution with strategic insight.
Aligning Goals, Scope, and Automation to Protect Business Value
Aligning on business objectives from the outset is essential. Waiting until development begins to discuss KPIs and ROI can create misalignment and unrealistic expectations. Early clarity on goals allows time to adjust use cases or scope so success metrics are achievable.
Case deflection targets must reflect what the agent can realistically resolve. Inquiries requiring systems the AI cannot access will always escalate, regardless of technical sophistication. Scoping boundaries early clarifies what is deflectable and establishes realistic success metrics.
Consultative engagement is critical. Proactively surfacing gaps between goals and technical capabilities, even when it requires difficult conversations, produces better outcomes than building strictly to specification. The most successful AI implementations combine technical execution with strategic guidance, ensuring solutions drive real business impact rather than simply functioning as intended.
Ready to unlock the full potential of AI-driven support without overpromising on what’s possible? Let’s chat and explore how to align technology, processes, and KPIs to deliver real results.