Design, build, and optimize AI-driven customer success automations using no-code/low-code tools and LLM APIs. Identify use cases across the customer lifecycle, integrate solutions with enterprise platforms, apply prompt engineering and RAG patterns, monitor performance, and collaborate cross-functionally to scale AI-enabled workflows while maintaining responsible AI practices.
As an AI Solutions Specialist, you will be a hands-on contributor on PointClickCare’s Customer Experience team, bridging the worlds of AI-powered customer success and intelligent workflow automation to drive measurable outcomes across the customer lifecycle. This role exists to bring together the strategic mindset of an Agentic Customer Success Manager with a practical ability to configure, connect, and optimize AI solutions — without requiring deep software engineering expertise.
You will work across enterprise platforms (Salesforce, Gainsight, Gong, and others), identifying high-value use cases, building and maintaining agentic workflows and automations, and partnering cross-functionally to embed AI into core customer engagement processes. You will use tools like prompt engineering, workflow orchestration platforms (n8n, Make, Zapier), and LLM APIs to build practical solutions — translating business needs into working automations rather than production-grade software.
This role is critical to PointClickCare’s strategy of scaling customer success and support through AI: it provides the hands-on capacity to identify use cases, stand up intelligent workflows, and continuously optimize them based on real performance data — freeing senior technical resources to focus on the most complex engineering challenges.
You will work across enterprise platforms (Salesforce, Gainsight, Gong, and others), identifying high-value use cases, building and maintaining agentic workflows and automations, and partnering cross-functionally to embed AI into core customer engagement processes. You will use tools like prompt engineering, workflow orchestration platforms (n8n, Make, Zapier), and LLM APIs to build practical solutions — translating business needs into working automations rather than production-grade software.
This role is critical to PointClickCare’s strategy of scaling customer success and support through AI: it provides the hands-on capacity to identify use cases, stand up intelligent workflows, and continuously optimize them based on real performance data — freeing senior technical resources to focus on the most complex engineering challenges.
Key Responsibilities
Identify & Execute AI Use Cases Across the Customer Lifecycle:
-Identify high-impact opportunities where AI agents and automations can improve customer adoption, retention, and operational efficiency.
-Design and execute AI-driven customer success programs including digital journeys, automated interventions, and agent-led workflows.
-Translate business requirements into working prototypes and production-ready solutions using no-code and low-code tools, LLM APIs, and prompt engineering.
-Drive continuous optimization of AI programs based on performance data and customer feedback.
-Identify high-impact opportunities where AI agents and automations can improve customer adoption, retention, and operational efficiency.
-Design and execute AI-driven customer success programs including digital journeys, automated interventions, and agent-led workflows.
-Translate business requirements into working prototypes and production-ready solutions using no-code and low-code tools, LLM APIs, and prompt engineering.
-Drive continuous optimization of AI programs based on performance data and customer feedback.
Build & Maintain Workflow Automations and Platform Integrations:
-Design and implement workflow orchestration pipelines using tools such as n8n, Make, Zapier, or custom-built logic layers.
-Integrate AI solutions across enterprise platforms (Salesforce, Gainsight, Gong, Pendo) via APIs, webhooks, and middleware.
-Manage data flows between systems, ensuring reliability and scalability of AI-driven processes without requiring engineering support for routine changes.
Prompt Engineering & AI Solution Optimization:
-Develop, test, and iterate on prompt engineering strategies to maximize model accuracy, consistency, and performance.
-Apply RAG (Retrieval-Augmented Generation) patterns and evaluation frameworks to improve solution quality.
-Build feedback loops to continuously refine deployed AI models and agents based on real-world usage data.
Data-Driven Insights & Performance Monitoring:
-Monitor AI program performance (engagement, success rates, efficiency metrics) and translate data into actionable improvements.
-Instrument AI solutions with performance tracking (latency, success rates, engagement metrics, cost efficiency).
-Help maintain dashboards and reports that show how AI solutions are performing; surface trends and recommendations to leadership
Cross-Functional Collaboration & AI Enablement:
-Partner with Customer Success, Product, Engineering, Operations, and Enablement to embed AI into core workflows and customer engagement strategies.
-Act as a subject matter expert on AI capabilities within the team — translating technical “agentic” concepts into business value for stakeholders.
-Stay current on emerging AI capabilities, model releases, and agentic frameworks to drive continuous innovation across the team.
Other Duties as Assigned: Perform additional responsibilities as required to support the success of the Customer Experience organization, including contributing to AI governance practices, evaluating new tools and platforms, training and enablement on AI soluitons and supporting strategic initiatives that advance PCC’s AI Solutions capabilities.
Minimum Qualifications
- AI Fluency: A solid understanding of AI/LLM concepts and the ability to translate “agentic” capabilities into practical business outcomes without requiring deep software engineering skills (AI Aptitude)
- Practical experience with workflow automation and orchestration tools (n8n, Make, Zapier, or equivalent) to connect systems and automate processes without writing production code (Workflow Automation)
- Analytical Fluency: Ability to interpret platform telemetry, usage data, and performance dashboards to derive actionable insights and recommendations (Analytics)
- Strategic Communication: Ability to present AI outcomes and program results to internal stakeholders, framing technical capabilities in terms of business value (Communication)
- Judgment about responsible AI deployment — understanding where AI adds value and where human judgment is required, especially in a regulated, customer-trust-sensitive environment (Judgment / Risk)
- Ownership mindset: bias toward execution, comfort with ambiguity, and a track record of following through on initiatives end-to-end (Ownership)
- Experience with CRM or Customer Success platforms (Salesforce, Gainsight, or similar) (Systems Knowledge)
- Ability to work collaboratively across teams and influence without authority (Collaboration)
- Bachelor’s degree, or equivalent combination of education and relevant experience (Education)
Preferred Qualifications
- 2–4 years in Customer Success, Professional Services, support operations, or a similar customer-facing role at a SaaS or technology company (Experience)
- Direct experience in a healthcare, regulated, or enterprise B2B SaaS environment with comparable scale and complexity
- Hands-on experience with prompt engineering, building automations, or configuring an AI agent in a work or personal context
- Experience integrating enterprise platforms (Salesforce, Gainsight, Gong, Pendo) via APIs or webhooks
- Familiarity with reporting tools such as Salesforce reports, Power BI, Tableau, or Gainsight dashboards
- Prior experience writing process documentation, runbooks, or playbooks for cross-functional teams
- Experience navigating compliance, privacy, or regulatory constraints (HIPAA, SOC 2, or similar)
- Industry domain expertise in healthcare; familiarity with EHR ecosystems, HL7/FHIR, or clinical data workflows
- Experience contributing to cross-functional projects spanning Customer Success, Product, Engineering, and Operations teams
- Coursework, certification, or self-directed learning in AI, data analytics, prompt engineering, or related technical fields
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