About Us
HiBob helps modern, mid-size businesses transform the way they manage people, giving HR and managers all they need to connect, engage, develop, and retain top talent. Since 2015, we’ve achieved consecutive triple-digit year-over-year growth, all backed by our amazing team of Bobbers from across the globe, making us the choice HRIS of over 5,500 midsize and multinational companies and over 1 Milion users.
Our HR platform is intuitive, data-driven, and built for the way people work today: globally, remotely, and collaboratively.
About the Role
We are looking for a CS AI Business Partner to help Customer Success turn AI strategy into practical business impact.
As CS scales, operational complexity around support, escalations, incidents, customer communication, knowledge management, services, and proactive customer management continues to grow. AI creates a major opportunity to improve efficiency, customer experience, visibility, and proactive service — but capturing that value requires dedicated ownership close to the business.
The CS AI Business Partner will consume the company-wide AI strategy, standards, and operating model led by AI Mind, and tailor them to the specific needs of the CS department across People, Processes, Technology, and Performance.
This is a hands-on business partner role. The person will partner with CS leadership, CS Ops, CX Delivery, Support, Services, CXE, AI Mind, and AI RevOps to identify high-impact opportunities, build and operationalize AI-powered workflows, agents, and automations, and help CS move from reactive operations to proactive customer intelligence. What We’re Looking For

• 3+ years of hands-on experience working with AI — building AI-powered solutions, agents, automations, or AI-driven workflows in a professional setting.
• 3+ years in roles requiring both technical depth and business acumen — such as solution architecture, systems design, technical consulting, product, operations, CS Ops, customer operations, or technology-driven business transformation.
• Strong understanding of Customer Success, support operations, customer operations, or post-sale customer journeys.
• Ability to translate business problems into practical AI solutions that are adopted by real users.
• Strong technical and architectural thinking, including workflow design, integrations, prompt logic, agent behavior, automation, and scalable systems.
• Understanding of AI solution design, including prompt engineering, context engineering, agent design, orchestration, workflow automation, integrations, and runtime behavior.
• Experience working with operational systems such as Zendesk, Slack, Notion, CRM, knowledge bases, data tools, workflow automation platforms, or similar tools.
• Strong execution mindset, with the ability to move from problem definition to prototype, rollout, measurement, and continuous improvement.
• Excellent communication and stakeholder management skills, including the ability to present to leaders, facilitate workshops, coach teams, and translate technical concepts into business language.
• Strong analytical skills, with the ability to define KPIs, identify patterns, measure impact, and improve performance over time.
• High adaptability, curiosity, and comfort working in ambiguity as AI tools and business needs evolve.
• Technical education or equivalent practical experience in Computer Science, Information Systems, Industrial Engineering, Data, Operations, or a related field.

Strong Advantage

• Experience working directly with CS, Support, CX Delivery, CS Ops, Services, or CXE teams.
• Experience improving CS or support KPIs such as TTR, SLA adherence, deflection, self-resolution, escalation quality, incident response, or customer communication quality.
• Familiarity with Zendesk architecture, automations, triggers, macros, routing, reporting, or support workflow design.
• Experience with incident management, escalation management, observability, logs, Datadog, or technical troubleshooting workflows.
• Experience with AI transformation, digital transformation, automation, enablement, or change management programs.
• Experience working with product, data, engineering, IT, or RevOps teams to define requirements and bridge business and technical needs.
• Experience in B2B SaaS or enterprise customer operations.

• Translate the company-wide AI strategy into a practical CS-specific execution plan across People, Processes, Technology, and Performance.

• Partner with CS leadership and operational teams to identify, prioritize, and deliver high-impact AI use cases.
• Build, test, optimize, and maintain AI agents, automations, and workflows for CS.
• Apply AI to improve support, escalations, incidents, customer communication, knowledge management, proactive outreach, and service delivery.
• Connect AI workflows to CS systems and channels such as Zendesk, Slack, Notion, CRM, knowledge bases, data sources, and internal tools.
• Help CS move from reactive support to proactive operational intelligence by surfacing risks, friction, bottlenecks, technical issues, and service opportunities earlier.
• Drive adoption through enablement, coaching, workshops, documentation, and hands-on support for CS teams.
• Define and track success metrics, including adoption, quality, accuracy, cost, efficiency, customer impact, and ROI.
• Capture field intelligence from users, workflows, and customer signals, and translate it into product requirements, process improvements, and strategic recommendations.
• Partner with AI Mind on methodology, standards, governance, reusable capabilities, and measurement, and with AI RevOps on cross-GTM customer intelligence and revenue-related workflows.

Success Looks Like

• AI automates meaningful operational work across CS.
• CS moves from reactive support toward proactive customer and operational intelligence.
• Reduced ticket ping pong and manual handoffs.
• Improved TTR, SLA adherence, deflection, and self-resolution.
• Faster, more scalable incident management and customer communication.
• Better visibility into customer sentiment, operational risk, recurring friction, and technical issues.
• More proactive identification of churn risks, escalations, customer friction, operational bottlenecks, and service opportunities.
• AI-generated insights become embedded into CS decision-making and operating rhythms.
• CS teams become more confident and capable in using AI independently.
• CS scales without proportional growth in operational overhead.