AI Adoption Lead – Perplexity

Job Title: AI Adoption Lead – Perplexity (IT & Business Enablement)

Location: Flexible (Onsite / Hybrid).

Experience: 5–8 years.

Work location- US - Dallas, TX is preferred. ( Hybrid role )

someone from Dallas would be great

So, an AI engineer role requiring Perplexity usage. It's not a development role, the main focus is training end users on how to use it in their day-to-day roles and configuring it for the client model is what they need.

Role Overview

We are looking for an AI Adoption Lead to drive the rollout and operationalization of Perplexity across IT and business functions. This role will act as the bridge between technology and business teams, identifying high-impact use cases, enabling adoption, and embedding AI-driven workflows into day-to-day operations.

Key Responsibilities

• Lead end-to-end adoption of Perplexity across IT and business units
• Identify, define, and prioritize practical use cases aligned to business outcomes
• Work closely with IT teams to integrate Perplexity into existing tools and workflows
• Engage with business stakeholders to drive awareness, training, and usage
• Develop playbooks, best practices, and usage guidelines
• Track adoption metrics, usage patterns, and value realization
• Act as the central point of contact for all Perplexity-related initiatives
• Provide continuous feedback to improve implementation and scale adoption
  
  

Required Skills & Experience

• 5–8 years of experience in digital transformation, AI enablement, or technology consulting
• Strong understanding of AI tools (e.g., Perplexity, ChatGPT, copilots) and their enterprise applications
• Experience working with cross-functional teams (IT + Business stakeholders)
• Ability to translate business needs into practical AI use cases
• Familiarity with workflow tools, APIs, and enterprise systems is a plus
• Strong communication and stakeholder management skills
  
  

Preferred Qualifications

• Experience in driving adoption of AI/automation tools in enterprise environments
• Background in pre-sales, consulting, or solution architecture
• Exposure to prompt engineering, knowledge management, or AI governance frameworks
  
  

Success Metrics

• Adoption rate across teams
• Number of active use cases implemented
• Measurable productivity improvements / efficiency gains
• Stakeholder satisfaction and engagement levels