AI Change Manager

WSP in India; Bengaluru; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

WSP in India seeks an AI Change Manager to drive adoption and literacy across GCCs, embedding AI into engineering, delivery and support workflows. The role focuses on sustainable change, business impact, and translating WSP AI strategy into executable initiatives across multi-country delivery centers.

Responsibilities include designing adoption waves, leading training, governing AI initiatives, and partnering with HR/L&D to reskill the workforce, with a focus on measurable adoption KPIs and

Qualifications

• 10–15+ years in change management or related consulting
• Experience driving multi-country transformation programs
• Experience with data analytics, AI, ML, or automation initiatives
• Strong understanding of enterprise AI and its business value
• Familiarity with AI rollout lifecycles (pilot, scale, adoption)
• Expertise in structured change methodologies and stakeholder communications
• Ability to facilitate workshops and engage leadership, managers, and engineers
• Data-driven with KPI definition, dashboards, and executive reporting

Responsibilities

• Design and execute unified AI change strategy across GCCs (India and other regions).
• Translate global AI initiatives into localized adoption plans.
• Lead AI training and enablement leveraging vendor capabilities.
• Define adoption waves: Learn, Apply, Scale, Optimize.
• Oversee impact of AI tools on workflows and document future ways of working.
• Engage with GCC leadership, project teams, and AI program offices.
• Lead communications for leadership, delivery, and engineers on value and risk.
• Develop workforce readiness through learning pathways and AI champions.
• Define and track AI adoption KPIs and run health checks and surveys.
• Ensure governance, risk, and responsible AI enablement with guardrails.

Skills

Change management

Transformation programs

Data analytics

AI/ML/Automation

Enterprise AI

Structured change methodologies

Communication & storytelling

Workshop facilitation

Leadership engagement

KPI definition & dashboards

Job description

Job Description

The AI Change Manager drives AI adoption and literacy across GCCs, embedding AI into engineering, delivery, and support workflows. This role focuses on sustainable change and business impact, translating WSP AI strategy into localized, executable initiatives across multi-country delivery centres. It operates at the intersection of AI capability, delivery, Human capital /Learning & Development, and governance.

AI Adoption & Change Strategy

• Design and execute a unified AI change strategy across GCCs (India, , Mexico, Colombia, Manila).
• Translate WSP global AI initiatives into localized adoption plans aligned with workforce maturity and delivery models.
• Drive AI training and enablement leveraging global and vendor capabilities.
• Define phased adoption waves: Learn, Apply, Scale, Optimize.
• Establish a repeatable operating model for AI rollouts

Workflow Transformation

• Oversee and assess the impact of AI tools (e.g., generative design, document intelligence, analytics) on existing workflows.
• Define and document future ways of working across WSP enterprise platforms (ERP, engineering, custom apps).
• Support creation of future AI ?role profiles and competency frameworks

Stakeholder Engagement

• Manage a complex stakeholder ecosystem within WSP including GCC leadership, project delivery teams, WSP global AI sponsors, and support function i.e. HR/IT/Risk .
• Act as the primary change interface between GCCs and WSP global AI program offices.
• Execute targeted communication strategies for:
• Leadership: value, risk, ROI
• Delivery: productivity, quality, capacity
• Engineers: usability, trust, skill growth
• Proactively address limitations, resistance, job displacement concerns, and trust issues

Workforce Readiness & Reskilling

• Conduct AI readiness and sentiment diagnostics across workforce segments.
• Partner with HR/L&D to design role-based learning pathways.
• Build and scale AI Change Champions / Super Users.
• Equip targeted groups and managers with toolkits to lead AI-driven transformation.

Adoption Measurement & Reporting

• Define and track AI adoption KPIs such as:
• Tool utilization and frequency
• Time-to-proficiency
• Productivity and cycle time improvements
• Quality and rework reduction
• Engineer experience
• Run adoption health checks and pulse surveys.
• Report progress with dashboards and actionable insights for leadership

Governance, Risk & Responsible AI Enablement

• Ensure responsible AI adoption aligned with best practice governance, security, and risk policies.
• Support development of guardrails, playbooks, and acceptable-use guidelines.
• Educate teams on ethical AI, bias awareness, and escalation mechanisms.
• Support audits and governance reviews as the change lead.

Qualifications

Required Skills & Experience

Core Experience

• 10-15+ years in change management, operating model design, or process consulting in GCC environments.
• Proven ability to drive large-scale, multi-country transformation programs.
• Experience in data analytics, AI, ML, or automation initiatives.

AI & Digital Transformation Skills

• Strong understanding of enterprise AI (automation, analytics, workflow transformation).
• Ability to translate AI into business value and workforce impact, beyond technology.
• Familiarity with AI rollout lifecycles (pilot, scale, adoption)

Change & Leadership Capabilities

• Deep expertise in structured change methodologies.
• Strong communication and storytelling skills for technical and non-technical audiences.
• Experience in workshop facilitation (design thinking, process mapping).
• Ability to engage effectively with leadership, managers, and engineers

Analytical & Execution Skills

• Data-driven approach to defining KPIs and measuring adoption.
• Experience with dashboards and executive reporting.
• Strong execution mindset with ability to manage ambiguity and evolving AI maturity

Overall Value Proposition

This role is critical in enabling GCCs to transition from AI experimentation to scaled, value-driven