AI Enablement Lead

GMO Globalsign Certificate Services; New Delhi; Hybrid; INR 2,500,000 - 4,000,000; Full time

Job summary

GlobalSign is seeking an AI Enablement Lead to accelerate AI adoption across the organization. Working within the IT Department and reporting to the CSO, you will own AI governance, policy, and the tool portfolio, preparing knowledge and data for AI use and leading AI-driven business process transformation.

You will measure value, develop capability across the workforce, and act as the internal catalyst from strategy to hands-on delivery.

Qualifications

• 5+ years of relevant professional experience.
• Demonstrable experience in AI, data, technology consulting, or IT strategy role, with hands-on delivery of AI or automation projects at organizational level.
• Strong understanding of modern AI concepts including large language models, AI agents, retrieval-augmented generation, and copilot architectures.
• Proven ability to define, track, and communicate value metrics and KPIs related to technology adoption and business impact.
• Experience establishing knowledge management, documentation, or data governance standards, and managing a software or SaaS portfolio including licenses, cost, and renewals.

Responsibilities

• Own and continuously optimize GlobalSign's AI usage policy, acceptable-use standards, and approval workflows, keeping them practical enough to encourage adoption while protecting the company's position as a publicly trusted certificate authority.
• Define and govern the AI operating model: platform selection, data usage, security-by-design, model lifecycle management, and integration with existing IT architecture.
• Set human-in-the-loop, review, and disclosure requirements for AI outputs used in regulated, audited, or customer-facing processes, and refresh policy as tool capabilities change.
• Scan the market for emerging AI models, agents, copilots, and platforms, and run structured evaluations against capability, total cost, security posture, data residency, and integration fit.
• Maintain reusable frameworks and reference architectures so departments build on a consistent foundation and advise leadership on build-versus-buy and investment priorities.
• Track license utilization and adoption by department and users, identifying under-used seats, unmet demand, and unsanctioned shadow AI.

Skills

5+ years

AI governance

KPI definition

Hands-on AI delivery

AI concepts (LLMs, agents)

Job description

ABOUT GLOBALSIGN

Established in 1996, GlobalSign is the leading provider of trusted identity and security solutions enabling businesses, large enterprises, cloud service providers and IoT innovators around the world to secure online communications, manage millions of verified digital identities and automate digital signing, authentication, and encryption. Its high- scale PKI solutions support the billions of services, devices, people, and things comprising the Internet of Everything (IoE). The company has offices in the Americas, Europe, and Asia.

JOB SUMMARY:

The AI Enablement Lead is responsible for accelerating the adoption and valorisation of artificial intelligence (AI) across GlobalSign. Working within the IT Department and reporting to the Chief Information and Security Officer, this role owns AI governance and policy, evaluates and rationalizes the AI tool portfolio, prepares the organization's knowledge and data for AI use, leads AI-driven business process transformation, measures the value delivered, and develops AI capability across the workforce. The AI Enablement Lead is the primary internal catalyst for AI-driven transformation, from strategy and tooling through to hands‑on delivery support .

Role & responsibilities

1. Drive AI Governance and Policy Optimization

• Own and continuously optimize GlobalSign's AI usage policy, acceptable-use standards, and approval workflows, keeping them practical enough to encourage adoption while protecting the company's position as a publicly trusted certificate authority.
• Define and govern the AI operating model: platform selection, data usage, security-by-design, model lifecycle management, and integration with existing IT architecture.
• Set human-in-the-loop, review, and disclosure requirements for AI outputs used in regulated, audited, or customer-facing processes, and refresh policy as tool capabilities change.

2. Evaluate Emerging AI Technologies and Optimize the Tool Portfolio

• Scan the market for emerging AI models, agents, copilots, and platforms, and run structured evaluations against capability, total cost, security posture, data residency, and integration fit.
• Rationalize the tool portfolio by consolidating overlapping tools and retiring those underused or superseded and support procurement and legal review of AI vendors.
• Maintain reusable frameworks and reference architectures so departments build on a consistent foundation and advise leadership on build-versus-buy and investment priorities.

3. Establish AI-Ready Knowledge and Data Management Practices

• Define how enterprise knowledge is structured, labelled, owned, and maintained so that it can be reliably retrieved and used by AI systems.
• Lead the design of retrieval-augmented generation foundations: source-of-truth repositories, indexing strategy, access controls, and rules for content freshness and ownership.
• Set data classification and handling rules governing which data may reach which AI systems and improve data quality in the systems feeding AI use cases.

4. Manage AI Adoption Metrics and Subscription Inventory

• Maintain a complete inventory of AI tools, agents, models, and subscriptions, including business owner, cost, contract terms, renewal dates, and governance status.
• Track license utilization and adoption by department and users, identifying under‑used seats, unmet demand, and unsanctioned shadow AI.
• Drive cost optimization through rightsizing, consolidation, and timely renewal or cancellation decisions.

5. Lead AI-Driven Business Process Transformation

• Map existing processes with department leads, identify automation and augmentation opportunities, and prioritize them by value, feasibility, and risk.
• Redesign workflows around AI capabilities rather than layering tools onto unchanged processes, translating business problems into scoped use cases with named owners.
• Move teams from proof‑of‑concept to production deployment, providing hands‑on delivery support and coordinating across departments to avoid duplication.

6. Measure, Track, and Report Business Impact

• Define AI value KPIs covering adoption, efficiency gains, cost, quality, risk reduction, and customer impact, and establish baselines before deployment.
• Build and maintain a central AI value dashboard showing active initiatives, their status, and their measured outcomes.
• Report to leadership and the Board on realized versus projected value, and recommend whether initiatives are scaled, sustained, or stopped.

REQUIRED SKILLS:

• 5+ years of relevant professional experience
• Demonstrable experience in AI, data, technology consulting, or IT strategy role, with hands‑on delivery of AI or automation projects at organizational level.
• Strong understanding of modern AI concepts including large language models, AI agents, retrieval‑augmented generation, and copilot architectures.
• Proven ability to define, track, and communicate value metrics and KPIs related to technology adoption and business impact.
• Experience establishing knowledge management, documentation, or data governance standards, and managing a software or SaaS portfolio including licenses, cost, and renewals.