AI Operations Analyst

1. AI-Driven Methodologies & Process Design

. Lead the design, stakeholder requirement gathering, vendor selection, management and operationalization of agentic-AI tools. This role bridges the gap between traditional Sales Ops (CRM, process mastery) and advanced automation (LLMs, RPA, Voice AI).

o Product Definition: Define requirements, success metrics, and workflows for AI capabilities.

o Process Mapping: Identify gaps in existing sales data for AI optimization.

o Roadmap Management: Prioritize and manage delivery across data, ML, and infra teams.

o Vendor Management: Lead evaluations for SaaS, LLM providers, and RPA platforms.

o Change Management: Own monitoring, QA, and business acceptance.

o Training: Upskill staff on new tools and iterate based on feedback.

. Automating Systematize Selling: Road Map reviewing, documenting, and standardize end-to-end sales processes (from lead generation to contracting) into a structured system including CRM. To further expand into a Quote-to- Cash (Q2C) system covering Sales, Operations, Procurement, Finance and HR.

. Implement AI Methodologies: Integrate AI tools (e.g., Voice Ai, CRM, SEO/ SEM GenAi, predictive Ai), Web LLMs,into daily workflows to provide Cadence and next-best-action recommendations, integrating Group Operations.

. Company Workflow Automation: Design and implement AI-driven routing automation for tasks such as CRM data entry, meeting summarization, lead routing, and technical follow-up email to enable quotations, invoicing,

proposals, Order fulfilment operations planning and technical designs with RPA tools.

. Process Bottleneck Elimination: Analyse and process data to identify sales bottlenecks to automate improvements and use AI to proactively remove administrative friction, freeing time for selling, delivery & customer satisfaction.

2. Data-Driven Sales Performance Management

. AI Forecasting & Analytics: Utilize predictive analytics to generate accurate sales forecasts and pipeline analytics,

providing early warnings on stalled deals or risk, and sales rep's and sales department's performance.

. Performance Metrics (KPIs): Develop and manage a robust dashboard of leading indicators (results, activity

metrics, conversion rates, pipeline size and velocity, average deal size, average days to close deals etc) to evaluate team and individual effectiveness. Provide accurate sales forecasting and market intelligence to management.

. Data Hygiene & Governance: Establish data standards and use AI to scan, cleanse, and enrich CRM data, ensuring a single source of truth.

. Sales Incentives: To implement process for review sales reward processes to better drive sales performance.

3. Sales Movement & GTM Enablement

. Targeting & Account Scoring: Automate AI-powered web LLMs to gain new market intelligence, prioritization to rank prospects-based project cycle, intent signals, buyer behaviour, timeliness & historical win data.

. Territory & Quota Modelling: Use data analytics to design equitable and motivating sales territories and quotas, ensuring balanced opportunity distribution for best ROI margins and win probabilities.

. Sales Automation Adoption: Drive the adoption of AI-powered enablement tools (e.g. Opportunity, Workflows, CRM, pitch analysis, AI sales coaching) and provide training to increase rep productivity.

. Strategic Business development: To provide market insights on opportunities, generate leads and support lead

gen. Intent Taxonomy: Creating a library of customer intents (e.g., Checking Price vs. Technical Support) so the AI routes the opportunity to the correct salesperson.

. Support Branding, Marketing: To support adoption of Ai branding and marketing in order to scale business. Social Media automation, Generative txt or images, SEO/SEM automation, competitor comparisons.