Business Operations Manager

Role Purpose

The Business Operations Manager is responsible for transforming business opportunities into scalable, AI-enabled, and operationally feasible solutions, while safeguarding delivery stability across the organization.

This role owns solution design, innovation execution, and PoC governance, ensuring that emerging technologies, AI capabilities, and R&D initiatives directly support revenue growth without disrupting running projects or core operations.

Key Responsibilities

1. Solution Architecture & Innovation Ownership

• Own end-to-end solution design for new opportunities, including features, specifications, architecture, and delivery models.
• Ensure all solutions align with ITWORX’s AI strategy, technical standards, security, and scalability requirements.
• Act as the final technical authority before commercial commitments are made.

2. R&D and AI Engineering Leadership

• Lead R&D and AI Engineering teams to develop innovative capabilities aligned with business priorities.
• Translate market, customer, and sales insights into practical R&D initiatives and AI accelerators.
• Ensure R&D outputs are reusable, productized, and scalable across projects and products.

3. PoC & AI Enablement Governance

• Own and govern the full PoC lifecycle—from ideation to execution and commercialization.
• Ensure PoCs clearly demonstrate value, feasibility, and ROI.
• Enforce capacity planning to prevent PoC and innovation activities from impacting live projects.

4. Commercial Enablement & Presales Support

• Support Sales and Presales teams during complex opportunity cycles, workshops, demos, and RFPs.
• Provide accurate technical inputs, estimates, and risk assessments.
• Enable confident go/no-go decisions based on technical readiness and delivery capacity.

5. Estimation, Costing & Margin Protection

• Own effort estimation, costing models, and delivery assumptions.
• Partner with Finance to ensure pricing supports target margins and long-term sustainability.
• Continuously improve estimation accuracy through feedback loops from delivery teams.

6. Cross-Functional Orchestration

• Act as a central coordination point across all departments to align innovation with execution.
• Ensure smooth handover from PoC to delivery (projects or products).
• Resolve cross-department dependencies, conflicts, and priorities.

7. Standardization & Knowledge Reuse

• Build reusable solution frameworks, reference architectures, PoC templates, and AI accelerators.
• Capture lessons learned and embed them into organizational standards.
• Promote knowledge sharing and innovation consistency across teams.

8. Risk Management & Executive Visibility

• Identify technical, operational, and financial risks early in the opportunity lifecycle.
• Provide transparent reporting to the COO on innovation pipeline, readiness, and impact.
• Recommend strategic investment, pause, or stop decisions.

Requirements:
Main Objectives

• Enable revenue growth through strong solution design and AI-driven innovation.
• Protect ongoing delivery by isolating R&D and PoC work from live projects.
• Accelerate time-to-market for new solutions and AI capabilities.
• Ensure technical excellence and scalability across all offerings.
• Maintain margin discipline through accurate estimation and cost control.
• Create an innovation engine that continuously feeds products, services, and sales pipelines.

Key KPIs

Innovation & Commercial Impact

• PoC-to-deal conversion rate (%)
• Revenue influenced by R&D and AI initiatives
• Time from PoC to production deployment

Operational Protection

• Number of delivery disruptions caused by R&D / PoC activities
• PoC capacity utilization vs plan (%)
• Adherence to innovation governance framework

Quality & Readiness

• Technical rework rate post-contract signature
• Solution approval rate by Architecture / Engineering
• Compliance with security and AI standards

Financial Performance

• Estimation accuracy (%)
• Margin variance between proposed and delivered solutions
• Cost efficiency of R&D and PoC initiatives

Organizational Maturity

• Reuse rate of AI accelerators and solution templates
• Knowledge adoption across teams
• Executive decision accuracy (go/no-go outcomes)