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.

Main Objectives

1. Enable revenue growth through strong solution design and AI-driven innovation.
2. Protect ongoing delivery by isolating R&D and PoC work from live projects.
3. Accelerate time-to-market for new solutions and AI capabilities.
4. Ensure technical excellence and scalability across all offerings.
5. Maintain margin discipline through accurate estimation and cost control.
6. 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)