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
- 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)