The challenge
ABUMET's current operations rely on manual, non-AI workflows that slow cycle times and introduce errors.
ABUMET is a top windows and glazing manufacturer in Nigeria, with its own production facility. This proposal outlines how Netcom Africa will partner with ABUMET to embed practical AI across sales, estimation, and operations.
01
Executive Summary
Our approach
A staged implementation that introduces practical AI adoption across teams, step by step.
Expected outcomes
Faster cycle times, reduced data-entry errors, improved customer responsiveness, and scalable efficiency.
02
About Netcom Africa
- Expertise in AI consultancy and digital process transformation.
- Engagement model: advisory and implementation support on a retainer basis.
- Lead consultant: Yen Choi (y.choi@netcomafrica.com).
03
Current State Assessment
- ABUMET is currently not using AI tools across the workforce to improve efficiency and productivity.
- Many internal processes are manual and involve repetitive data entry and document creation.
- Existing workflows are prone to errors and inconsistencies due to human dependency.
- Practical example: Modifications to customer quotations are time-consuming and can delay sales response and closure.
- The salesperson owns the full process from Quote-to-Cash.
- Most customer inquiries are received via email.
- The salesperson collects requirements, which can take 1-2 weeks to complete in some cases.
- In many cases, the client does not have exact measurements at the start.
- A quotation number is created, and an Excel file is generated from a pre-made template.
- The estimation team develops a design and sends requirements into Logical for an exact BOQ (10 minutes to 1 day).
- Pricing is updated based on current material prices and exchange rates.
- Overheads, premiums, and accessory pricing are added.
- A cover letter is prepared.
- The estimate and BOQ are sent to the customer.
- Customers often struggle to interpret the BOQ because it is presented in bulk aggregate quantities for the full project.
- The customer may request modifications, and the process repeats.
Platform requirement: The solution platform will sit on top of ABUMET's existing Microsoft 365 environment.
- Customer inquiries from email are ingested into a structured AI-assisted intake workflow.
- Sales uses a guided requirements assistant (custom sales chat guide) to collect complete project details faster and more consistently.
- Where measurements are incomplete, AI prompts the salesperson and customer with targeted follow-up questions and standard measurement checklists.
- A quotation draft is auto-generated from standardized templates with a unique quotation reference.
- Estimation: AI creates a rough BOQ based on learned parameters and trained reference cases, focused on the top 3 products and using no more than 10 elements (e.g., sliding windows and doors).
- The estimation team validates and refines design inputs, then confirms exact BOQ output in Logical.
- Pricing tables are automatically refreshed using current material pricing and exchange-rate inputs.
- Overheads, premiums, and accessories are auto-calculated using configurable business rules.
- A customer-ready cover letter and quotation summary are generated automatically and reviewed by sales.
- The customer receives both a detailed BOQ (technical/procurement) and a simplified, requirement-aligned summary mapping scope to deliverables and costs.
- Customer modification requests are processed through guided change workflows, with rapid re-estimation and version-controlled quotation updates.
- Final approved quotation transitions into downstream execution with clearer handoff data for operations, inventory, and supply chain.
Expected impact
- Faster turnaround for first quotation issuance and revisions.
- Reduced manual data entry and fewer process errors.
- Improved customer understanding of estimates and scope.
- Better consistency across sales, estimation, and finance inputs.
04
Transformation Objectives
Standardize & accelerate
Key business workflows with AI-enabled support.
Improve accuracy
In data handling, quotation updates, and customer communication.
Equip staff
With practical AI skills specific to ABUMET use cases.
Build a foundation
For future AI-driven sales, quotation, and operational systems.
05
Proposed Implementation Roadmap
Click any stage to expand its objective, scope, and work-package deliverables.
Objective
Conduct a comprehensive discovery and assessment of ABUMET's current AI readiness, technology landscape, and organizational capability to adopt AI-driven solutions.
Scope
- Current technology stack and infrastructure audit (Microsoft 365 environment review, systems integration points, data architecture).
- Assessment of AI tools, platforms, and available updates within the current environment.
- Current adoption levels across the organization (tools usage, digital maturity, skill gaps).
- Key business process mapping and workflow analysis (sales, quotations, inventory, supply chain).
- Bottleneck and pain-point identification with quantified impact assessment.
- Competitive benchmarking and industry best-practice review for AI in manufacturing and B2B sales.
- Data readiness assessment (data quality, availability, governance, compliance).
- AI opportunity matrix: prioritized use cases with business impact scoring and implementation feasibility.
- Change management and organizational readiness assessment.
Deliverables
Work Package A — Initiation and Baseline
- 0.1 Project Kickoff and Stakeholder Alignment — confirm objectives, scope, success criteria, and governance model.
- 0.2 Technology and Tooling Audit — assess Microsoft 365 environment, integration points, and current AI-capable tools.
- 0.3 Adoption and Skills Baseline — evaluate current usage, user maturity, and capability gaps across teams.
Work Package B — Process and Data Discovery
- 0.4 Business Process Discovery — map key workflows (sales, quotations, inventory, supply chain) and handoffs.
- 0.5 Data Readiness Assessment — review data quality, availability, structure, and governance readiness.
Work Package C — Opportunity and Prioritization
- 0.6 Opportunity and Benchmark Analysis — identify high-impact AI use cases and benchmark against industry best practices.
- 0.7 Prioritization Workshop — rank use cases by business value, feasibility, risk, and time-to-impact.
Work Package D — Reporting and Mobilization
- 0.8 AI Readiness Assessment Report — deliver consolidated findings, recommendations, and target-state architecture.
- 0.9 Stage Planning and Mobilization — provide phased implementation roadmap, resourcing guidance, and Stage 1 mobilization plan.
Objective
Train general staff on practical, role-specific usage of Microsoft Copilot and AI tools.
Scope
- Abumet-specific use cases for daily tasks.
- Prompting best practices, responsible usage, and quality checks.
Deliverables
Work Package A — Needs Analysis and Curriculum Design
- 1.1 Role-Based Training Needs Analysis — segment users and define role-specific training outcomes.
- 1.2 Training Curriculum Design — build ABUMET-specific curriculum, scenarios, and practical use cases.
Work Package B — Core Training Delivery
- 1.3 Session 1 Delivery — first instructor-led training session (up to 20 participants).
- 1.4 Session 2 Delivery — second instructor-led training session (up to 20 participants).
Work Package C — Advanced Delivery and Enablement Assets
- 1.5 Session 3 Delivery — third instructor-led training session (up to 20 participants).
- 1.6 Enablement Materials Pack — quick-reference guides, prompt templates, and usage playbooks.
Work Package D — Adoption Review and Recommendations
- 1.7 Adoption Review and Recommendations — assess usage outcomes and provide next-step recommendations.
Objective
Build a custom AI-assisted sales conversation guide for customer-facing teams.
Scope
- Sales approach guidance.
- Technical guidance support.
- Requirements collection framework.
Deliverables
Work Package A — Process Discovery and Flow Mapping
- 2.1 Sales Process Documentation — document current sales workflows, customer interaction patterns, and decision points.
- 2.2 Customer Inquiry Flow Mapping — map inquiry types, escalation paths, and touchpoints from email to quotation handoff.
Work Package B — Prompt and Playbook Design
- 2.3 Prompt Library Development — create and test prompt templates for common sales scenarios.
- 2.4 Sales Chat Playbook — structured, role-specific playbook with conversation flows and best practices.
Work Package C — Integration Planning and Pilot
- 2.5 Integration Planning — design integration points with email, CRM, and quotation systems.
- 2.6 Pilot Testing with Sales Team — hands-on pilot with 2-3 sales reps; gather feedback and refine prompts.
Work Package D — Training and Reference Enablement
- 2.7 User Training — train sales team on using the chat guide, including hands-on practice and Q&A.
- 2.8 Reference Materials — quick-reference guides, FAQs, and troubleshooting documentation.
Objective
Develop a quotation support system to produce rough estimates based on past customer inputs and historical patterns.
Scope
- Input standardization.
- Estimation logic aligned with prior quotation data.
- Human review workflow before customer submission.
Deliverables
Work Package A — Discovery and Data Readiness
- 3.1 Historical Quotation Data Audit — analyze and standardize past quotation records; identify data quality issues.
- 3.2 BOQ Element Mapping & Parameters Documentation — map top 3 products, 10 key elements, and learning parameters.
Work Package B — Estimation Foundation Setup
- 3.3 AI Model Training — train estimation model on clean, standardized quotation data with accuracy baseline validation.
- 3.4 System Configuration — configure pricing rules, overhead/premium algorithms, and exchange-rate update mechanisms.
Work Package C — Workflow and Integration Build
- 3.5 Integration with Logical & Pricing Systems — design and implement data flow between AI system, Logical, and pricing databases.
- 3.6 Prototype Development — build functional prototype with UI for estimation input and rough BOQ output review.
Work Package D — Validation and Launch
- 3.7 User Acceptance Testing (UAT) — validate accuracy, speed, and usability; iterate based on feedback.
- 3.8 Customer-Ready Summary Template — dual-output templates (detailed BOQ + simplified customer summary).
- 3.9 Deployment & Go-Live Support — deploy to production; provide go-live training and initial production support.
Objective
Identify and prioritize practical AI use cases for inventory management automation.
Scope
- Inventory process mapping and bottleneck identification.
- AI use case discovery workshops for inventory and related supply chain workflows.
- Prioritized automation roadmap with business impact scoring.
Deliverables
Work Package A — Process and Data Baseline
- 4.1 Current Inventory Process Documentation — document end-to-end inventory workflows, decision rules, and interdependencies.
- 4.2 Data Quality Assessment — audit inventory records, completeness, consistency, and readiness for AI analysis.
Work Package B — Bottleneck and Workflow Analysis
- 4.3 Bottleneck and Pain-Point Analysis — identify manual data entry, process delays, and inefficiency hotspots.
- 4.4 Supply Chain Workflow Mapping — map supplier interactions, reordering processes, lead times, and procurement workflows.
Work Package C — Use Case Discovery and Prioritization
- 4.5 AI Use Case Discovery Workshops — identify and validate AI use case opportunities with teams.
- 4.6 Use Case Register — prioritized register with business cases, success metrics, and complexity scoring.
- 4.7 Pilot Shortlist — select top 2-3 use cases for proof-of-concept; define success criteria and data requirements.
Work Package D — Business Case and Roadmap Finalization
- 4.8 Business Case Development — detailed business case for each pilot use case (ROI, effort, risk, dependencies).
- 4.9 Phased Implementation Roadmap — phased roadmap with resource and timeline estimates.
06
Engagement Model
The engagement model will be subject to mutual agreement based on ABUMET's requirements and the agreed scope for each stage or support period. Netcom Africa may provide services on a consultancy retainer basis, with support arrangements tailored to customer needs.
Support may include
- Strategy, implementation, user enablement, and iterative optimization.
- AI steering committee support and governance facilitation.
- Regular check-in meetings with staff, including guidance, review, and progress tracking.
- Training and ongoing knowledge transfer.
- Technical support, ongoing development, bug fixes, additional features, and advisory services.
Engagement cadence
- Weekly working sessions.
- Monthly progress reviews.
- Stage-based delivery checkpoints.
07
Commercials & Commercial Terms
Hourly Rates
- Yen ChoiUSD 600/hr
- Senior EngineerUSD 200/hr
- Junior EngineerUSD 75/hr
Provided for guidance and applied by mutual agreement in the case of additional scope or variations.
Stage 0 Investment
- Work Package AUSD 1,500
- Work Package BUSD 1,200
- Work Package CUSD 400
- Work Package DUSD 600
Rough Order of Magnitude
- Stage 1USD 2,000–4,000
- Stage 2USD 5,000–10,000
- Stage 3USD 7,000–15,000
Payment Terms
Payment in advance, subject to availability of resources.
Travel & Expenses
Travel and out-of-pocket expenses are charged to the client at cost.
Travel Billing
When travel is required, an additional daily premium applies on top of existing project stage charges, calculated as 50% × applicable hourly rate × 4 hours per day to account for travel time.
08
Governance & Ways of Working
Joint steering
Between ABUMET stakeholders and Netcom Africa.
Clear ownership
By stage, with decision checkpoints.
KPI tracking
For adoption, speed, quality, and business impact.
Risk management
Risk and dependency management throughout implementation.
09
Success Metrics
- Reduction in average quotation modification turnaround time.
- Reduction in manual data-entry error rates.
- AI adoption rate across trained staff.
- Sales response-time improvement.
- Process throughput improvements in targeted workflows.
10
Assumptions & Dependencies
- Availability of relevant historical quotation/process data.
- Participation of key users for workshops and testing.
- Access to current tools, systems, and process documentation.
- Strong internal executive sponsor to support change management and adoption.
11
Next Steps
- Confirm Stage 0 scope, priorities, and success criteria.
- Conduct a stakeholder alignment workshop to validate assumptions, data availability, and governance.
- Finalize retainer structure and statement of work.
- Mobilize Stage 0 assessment delivery and produce the AI Readiness Assessment Report.