Starting with data and AI: Understand the value, estimate the effort, and see the path forward

You know data and AI can improve efficiency, reduce costs, and enhance decision-making. But before investing in new systems or initiatives, you need clarity: What data do you actually have? What's its quality? Where are the gaps? And which opportunities are worth pursuing first?

Without this foundation, organizations risk investing in technology that doesn't align with their data capabilities or business priorities.

We conduct structured discovery through three phases:

Preparation - Review your process documentation, system architecture, current pain points, and operational challenges through the provided materials and preliminary discussions

Discovery sessions - On-site or virtual structured interviews with cross-functional teams covering strategy, operations, technology, quality, and organizational readiness. Includes facility walkthrough (if applicable), system architecture review, and initial findings presentation

Analysis and reporting - Comprehensive assessment development with quantified maturity scores, industry benchmarking, gap analysis by functional area, and actionable recommendations

A comprehensive, quantified assessment of your current data landscape and a clear path forward:

• Data and AI Readiness Index - Quantified assessment across 9 capability areas, including operations digitization, data quality, IT infrastructure, analytics maturity, and AI readiness
• Gap analysis with value identification - What's working, what's missing, and high-potential opportunities ranked by estimated impact and feasibility
• Prioritized implementation roadmap - Specific next steps with timeline estimates, resource requirements, and minimum technical standards needed for success

Format: Comprehensive assessment report including current state analysis, dimension-by-dimension evaluation with detailed findings, your Data and AI Readiness Score with industry benchmarking, gap analysis identifying improvement priorities, ROI evaluation for recommended initiatives, and a proposed proof of concept with defined success criteria to guide next steps.

Timeline: 2–3 weeks, including on-site discovery sessions if necessary (2-3 days)

Participants: Cross-functional team including operations, quality, IT, planning, and leadership

Investment: Price available on request, reflecting scope and complexity. Travel expenses are invoiced separately if on-site.

• Quantified, not subjective - Objective scoring framework provides a measurable baseline and enables progress tracking over time
• Industry-contextualized - Benchmarking shows how you compare and sets realistic improvement targets for your sector
• Integrated perspective - Evaluates technology and operations together, not in isolation
• Vendor-agnostic recommendations - Focused on your needs and constraints, not specific tool preferences
• Action-oriented outcome - Bridges assessment to implementation by identifying specific POC opportunities with defined success criteria

AI and advanced analytics offer proven benefits across industries, but knowing where to start is difficult. You need to separate hype from practical applications, understand what's possible with your current data, and identify opportunities that align with your business priorities and operational reality.

Generic use cases don't account for your specific data readiness, organizational constraints, or strategic goals.

• Structured scoring methodology - Objective prioritization framework eliminates subjective ranking
• Collaborative discovery - We facilitate, you identify opportunities (not consultant-driven recommendations imposed on your organization)
• Grounded in reality - Accounts for your actual data readiness from assessment or rapid inventory
• Sprint-based framework - Connects directly to implementation with a clear, actionable execution approach
• Industry context with custom innovation - Demonstrates proven applications while helping identify unique opportunities specific to your situation
• Vendor-agnostic focus - Emphasis on solving problems, not promoting specific technologies or platforms

Structured facilitation through the workshop session:

Context setting - Review current operational challenges, business priorities, and relevant AI/analytics applications in your sector

Opportunity discovery - Facilitated brainstorming across functional areas relevant to your organization (e.g., process optimization, quality improvement, predictive maintenance, demand forecasting, resource optimization)

Evaluation and prioritization - Structured scoring of each opportunity using a consistent framework:

• Business impact (cost savings, efficiency gains, quality improvements)
• Data readiness (what exists, quality level, what's needed)
• Implementation feasibility (time, cost, organizational change)
• Strategic alignment (fit with business goals)

Sprint planning - Define specific implementation sprints for top 3–5 opportunities, each with: goal, problem solved, required data, technical approach, success metrics, timeline

A facilitated workshop that produces actionable prioritization:

• Prioritized use case catalog - AI and analytics opportunities scored and ranked by business impact, data readiness, and implementation feasibility, specific to your operations
• Technical readiness evaluation - Assessment of what your current data and systems can support today, versus what needs improvement
• Implementation framework - Sprint-based approach showing how to tackle each opportunity with clear sequencing
• Recommended pilot project - Specific first step with defined scope, success criteria, and resource requirements

Format: Workshop summary report, detailed use case catalog with scoring rationale, prioritization matrix with visual quadrant chart, sprint roadmap for top opportunities

Timeline: 1 day workshop + 1 week follow-up documentation

Participants: 4–6 people recommended, representing operations, quality, maintenance/engineering, planning, IT, and leadership (must include decision-makers)

Investment: Price available on request. Can be conducted standalone or as a follow-up to the Assessment. Travel expenses are invoiced separately if on-site.

Before committing to large-scale data or AI initiatives, you need proof that the approach works for your specific operations, with your actual data, in your environment. Without validation, organizations risk investing in solutions that don't deliver expected value or that encounter unforeseen implementation barriers.

A well-designed POC demonstrates value, tests feasibility, and builds organizational buy-in before major investment.

Structured sprint implementation in three phases:

Sprint planning - Define specific problem to solve (based on assessment/workshop recommendations), identify target area, confirm data sources and access requirements, set success criteria (business outcomes + maturity improvements)

Implementation - Data collection and integration setup, dashboard/model development with iterative testing, refinement based on user feedback, and adoption support to ensure actual usage

Validation and handover - Performance measurement against success criteria, comprehensive documentation and knowledge transfer, focused re-assessment of affected areas to quantify improvement, scale-up recommendations, and roadmap

A functional proof of concept that demonstrates measurable value and serves as a template for scaling:

Common POC types (adapted to your industry and operations):

• Real-time operational dashboard - Live monitoring and performance tracking
• Quality analytics dashboard - Statistical process control, defect analysis, root cause identification
• Predictive analytics prototype - Early warning system for equipment failures or process deviations
• AI-powered inspection or classification - Automated defect detection, document processing, or pattern recognition
• Optimization model - Resource scheduling, throughput prediction, or demand forecasting

Each POC includes:

• Functional system with live data integration
• Data architecture and integration documentation
• User training and adoption support
• Post-POC assessment showing quantified improvements
• Scale-up plan for broader deployment

Timeline: 6–8 weeks of implementation

Investment: Price available on request. Complexity varies by POC type—operational dashboards (lower complexity), predictive analytics (medium complexity), AI/computer vision (higher complexity). Investment includes full implementation cycle, data integration setup, user training, documentation and knowledge transfer, and post-POC assessment. Travel expenses are invoiced separately if on-site.

Problem solved: Downtime events and productivity losses go unrecorded when operators rely on manual logging

Target area: Specific production line or equipment group

What we build:

• PLC connection for automated data capture (run/idle state, part counts, machine status)
• Digital downtime reason entry (replaces manual logs)
• Real-time dashboard showing OEE, availability, performance, and quality
• Historical trend analysis and reporting
• AI-enhanced capabilities: anomaly detection for equipment performance patterns, predictive alerts for potential downtime events, and automated root cause suggestions based on historical data

Deliverables:

• Functional dashboard with live data and AI-powered insights
• Data integration architecture documentation
• User training and adoption support
• Post-POC assessment showing quantified improvements
• Scale-up plan for additional lines/equipment

• Measurable outcomes - Success defined by business results AND quantified data maturity improvements
• Proof before scale - Validate approach with real data in your environment before major investment
• Your actual operations - Uses your real data and processes, not simulated scenarios
• Clear success criteria - Agreed upfront, measured objectively at completion
• Built to scale - Architecture designed to support expansion from day one
• Vendor-agnostic implementation - Right tool for your specific problem, avoiding unnecessary lock-in
• Quantified impact - Post-POC re-assessment proves ROI and validates improvements with data
• Adoption support included - Ensures people actually use the system, not just that it's deployed