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AI Assembly

Framework for Successful AI/ML Adoption


Enterprises attempting to leverage Artificial Intelligence (AI) and Machine Learning (ML) technologies face significant hurdles. Surveys indicate that almost 60% to 85% of AI/ML models never reach deployment. The root causes include the need for multidisciplinary collaboration, data quality improvements, and preparation for post-production needs and challenges.


60% to 80%

of AI/ML models never reach deployment

Challenges for AI/ML Adoption

Clear Business Objectives & Strategy

Many enterprises pursue AI/ML initiatives based on a broad understanding of potential use cases, lacking specificity, measurability, and viability.

Data Quality and Management

Ensuring the availability, accuracy, and completeness of data is critical, yet often difficult to achieve

Multidisciplinary Collaboration

Successful AI/ML projects require close collaboration between data scientists, engineers, domain experts, and business stakeholders, which can be challenging to coordinate


Scaling AI/ML models from pilot projects to full production can be technically challenging and resource-intensive

Monitoring and Maintenance

Post-deployment, AI/ML models need continuous monitoring, maintenance, and updates to remain effective and accurate

Talent and Skill Gaps

There is a high demand for skilled AI/ML professionals, making it difficult to find and retain the necessary talent

AI Assembly Framework

AI Assembly, an enterprise AI/ML adoption framework, addresses these obstacles and streamlines the path to AI/ML adoption.

Its structured methodology focuses on collaboration, innovation, and practical implementation, guiding enterprises from initial exploration to successful operational integration.

This framework brings together business, data, and IT teams with data scientists and engineers. Together, they invest time and resources to identify, prioritize, and develop use cases based on their impact, viability, and data availability. Once identified, the framework provides a well-defined, iterative approach to deliver AI/ML-powered outcomes to production.

Three Phases of AI Assembly

Invest Phase
- Cross-functional collaboration
- Testing
- Validation of AI/ML projects
- MLOps strategy formulation
- Explainability
- Risk assessment
Develop Phase
- Data collection
- Cleansing
- Analysis
- Feature engineering
- Model training
- Deployment
Harvest Phase
- Monitoring
- Governance
- Training
- Communication

Key Advantages of AI Assembly

  • Accelerated Time to Market: Speed up from several months to weeks
  • Lower Risk of Failure: Test and validate use cases with stakeholders
  • Cost Savings: Smart planning and resource efficiency for maximum ROI
  • Trust and adoption: Comprehensive data management for accuracy
  • Competitive Advantage: Continuous Optimization for dynamic business needs
Download the complete the AI Assembly Guide

Why GS Lab | GAVS as Implementation Partner

Multidisciplinary teams

We have a cohesive interdisciplinary team experienced with multiple end-to-end AI/ML projects

Pragmatic Approach

We help establish feasibility with rudimentary models and then implement a complete pipeline till model deployment and monitoring

Domain led Solutions

Our deep domain understanding across healthcare, supply chain, Life Sciences, pharm, hi-tech and BFSI helps us carve industry-centric solutions for our customers

Higher Success Rate

With an 85% success rate for AI/ML projects, we ensure your investments meet your end goals

Modular Services

Solutions and services carved specifically for each stage of AI Assembly

Innovative Business Models

Equally invested in partnership with shared risk