Growth Opportunities in Test & Measurement for Artificial Intelligence, 2024-2028

Safety and Ethical Considerations Regarding the Increasing Use of AI Across Industries will Drive Growth

Artificial intelligence (AI) is a relatively new technology, and it is critical that AI solutions are thoroughly tested for varied situations, edge cases, and preferences. The primary aim of a test & measurement (T&M) approach for an AI-driven company is to verify that the AI model's functionality is in line with business requirements while ensuring high-quality performance metrics. Regulations stemming from growing concerns over the safety risks and ethical considerations presented by the increasing use of AI across industries also call for thorough testing and validation. AI models are thus consistently and repeatedly tested at several levels and stages during model development.

This study analyzes the T&M for AI industry. It examines the factors driving and restraining its growth and identifies the opportunities emerging from the changes in this space for market players and stakeholders to leverage. It analyzes global industry trends and sheds light on the evolving landscape of AI regulation, gauging the impact of these developments on the industry. The base year is 2023, and the forecast period is from 2024 to 2028.

Introduction

• AI model should be tested at various levels including unit, integration, system, and acceptance testing. It should also be tested at the pre-deployment, deployment, and post-deployment stages. 
• The model must also undergo testing for various factors, including functionality, performance, resilience, fairness, and interpretability. 
• Testing at various levels and phases helps to identify and resolve problems early, ensuring that the model meets the expectations and requirements of stakeholders. 
• The quality and quantity of the test data can have a significant impact on the model testing outcomes. 
  
  • T&M companies prefer test data that is demonstrative, varied, and relevant for a particular model domain and use case. Same data for training as well as model testing is not recommended, as this can lead to overfitting and poor generality.
• Collecting data from several sources and integrating and validating the same are the initial steps of AI systems testing, followed by algorithm testing. 
  
  • Algorithms used for data processing to derive understanding are the most important part of AI systems. Algorithm testing includes model validation, learnability, and algorithm efficiency - natural language processing, image processing, machine learning (ML), and deep learning. 
  • AI systems also need rigorous performance and security testing, which also include regulatory compliance. 
• Responsible AI model testing demands cooperation and communication between team members and other stakeholders.  
  
  • It is critical that the testing objectives, strategies, results, and feedback are shared with team members, clients, and users, and their input and suggestions are taken into consideration. 
  • By collaborating and communicating, a culture of learning, transparency, and trust is fostered. Also, it is ensured that the model testing aligns with the project vision and value. 
• With rapid developments in AI, including growing tool complexity, the market for AI testing tools is growing. Different AI testing types include functional testing, performance testing, usability testing, security testing, data evaluation, model evaluation, robustness validation, integration testing, and white box and black box testing. Dekra, Qualitest Group, TrustDevLab, Infosys, and PrimeQA solutions are a few examples of AI service providers in the T&M space.

The Impact of the Top 3 Strategic Imperatives on the Test & Measurement (T&M) for AI Industry

Comprehensive Testing Across Multiple Levels

Why:

• AI models require extensive testing at various levels - unit, integration, system, and acceptance - to ensure they function correctly in diverse scenarios.
• Continuous testing throughout the model development lifecycle helps identify and resolve issues early, aligning the model's performance with business requirements.

Analyst Perspective:

• A robust T&M approach is essential for AI-driven companies to maintain high-quality performance metrics. By implementing a multi-level testing strategy, organizations can enhance the reliability and effectiveness of their AI models, ultimately leading to better stakeholder satisfaction and trust in AI solutions.

Quality and Relevance of Test Data

Why:

• The quality and diversity of test data significantly influence the outcomes of AI model testing. Using the same data for both training and testing can lead to overfitting, which undermines the model's generalizability.
• T&M companies prioritize demonstrative and varied test data that is relevant to specific model domains and use cases.

Analyst Perspective:

• Investing in high-quality, relevant test data is crucial for successful AI model validation. Organizations that focus on diverse data sources will likely see improved model performance and adaptability, positioning themselves favorably in the competitive AI landscape.

Collaboration and Communication in Testing

Why:

• Effective AI model testing necessitates collaboration among team members and stakeholders to ensure that testing objectives and results are clearly communicated.
• Gathering input and feedback from various parties fosters a culture of learning and transparency, which is vital for aligning testing efforts with project goals.

Analyst Perspective:

• Promoting a collaborative environment enhances the testing process and ensures that AI models meet the expectations of all stakeholders. Organizations that prioritize communication in their T&M strategies will likely experience greater innovation and success in their AI initiatives, as they can adapt more readily to feedback and changing requirements.