aster.cloud aster.cloud
  • /
  • Platforms
    • Public Cloud
    • On-Premise
    • Hybrid Cloud
    • Data
  • Architecture
    • Design
    • Solutions
    • Enterprise
  • Engineering
    • Automation
    • Software Engineering
    • Project Management
    • DevOps
  • Programming
    • Learning
  • Tools
  • About
  • /
  • Platforms
    • Public Cloud
    • On-Premise
    • Hybrid Cloud
    • Data
  • Architecture
    • Design
    • Solutions
    • Enterprise
  • Engineering
    • Automation
    • Software Engineering
    • Project Management
    • DevOps
  • Programming
    • Learning
  • Tools
  • About
aster.cloud aster.cloud
  • /
  • Platforms
    • Public Cloud
    • On-Premise
    • Hybrid Cloud
    • Data
  • Architecture
    • Design
    • Solutions
    • Enterprise
  • Engineering
    • Automation
    • Software Engineering
    • Project Management
    • DevOps
  • Programming
    • Learning
  • Tools
  • About
  • Technology

Mastering AI Quality For Successful Adoption Of AI In Manufacturing

  • aster.cloud
  • January 29, 2024
  • 5 minute read

Managing AI quality enables industrial organizations to increase their productivity and sustainability.

Image: iStockphoto.


Partner with aster.cloud
for your next big idea.
Let us know here.



From our partners:

CITI.IO :: Business. Institutions. Society. Global Political Economy.
CYBERPOGO.COM :: For the Arts, Sciences, and Technology.
DADAHACKS.COM :: Parenting For The Rest Of Us.
ZEDISTA.COM :: Entertainment. Sports. Culture. Escape.
TAKUMAKU.COM :: For The Hearth And Home.
ASTER.CLOUD :: From The Cloud And Beyond.
LIWAIWAI.COM :: Intelligence, Inside and Outside.
GLOBALCLOUDPLATFORMS.COM :: For The World's Computing Needs.
FIREGULAMAN.COM :: For The Fire In The Belly Of The Coder.
ASTERCASTER.COM :: Supra Astra. Beyond The Stars.
BARTDAY.COM :: Prosperity For Everyone.

  • AI is expected to transform manufacturing and supply chains, making it critical for businesses to stay competitive.
  • Concerns about security, data protection, and regulatory uncertainties present serious challenges to AI adoption.
  • Managing the quality of AI systems offers a systematic approach to evaluating risks and determining whether an AI system satisfies key requirements throughout its life cycle.

Artificial intelligence (AI) has emerged as the cornerstone of a reimagined manufacturing landscape, with 89% of executives across industries regarding AI as essential to achieve their growth objectives and aiming to implement it in their operations. Yet, concerns about security, data protection, regulatory compliance or performance issues present serious challenges to AI adoption in manufacturing and supply chains. AI also comes with intrinsic risks that may be exacerbated if not carefully considered. The lack of standardised processes to qualify AI products and assess risks when integrating and operating AI solutions results in a complex and often unclear path.

Effectively managing the quality of AI systems enables industrial organizations to overcome these challenges and harness the full potential of AI for greater productivity, flexibility, sustainability and workforce engagement while mitigating the associated risks.

What is AI quality and why is it important?

Quality has always been a key aspect of industrial operations. Quality assurance, essential in every stage of a manufactured product’s life cycle, parallels AI quality, which is a systematic approach to evaluating the degree to which an AI system meets specific requirements throughout its life cycle.

In assessing the quality of an AI system, six critical pillars are considered:

  • Safety: focusing on potential harm to people or property.
  • Security: evaluating cybersecurity risks and AI-specific threats.
  • Legal: ensuring compliance with regulations and contractual obligations.
  • Ethics: aligning the system with the company’s values and ethical principles affecting stakeholders.
  • Performance: confirming the system’s effectiveness and accuracy.
  • Sustainability: examining whether its development and operations have been conducted with environmental considerations.
Read More  AMD And Qualcomm Collaborate To Optimize FastConnect Connectivity Solutions For AMD Ryzen Processors

Collectively, these elements determine the AI system’s overall suitability and impact.

Six AI quality pillars to be considered throughout the AI system life cycle and data life cycle, taking sector specific requirements, industry best practices, and all applicable standards and regulations into account. Image: TÜV SÜD

AI quality in manufacturing: where do we currently stand?

To understand the most common quality gaps when implementing AI in manufacturing, and collectively identifying solutions to address them for long-term successful, responsible, and sustainable outcomes, the World Economic Forum, in collaboration with TÜV SÜD, conducted an in-depth analysis of AI quality as part of the AI-Powered Industrial Operations Initiative. TÜV SÜD’s AI quality framework was leveraged to conduct an “AI Quality Readiness Analysis” – consisting of a risk analysis and a maturity profiling – for five manufacturing use cases. After identifying the relevant quality pillars, 60 risk characteristics were analysed, out of which 21 were highlighted based on their frequency of occurrence and maximum level of risk observed.

Over 60 risk characteristics were analysed for each of the 5 use cases. 21 points of interest were highlighted based on frequency of occurrence and maximum level of risk. Image: TÜV SÜD

The assessments revealed that the highest percentage of risks were in the performance and security pillars. Risk criteria such as functional suitability, maintainability, and confidentiality were highlighted. Each risk characteristic represents a quality consideration that – if not met – introduces risk in the application of the AI system. For instance, ensuring functional suitability is paramount to guaranteeing that AI systems meet their intended objectives and perform effectively within their designated contexts. This is not only key for achieving reliable and accurate outcomes, but also for avoiding unintended consequences and maintaining user trust.

The security pillar reported comparatively higher levels of risk due to the sensitivity of the data processed by some of the assessed use cases. However, residual risks across all pillars were found to be generally well-managed, with effective risk mitigation strategies – such as robust security measures, proactive monitoring, and a culture of security awareness – bringing the maximum risk down to an acceptable level.

Read More  Claude On Amazon Bedrock Now Available To Every AWS Customer

18 maturity dimensions were assessed, revealing gaps in 12 dimensions. Image: TÜV SÜD

The aggregated results of the maturity profiling indicate well-managed organizational maturity across the assessed use cases. High maturity levels were reported in dimensions such as compliance and strategy, as well as in cybersecurity, with organizations implementing robust policies and processes for managing AI-related cybersecurity threats.

Some maturity gaps were identified in the oversight and process management dimensions. Organizations displayed a tendency to overlook certain aspects of the AI life cycle, particularly in outlining a robust decommissioning plan. Testing approaches did not always account for AI-specific risks, such as adversarial attacks. While several testing tools were adopted across the assessed use cases, these tools were not always systematically employed. Adopting a structured approach towards testing and controls is necessary for ensuring the overall reliability and robustness of AI applications and minimising the impact of any AI failures.

Consultation with our broader community of industry and technology experts led to the elicitation of best practice solutions to address the identified maturity gaps. Key considerations include upskilling talent through training and development programmes, aligning business strategy with technical products, and implementing a robust failure management system.

The community also highlighted the importance of viewing AI quality as a continuous process rather than a one-off assessment. Routine evaluations of AI systems, their associated risks and risk mitigation strategies, as well as ongoing documentation and updates, are essential to ensuring the quality of AI systems and are to be operationalised as part of a robust quality management system.

The way forward

To successfully capture the full potential from AI in manufacturing in driving productivity, agility, sustainability and workforce augmentation, and achieve long-lasting results, it is essential for organizations to recognise that AI solutions, distinct from conventional automation systems, come with their own set of inherent risks that require systematic management.

Read More  AWS Expands Access To Free Cloud Skills Training On Its Mission To Educate 29 Million People By 2025

A robust approach for identifying and evaluating these risks in the development and deployment of AI systems enables manufacturers to effectively integrate AI into their industrial processes, for a smooth and efficient implementation and operation. This not only facilitates AI adoption and compliance with regulations but also cultivates trust in AI technology. Furthermore, ongoing assessment and adaptation helps organizations remain agile and responsive to new opportunities and challenges in this rapidly evolving field.

With a holistic AI quality strategy, AI’s transformative potential can be responsibly and effectively leveraged, unlocking the next wave of value for businesses, workers, society, and the environment.

By: Kyriakos Triantafyllidis (Head of Growth and Strategy, Centre for Advanced Manufacturing and Supply Chains, World Economic Forum) and Andreas Hauser
(CEO Digital Service, TÜV SÜD)
Originally published at: World Economic Forum


For enquiries, product placements, sponsorships, and collaborations, connect with us at [email protected]. We'd love to hear from you!

Our humans need coffee too! Your support is highly appreciated, thank you!

aster.cloud

Related Topics
  • Advance Manufacturing
  • AI
  • Artificial Intelligence
  • Davos Agenda
You May Also Like
View Post
  • Computing
  • Multi-Cloud
  • Technology

Reliance on US tech providers is making IT leaders skittish

  • May 28, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

Examine the 4 types of edge computing, with examples

  • May 28, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

AI and private cloud: 2 lessons from Dell Tech World 2025

  • May 28, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

TD Synnex named as UK distributor for Cohesity

  • May 28, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

Broadcom’s ‘harsh’ VMware contracts are costing customers up to 1,500% more

  • May 28, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

Weigh these 6 enterprise advantages of storage as a service

  • May 28, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

Pulsant targets partner diversity with new IaaS solution

  • May 23, 2025
View Post
  • Computing
  • Multi-Cloud
  • Technology

Growing AI workloads are causing hybrid cloud headaches

  • May 23, 2025

Stay Connected!
LATEST
  • 1
    Just make it scale: An Aurora DSQL story
    • May 29, 2025
  • 2
    Reliance on US tech providers is making IT leaders skittish
    • May 28, 2025
  • Examine the 4 types of edge computing, with examples
    • May 28, 2025
  • AI and private cloud: 2 lessons from Dell Tech World 2025
    • May 28, 2025
  • 5
    TD Synnex named as UK distributor for Cohesity
    • May 28, 2025
  • Weigh these 6 enterprise advantages of storage as a service
    • May 28, 2025
  • 7
    Broadcom’s ‘harsh’ VMware contracts are costing customers up to 1,500% more
    • May 28, 2025
  • 8
    Pulsant targets partner diversity with new IaaS solution
    • May 23, 2025
  • 9
    Growing AI workloads are causing hybrid cloud headaches
    • May 23, 2025
  • Gemma 3n 10
    Announcing Gemma 3n preview: powerful, efficient, mobile-first AI
    • May 22, 2025
about
Hello World!

We are aster.cloud. We’re created by programmers for programmers.

Our site aims to provide guides, programming tips, reviews, and interesting materials for tech people and those who want to learn in general.

We would like to hear from you.

If you have any feedback, enquiries, or sponsorship request, kindly reach out to us at:

[email protected]
Most Popular
  • Understand how Windows Server 2025 PAYG licensing works
    • May 20, 2025
  • By the numbers: How upskilling fills the IT skills gap
    • May 21, 2025
  • 3
    Cloud adoption isn’t all it’s cut out to be as enterprises report growing dissatisfaction
    • May 15, 2025
  • 4
    Hybrid cloud is complicated – Red Hat’s new AI assistant wants to solve that
    • May 20, 2025
  • 5
    Google is getting serious on cloud sovereignty
    • May 22, 2025
  • /
  • Technology
  • Tools
  • About
  • Contact Us

Input your search keywords and press Enter.