21 Aug
2018

Five Pillars of a Successful AI-based Transformation

The innovations resulting from the arrival of artificial intelligence (AI) are within reach for many companies. When it comes to using AI and IIoT, consider the five pillars to make a successful AI-based transition and adopt leaner and more agile work practices.

Artificial Intelligence
Industry 4.0
Lean Manufacturing
IIoT
Les cinq piliers d'une transformation réussie basée sur l'IA

The innovations resulting from the arrival of artificial intelligence (AI) are within reach for many companies. When it comes to using AI and the Industrial Internet of Things (IIoT,) Senior Vice President, Global Solutions & Innovation at Hitachi Consulting Philip Townsend says these things need to “work together securely at commercial or industrial scale to create business value.” In a report he wrote with Vice President Organization Effectiveness at Hitachi Consulting Susan Anderson and Hitachi Fellow Dr. Kazuo Yano, they discuss the “Five pillars of a successful AI-based transformation,” and more with a view to helping your manufacturing plant more lean and agile with AI and IIoT.

Here are the five pillars of a successful AI-based transformation according to Townsend, Anderson, and Yano.  

 

  1. Demonstrate effective digital leadership: Leaders wanting to create new value streams from AI need to be collaborative with  groups like R&D and IT, but also recognize that the digital vision ultimately cascades from the  top down through the rest of the organization.
     
  2. Enhance customer engagement: In practical terms, this means putting the customer at the center of the AI vision and building the capabilities required to move ever closer to customers, end-users, suppliers  and investors.
     
  3. Improve the operational environment. This pillar is foundational because AI and other digital capabilities enable organizations  to act faster, smarter and more cohesively, with unprecedented levels of clarity and precision.
     
  4. Evolve the core architecture: An essential component of digital transformation using AI is evolving the  core architecture. Streamlined, secure and robust IT drives the digital enterprise, creating a more  responsive digital core that provides intuitive access to business data and apps that enable greater efficiency and effectiveness.  

    Source + read the complete report.

 

Want to learn more?
Download the ebook
Related blog articles

Articles connexes

Retour au blog
Nous vous remercions ! Votre demande a bien été reçue !
Oups ! Un problème s'est produit lors de l'envoi du formulaire.
3
Août 2023

Adopter l'IIoT: la voie intelligente pour permettre aux fabricants d’équipement d’origine de prospérer grâce à un partenaire expert en technologies d’usine intelligente IIoT

French
3
Août 2023

Embracing IIoT: The Smart Path for OEMs to Thrive with a Leading IIoT Smart Manufacturing Partner

English
21
Juillet 2023

CDAP: $15,000 Grant To Jump Start Canadian Manufacturing Digital Transformation Projects

English

Articles connexes

Retour au blog
Nous vous remercions ! Votre demande a bien été reçue !
Oups ! Un problème s'est produit lors de l'envoi du formulaire.
29
Oct. 2021

Your Factory's Digital Manufacturing Roadmap: A 5+ Item Checklist!

Learn how to achieve your digital manufacturing goals by implementing an effective roadmap your team can easily follow.

English
4
Juillet 2018

The 3 Central Tasks of Data-Driven Transformation

BCG's cost-effective and evidently successful three-step approach addresses a timeline for digital transformation in companies across sectors. The approach begins with pilot projects of rapid digitization to a final data governance system leading to a full-fledged digital transformation.

English
31
Juillet 2018

3 Ways Monitoring & Analytics Maximizes Operations Performance

With a variety of conditions that have led to an increase in the cost of raw materials, in the cost of accessing raw materials and to a decrease in growth, manufacturers are looking for new solutions to maximize operations performance. Enter manufacturing monitoring and analytics

English