Embedding AI at Enterprise Scale for Creating Meaningful Value

//Embedding AI at Enterprise Scale for Creating Meaningful Value

Embedding AI at Enterprise Scale for Creating Meaningful Value

Scaling and employing Enterprise AI presents a colossal challenge in all spheres of business, whether finance, healthcare, government, retail or manufacturing. Enterprise AI can be pivotal to organizations’ success, given it can solve business problems in a manner not even possible in the past.

Many organizations have done proof of concepts, but the hard part is moving this into production and ensuring the solution is both scalable and repeatable.
The key to deploying a successful AI project lies in building a roadmap that addresses people, process, and platform. Integrating machine learning and deep learning methodology into organizations’ business core and turning it into a strategic asset that continues to grow exponentially in volume, variety, and complexity will offer you a significant competitive advantage.

 

The Journey into Building an AI Business Strategy

Making Enterprise AI models a reality requires a comprehensive understanding of every component of AI configuration. Fruitful AI business strategies encompass four pillars, which, together, provide a solid foundation for innovation based on cutting-edge science and a well-scoped AI project.

  • Widespread AI Expertise
  • Relevant Domain Expertise
  • Explainable, Auditable and Well-Suited Infrastructure
  • Resourceful Partners
A successful Enterprise AI strategy can be implemented at different corporate AI maturity stages of a business.
  • The Innovate & Inspire Stage: Determining how your AI project can bring value or meaningful science to your business
  • The Pilot & Prove Stage: Implementing an AI project to improve your business’ end goal
  • The Refine & Repeat Stage: Streamlining and refining towards obtaining a project that is more accurate, quicker, and less biased.

Enterprise-scale AI strategies thrive with the right end-to-end view, framework, system requirements and information architecture. But mostly, they rely on collaboration between resources with multiple skillsets brought together under a common grid to increase the value of AI and science.

  • The Data Scientist: Writes algorithms, devises AI models, conducts trials and proofs of concept.
  • Line of Business: Determines needs and makes a business case out of an AI strategy.
    Head of IT: Ensures the solution ties in well with existing infrastructure.

R2i Enterprise AI Co-Creation Workshops

At R2i, we believe in the strength that comes from working with partners and individuals with longstanding industry experience towards creating a well-tuned AI model from clean, refined data that will bring value to your organization.

R2i can provide you with the software, architecture, infrastructure, technical support, and cloud-based services needed for implementing a successful AI strategy at enterprise scale. Our hands-on approach to helping our clients combined with our recognized expertise, make us a trusted partner for leading your business to a path to enterprise AI.

Through our IBM/R2i co-creation labs, our clients gain valuable insight on how to understand their business challenges, optimize their solution through design thinking and define the framework of their solution. To this end, we invite you to watch our webinar “AI at Enterprise Scale: “From Science Project to Meaningful Science” which will enlighten you on the subject and show you the potential for creating meaningful value through AI.

Contact us today to learn more about how to build an Enterprise AI strategy for your business. Our team here at R2i will gladly show you the ropes and guide you through the steps towards transforming your enterprise.

To view or download the video of the Webinar, please complete the form below:



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2020-07-31T15:50:46-04:00 July 10th, 2020|Blog|
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