Enterprise Ai Solution

Enterprise Ai Opportunities and Challenges

The next generation of digital assets for enterprises are core Ai capabilities that will make them an Ai-fueled organization. Going beyond a handful of Ai applications will need a digital transformation for enterprises. To deliver Ai capabilities into their products, services, and workflows, including to their partners, enterprises must consider Ai applications very much like content.

Enterprises seek a comprehensive Ai platform to integrate pragmatic Ai into mission-critical enterprise processes more rapidly and cost-effectively, without cloud vendor lock-in, to lower the cost per insight. Zeblok's Ai-MicroCloud^® provides a turnkey, cloud-native environment, deployed to their existing infrastructure, supporting a hybrid cloud strategy, enabling them to easily create their own Ai ecosystem, Ai assets, and Ai-AppStore.

But to develop this digital transformation Ai ecosystem there are challenges to be solved in three broad categories:

Data Comprehension

According to Gartner, 85% of Ai projects fail. We believe there is a data comprehension gap. Additionally, Ai modeling environment needs to handle High-Performance Computing natively to deal with very large datasets needed for model training and optimization.

Lowering the cost per Insight

Enterprises must confront the complexity of integrating multiple independent Ai software vendors (ISVs) and internally developed Ai capabilities. There is a price-to-performance gap and a digital asset curation gap (think AppStore). Additionally, there is latent demand for profiling existing Ai inference engines and optimizing existing Ai models to lower the cost of ownership.

User Experience

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform. Software developers prefer to use existing development studio environments and prefer to integrate via SDK. API-driven approach to integrating with any existing analytics platform and delivering Ai capabilities as a service is a key requirement.

Diversity & Ecosystem Challenges

Diverse ISVs

According to Gartner, 85% of Ai projects fail. We believe there is a data comprehension gap. Additionally, Ai modeling environment needs to handle High-Performance Computing natively to deal with very large datasets needed for model training and optimization.

Diverse Hardware

Enterprises must confront the complexity of integrating multiple independent Ai software vendors (ISVs) and internally developed Ai capabilities.

Diverse Cloud

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform. Software developers.

Complexity

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform.

Operational Challenges

Workflow

According to Gartner, 85% of Ai projects fail. We believe there is a data comprehension gap. Additionally, Ai modeling environment needs to handle High-Performance Computing natively to deal with very large datasets needed for model training and optimization. `

Roles

Enterprises must confront the complexity of integrating multiple independent Ai software vendors (ISVs) and internally developed Ai capabilities.

ML Ops

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform. Software developers.

Continuous Evolution

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform.

Technical Challenges

Orchestration

According to Gartner, 85% of Ai projects fail. We believe there is a data comprehension gap. Additionally, Ai modeling environment needs to handle High-Performance Computing natively to deal with very large datasets needed for model training and optimization.

Insight Quality

Enterprises must confront the complexity of integrating multiple independent Ai software vendors (ISVs) and internally developed Ai capabilities.

Optimization

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform. Software developers.

Heterogenous Architecture

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform.