In an era marked by escalating economic challenges and environmental uncertainties, businesses are placing data-driven insights at the forefront of their strategies. To navigate these complexities and foster efficiency and resiliency, many companies have delved into the realm of advanced analytics and artificial intelligence (AI). However, despite the promise AI holds, the alarming statistic that between 83% and 92% of AI projects fail, as reported by Fortune magazine, casts a shadow of doubt over their successful implementation.

As the priorities for executives in 2023 continue to revolve around harnessing data's potential, it's crucial to address the underlying reasons for AI project failures and discover how to avoid becoming another statistic.

Challenges Leading to AI Project Failures

While organizations have been dedicated to assembling the right talent and software for their AI endeavors, the transition from conceptualization to tangible ROI remains a stumbling block. Inadequate foundational technologies often impede the shift from experimental AI to a production-ready solution. This lack of proper infrastructure is a common culprit cited for failed AI projects. Concerns about cost, complexity, and strategy, coupled with outdated legacy hardware and challenges tied to public cloud utilization, contribute to this predicament. Amid the excitement of AI software tools, the importance of selecting the right hardware has often been overlooked.

Foundational Considerations for AI Success

For AI to deliver substantial value, it must rest upon purpose-built infrastructure. Essential components include compute power, memory architecture, data processing, storage, and security. Neglecting these foundational elements hampers AI's potential to generate meaningful insights and impacts the project's overall success.

Data as the Driving Force

The success of AI initiatives hinges on data democratization – making data accessible across the organization, from staff to customers and suppliers. Effective management of data requires the right technologies and hardware, forming the backbone of a successful AI strategy.

A Focus on Outcomes

While analytics and AI hold the potential to unlock invaluable insights from various data sources, the key lies in successful deployment. Enterprises must conduct honest assessments of their capabilities to not only develop but effectively implement AI and analytics projects. Modernizing critical infrastructure and hardware is a prerequisite for supporting AI development and deployment across edge, data center, and cloud environments. This modernization journey not only enhances data security and accessibility but also aligns with future IT and business objectives.

Fast-Track Your Success with AI Incubator

Understanding the formidable resources required for transformative change, we present the AI Incubator –designed to assist businesses in implementing AI-powered solutions for their daily challenges. Made up of top industry experts from Siemens, NVIDIA, and NVIDIA Elite partners, we harness the potential of Artificial Intelligence to bring real solutions to market that are currently underserved by AI.

Our ecosystem partners bring a wealth of experience, demonstrated by:

• Elite pool of 50+ experts, architects and engineers (from Siemens, NVIDIA and others)
• Pre-vetting and validation of projects
• Maintaining IP ownership
• Minimizing risks (reputation, resources)
• Real learnings across all projects / use cases

Location: Based in the heart of Silicon Valley, the AI Incubator operates at the Plug & Play Tech Center in Sunnyvale, CA.

Ready? Apply now and be a part of shaping tomorrow's world or visit our website for more information! The deadline to apply is November 15th.

Warm regards, The AI Incubator Team