Generative AI

Why Companies Often Stumble in Their AI Launch: A Deep Dive

By September 14, 2023December 13th, 2023No Comments

In the decades following Alan Turing’s landmark paper on Computing Machinery and Intelligence, the promise of Artificial Intelligence (AI) has evolved from pure theory to an omnipresent technology. AI now influences a spectrum of industries, reshaping how we consume entertainment, diagnose critical illnesses, and drive business innovation. 

However, beneath these promising advancements lies a sobering reality: many AI initiatives stumble before making a real-world impact. Here, we explore why, despite its potential, many companies face challenges when diving into AI.

The Hurdles to Effective AI Deployment

The Great Expectation vs. Reality Gap

While AI’s potential applications span an impressive range, achieving these results often proves elusive. Recent data indicates a troubling rate of failed AI projects; some studies even suggest that a mere 12% of AI initiatives successfully transition from pilot stages to full-scale production. This disparity begs the question: What is causing these hiccups?

 Root Causes of AI Failures

  • Reproducibility Issues: Often, an initial AI solution might display outstanding performance, but replicating its success proves challenging.
  • Team Misalignment: A disconnect between Data Science and MLOps teams can hamper the full potential of AI models.
  • Scaling Struggles: To make a meaningful impact, AI needs to operate at a vast scale, which many organizations struggle to achieve due to various constraints.

Enter the realm of Applied AI, which seeks to bridge these gaps.

 What Exactly is Applied AI?

Defined, Applied AI emphasizes the tools and strategies required to move AI from a mere experiment to a critical production asset. It stresses not only the creation and launch of AI models but also the importance of obtaining tangible, real-world outcomes. The industry needs an Application-Level AI Technology Platform that a) provides a tightly integrated technology stack and b) enables an iterative deployment and discovery of the AI experience to realize the ROI.

A common misconception is that AI predominantly revolves around programming. In reality, AI encompasses an intricate ecosystem of tools, processes, and infrastructure components.

 Critical Components for AI Success

 1. Data Management: The Lifeblood of AI

  • Data Warehousing: Efficient data storage solutions, like Hadoop, can cope with AI’s rigorous data demands.
  •  Understanding Data: A comprehensive data catalog aids in the comprehension and utilization of available data.
  • Ensuring Data Quality: Maintaining data accuracy from inception to production is non-negotiable.
  •  Optimized Data Pipelines: The foundation for data flow and processing must be robust and fine-tuned.

 2. Networking for AI: Beyond Traditional Boundaries

  • Deep learning solutions, at their core, rely on effective communication protocols. As AI models handle vast amounts of information, conventional networking solutions often fall short. An AI-ready network requires a revamped infrastructure, emphasizing performance, scalability, and real-time data transmission.

 3. Efficient AI Data Processing & Training

  • Harnessing the Power of GPUs: AI models, especially deep learning, need significant computational resources. Graphics processing units (GPUs), with their parallel processing capabilities, are the go-to choice for many enterprises.

 4. Functionality: The Technical Backbone

  • Model Handling: This involves storing, evaluating, and updating various AI models efficiently.
  • Feature Engineering: Creating new, impactful data features can drastically improve model performance.
  • Model Evaluation: Companies need strategies for comparing different AI models, ensuring only the best is in play.

 5. Governance: AI’s Guiding Principles

  • Access & Control: Ensuring only authorized individuals modify AI models can mitigate potential risks.
  • Change Management: Effective version control systems are indispensable in the dynamic world of AI.

 6. Continuous Monitoring

  • Performance Tracking: As AI models can degrade over time, real-time monitoring is a must to identify and rectify issues promptly.

Applied AI in Action: Industry Pioneers

Several forward-thinking enterprises are already leveraging Applied AI to reshape their business landscapes.

Target’s Insightful Innovations

By consolidating data from diverse sources, Target has been harnessing AI’s power to offer a more personalized shopping experience, predicting significant life events that may influence consumers’ purchasing patterns.

Starbucks’ Deep Brew Revolution

Starbucks’ AI journey, termed “Deep Brew,” aims to revolutionize every business facet. By integrating comprehensive data streams, Starbucks is pioneering initiatives ranging from personalized recommendations to predictive maintenance of their coffee machines.

Facebook’s AI Mastery

Facebook, with its colossal user base, is pushing the AI envelope. They are leveraging AI for diverse tasks, from content moderation to targeted advertising. Their advanced AI strategy encompasses areas like computer vision, multilingual technologies, and VR experiences.

Wrapping Up

While the AI realm holds immense promise, the journey to successful AI implementation is fraught with challenges. Organizations must recognize these hurdles and, with the help of Applied AI, craft a strategic approach that ensures AI’s transformative potential is fully realized.

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