AI Adoption: Overcoming Industry Resistance and Driving Acceptance

Gen AI is set to revolutionize industries by enhancing productivity and streamlining operations. This article will discuss the pathways enterprises can use to adopt AI.

DQC Bureau
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AI Adoption Overcoming Industry Resistance and Driving Acceptance

AI Adoption

Artificial intelligence (AI), specifically Generative AI, is poised to transform various industries in the coming decade. Leveraging AI helps businesses improve productivity, streamline customer service, scale their product R&D, and more.


According to PwC, AI is expected to improve productivity by 40% by 2035. PwC also estimates that AI could contribute up to $15.7 trillion to the global economy by 2030. McKinsey's simulation shows that around 70% of companies may have adopted at least one type of AI technology by 2030. 

Yet, enterprises state several barriers to AI adoption, many of which arise from misunderstandings of the technology. This article will discuss the pathways enterprises can use to adopt AI and explain why the risk of not adopting AI is far greater than the challenges it poses during adoption.

Understanding the Latest AI Models


The first step to adopting AI for enterprise use cases involves understanding the latest AI models that are emerging, and the route to training, deploying, and inferring with them. The cutting-edge AI models in the current landscape are all built on deep learning architectures.

 Recent large language models (LLMs), such as variants of Llama-3 or Mistral, and various fine-tuned versions of these models, have set new benchmarks in natural language processing and language generation tasks. Similarly, image generation models like those from Stable Diffusion, and models like Music Gen or Audio Gen, offer excellent capabilities for media generation.

These models have demonstrated remarkable capabilities in understanding and generating human-like text, recognizing and classifying images, and creating realistic synthetic media. Variants of Yolo models, on the other hand, help build powerful object detection and computer vision applications.


Enterprises can leverage these models to automate and enhance processes. For instance, customer service departments can deploy LLMs with vector stores to build sophisticated chatbots that efficiently handle inquiries and reduce human agents' workload. Similarly, R&D teams can use computer vision models for quality control in manufacturing, ensuring higher accuracy.

The first step to AI adoption, therefore, is to track and understand the latest generation of AI models that are emerging. One of the best platforms to discover and explore these models is Hugging Face or via open-source applications released by developers on GitHub.

Training AI Models


To fully leverage the capabilities of AI models, enterprises need to train and customize them for their specific needs. While training AI models requires advanced GPUs and high-quality data, there are advanced platforms now easily accessible to enterprise developers that allow them to do so without any code.

An example of such a platform is Tir on E2E Networks, where developers can easily pick a base model, provide the training data, select the GPU to be used, and perform the training task, all without any programming effort.

The key fact to keep in mind before training AI models is the availability of high-quality data. Data is the cornerstone of effective AI training. Enterprises must invest in robust data collection and preprocessing pipelines to ensure their models are trained on accurate and relevant data. High-quality data leads to better model performance, while poor data quality can result in inaccurate outcomes.


Deploying AI Models

Once trained, AI models can be deployed in a production environment. This step involves integrating the models into existing systems and ensuring they operate efficiently under real-world conditions. AI-focused platforms now offer easy ways to deploy a trained AI model. Alternatively, developers can explore container technologies, such as Docker. Kubernetes, an orchestration tool, can then be used to manage these containers.

Cloud platforms, especially AI-focused ones, play a crucial role in deploying AI models at scale. Developers should use advanced GPU nodes, such as those using H100 or even HGX H100 or 8xH100, specifically designed for AI workloads. These services enable enterprises to scale their AI infrastructure on demand, ensuring they can handle varying loads without significant upfront investment in hardware.


Integration and Building Enterprise Applications

Integrating AI models involves embedding the AI model into the enterprise's existing workflows and systems. This can include integrating AI-driven insights into business intelligence platforms, CRM systems, building coding assistants, AI analysts, or other operational tools. Seamless integration ensures that AI models provide actionable insights and support decision-making processes without disrupting existing operations. Continuous monitoring and management of AI models in production are also crucial to ensure they maintain accuracy and reliability over time.

Gaining Competitive Edge


The risks of ignoring AI adoption are substantial. Enterprises that fail to embrace AI risk falling behind competitors who leverage AI for innovation and operational improvements. As more companies integrate AI to innovate and enhance their operations, those that lag in adoption may find it increasingly difficult to compete effectively.

AI enables businesses to optimize processes, improve product offerings, and provide superior customer experiences. Without AI, enterprises risk falling behind in innovation and efficiency. In the long term, these inefficiencies and missed opportunities can erode an enterprise's market position and profitability, making the challenge of adopting AI far outweighed by the risk of not doing so.

AI, on the other hand, can drive innovations that unlock new business models, improve product features, and personalize customer experiences, providing a competitive edge in the market. AI-powered tools such as chatbots and virtual assistants provide instant customer support, improving satisfaction and loyalty.

AI can automate routine tasks, freeing up human workers for more complex and creative activities. Most interestingly, as we are witnessing in domains like drug discovery or material science, Generative AI is helping identify promising compounds and materials, which otherwise would have taken much longer.

As we step into the AI decade, the pace of innovation driven by AI will accelerate. Enterprises need to approach this as an opportunity to gain an edge over competitors by leveraging the powerful capabilities the current generation of AI models unlocks. Ultimately, the risk of ignoring AI is too high, and hurdles in adoption are simplified once they start building.

By - Tarun Dua, Founder, E2E Networks Ltd – Cloud Computing Platform

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