How organizations can tackle the cost barrier to AI adoption
4 min read
The promise of AI is intriguing, offering businesses the potential to unlock unprecedented efficiency, innovation and growth. Yet, for many, this potential remains frustratingly out of reach. The high cost of entry, driven by factors like unpredictable cloud pricing models and demanding compute requirements, is creating a significant barrier to adoption, particularly when it comes to powerful new tools like Generative AI.
The solution lies in democratizing access to AI – making this game-changing technology affordable, accessible and achievable for businesses of all sizes. By breaking down the cost barrier, businesses can empower a new wave of innovation and unlock the true potential of AI tools across every industry. Fortunately, we are already seeing signs of progress.
Breaking Down the Barriers: Commoditizing AI for All
Just as cloud computing transformed the technology landscape by making computing power widely accessible, AI is on a similar trajectory. The commoditization of AI, driven by factors like standardized models and accessible platforms, will be crucial in making it affordable and attainable for all types of businesses.
Crucially, this journey towards accessible AI will rely heavily on collaboration and partnership. By working together, businesses can pool resources, share expertise, and develop tailored AI solutions that address their specific needs while navigating the cost and efficiency challenges of high-compute workloads. This collaborative approach will be essential in ensuring that AI benefits everyone.
The potential applications of AI are vast, spanning every industry imaginable. As AI becomes more commoditized, we can expect to see a surge in innovation as businesses of all types discover how to leverage this technology to solve their unique challenges.
From Cloud to Edge: The Technologies Democratizing AI Access
One of the key technologies driving the democratization of AI is Edge computing. Edge computing brings AI capabilities closer to data sources, enabling real-time processing and decision-making in various industries. No-code/low-code AI platforms empower users with limited programming knowledge to build and deploy AI models without extensive coding, broadening the accessibility of AI development. AutoML tools automate model selection, training, and optimization, simplifying the AI development process for non-experts.
Additionally, federated learning allows AI models to be trained across decentralized edge devices, addressing privacy concerns and enabling wider participation in AI model training. These advancements are poised to expand access, simplify development, and facilitate the deployment of AI in diverse applications and environments.
Another area of democratizing innovation has been allowing some AI models to run on CPUs instead of GPUs. Large Language Models (LLMs) process massive datasets during training, and most of their computations during both training and inference involve matrix multiplications, typically performed in parallel. GPUs, with their thousands of cores, are inherently designed to support highly parallel computation much better compared with CPUs. This is one key reason GPUs are far more suitable for running LLMs as compared with CPUs. In addition, the high memory bandwidth of GPUs is better suited to shuttling the many intermediate data points involved in LLM calculations between memory and processing units.
However, with recent improvements such as quantization, State Space Models, and frameworks like MLX which use unified memory, we are starting to make it possible to run SLMs (Small Language Models) or some quantized LLMs on CPUs. This is another clear example of technologists understanding more about how to harness AI’s capabilities and apply technology in a practical way
A Future Powered by AI: Unlocking Potential Across Industries and Society
The trajectory of AI suggests that it may be poised to redefine the fabric of not only underlying technology, but also the process of how we work, collaborate, and even communicate. This profound shift will foster innovation, economic growth, and societal advancement. The key will be to ensure this shift is completed ethically and safely so that it remains a positive and not a negative transition.
To foster wider AI adoption, organizations need to address these multifaceted barriers through a holistic approach that encompasses technical, organizational, ethical, and regulatory considerations. But before we reach there it is important to address the challenges that are hindering AI adoption. The prevalent skills shortage will be a huge challenge for AI adoption. From a senior leadership point of view, the skills shortage would make AI implementation harder as no one can help handle the complexity of integrating AI solutions with existing systems and workflow.
As a first step to remedying this, there is the need to upskill workers so that there is limited resistance to change within organizations and so that employees with the correct skills can work alongside AI systems. This would alleviate concerns around data privacy and security concerns of the AI systems as there will be a trust from all workers on AI systems.
I’m confident that today’s generation of leaders are up to the challenge of democratizing AI. The signs of progress are everywhere, from the rise of edge computing to the ability to run AI models on CPUs, demonstrating a commitment to making AI more accessible and affordable. This drive towards democratization will unleash a wave of innovation across every sector, reshaping not just our technology but also how we work and connect.
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