AI Commoditisation: Business Threat or Opportunity?
Discover how AI commoditisation marks a pivotal shift in business, transforming accessibility and driving innovation in the digital era.
Key takeaways
- AI Commoditisation: The transformation of AI into a widely accessible and affordable commodity, similar to mass-market products, enabling businesses of all sizes to leverage AI technology.
- Drivers: Advances in technology, cloud computing, and open-source frameworks have made AI tools more accessible, fostering AI commoditization.
- Opportunities: AI commoditisation democratizes access to powerful AI capabilities, promoting competitive advantage and innovation across industries.
- Challenges: Quality control, scalability, ethical considerations, and market saturation pose significant challenges to businesses adopting commoditised AI solutions.
- Considerations: Businesses must evaluate the quality, reliability, and scalability of AI solutions, focusing on long-term growth and ethical use of data.
- Impact: While commoditised AI offers vast opportunities for digital transformation and competitive advantage, businesses must navigate potential risks to fully benefit.
The rise of AI
Humanity has consistently advanced thanks to several era-defining discoveries, ushering us into new worlds far different from what came before. Fire brought us out of the dark, and coal and oil enabled us to comfortably traverse continents, while electricity and radiowaves connected much of the world and formed new industries.
Then, the internet created a digital world out of our collective consciousness and the digital Big Bang lead us in an entirely new direction. This laid the foundation for perhaps one of humanity’s largest leaps forward from what came before – and our generation’s contribution – artificial intelligence (AI).
AI has rapidly advanced every industry on the planet and revolutionised the way we live, work, and connect with each other. It is progressing so fast, that we are now facing questions about how to control AI and ensure it works together with us and for the benefit of humanity.
But as AI technology becomes more widespread and accessible, a new phenomenon is emerging – the commoditisation of AI.
What is AI commodisation?
Like any service or product that can be replicated at scale, AI is becoming commoditised.
Commoditisation happens when a product or service is transformed into a standardised and readily available commodity. Go into any H&M and you can purchase a Nirvana or NASA shirt without ever having to go to a concert…or space. This is commoditisation – a socialist ethos (available for all) with a capitalist nuance (at a price).
But what does this mean for AI? Well, it simply means that AI technology and solutions are now becoming packaged into widely accessible and affordable offerings for businesses.
There are many types of AI designed to perform various functions according to their capabilities and functions. This means that not all types of AI can be commoditised in the same way. While some AI applications like machine learning (ML) algorithms for data analysis can be easily commoditised, others such as those that power neural networks or digital twins require more specialised and tailored solutions.
However, the most relevant case of AI commoditisation is undoubtedly Large Language Models (LLMs) such as ChatGPT. They use AI, natural language processing (NLP), machine learning (ML), and deep learning (DL) to generate human-like responses. Not long ago, these AI technologies were only used in labs and large corporations or by AI/IT specialists. Today, everyone can access them via a single tool, allowing anyone to learn or write anything in seconds (making copywriters everywhere weep in streams of sad vowels and consonants).
What is driving AI commoditisation?
Advancements in AI technology, like evolving algorithms, faster processing power, and data availability have played key roles in AI commoditisation, making AI tools and solutions more accessible and easier to implement.
Cloud computing has revolutionised the business world through digital transformation, making AI technologies and tools widely accessible to companies big and small via subscriptions. Now, most companies do not need big tech stacks or large data processing capabilities as they can simply outsource most of their IT requirements.
The expansion of open-source AI frameworks and libraries, together with the availability of cloud-based AI services, has made entry into the AI space far simpler for businesses. This accessibility allows organisations of all sizes to benefit from AI capabilities without needing extensive resources or technical expertise.
What businesses should look for when adopting AI commodities
For businesses searching for AI-as-a-Service providers or considering adopting cloud-based AI solutions, there are key considerations to take into account.
Quality and reliability
Not all AI solutions that have been commoditised perform the same so it’s important to evaluate their quality and reliability. Look at the accuracy, performance, and security of the actual AI. Additionally, researching your vendor’s reputation through customer reviews and independent assessments can give you a good idea about the calibre of developers working to create AI commodities.
Scalability
Scalability is paramount when adopting AI solutions. You need to find out if the AI solution is able to scale with your business needs and support growth in the future. You don’t want to have to continue purchasing different AI solutions to accommodate growth.
Benefits
AI commoditisation allows businesses of all shapes and sizes to leverage the far-reaching capabilities of AI. Like all as-a-service offerings, it democratises powerful technology at a far more affordable rate compared with bespoke AI development. It also encourages market competition, providing more access to AI than ever before while galvanising our global digital transformation.
Risks
Despite the many advantages, challenges associated with AI commoditisation do exist. Saturation of the market with similar offerings might lower the overall quality, which might result in the need for bespoke AI solutions (going against the purpose entirely). Additionally, with more entities leveraging AI solutions, quality control issues will emerge due to oversight being unable to handle the volume. Furthermore, ethical considerations regarding data usage and privacy are big challenges for businesses to navigate.
Summary
AI commoditisation presents opportunities but also challenges for businesses. Commoditised AI solutions provide businesses of all sizes with easy accessibility to powerful tools at affordable subscriptions. This packs the potential for a competitive advantage in the business world. However, issues surrounding quality, reliability, and scalability must be identified to thwart any obstruction to a business’s competitiveness.
Ultimately, AI commoditisation is a powerful opportunity to empower businesses to drive innovation and unlock new possibilities.
Mario Grunitz
Working Machines
An executive’s guide to AI and Intelligent Automation. Working Machines takes a look at how the renewed vigour for the development of Artificial Intelligence and Intelligent Automation technology has begun to change how businesses operate.
