Discover how AI commoditisation marks a pivotal shift in business, transforming accessibility and driving innovation while reshaping competitive landscapes in unprecedented ways.
Humanity consistently advances through era-defining discoveries, ushering us into transformative worlds far different from what came before. Fire brought us out of darkness, coal and oil enabled comfortable continental travel, while electricity and radio waves connected our world and spawned entirely new industries.
Then, the internet created a digital universe from our collective consciousness, and the digital revolution led us in entirely new directions. This laid the foundation for perhaps humanity’s most significant leap forward—and our generation’s defining contribution: artificial intelligence (AI).
AI has rapidly advanced every industry on the planet and revolutionised how we live, work, and connect with each other. The technology progresses so rapidly that we now face questions about controlling AI and ensuring it collaborates with humanity for collective benefit.
But as AI technology becomes increasingly widespread and accessible, a transformative phenomenon emerges: the commoditisation of AI.
Understanding AI commoditisation in 2025
Like any service or product that can be replicated at scale, AI is experiencing comprehensive commoditisation. Commoditisation transforms products or services into standardized, readily available commodities. Think of how you can walk into any retailer and purchase branded merchandise without attending the original events or experiences—this represents commoditisation’s essence: universal accessibility within market-driven frameworks.
The AI market is expected to grow by at least 120% year-over-year, with 63% of organisations intending to adopt AI globally within the next three years. This means AI technology and solutions are becoming packaged into widely accessible and affordable offerings for businesses of all sizes.
There are many types of AI designed to perform various functions according to their capabilities and applications. This means not all AI types can be commoditised identically. While some AI applications like machine learning algorithms for data analysis can be easily commoditised, others such as those powering neural networks or digital twins require more specialised and tailored solutions.
However, the most relevant case of AI commoditisation involves Large Language Models (LLMs) such as ChatGPT. These systems use AI, natural language processing (NLP), machine learning (ML), and deep learning (DL) to generate human-like responses. Previously, these technologies were confined to laboratories and large corporations. Today, everyone can access them through single platforms, enabling anyone to learn or create content within seconds.
Driving forces behind AI commoditisation
Technological advancements, cloud computing evolution, and open-source framework expansion have played pivotal roles in AI commoditisation, making AI tools more accessible and easier to implement across diverse business environments.
Cloud computing has revolutionised business operations through digital transformation, making AI technologies widely accessible to companies regardless of size via subscription models. Most organisations no longer need extensive tech stacks or large data processing capabilities as they can outsource IT requirements through cloud services.
The expansion of open-source AI frameworks and libraries, combined with cloud-based AI service availability, has simplified entry into the AI space significantly. This accessibility allows organizations of all scales to benefit from AI capabilities without requiring extensive resources or specialized technical expertise.
Revolutionary business opportunities in 2025
Enterprise transformation and competitive advantage
In enterprise software, AI is exposing opportunities for startups to disrupt some of the largest horizontal systems of record. For decades, systems like Salesforce, SAP, Oracle, and ServiceNow maintained strong positions due to deep product surfaces, implementation complexity, and centrality to business-critical data. Now, those competitive moats are degrading.
With AI’s ability to structure unstructured data and generate code on demand, migrating to new systems is faster, cheaper, and more feasible than ever. Agentic workflows are replacing routine data entry, and typical implementation projects that required armies of systems integrators are being accelerated by orders of magnitude.
Enhanced productivity and workforce transformation
AI is expected to improve employee productivity by 40%, while 60% of business owners believe AI will increase their productivity. The manufacturing sector will likely see the greatest benefit, with a projected gain of $3.8 trillion by 2035.
Rather than shrinking workforces, AI will welcome new team members: digital workers known as AI agents. These could easily double knowledge workforce capabilities in roles like sales and field support, transforming speed to market, customer interactions, and product design.
Accelerated innovation and development cycles
AI can help organisations iterate designs in hours rather than weeks, test solutions virtually before building prototypes, and troubleshoot problems before moving to production. Adopting AI in R&D can reduce time-to-market by 50% and lower costs by 30% in industries like automotive and aerospace.
Strategic considerations for AI adoption
Quality and reliability evaluation
For businesses exploring AI-as-a-Service providers or considering cloud-based AI solutions, several key considerations require careful attention. Not all commoditised AI solutions perform identically, making it essential to evaluate their quality and reliability thoroughly.
Assess the accuracy, performance, and security of AI systems while researching vendor reputations through customer reviews and independent assessments. This provides valuable insights into the caliber of developers creating AI commodities and their long-term viability.
Scalability and growth alignment
Scalability remains paramount when adopting AI solutions. Determine whether AI solutions can scale with business needs and support future growth. Avoid purchasing different AI solutions to accommodate expansion—seek comprehensive platforms that evolve with organisational requirements.
Data strategy and modernization
AI will “pay back” data modernisation investments when done correctly. Organizsations need enterprise-wide approaches to data, but don’t need to perfect everything simultaneously. Set priorities for which data architecture segments should provide value first, then focus on modernizing precisely the right data—no less, but also no more.
Navigating challenges and risks
Market saturation and quality control
Despite numerous advantages, challenges associated with AI commoditisation do exist. Market saturation with similar offerings might lower overall quality, potentially creating need for bespoke AI solutions that contradict commoditisation’s purpose.
Additionally, with more entities leveraging AI solutions, quality control issues emerge due to oversight capabilities struggling to handle increasing volumes. Organisations must prioritise “deepfake defense” as AI becomes more sophisticated, requiring investment in technologies and strategies to detect and mitigate synthetic media threats.
Ethical considerations and governance
Ethical considerations regarding data usage and privacy represent significant challenges for businesses to navigate responsibly. Leaders need to contend with external threats, including intellectual property infringement, AI-enabled malware, and internal threats arising from AI adoption processes.
Measuring success and avoiding automation excess
Apply operational, KPI-focused approaches to measure business-relevant metrics for AI such as new revenue, accelerated project delivery, productivity improvements, and enhanced experiences. However, ensure metrics don’t encourage excessive automation—human oversight and leadership of AI will always remain essential.
The transformative opportunity ahead
92% of companies plan to increase AI investments over the next three years, yet only 1% of leaders consider their companies “mature” in AI deployment. This presents unprecedented opportunities for organizations that can successfully navigate AI commoditisation.
AI commoditisation democratises access to powerful tools at affordable subscription rates compared to bespoke AI development. It encourages market competition, providing broader access to AI capabilities while accelerating global digital transformation initiatives.
The challenge lies in balancing accessibility with quality, ensuring that commoditised AI solutions provide genuine business value rather than superficial technological integration. Organizations must identify core value drivers and explore how AI can reimagine processes and rebuild experiences to realize substantial returns on investments.
Conclusion: Embracing AI’s transformative potential
AI commoditisation presents both opportunities and challenges for forward-thinking businesses. Commoditised AI solutions provide organizations of all sizes with unprecedented access to powerful tools through affordable, scalable platforms. This democratization packs potential for competitive advantage and meaningful innovation across industries.
However, success requires careful consideration of quality, reliability, scalability, and ethical implications. Organizations must navigate potential risks while fully capitalizing on AI’s transformative capabilities.
As we’re at the start of a once-in-a-generation shift from systems of record to systems of action, AI commoditisation represents a powerful opportunity to empower businesses to drive innovation, unlock new possibilities, and create sustainable competitive advantages in an increasingly AI-driven world.
The organisations that will thrive are those that view AI not merely as a cost-cutting tool, but as a collaborative partner in reimagining what’s possible, enhancing human potential, and creating value that serves both business objectives and broader societal progress.
