Porter’s Five Forces model, developed by Michael E. Porter in 1979, remains a cornerstone for analysing the competitive forces that shape industries and strategies within organisations. This framework provides a structured lens through which to examine not just the field of artificial intelligence (AI) and machine learning (ML) but the broader spectrum of modern organisational challenges and opportunities.
This post focuses on how organisations across various industries can use Porter’s Five Forces, alongside AI and machine learning, to navigate the complexities and challenges of the contemporary business landscape.
Porter’s Five Forces Model (PFFM)
Porter’s model lists five ‘forces’ that drive competitive pressure within an industry. These are:
- Competitive Rivalry
- Supplier Power
- Buyer Power
- Threat of Substitution
- Threat of New Entry
Organisations naturally tend to focus on their direct competitors. The result is that these entities can be blindsided by less obvious threats. By using PFFM, organisations take a broad perspective on the competitive landscape.
Competitive Rivalry
This is the force of direct competition. In this phase, an organisation considers the following:
- How many competitors there are (and who they are)
- The quality of their competing products
In a market which contains many competitors, organisations typically compete by lowering prices. Organisations can also compete by providing a product that is far superior to those of competitors – although this can be extremely challenging.
Impact of AI on Competitive Rivalry:
- AI-driven industries, including generative AI and NLP, are marked by intense competition, driven by rapid technological advancements and a plethora of active players ranging from tech giants to smaller startups. This rivalry shapes the strategic decisions organisations must make to sustain their competitive position.
- Generative AI and NLP: Competition in generative AI and NLP is particularly fierce, with major players like OpenAI, Google, Microsoft, and Facebook continuously pushing the envelope. To stay competitive, organisations must invest in cutting-edge research and forge strategic partnerships.
- Product Development and Prompt Engineering: In the realm of product development, prompt engineering has emerged as a crucial skill. Crafting effective prompts can significantly enhance the performance of LLMs in specific tasks, offering a competitive advantage.
- Agentic Workflows: The integration of AI into agentic workflows—where AI agents autonomously perform complex tasks—can revolutionise productivity and operational efficiency.
Supplier Power
The greater the number of actual or potential suppliers an organisation has, the more power it has over individual suppliers, and the better it is able to secure relevant products or services. The inverse situation applies if there are few suppliers.
Impact of AI on Supplier Power:
- In the AI and ML industry, suppliers primarily consist of data providers, cloud computing services, and specialised hardware manufacturers. The bargaining power of these suppliers can significantly impact an organisation’s operational efficiency and cost structure.
- Cloud Computing and Hardware: Cloud service providers like AWS, Google Cloud, and Microsoft Azure offer scalable computing resources essential for training and deploying AI models. Additionally, specialised hardware suppliers, such as NVIDIA for GPUs, hold significant influence.
- Data Providers: Access to quality data is paramount for training effective AI models. Organisations can mitigate this power by developing in-house data collection capabilities or leveraging synthetic data generation techniques to supplement their datasets.
Buyer Power
Buyer power measures the options an organisation’s customers have in the market. If customers only have a few options from which to procure a product or service, then buyers have less “buyer power”. If there are many suppliers, then the buyers have greater “buyer power.” The organisation’s power is inversely proportional to the buyer’s power.
Impact of AI on Buyer Power:
- Buyers in various industries now expect high-quality, reliable, and customisable AI solutions. Large enterprises possess significant bargaining power due to their financial resources and ability to negotiate favourable terms.
- End consumers’ power rises as awareness and understanding of AI technologies grow. Consumers increasingly expect transparency, privacy, and ethical considerations in AI products.
- Regulatory Bodies: Regulatory authorities impose standards and compliance requirements that organisations must meet. The growing focus on ethical AI and data privacy regulations has heightened the power of regulatory bodies.
Threat of Substitution
The threat of substitution refers to the ability of an organisation’s customers to find a substitute for its services or products. Here, a substitute refers to a different way of achieving the same – or a superior – outcome.
Impact of AI on the Threat of Substitution:
- Traditional Software Solutions: Traditional software solutions can sometimes substitute for AI-based applications, particularly if they offer the required functionality at a lower cost.
- Emerging Technologies: New technological advancements, such as quantum computing and edge AI, could potentially substitute existing AI and ML solutions.
- Retrieval Augmented Generation (RAG): This hybrid approach can outperform purely generative models in specific applications, necessitating continuous innovation and adaptation by organisations.
Threat of New Entry
This is a measure of the barrier to entry for new players (competitors) in the market. A low barrier to entry is bad for an incumbent, and a high barrier to entry is good for an incumbent.
Impact of AI on the Threat of New Entrants:
- The AI and ML industry is characterised by significant barriers to entry, primarily due to the high capital requirements for research and development (R&D), the need for specialised talent, and the complexities of regulatory compliance.
- Open-source frameworks like TensorFlow, PyTorch, and Hugging Face Transformers have democratised access to sophisticated AI tools, enabling startups and smaller enterprises to compete with established players.
- Generative AI and LLMs: These models require substantial computational resources for training and fine-tuning. However, organisations can leverage existing pre-trained models and focus on fine-tuning them for specific applications, reducing the initial investment and technical requirements.
- Open Source and Model Fine-Tuning: Fine-tuning models on proprietary data allows new entrants to create competitive products without developing models from scratch.
Conclusion
Porter’s Five Forces model offers a comprehensive framework for organisations across various industries to analyse their competitive environment and formulate strategic initiatives. By understanding the threats and opportunities presented by new entrants, suppliers, buyers, substitutes, and industry rivalry, organisations can better navigate the complexities of the modern business landscape.
In particular, leveraging open-source resources, focusing on model fine-tuning, staying abreast of regulatory developments, investing in emerging technologies, and excelling in AI-driven workflows can provide substantial competitive advantages. As AI continues to evolve, organisations must remain agile and innovative, continually reassessing their strategies to maintain their position in this rapidly changing landscape.
