Artificial intelligence (AI) is one of the most transformative and disruptive technologies of our time, affecting various aspects of our lives, such as work, education, entertainment, health, and security. AI technologies and services are also creating a huge and dynamic market, with a projected global revenue of $327.5 billion by 2021. However, as the AI market grows and evolves, so do the roles and interests of the two main parties involved:
The AI consumers and the AI suppliers. Who are they, and what are their motivations and challenges? How do they influence and interact with each other in the AI market? And most importantly, who will have more control and power over the future of AI technology? In this blog, we will explore these questions and more, by analyzing the factors and dynamics that shape the AI tech market, and the implications and opportunities for both AI consumers and suppliers.
Rise of AI in the tech market
AI is defined as the ability of machines or systems to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, and natural language processing. AI can be classified into different types, such as narrow AI, which focuses on specific tasks or domains, and general AI, which aims to achieve human-like intelligence across various domains. AI can also be categorized into different techniques, such as machine learning, deep learning, natural language processing, computer vision, speech recognition, and robotic process automation.
The global AI market size was valued at $150.2 billion in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 36.8% from 2023 to 2030. The revenue forecast for 2030 is projected to reach $1,345.2 billion. The main drivers of the AI market growth are the increasing adoption of cloud-based services and platforms, the rising demand for intelligent virtual assistants, the growing investment in research and development, and the emergence of new AI applications and use cases.
One of the most recent and disruptive AI trends is generative AI (gen AI), which refers to the ability of AI systems to create new content or data, such as images, text, audio, or video, based on existing data or inputs. Gen AI tools use techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers, to generate realistic and high-quality outputs that can mimic or enhance human creativity. Gen AI has been used for various purposes, such as content creation, data augmentation, image synthesis, style transfer, text summarization, and natural language generation.
The latest annual McKinsey Global Survey on the current state of AI confirms the explosive growth of gen AI tools. Less than a year after many of these tools debuted, one-third of the survey respondents say their organizations are using gen AI regularly in at least one business function. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work.
Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. Gen AI is expected to have a significant impact on the business and society, as it can enable new forms of innovation, communication, and expression, as well as pose new challenges and risks, such as ethical, legal, and social implications.
Consumer demands and Preferences
Consumer demands and preferences in the AI tech market are influenced by various factors, such as the functionality, emotion, communication, adaptation, relationship and privacy of AI-based solutions.
Consumers perceive AI as a multidimensional and relational concept, which they compare and contrast with human beings. Consumers consider AI marketing communication to be unavoidable and generally acceptable, but they also have some concerns and expectations about the benefits and drawbacks of AI tech.
AI tech can help companies capture, analyze, and utilize personalized customer data at scale, and deliver customized and seamless omnichannel experiences. AI tech can also enable product innovation and customer satisfaction by extracting valuable insights into consumer trends and preferences from big data and machine learning model.
Analysis of AI suppliers: The contenders for market dominance
AI suppliers are the companies that provide AI solutions, platforms, and tools to other businesses or consumers, either as a core product or as a complementary service. AI suppliers can be classified into different categories, based on their size, scope, and specialization. For example, some AI suppliers are large and diversified technology companies, such as Google, IBM, Alibaba, and Amazon, that offer a range of AI and cloud services to various sectors and markets.
Some AI suppliers are research-oriented organizations, such as OpenAI and DeepMind, that focus on creating and promoting artificial intelligence that can achieve general intelligence and benefit humanity. Some AI suppliers are niche and specialized companies, such as Nvidia and Builder.ai, that provide specific AI solutions or platforms for certain domains or applications.
The competition among AI suppliers is intense and dynamic, as they compete on various dimensions, such as innovation, quality, price, and customer satisfaction. AI suppliers also face different sources of competitive pressure, such as existing rivals, potential entrants, substitutes, and buyers. The competitive advantage of AI suppliers depends on several factors, such as their access to data, talent, and capital, their ability to scale and integrate their AI solutions, and their reputation and trust among customers and regulators.
The market dominance of AI suppliers is not easy to measure or assess, as it may vary across different segments, regions, and time periods. However, some indicators that may reflect the market position and performance of AI suppliers are their market share, revenue, profitability, growth, and valuation. Based on these indicators, some AI suppliers may have a stronger or weaker position in the market, and may pose more or less competitive threats or opportunities to other market participants.
The analysis of AI suppliers: The contenders for market dominance is a valuable and relevant topic, as it can help to understand the current and future trends and implications of AI in the economy and society. By examining the strengths, weaknesses, opportunities, and threats of AI suppliers, and how they interact and influence each other, the analysis can provide insights and recommendations for various stakeholders, such as businesses, consumers, regulators, and policymakers.
Strategies for success in the AI market
- Design an AI marketing strategy that aligns with the business goals and customer needs, and leverages the current and future capabilities of AI. A useful framework for designing an AI marketing strategy is to categorize AI applications along two dimensions: intelligence level and integration level. Intelligence level refers to how advanced and autonomous the AI is, ranging from simple task automation to complex machine learning. Integration level refers to how well the AI is embedded into the broader platform or system, ranging from stand-alone applications to integrated solutions.
- Establish a clear and effective governance structure for AI initiatives, that defines the roles, responsibilities, and accountabilities of the various stakeholders, and ensures alignment, coordination, and communication across the organization.
- Build strong and strategic partnerships with external AI providers, such as cloud platforms, software vendors, and research organizations, that can offer complementary AI solutions, platforms, and tools, and help accelerate and scale the AI adoption and innovation. The partnerships should be based on mutual trust, value, and benefit, and should involve close collaboration and co-creation.
- Invest in developing and retaining the right talent and skills for AI, both technical and non-technical, and foster a culture of learning and experimentation. The talent and skills for AI include data scientists, engineers, analysts, and developers, as well as business leaders, managers, and users, who can understand, apply, and leverage AI for their respective domains and functions. The culture of learning and experimentation involves encouraging curiosity, creativity, and risk-taking, and providing opportunities and resources for training, education, and development.
- Ensure the availability and quality of data for AI, as data is the fuel and foundation for AI. Data availability and quality refer to the quantity, variety, timeliness, accuracy, and completeness of the data that is collected, stored, and processed for AI purposes. Data availability and quality can be improved by adopting data management best practices, such as data governance, data integration, data cleansing, data security, and data privacy.
Speculating on the future of AI and the potential frontrunners
The AI market becomes more balanced and collaborative, with a mix of large and small AI suppliers, that offer a variety of AI solutions, platforms, and tools to their customers, and that cooperate and compete with each other, in a fair and ethical manner. These AI suppliers recognize the benefits and drawbacks of AI, and adopt effective strategies for success in the AI market, based on the best practices and insights from the leading AI suppliers and users.
They also engage and involve their customers and other stakeholders, such as regulators and policymakers, in the development and deployment of AI, and ensure that AI is used for good and not evil. The advantages of this scenario are that the AI market becomes more efficient, diverse, competitive, and innovative, and that the AI suppliers can provide high-quality and reliable AI solutions to their customers, while also addressing the challenges and risks of AI, in a responsible and sustainable manner. The disadvantages of this scenario are that the AI market may still face some uncertainties and complexities, and that the AI suppliers may have to balance and compromise between their own interests and those of their customers and other stakeholders.
