- Sopra Steria Next's forward-looking study predicts that the global AI market will grow from $540 billion in 2023 to $1,270 billion in five years.
- It suggests an innovative methodology for analysing the market, based on four AI categories - AI for Machines, AI for Processes, AI for Humans, AI for Software -, called archetypes, themselves each quantified and projected.
- This segmentation enables decision-makers to take better account of the diversity of AI, as well as the challenges, opportunities and risks associated with each AI category, and thus define a strategy tailored to their context and objectives.
- Finally, the study sheds light on the 4 challenges involved in deploying AI at scale.
Paris, September 17, 2024 - Sopra Steria Next, the consultancy division of the Sopra Steria Group, a major player in European technology, today publishes a ground-breaking study of the artificial intelligence market. Beyond numerical forecasts, the study introduces an innovative market analysis methodology, structured around four AI categories, or archetypes, to provide decision-makers with a clear strategic reading of this fast-growing sector.
Fabrice Asvazadourian, CEO of Sopra Steria Next, emphasises the importance of this approach: "Our four categories provide managers with a strategic compass in the complex and multifaceted world of AI. This structuring of the market enables our customers to optimise their investments and ensures they fully explore AI’s potential without overlooking any areas. In this way, we are transforming AI from a technological concern into a genuine lever of business performance."
A rapidly expanding market
The Sopra Steria Next study reveals that the global AI market, estimated at $540 billion in 2023 (or 6% of the global IT market), may reach $1.27 trillion by 2028, representing 10% of the global IT market. This spectacular growth of 19% year-on-year over the next 5 years, three times that of the IT market as a whole, will be driven by major technological advances in each of the four Artificial Intelligence categories:
AI for Machines
Industrial AI will enable not just machines, but entire factories and supply chains to operate ever more intelligently. This development is made possible by the deployment of 5G/6G networks and the proliferation of connected sensors, which will enable a quantum leap in the capture, aggregation and processing of data. The flagship industrial AI technology will undoubtedly be digital twins and their networking in the industrial metaverse.
Industrial AI is expected to grow by 13% a year to reach $330 billion in 2028, or 26% of the global AI market.
AI for Processes
Intelligent Process Automation focuses on management activities, enabling a new wave of process automation. This category can be found particularly in financial services and public services, as well as in support function processes (i.e. HR, Finance, Compliance, etc.). In addition to end-to-end automation, new AI tools can also be used to better detect anomalies or fraud.
The development of this category will be driven by competitive convergence between publishers of automation solutions (e.g. RPA or OCR), those of workflow management solutions (e.g. BPM or process mining) and the major ERP publishers to develop integrated intelligent automation platforms that make available to companies the various solutions that are already interconnected. This category could peak at $390 billion in 2028, or 31% of the AI market, thanks to growth of 18% per year over the period.
AI for Humans
AI at the service of Humans encompasses successive generations of decision support tools and different types of virtual assistants (Copilot, HuggingFace, ChatGPT, etc.). This category is developing particularly in the financial, health, e-commerce and media sectors.
Boosted by the growing maturity of generative AI and the complementary nature of predictive and generative AI, this category should see the biggest growth in terms of volume, rising from $130 billion to $380 billion over 5 years and representing 30% of the total AI market.
AI for Softwares
AI applied to IT development includes all the tools for automating the development process and assisting code generation and is benefiting from the growing interest in low-code/no-code applications.
This AI category applied to IT development is expected to more than triple in size to reach $170 billion by 2028, representing 25% annual growth. To illustrate this trend, Sopra Steria Next draws on the experience of the more than 40,000 IT professionals in the Sopra Steria group. For certain types of activity and computer languages, leveraging AI tools results in a 10% to 40% reduction in the number of bugs and at least 2 hours saved per week for developers.
More than just figures, a strategic management tool
In addition to numerical forecasts, Sopra Steria Next has positioned its methodology as a genuine strategic management tool, highlighting investment strategies in artificial intelligence that differ across various sectors.
"By combining these four categories, we offer our customers a 360-degree view that aligns AI investments with business priorities, identifies use cases that are mature for deployment and those still in the exploration phase, and accelerates the deployment of AI on a large scale," explains Fabrice Asvazadourian.
For instance, financial services divide their AI investments evenly between 3 of the 4 categories (i.e. Process, Human and Software). In contrast, sectors like manufacturing, energy and defence focus most of their investments on industrial AI, while the healthcare sector maintains a balanced focus on industrial and human AI. This sectoral approach covers a wide range of industries, from financial services to healthcare, government, defence and security, aerospace, manufacturing, energy, transport and logistics, retail, and telecoms and media.
In addition, the study indicates the 10 or so priority IA use cases to be implemented from 2024 for each sector.
Successfully scaling up AI
"Beyond focusing on the development of AI algorithms to address priority use cases, the real challenge is to successfully industrialise them. Today, only 1 in 7 algorithms reaches the production phase," adds Fabrice Asvazadourian.
This means tackling four challenges simultaneously:
- Focus efforts on mature use cases, avoiding blind spots by exploiting the complementary nature of Predictive AI and Generative AI.
- Modernise Data/IA technology platforms to manage large volumes of unstructured or even synthetic data, and equip themselves with the relevant AI solutions
- Integrate new AI algorithms into industrial processes without degrading their performance and guaranteeing their traceability and scalability over time.
Secure recruitment and ensure the development of skills in AI and the adoption of these new tools by all employees.
Faced with the major challenge that large-scale deployment of AI represents for European companies, Sopra Steria Next has developed dedicated offerings, ranging from training and onboarding to the creation of POCs, the modernisation of Data/AI technology platforms and industrialisation with the setting up of AI Factories. This holistic approach enables companies to develop AI gradually, according to their needs and maturity.
With this study and its new methodology, Sopra Steria Next aims to provide business leaders with the tools and lessons they need to navigate the complex AI landscape, guide their investment strategies, and make AI a real driver of growth and innovation for their companies.
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