AI Market Growth
The AI market is expected to grow by 35% in 2025 compared to 2024, with a compound annual growth rate (CAGR) of 41% from 2023 to 2028. And although generative AI (GenAI) is gaining importance, predictive AI will continue to dominate the market.
GenAI Challenges and Opportunities
The large investments required to train and operate GenAI models raise questions about their long-term financial viability. The emergence of systems capable of creating video outputs and using moving images and sound as input data makes efficient power management and processing crucial.
Adoption of Agent AI
Agent AI, which enables systems to complete complex tasks autonomously, will accelerate the adoption of GenAI in enterprises. These capabilities allow AI agents to better understand human intentions and act independently.
Small Language Models (SLMs)
SLMs require less processing power and are easier to customise, making them attractive for local or edge deployments. They have significant implications for real-time applications that require low latency.
Data Management
The need for robust data management and governance strategies will be a priority for scaling GenAI. Information security and proper data management will be crucial to avoid leaks of sensitive information.
AI Consulting Models
Pricing models for AI consulting services will evolve, becoming increasingly linked to project deliverables and timelines. RAG techniques combine GenAI with detailed data to provide accurate and relevant information, improving the adoption of agent AI.
On the other hand, there will be an increase in traction. Open source models will gain traction, offering accessibility and transparency, albeit with security concerns.
Predictions for 2025
- Mature Approach to GenAI: Organisations will adopt a more mature approach to implementing GenAI, developing realistic roadmaps and measuring the impact of projects.
- Adoption of Agent AI: Agent AI will accelerate GenAI adoption, improving productivity through complex automation.
- SLMs and Local Deployments: SLMs will enable more organisations to consider local or edge deployments, optimising performance and security.
- Investment in Data Management: The need to scale GenAI will force executives to prioritise investments in robust data management strategies.
- Outcome-Based Consulting: Pricing models for AI consulting will become more tied to project outcomes and timelines.
The role of digital transformation
AI is central to digital transformation, enabling processes and services to learn and adapt to the environment.
It can analyse and solve complex operations faster than humans, improving productivity and decision-making.
Process Transformation
- Process Automation: AI can automate manual tasks, reducing errors and increasing efficiency.
- Improved Decision Making: AI’s predictive analytics capability helps make more informed and strategic decisions.
- Cost Reduction: By automating processes and improving efficiency, AI helps reduce operational costs.
Business Applications.
- Customer Experience: Personalised recommendations, chatbots for customer service, and audience segmentation.
- Fraud Detection: In the financial sector, AI is used to detect and prevent fraud.
- Industrial Process Improvement: Process optimisation in sectors such as industry, healthcare and education.
Specific AI Solutions
- Space Insights: Analysis of customer profiles and visitor traffic in retail outlets.
- Tourism Insights: Understanding visitor behaviour in the tourism sector using Big Data and AI techniques.
