Insights & Analysis
Jun 19, 2025
Generative AI’s Next Frontier: Six Trends Reshaping Business and Technology
Generative AI is evolving at a breakneck pace, moving beyond novelty use cases towards becoming a core organisational capability. For enterprise leaders, keeping up with technical milestones isn’t enough. What matters is understanding how these trends converge to reshape strategy, workflows, and competitive advantage.
Here are six key trends redefining generative AI and what they mean for your business.
1. Multimodality: The New Standard
Generative AI is no longer confined to a single type of data. The rise of multimodal AI—models that process and generate text, images, audio, and video—is closing the gap between human and machine perception.
✅ Examples:
Google Gemini can analyse a photo of cookies and produce a recipe.
AI systems now integrate image and text generation, video analysis, and even audio-to-image transformations.
🔎 Strategic implication:
Multimodal AI will unlock richer, more intuitive interfaces and products. Businesses must prepare for user expectations that span formats, not just tasks.
2. The Duality of Scale: Bigger and Smaller Models
AI development is diverging along two paths:
Large Language Models (LLMs): Massive, general-purpose models trained on vast datasets for complex reasoning and creative tasks.
Small Language Models (SLMs): Efficient, task-specific models ideal for edge devices, with lower compute requirements and greater interpretability.
✅ Example:
SLMs enable AI-powered features directly on smartphones or IoT devices, bypassing cloud latency and improving privacy.
🔎 Strategic implication:
Choose model scale based on workflow context and operational constraints. A balanced AI stack combines the power of LLMs with the deployability of SLMs.
3. The Rise of AI Agents
Generative AI is advancing from single-turn prompts to autonomous AI agents capable of goal-oriented planning, tool use, and multi-step workflows. These agents can:
Understand objectives
Interact with tools and APIs
Make decisions to achieve outcomes
Collaborate with other agents to orchestrate complex tasks
✅ Example:
OpenAI’s Operator platform coordinates agents across enterprise systems, while CrewAI and LangGraph enable agentic orchestration.
🔎 Strategic implication:
Shift focus from prompting to workflow design and agent orchestration. Leaders must build operational and governance layers to harness agents safely and scalably.
The Growth of Open Source AI
A significant movement towards open-source AI is democratising access and innovation. Companies are releasing model architectures, code, and datasets publicly, fostering collaboration and reducing dependence on proprietary systems.
✅ Example:
Hugging Face models dominate developer adoption, and many organisations plan to increase open-source AI use despite security concerns.
🔎 Strategic implication:
Evaluate open-source models to accelerate development and avoid vendor lock-in—but build robust security, privacy, and compliance controls around them.
Advancements in Video Generation
Generative AI is entering video. Tools like OpenAI’s Sora can produce realistic, coherent video clips from text prompts.
✅ Impacted industries:
Entertainment, marketing, content creation, education, and virtual experiences.
🔎 Strategic implication:
Video generation will democratise production, compress timelines, and unlock new creative formats. Early adopters will gain brand and operational advantages.
Changelog
