Evolving Your AI Strategy: Consider Multimodal Generative AI
As artificial intelligence continues to advance, enterprises are beginning to realize that single-modality AI—where models process only text or images—limits the potential of their AI investments. The future lies in multimodal Generative AI (Gen AI), where AI can seamlessly integrate and process multiple data types, including text, images, audio, and video, to drive richer insights and more intelligent automation.
According to Gartner, 40% percent of generative AI (Gen AI) solutions will be multimodal (text, image, audio and video) by 2027, up from 1% in 2023.
Why Multimodal Gen AI?
Imagine trying to understand a movie with only its script but without visuals or sound—it would be an incomplete experience. That’s how many enterprises approach AI today. Multimodal Gen AI enhances AI’s ability to comprehend and generate responses based on a diverse range of data inputs, leading to:
- Better Decision-Making: Integrating structured and unstructured data (text, images, and video) for more comprehensive insights.
- Enhanced Customer Interactions: Improving AI-driven customer service with voice, visual, and textual engagement.
- More Efficient Automation: Enabling AI to process documents, video feeds, and audio inputs simultaneously.
- Greater Innovation: Empowering new use cases in marketing, product design, and digital content generation.
Challenges in Adopting Multimodal AI
While the benefits are clear, implementing multimodal Gen AI requires significant investments in infrastructure, data management, and governance. Key challenges include:
- Data Complexity & Integration: Managing multimodal data requires new strategies for metadata tagging, vector embeddings, and storage solutions.
- Computing Power & Scalability: Multimodal AI demands high-performance compute resources, often requiring cloud-based AI infrastructure.
- AI Model Selection & Training: Organizations must experiment with different AI models and refine them for specific business needs.
- Governance & Ethics: Handling multimodal data raises new privacy, compliance, and security concerns that must be proactively managed.
Steps to Implement Multimodal AI Successfully
1. Define an AI Strategy & Use Cases
Enterprises should identify high-impact use cases for multimodal Gen AI, ensuring AI solutions align with business objectives and data readiness.
2. Invest in AI Development & Integration
Organizations must develop and fine-tune AI models while incorporating best practices for multimodal AI. Ensuring seamless integration into existing workflows will be crucial for success.
3. Modernize Infrastructure
Deploying high-performance AI environments with scalable cloud and hybrid solutions will be essential to support multimodal AI workloads efficiently.
4. Establish Governance & Compliance
Creating AI governance frameworks that align with industry regulations and ethical considerations ensures responsible AI usage while protecting enterprise and customer data.
Getting Started with Multimodal AI
Organizations that embrace multimodal Gen AI today will gain a competitive advantage, unlocking new efficiencies and innovation opportunities. Whether just beginning an AI journey or looking to enhance existing initiatives, taking a structured and strategic approach will be key to success.
How InterVision Can Help
InterVision offers expert guidance and services to help you business successfully implement its AI strategy. Our team assists in defining AI roadmaps, developing and integrating multimodal AI models, and modernizing IT infrastructure to support AI workloads. Additionally, we provide governance frameworks to ensure compliance and to reduce risks. With InterVision’s expertise, organizations can accelerate your AI initiatives and drive meaningful business outcomes.
