Traditional AI systems, built to handle one data type at a time, are no longer sufficient. Read on to discover how businesses are employing multimodal AI and find out if you are ready to do the same.
Multimodal AI: Key Points
- Multimodal AI-powered marketing assistants reduce content tagging and enrichment time by up to 70%, dramatically accelerating campaign delivery.
- Nike’s “Never Done Evolving” campaign, powered by multimodal AI, saw a 1082% increase in organic YouTube views, becoming the brand’s most-watched content to date.
- Calm achieved a 3.4% increase in daily mindfulness practices among users by using multimodal AI for personalized content recommendations.
Multimodal AI Models Powering Business Innovation
Companies are seeing real benefits from generative AI, with boosts in revenue and cost savings similar to earlier analytics gains, according to McKinsey.
In practice, multimodal AI can improve search, streamline data analysis, optimize supply chains, and even help develop and test software.
Key Benefits of Multimodal AI for Business Strategy
Early users of AI marketing assistants cut task time by up to 70%
Multimodal AI tools are already enabling new efficiencies and capabilities in business operations.
For example, Bynder, a digital asset management firm, reports that early users of AI-powered marketing assistants cut the time spent on tasks like tagging and enriching content by up to 70%.
- Dramatic efficiency gains: Marketing and content teams are experiencing significantly faster project turnaround times with the help of AI agents.
- Enhanced decision support: By combining text, visual, and other data, these models improve forecasting and root‑cause analysis (e.g. smart assistants that read documents and dashboards).
- Broader automation: Tasks that span systems and teams — from generating reports to orchestrating customer campaigns — can be automated end-to-end with natural-language prompts.
- New product and service innovation: Businesses are embedding multimodal AI into products (for example, AR shopping apps or voice assistants), creating new revenue streams.
- Competitive advantage: According to IBM’s study, over 70% of the highest performing executives believe that competitive advantage depends on having the most advanced generative AI.
Real-World Business Applications of Multimodal AI
By enabling machines to interpret and integrate diverse types of information, multimodal AI is unlocking innovative applications in customer service, marketing, healthcare, manufacturing, and more.
Let’s see how multimodal AI is being practically applied to solve real-world business challenges and drive smarter, more efficient operations.
- L’Oréal: Multimodal AI in media and content
- Calm: Personalized brand experiences in real time
- Intercom: Multimodal chatbots for faster resolutions in customer support
- Nike: Marketing with AI storytelling
L’Oréal: Multimodal AI in Media and Content
L’Oréal has embraced multimodal AI to revolutionize its marketing and product development processes.
By integrating Google's Imagen 3 and Veo 2 models within its internal GenAI Beauty Content Lab, CREAITECH, L’Oréal aimed to enhance creativity, streamline content production, and uphold ethical standards in AI usage.
What Did L’Oreal Achieve?
- Cut concepting time from weeks to days
- Improved speed-to-market for campaigns and product launches
- Reduced production costs significantly
- Established a Responsible AI Framework focused on ethical standards
- Set a new industry benchmark for transparency, authenticity, and sustainability in AI use
More of Multimodal AI in Content
Other examples of multimodal AI in content include:
- Mondelez, which is the company behind brands like Oreo and Cadbury, spins up campaign visuals worldwide and aims for ~25% higher ROI on this content.
- Puma reports using an AI model that auto-generates localized product photos on its site, saving design time and raising click-through by 10% in India.
- JPMorgan Chase’s marketing team has run Persado’s AI to rewrite ad copy, resulting in up to 450% more clicks on campaigns.
- Moody’s sales trainers cut video production from ~4 hours to just 30 minutes, and corporate L&D departments translate 100 hours of narration in 10 minutes.
- Nutella ran an AI campaign that generated 7 million unique jar labels (no two alike), and sold every single jar.
Calm: Personalized Brand Experiences in Real Time
Calm, a leading wellness app, integrated Amazon Personalize to enhance user engagement by delivering tailored content recommendations.
Facing a tenfold increase in its content library, including contributions from celebrities like LeBron James and Ariana Grande, Calm sought to simplify content discovery for its users.
What Did Calm Achieve?
- Delivered personalized in-app content without requiring deep ML expertise
- Helped users discover content that matched their individual preferences
- Achieved a 3.4% increase in daily mindfulness practices across the user base
- Scaled AI-driven personalization efficiently and effectively
More of Multimodal AI for Personalization
How others utilized multimodal AI for personalization:
- Starbucks uses its in‑house “Deep Brew” AI to analyze order histories and service patterns, enabling personalized drink suggestions even at drive-thru windows.
- Ferrari built an AI‑driven configurator by applying large-language models and Amazon Personalize, it now offers customers “millions” of hyper-personalized car configurations, cutting configuration time by 20% and boosting sales leads.
- Users and revenues at Spotify grew ~10× in a decade in large part due to its recommendation engine.
Intercom: Multimodal Chatbots for Faster Resolutions in Customer Support
Intercom has significantly enhanced its AI-powered customer service agent, Fin, by introducing multimodal capabilities that extend beyond traditional text interactions.
These advancements enable Fin to provide comprehensive support across various communication channels, improving the overall customer experience.
What Did Intercom Achieve?
- Reduced resolution times by supporting voice, text, and image-based communication
- Increased customer satisfaction through more flexible, user-friendly interactions
- Ensured consistent, on-brand responses by learning from company-specific guidelines
- Enhanced overall support quality with personalized, AI-driven assistance
More Multimodal AI Chatbots
Other AI agents examples include:
- MetLife deployed Cogito’s AI (analyzing speech tone and language) across 10 call centers, resulting in NPS↑14 pts, “Perfect Call” scores +5%, 6.3% more issues resolved, and handle time –17%.
- Upworkcredits an AI triage bot (Forethought) for jumping its chat self‑serve rate from 45% to 65% by routing tickets based on detected sentiment.
- AI in the order/OSS systems (Ericsson with T-Mobile) cut order fallout by 95% and speed issue identification 90%, dramatically improving CX.
Nike: Marketing with AI Storytelling
Nike used advanced AI/ML in its “Never Done Evolving” campaign, creating an AI-generated tennis match between Serena Williams at age 17 and 35.
The project modeled each era’s playing style and rendered a realistic video match to tell a compelling brand story.
What Did Nike Achieve?
- Reached 1.7 million viewers for the grand final on YouTube
- Achieved a 1082% increase in organic views compared to typical Nike content
- Set a new record for Nike's highest organic views on YouTube
- Earned industry awards, proving the creative power of generative AI for high-impact campaigns
Core Multimodal AI Models and Technologies
Several key AI models are driving these transformations. Let’s have a closer look on which ones:
Model | Business Benefits |
Enhances productivity with high-quality text generation and image interpretation for tasks like reporting, analysis, and documentation. | |
Enables real-time, multi-input interaction (text, image, audio, video), ideal for customer support, accessibility, and automation. | |
Powers smarter image search, content moderation, and product tagging using natural language input. | |
Supports dynamic visual analysis and language tasks, useful in fields like surveillance, media, and real-time decision-making. | |
Fuels creative tools and visual assistants, improving user experiences in areas like design, ecommerce, and interactive media. |
Traditional vs. Multimodal AI: Business Outcomes Comparison
To truly understand the value of multimodal AI, we have to compare it with traditional AI models.
The table below offers a quick overview of the key differences in business outcomes between these two approaches.
Function | Traditional AI | Multimodal AI |
Content generation | Text or image-only generation | Integrated text + image + voice generation |
Customer experience | Static personalization | Dynamic UX based on real-time behavioral and visual data |
Support automation | Text-based chatbots | Emotion-aware bots using voice and facial recognition |
Product innovation | Based on limited user inputs | Data fusion from video, speech, and usage patterns |
Decision speed | Delayed insight via single stream | Real-time analytics from multiple input sources |
Decision Matrix: Is Your Business Multimodal-Ready?
By now you should already have a clear vision of what multimodal AI models are and how they can help your business, but how do you know if your business is truly ready to go there?
This decision matrix provides a simple yet structured approach to evaluate your organization’s current capabilities, necessities, and determine whether you're prepared to dive into multimodal, headfirst.
Readiness Factor | Indicator |
Data Variety | You collect or generate text, images, video, and audio data |
Tech Maturity | Your systems support API integration or use modern cloud architecture |
Team Capacity | You have marketing/analytics teams open to AI-assisted workflows |
Strategic Priority | Personalization, automation, or experience design are top goals |
Adoption Challenges and How to Navigate Them
While the benefits of a multimodal strategy are compelling, successfully implementing it across your organization requires overcoming several adoption hurdles.
These challenges span legal, technical, and organizational domains, and failing to address them can hinder progress or expose your business to significant risks.
Below are three of the most common obstacles to multimodal adoption along with proven strategies to help you navigate them effectively.
Data Privacy and Regulatory Risk
Multimodal systems often rely on sensitive user inputs like facial recognition, voice data, and biometric indicators.
These inputs fall under strict regulatory frameworks such as GDPR (Europe), HIPAA (healthcare in the US), and CCPA (California).
Mishandling this data can result in legal penalties and reputational damage.
How to mitigate this challenge:
- Encrypt data both at rest and in transit to protect against breaches.
- Implement clear, transparent consent mechanisms to ensure users understand how their data will be used.
- Select AI models and platforms that offer built-in audit logging and compliance features to support accountability and traceability.
System Integration Complexity
As Jordan Brown, Founder of Omnie, emphasizes, “Equally important is adopting scalable AI solutions that can grow with the business while remaining flexible to technological advancements.” This ensures your AI infrastructure isn’t just effective today, but adaptable tomorrow.
That said, for multimodal AI to deliver value, it must integrate smoothly with existing tools like customer relationship management (CRMs), digital asset management (DAM), and support ticketing systems.
This level of integration can become a major technical hurdle, especially in legacy environments.
Here’s how to mitigate challenges in this area:
- Choose models that offer robust and flexible APIs to ensure compatibility with your current tech stack.
- Begin with modular deployments, such as automating specific content or support tasks, to reduce risk and complexity.
- Use pre-trained, plug-and-play platforms that minimize integration friction and allow for rapid prototyping.
Skill Gaps
While implementing multimodal AI doesn’t require a team of PhDs, your staff does need foundational knowledge of prompt engineering, model behavior, and ethical considerations.
Without these skills, teams may misuse tools or miss out on their full potential.
Brown here highlights the need to train teams to work alongside AI, fostering collaboration between automation and human expertise to deliver exceptional customer experiences.
How to overcome this challenge:
- Offer hands-on workshops to train team members in prompt design and model interaction.
- Develop internal “AI playbooks” tailored to marketing, customer experience, and content teams.
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Multimodal AI Models FAQs
1. Who benefits the most from multimodal AI models?
Marketing, support, product teams, and analytics functions see the most immediate gains, especially in customer-facing, content-heavy environments.
2. What makes multimodal AI different from traditional AI?
Multimodal AI can process and combine different types of input, like text, images, audio, and video, making it more flexible and context-aware than models limited to a single data type.
3. How can businesses get started with multimodal AI?
Start with specific use cases, such as automating image tagging, improving search with natural language, or enhancing customer support. Leverage existing tools and APIs from providers like OpenAI, Google, and Meta to experiment and scale gradually.
