Thomas stands at his office window, reviewing the latest client request. Forty-seven pages of technical specifications, plus sketches, photos of the existing facility, and an audio file with further explanations from the head of purchasing.
In the past, his team would have needed days to sift through all this information and prepare a suitable proposal. Today? His new AI system analyzes text, images, and audio simultaneously—and delivers a structured summary, complete with initial solution ideas, in just minutes.
Welcome to the world of multimodal artificial intelligence.
What Is Multimodal AI and Why Now?
Multimodal AI refers to AI systems capable of processing various data types at once—text, images, audio, and increasingly, video. Unlike specialized single-mode solutions, these systems understand context across multiple sensory channels.
The breakthrough came in 2023 with models like OpenAI’s GPT-4V, the first to interpret text and images together. Google followed with Gemini, and Microsoft integrated multimodal features into Copilot.
But why is this relevant for your business?
The answer lies in the reality of your business processes. Information rarely comes in as plain text alone. Customers send photos of broken parts, colleagues explain complex issues via voice messages, and key details are buried in technical drawings.
So far, you had to compile this information manually. That takes time—and in your business, time is money.
The Revolution Is in the Combination
A practical example: Your service technician photographs a faulty machine part, records a brief explanation via smartphone, and adds three keywords. A multimodal AI identifies the part, understands the issue from the audio explanation, and automatically suggests the correct replacement part number.
This is not science fiction—it works today.
The Three Pillars of Multimodal AI in Business
Pillar 1: Computer Vision—When Machines Learn to See
Computer vision analyzes and interprets image content. For your business, this means:
- Automated quality control through image recognition
- Document analysis for drawings and plans
- Inventory tracking via photo capture
- Damage documentation in service operations
A machinery manufacturer from Baden-Württemberg uses computer vision to automatically categorize incoming customer photos. What used to take 20 minutes of manual work now takes just seconds.
Pillar 2: Natural Language Processing—Understanding and Generating Language
This is where modern AI systems really shine. They comprehend not just what is written, but also the context and intent behind the words.
Practical applications:
- Automatic email classification and forwarding
- Proposal generation based on customer inquiries
- Summarizing lengthy documents and minutes
- Translation of technical documentation
Anna from HR uses NLP to pre-sort applications. The system recognizes not only qualifications but also cultural fit for the company.
Pillar 3: Speech Recognition—Turning Audio Into Knowledge
Speech recognition has long since outgrown basic dictation functions. Modern systems understand context, emotions, and can even distinguish between different speakers.
Business applications:
- Automated meeting transcription
- Customer service analysis to improve quality
- Voice-controlled warehouse management
- Training analysis and feedback generation
Markus’ IT team uses speech recognition to automatically categorize support calls and identify the most common issues. This not only saves time, but also proactively increases system stability.
Practical Use Cases for SMEs
Proposal Generation: From Days to Hours
Imagine a customer sends you photos of their current setup, a PDF with technical requirements, and a voice message outlining additional preferences.
A multimodal AI analyzes all three sources simultaneously:
- The images reveal the type and condition of the system
- The PDF provides exact specifications
- The audio file contains important secondary requirements
The system generates a structured requirements catalog and suggests suitable solution approaches. Your proposal team can get started immediately on the technical work, instead of spending hours gathering and sorting information.
Service Optimization: Getting to the Heart of the Problem Faster
A service technician receives a work order. Instead of a brief error description, he has access to:
- Photos of the affected components
- Audio recordings of the noises
- Historical service data in text form
The AI combines all the information and suggests not only likely causes, but also the optimal spare parts for the first visit. This drastically reduces repeat trips.
Knowledge Management: Ending Information Silos
Every company has a wealth of knowledge—spread across emails, presentations, manuals, training videos, and in employees’ minds.
Multimodal AI finally makes this knowledge accessible. For example: A new team member asks via chat, “How do I set up machine XY for product Z?”
The system automatically searches:
- Text documents for procedural descriptions
- Videos for setup sequences
- Images for sample settings
- Audio recordings of expert explanations
The answer comes as a structured guide—with text, relevant images, and links to video snippets.
Quality Control: Precision Meets Efficiency
You already photograph your products for documentation? Let those images do the work.
Computer vision detects deviations the human eye might miss. Combined with text documents on quality standards and auditors’ audio comments, you get a seamless quality report.
A food manufacturer in Bavaria uses this approach: Batch images, combined with sensor data in text and shift supervisors’ audio comments, are automatically turned into structured quality reports for traceability.
Challenges and Realistic Limitations
Honesty is essential for sound advice. Multimodal AI is not a silver bullet for every business problem. There are distinct boundaries and challenges you should be aware of.
Data Quality Determines Success
An AI is only as good as the data you feed it. Blurry images, poor audio quality, or unstructured text lead to useless results.
What this means for your company: Before investing in multimodal AI, you should honestly assess your data quality. Sometimes it’s wiser to improve data collection first.
Complexity in Integration
Multimodal systems are technically more demanding than pure text-based AI. They require more computing power, more complex interfaces, and often special hardware for image processing.
Markus knows this pain all too well: It took three months longer than planned to integrate into his existing ERP system. The reason? Unexpected compatibility issues with image processing.
Data Privacy and Compliance
Images and audio files can contain particularly sensitive information. A photo of your production hall reveals more about your company than a text document.
When using multimodal AI, you must be even more careful to check:
- Which data the system processes
- Where this data is stored
- Who has access to the raw data
- How you ensure GDPR compliance
Cost-Benefit Analysis
Multimodal AI is more expensive than simple chatbots. Hardware requirements are higher, license fees increase, and implementation effort grows.
Be honest: How much time are you really saving? How often do you actually process complex multimodal requests? Sometimes a simpler solution is entirely sufficient.
Employee Acceptance
The more complex the AI, the higher the barrier for your employees. While a text chat is intuitive, multimodal interaction often requires training.
Anna found that her colleagues use the new AI’s text features daily, but only rarely the image recognition. Why? No one had shown them how to create high-quality photos for analysis.
Implementation Strategies for B2B Companies
Step 1: Use Case Assessment
Don’t start with the technology—start with your business processes. Where are you losing time today due to manual information processing?
Ask yourself these questions:
- Which of your processes regularly involve different data types?
- Where do employees frequently switch between different systems?
- Which recurring tasks are disproportionately time-consuming?
Thomas identified three core processes: proposal generation, service planning, and quality documentation. All three involve text, images, and often voice notes.
Step 2: Proof of Concept With Real Data
Theoretical demos may impress, but don’t help you decide. Insist on a proof of concept using your real data and processes.
Consciously choose a typical but not too complex case. The goal: develop realistic expectations and measure concrete time savings.
Step 3: Gradual Rollout
Don’t roll out multimodal AI across the entire company all at once. Start with one team, one process, one application.
Anna started with her recruiting team. Only after three months of successful use did she roll out the system to other HR processes.
Step 4: Employee Enablement
The best AI is useless if your employees can’t use it effectively. Set aside enough time for training sessions—not just technical introductions.
Your people need to understand:
- When to use which modality
- How to create high-quality inputs
- How to critically evaluate outputs
- What the system’s limitations are
Step 5: Continuous Optimization
Multimodal AI systems learn through use. The more high-quality examples you provide, the better the results.
Establish a feedback loop: Which requests work well? Where are the sticking points? Which new use cases emerge from daily use?
Markus holds monthly review sessions. His team discovered that the AI could help with budget planning—a use case no one originally anticipated.
Outlook and Recommendations
What’s Next?
The development of multimodal AI is accelerating rapidly. Video analysis is expected to improve significantly and become far more affordable in the next few years. Real-time processing will become standard. Integration between different modalities will be seamless.
For your company, this means: What is still complex and expensive today will be standard tomorrow. But waiting is not the best strategy.
Why You Should Act Now
Early adopters have a critical advantage: They gain experience while the competition hesitates. They develop skills, optimize processes, and build employee trust in the new technology.
Thomas sums it up: “We could have waited until everything was perfect. But then our competitors would have had a two-year head start.”
Concrete Next Steps
If you want to get started now, we recommend the following approach:
- Conduct a current state analysis: Document a typical workday for your key staff. Where do different data types come together?
- Identify quick wins: Look for simple but frequent tasks that would benefit immediately.
- Set a budget: Plan realistically—not just for the technology, but also for training and change management.
- Evaluate partners: Choose an implementation partner who understands your industry and has already completed similar projects.
Brixon’s Role in Your AI Journey
At Brixon, we understand the challenges mid-sized B2B companies face. We offer end-to-end support: from strategic planning and technical implementation to long-term support.
Our approach is pragmatic: We first analyze your specific requirements, then develop customized solutions, and support you through the rollout. No academic distractions—just measurable results.
One thing is clear: Multimodal AI is no longer a trend. It’s becoming a core component of modern businesses. The question isn’t if, but when—and how—you’ll make the leap.
Frequently Asked Questions
How much does it cost to implement multimodal AI in a mid-sized company?
Costs vary greatly depending on the use case and complexity. For an initial proof of concept, you should budget between €15,000 and €30,000. Full implementation for specific business processes typically ranges from €50,000 to €150,000. In addition, you can expect ongoing license fees of about €500 to €2,000 per month, depending on usage intensity.
How long does it take for multimodal AI to deliver productive results?
In simple use cases, you can see initial results after 4–6 weeks. For more complex integrations into existing systems, you should plan for 3–6 months. Most companies achieve full productivity after 6–12 months, once all employees are trained and processes have been optimized.
What are the technical requirements for my company?
Most modern multimodal AI systems run cloud-based, so no special hardware is required. What’s important: A stable internet connection (at least 50 Mbit/s), up-to-date browsers on workstations, and organized data storage. For especially data-sensitive applications, there are on-premise options—but these require powerful servers.
How do I ensure sensitive company data stays protected?
Choose GDPR-compliant providers with servers located in the EU. Use encryption for all data transfers and clearly define access rights. For highly sensitive data, on-premise solutions or provider compliance certifications are advisable. Be sure to get written confirmation of data deletion policies.
Can multimodal AI replace my existing ERP or CRM systems?
No, multimodal AI does not replace your core systems, but rather complements them intelligently. It analyzes and processes information, which is then imported into your existing systems. Most providers offer interfaces for common ERP and CRM systems, ensuring seamless integration.
How do I recognize trustworthy multimodal AI providers?
Trustworthy providers show you concrete reference projects in your industry, offer in-depth proofs of concept using your data, and can transparently explain the technical details. Avoid providers who make unrealistic promises or are unclear about pricing. Look for relevant certifications and ask about support hours and training options.
Which industries benefit most from multimodal AI?
Industries with high documentation requirements benefit the most: mechanical engineering, automotive, medical technology, architecture, and engineering. Service-intensive businesses such as facility management or technical support also see rapid advantages. In general, the more different data types your processes involve, the greater the benefit.
