Copy.ai – a platform for automating marketing and sales processes – has published a piece on how multimodal AI capabilities are changing the approach to Go-to-Market (GTM) strategies.
In short: modern models can work not only with text but also with tables, charts, and images. This allows for the seamless connection of different departments that previously relied on siloed tools and fragmented data formats.
What is Multimodal Fluency?
The Copy.ai article introduces the term «multimodal fluency». This refers to a system's ability to simultaneously process and generate content in various formats: text, structured data, and visual elements.
Simply put, the same model can analyze a metrics table, draft an email to a potential client, and prepare a visualization for a presentation – all without switching between tools or losing context.
For companies, this is mission-critical, as the traditional workflow typically looks like this: marketers write copy in one service, analysts build dashboards in another, and sales managers handle correspondence in a third. Between these stages, «grunt work» inevitably arises: data copying, the risk of manual errors, and significant time drains.
Multimodal AI Use Cases for Sales and Marketing Automation
How It Works in Practice
Copy.ai describes several scenarios where multimodality significantly streamlines workflows.
The first scenario is the automated creation of personalized materials for clients. The system can extract data from a CRM (for instance, information about a company, its scale, and its industry), select relevant case studies, draft an email, and supplement it with visual elements: infographics or product screenshots. All of this happens within a single process, without manual gathering or formatting.
The second scenario is campaign performance analysis. Instead of manually consolidating data from various sources (ad platforms, web analytics, sales reports), the system can independently extract the necessary information, cross-reference it, and prepare a final report – in both text and visual form.
The third scenario is the preparation of materials for internal needs. For example, creating presentations for management or training guides for new employees. The system takes real-time data, structures it, and formats it as required.
The Evolution of Multimodal Capabilities in Language Models
Why This Is Becoming Possible Now
Copy.ai notes that until recently, such tasks required either a whole suite of specialized tools or significant development costs. Today, multimodal capabilities are built directly into language models, which fundamentally changes the economics of automation.
Previously, integrating different systems was expensive and complex: it required setting up APIs, synchronizing data, and training employees to use new software. Now, most of this work can be performed automatically – the model itself understands the data structure, extracts valuable insights, and generates the output.
This doesn't mean that all problems are solved. Accuracy remains a pressing issue: models can still make mistakes in data interpretation or generate incorrect content. Oversight is essential: automation requires fine-tuning and verification, especially when it comes to client-facing interactions. Adaptation is also key: companies need to rethink internal processes to effectively leverage these new opportunities.
Benefits of Multimodal AI for Cross Departmental Workflows
What This Changes for Business
Copy.ai sees multimodality as a way to bridge the gap between departments. When a single system works with different data formats, the need for constant manual hand-offs between teams disappears.
Marketing can receive feedback from sales faster, and sales managers can use materials prepared by marketing without additional adaptation. Analytics become more accessible, even for specialists who do not work with data professionally.
This does not eliminate the need for experts; rather, it changes the nature of their work. Less time is spent on routine tasks like copying data or formatting reports, and more is dedicated to strategic goals, verifying results, and live communication with clients.
The material also emphasizes that multimodality is not a one-off improvement, but a vector of development. Models will become more accurate, integrations deeper, and use cases more diverse. Companies that start experimenting with these technologies today are gaining invaluable experience for the future.
Challenges and Limitations of Implementing Multimodal AI
Open Questions
Despite the broad possibilities, limitations remain. Multimodal systems are demanding regarding data quality: if CRM information is incomplete or outdated, automation will not yield results. They require configuration – there is no universal «out-of-the-box» solution for every company. And, of course, they require supervision – automatically generated content must be checked, especially if it is intended for external communication.
Furthermore, it is not yet clear how the tool market will evolve. Currently, multimodal features are being integrated into platforms like Copy.ai, but whether this will become an industry standard or remain the advantage of only a few players – only time will tell.
Nevertheless, the direction looks extremely promising. The ability to work with different data formats within a single system simplifies key business processes, which is especially critical in fields where response speed and personalization are paramount.