Source: Notion.ai
“The future of pricing in this space will likely be a mix of these models, customized to the value provided and the target customer segment."
— Rajiv Pratap, Head of Strategic Verticals, Glean
Broader commercial aspects
Monetizing generative AI extends beyond pricing considerations. Organizations should also address broader commercial aspects to maximize the potential of their offerings:
Defining your role in the generative AI ecosystem
Identifying your organization’s role in the generative AI ecosystem is essential for strategic decision-making. This can be anchored in proprietary model development, leveraging proprietary data, or utilizing in-house expertise and customer insights.
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Proprietary model development: Companies like Google, Nvidia and Adept have excelled in developing their own LLMs, offering full control over model evolution and changing the cost structure of the business. This might be your path if your strength lies in AI research.
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Harnessing proprietary data: For organizations that possess proprietary data but lack the expertise to build models, collaborating with an AI model provider can be beneficial. Companies like Jasper, Glean and Tome all help enterprises turn proprietary data into powerful assets while contributing to the training and refinement of the AI model.
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Leveraging in-house expertise or customer base: Using domain knowledge or customer insights to train and fine-tune AI models can be effective. Collaboration with AI providers can enhance models with your unique industry insights.
This analysis aligns with the “build vs. buy” decision-making process that often happens within enterprise environments. Your position in the ecosystem guides the commercial partnerships you should pursue, leading to more informed decisions and setting the stage for maximizing the potential of generative AI offerings.
Commercial partnerships
Organizations can pursue partnerships with relevant platforms, such as document management system or project management solutions, to access a broader market and enhance the value proposition of generative AI solutions. Integration with complementary tools can create synergies and offer customers a more comprehensive solution. For example, a number of major enterprises, including Box, Salesforce and Canva, have announced plans to build products using Google Cloud’s generative AI functionalities.
Emphasizing privacy, security and control
Privacy protection is crucial in the generative AI domain. A notable example is IBM’s Watson, which faced scrutiny when questions arose about its handling of sensitive patient data during the development of its healthcare-focused AI solutions. This incident underscored the necessity for rigorous data protection measures when dealing with client data or sensitive intellectual property in generative AI applications.
Focusing on customer outcomes
Successful monetization requires an emphasis on customer outcomes. Creating helpful resources, such as buyer’s guides, tutorials and user testimonials, can assist customers in understanding the benefits and value of the solution, thus ensuring the “stickiness” of the product.
For instance, Adobe’s Sensei platform, an AI and machine learning technology, provides detailed tutorials and use-case demonstrations to potential users. This approach effectively illustrates how its AI-powered tools can enhance design workflows and result in better creative outcomes, thereby showcasing the tangible value of the solution to new customers (see Figure 9).
