A recent Gartner poll revealed that a whopping 70% of executive leaders are exploring generative AI solutions, with 19% already in pilot or production modes. With generative AI investments primarily focused on content creation and customer experience, CMOs have a major stake in identifying how generative AI technology will and won’t be integrated into the enterprise roadmap.
When it comes to AI-generated content creation, there are three main adoption paths brands can take: using public tools, building proprietary tools, or levergaging enterprise tools. Each option offers unique benefits and drawbacks. In this guide, we’ve synthesized the pros and cons of each to help you determine the best fit for your brand’s requirements, budget, and technical capabilities.
Let’s dive in.
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Option 1: Public Generative AI Tools
Public tools, such as ChatGPT and Google’s Bard, provide open access to pre-trained generative AI models. These tools allow users to generate content through an unstructured, conversational interface.
Advantages of Public Generative AI Tools
- Fast and inexpensive way to create content: Public AI tools like Bard and ChatGPT provide a convenient and cost-effective method for generating content. This saves businesses valuable time and money by automating the content creation process.
- Synthesizes information and interprets style instructions: These AI solutions can effectively summarize and revise copy by understanding style instructions. They can analyze and combine information from various sources, creating cohesive and well-structured outlines or drafts.
- Generates themes, topics, and ideas for new content: With the right prompts, public AI solutions can generate fresh and innovative ideas for content creation. This can be particularly useful for businesses looking to diversify their content marketing strategies.
- Creates new content for straightforward copywriting tasks: Public AI tools can be utilized to quickly generate content for tasks that require straightforward copywriting. This can help businesses meet their content creation demands efficiently.
- Optimizes text-based and visual content: AI models like ChatGPT and Bard can assist in optimizing both text-based and visual content. Whether it’s refining written content or improving visual elements, these tools can enhance or revise a business’s existing marketing materials.
Disadvantages of Public Generative AI Tools
- Challenging to scale, monitor, and streamline usage across team members: Managing the usage of public AI solutions can be difficult, especially when multiple team members are involved. It is challenging to maintain consistency, track individual contributions, and ensure everyone adheres to the same standards.
- Potential for generating false information: Public AI models, although powerful, may occasionally generate inaccurate or false information. Businesses need to carefully review and fact-check the content produced to avoid disseminating incorrect or misleading information.
- Intellectual property and copyright issues: Public AI tools rely on pre-trained models, which may raise concerns regarding intellectual property and copyright. First, because models may have been trained on others’ copyrighted material. Second, because any information you feed into the tool may be stored or ingested into the model’s training data, thus exposing sensitive brand material and data. Businesses must be cautious when using these tools to ensure compliance with copyright laws and avoid any legal complications.
- AI trained on outdated or limited data: Public AI solutions may be trained on outdated or limited datasets, which can impact the accuracy and relevance of the content generated. This can make it challenging to maintain up-to-date and high-quality content for businesses that require current and precise information.
- Potential reinforcement of bias, prejudice, and misinformation: Public AI models learn from the data they are trained on, which could include biased or prejudiced content. Unchecked usage of these tools may inadvertently exacerbate existing biases, prejudices, and misinformation present in the training data, leading to skewed or problematic output.
While public solutions offer fast and cost-effective content generation, they come with challenges related to scalability, accuracy, and security.
Option 2: Proprietary Generative AI Tools
Proprietary tools are developed, owned, and maintained by your company to meet your specific requirements. This can be a good choice if you have unique use cases or need more control over your AI models’ training data and development.
Advantages of Proprietary Generative AI Tools
- Hyper-customized to your specific business needs: Proprietary AI tools can be tailored precisely to meet the unique requirements of your company. This customization ensures that the AI models are optimized for your specific workflows, processes, and data, leading to more accurate and efficient results.
- Greater control and protection over the underlying AI technology: When using proprietary AI tools, your company retains full control over the technology. This control allows you to protect sensitive data, confidential information, and trade secrets, reducing the risk of leakage or misuse.
- Increased competitive advantage with intellectual property (IP) rights: By developing proprietary AI tools, your company gains IP rights. This ownership gives you a significant competitive advantage, as it prohibits others from using or replicating your AI technology, enhancing your market position.
- Ability to incorporate domain expertise/proprietary data or content into models: Proprietary AI tools enable you to leverage your company’s domain expertise and proprietary data to enhance the accuracy and relevance of the models. By incorporating internal knowledge and unique content, you can achieve more tailored and company-specific outputs.
Disadvantages of Proprietary Generative AI Tools
- Limited potential to benefit from collaboration, knowledge-sharing, and community-driven advancements: When using proprietary AI tools, you miss out on the collaborative nature of open-source AI ecosystems. You might not be able to tap into the collective intelligence, knowledge-sharing, and advancements driven by the broader AI community.
- Significantly higher costs including upfront development fees and ongoing maintenance: Developing and maintaining proprietary AI tools can be costly. These expenses include upfront development fees, requiring a substantial investment, as well as ongoing maintenance costs. It’s essential to carefully assess whether the benefits outweigh the expenses.
- Limited flexibility and scalability with difficult to modify algorithms and inability to integrate with other systems: Proprietary AI solutions may have constrained flexibility and scalability. Customized algorithms may be challenging to modify or adapt, limiting your ability to respond quickly to evolving business needs. Additionally, integrating proprietary AI tools with other systems might be complex, hindering seamless interoperability.
- Requires an extremely high volume of training data and training time: Training proprietary AI models often demands a significant amount of high-quality training data, which can be time-consuming and resource-intensive to gather and curate. It may take more time to achieve meaningful results compared to utilizing pre-trained models or leveraging larger datasets available in open-source alternatives.
Proprietary AI tools offer hyper-customization, control, protection, and competitive advantage, while also incurring higher costs and limitations in collaborative potential, flexibility, scalability, and training requirements.
Option 3: Enterprise AI Solutions
Enterprise tools, lik Skyword’s ATOMM™, are designed to cater to large-scale organizations with complex marketing needs. These tools offer task-specific generative AI capabilities in a more secure and controlled interface.
Advantages of Enterprise Generative AI Tools
- Content creation at scale with more quality control: Enterprise AI solutions enable large-scale organizations to create content efficiently while helping to ensure that content is consistently produced in a manner that meets the organization’s requirements.
- Solution optimization and ongoing development is handled for you: With enterprise AI solutions, businesses can rely on the expertise of the solution provider to continuously optimize and enhance the AI tools. This saves the organization from investing time and resources into managing the technology themselves, allowing them to focus on core business activities.
- Time, cost, and resource efficiency: Implementing enterprise AI solutions can result in significant time, cost, and resource savings for organizations. By automating repetitive tasks and streamlining workflows, businesses can allocate their resources more efficiently, reduce costs, and free up time for more strategic initiatives.
- Integration with existing tools and workflows: Enterprise AI solutions are typically accessible within existing enterprise tools or designed to integrate with existing tools and workflows. This allows for a faster adoption and implementation process.
- More secure than public AI tools: Enterprise AI solutions prioritize robust security features, providing organizations with a higher level of data protection compared to public AI tools. This minimizes the risk of data breaches and ensures compliance with industry regulations, safeguarding sensitive information.
Disdvantages of Enterprise Generative AI Tools
- Quality and reliability depends on provider: The quality and reliability of enterprise AI solutions can vary depending on the chosen provider. It is essential to thoroughly evaluate the provider’s expertise, AI security policies, and experience with your brand’s application use cases.
- Data quality concerns: The effectiveness of enterprise AI solutions heavily relies on the quality of the data used. If the provider’s data sources are unreliable or outdated, it can negatively impact the accuracy and reliability of the generated content. Businesses must ensure that the provider has robust data quality practices in place.
- Cost of initial investment and onboarding: Implementing enterprise AI solutions often requires some initial investment in terms of licensing fees, hardware, and training. Additionally, the onboarding process may involve a learning curve for employees, which can temporarily impact productivity. Organizations need to carefully evaluate the cost implications and assess the return on investment before committing to such solutions.
- Can be complex and difficult to operationalize: Enterprise AI solutions, due to their complexity, can be challenging to operationalize. They may require specialized expertise or dedicated resources to effectively configure, deploy, and maintain. Organizations need to consider their internal capabilities and potential resource gaps before adopting these solutions.
These tools necessitate careful selection of reliable providers and consideration of data quality and operational complexities.
Here at Skyword, we support brand marketing clients with ATOMM, a generative AI engine specifically designed for content marketing. ATOMM™ utilizes the latest GPT models to turn original content created by humans into new customized assets for different audiences and channels.
Our application mitigates generative AI risks by combining AI with human input to produce original, credible, and differentiated content. Grammar, style, and plagiarism checks are automated, followed by human editorial review to uphold your brand’s reputation.
Through a secure API, we ensure the privacy and confidentiality of your proprietary information. Your data is never stored, exposed, or ingested into AI training models.
Ultimately, the choice of which path to take may not be singular or linear. With public tools offering convenience and cost-effectiveness, proprietary tools offering customization and control, and enterprise solutions offering scalability and efficiency, the right decision may be to pursue all, some, or a hybrid approach. As the landscape continues to evolve, it’s crucial to stay informed, reserve budget for piloting solutions, and be crystal clear on your brand’s requirements, budget, priorities, and technical capabilities.
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