The economics of generative AI: 3 common strategies to save or make money

Generative AI and Large Language Models (LLMs) are not just cutting-edge technologies; they're practical tools that help companies save money and create new revenue streams. In this discussion, I'll share how we can use these AI technologies strategically to

• Automate internal processes
• Generate content for monetisation
• Develop profitable software products

Each of these approaches has its own risks and benefits, and I'll look at these from my perspective.

Strategy #1: Internal Process Automation

Implementing Generative AI and LLM for internal automation can significantly reduce operational costs and increase efficiency. By using AI agents that interact with APIs, we can automate complex tasks and workflows to achieve goals without constant human intervention.

For example, AI agents powered by LLMs can:

• interact with different software systems: They can connect to internal databases, CRM systems or third-party services via APIs to retrieve, process and update information.
• Automate routine tasks: Tasks such as data entry, report generation, and email responses can be handled by AI agents, freeing up staff for more strategic work.
• Perform multi-step operations: AI agents can follow complex instructions to perform tasks such as scheduling meetings across time zones, managing inventory, or processing transactions.

By Using AI Agents, we can:

• Streamline operations by automating repetitive and time-consuming tasks
• Improve accuracy by reducing human error
• Improve scalability, as AI agents can handle increased workloads without additional human resources

Risks

However, there are considerations to keep in mind:

• Unpredictable behaviour: AI agents may not always perform as expected, especially in unforeseen scenarios.
• Security concerns: As AI agents interact with APIs and may access sensitive data, robust security measures are essential.
• Integration challenges: Ensuring that AI agents work seamlessly with existing systems can be complex.

Mitigation strategies:

• implement strict access controls: Limit the data and systems that AI agents can access.
• Monitor and log AI activities: Keep track of actions taken by AI agents for auditing and troubleshooting.
• Include human oversight: Critical tasks can require human approval before completion.

Strategy #2: Content generation (static)

Generative AI and LLMs excel at creating content, making them valuable tools for knowledge generation that can be monetised. Companies can use these models to produce high-quality blog posts, articles, music, or even software that can generate revenue through advertising or subscriptions.

Static content is usually generated (a long time) before it is presented to the user, e.g. an AI-generated blog post. This gives the creator more 'time' to review and quality control the content before it is presented to the user, including humans in the loop. Dynamic content, on the other hand, is generated on the fly during a user interaction (e.g. chat) and can only be checked using automated processes and algorithms.

For example, we can:

• Create engaging blog content: Attract readers to our website, increasing advertising revenue.
• Develop educational materials or e-books: Offer them as paid content or as part of a subscription service.
• Create code snippets or software tools: Make these available to users on a subscription basis.

Because the content is static, we have the advantage of:

• Pre-publication review: Ensure accuracy, quality and adherence to brand guidelines.
• Batch processing capabilities: Using APIs, such as OpenAI's Batch API, to efficiently generate large volumes of content, saving time and reducing costs.

A "benefit" of generating static content is the ability to use batch processing, which significantly reduces the cost of computing by processing multiple items simultaneously. This method optimises resource utilisation by scheduling operations during off-peak hours, thereby saving on computing costs. In addition, batch processing ensures quality control by allowing thorough review and editing of content before it reaches the end user. By using batch processing APIs, such as those offered by OpenAI, organisations can efficiently generate large volumes of content in a cost effective manner.

Risks

We need to be careful about:

• Quality and originality of content: AI could produce content that doesn't make sense or is too similar to existing work.
• Bias and appropriateness: AI-generated content may inadvertently contain biased or inappropriate material.

Mitigation strategies:

• Implement rigorous review processes: Every piece of content is reviewed by humans before it is published.
• Use of plagiarism detection tools: Ensure originality of content to avoid intellectual property issues.
• Apply content filters and guidelines: Guide the AI to produce content that meets our values and standards.

Strategy #3: Monetisable software products

Generative AI and LLM can be used to create innovative applications that generate content (media, text, etc.) on the fly based on user needs, which can then be monetised directly or indirectly (for example, through customer service for products sold by the company).

Examples include:

• AI writing assistants: Tools that help users write emails, reports or creative writing.
• Chatbot platforms: Providing customer service or personal assistance.
• Personalised recommendation systems: Improve user experience in e-commerce or content platforms.

Using AI for code generation accelerates product development, allowing us to:

• Get software to market faster.
• Reduce development costs
• Remain competitive in rapidly changing markets

Risks

Integrating AI, especially for real-time content generation, carries significant risks:

• Unpredictable results: AI models may produce unexpected or inappropriate responses to user input.
• Inability to pre-vet real-time content: Unlike static content, we can't review AI output before it reaches the user.
• User trust and satisfaction: Negative experiences can damage our reputation and user retention.

Mitigation strategies:

• Implement security measures: Use content moderation filters and establish guidelines for acceptable output.
• Enable user feedback mechanisms: Allow users to report problems and help us refine the AI's performance.
• Continuously monitor and update AI models: Ensure they are aligned with user expectations and compliance requirements.

Bringing it all together

The real magic happens when these strategies are intertwined, creating a synergistic effect that amplifies their individual benefits.

Creative Combinations:.

• Automated tools for streamlined operations:** Developing AI-driven software that improves internal processes across departments.
• AI-enabled marketing:** Personalising content and recommendations to engage customers more deeply.
• Customer Interaction Automation:** Deploy intelligent chatbots to seamlessly handle inquiries, gather feedback and assist with sales.

By exploring and implementing these combinations, organisations can fully harness the potential of Generative AI and LLM to drive growth and innovation across all facets of their business.