Generative Artificial Intelligence is no longer a futuristic concept. It is a business imperative. As technologies evolve at (even more) unprecedented speeds, organizations across industries need practical strategies to harness AI’s potential. The below provides business leaders and technology managers with a blueprint for understanding, evaluating and implementing AI technologies that drive value.
A post by Sam Altman, CEO of OpenAI, reported last week that the company is losing money on its top tier ChatGPT Pro, which was launched at $200 per month in December 2024. The tier offers users unlimited access to OpenAI’s latest model, OpenAI o1, as well as access to its Sora AI video generator and Advanced Voice Mode. In other words, in case it was not obvious, the more subscribers, the bigger the loss. There is also the possibility that Altman managed to slide in an ad for OpenAI’s top tier of course.
There is also a free tier and the limited access Plus model for $20 per month.

It was refreshing that the CEO accepted responsibility for the pricing and the loss, which he did. OpenAI had projected losses of about $5 billion (US) and revenue of $3.7 billion for 2024 as of last summer. Amazingly, in one recent report from Jfrog ML 87% of AI users said they were leveraging OpenAI’s model. It highlights how market share could come with financial challenges. Some may recall that Amazon lost money for years before becoming what it is today.
Assuming that $200 per month is a ceiling for what most people will pay for access, and knowing that the company is still losing money, it is reasonable to deduce that the company needs to make up for the shortfall and encourage uptake and usage elsewhere.
With that in mind, here is some help for Altman and co. to present the need for their solution and gain more customers and revenue. The use cases could apply to other providers like Cohere, DeepSeek-R1, Anthropic, AWS Bedrock or Google Vertex as well. Below is a summary of the utility and use cases for AI both in general and by vertical.
Recommendations for AI implementation
- Start Small, Scale Smartly: Begin with a pilot project to demonstrate value before full-scale deployment.
- Data Strategy: Emphasize the importance of a robust data management strategy, including data quality and governance.
- Continuous Learning: Implement a feedback loop for model improvement and adaptation to changing needs.
- Ethical Considerations: Incorporate ethical AI principles into the deployment process.
Steps and prerequisites to deploy AI:
- Assess needs and corresponding applications.
- Budget for and procure the technologies and resources needed for data ingestion, integration, testing and on-going optimization.
- Ingest in-house, public or both types of data including structured, unstructured or both types of data.
- Designate team members responsible for the model, policies and operation including the HITL (Human-In-The-Loop) model requirement.
- Map and connect data with designated business outputs and desired outcomes.
- Train employees and stakeholders.
- Continuously monitor and review risks, security and evolving regulatory compliance (see above designated team requirement).

Mainstream and commercially available AI that could be deployed today could take different forms. The actual ones utilized are dependent on the use case. These include:
- Analysis and didactic AI
- Conversational and Personal AI
- Developer AI
- Image/Visual AI (or multi-modal AI potentially)
- Predictive AI
- Process AI
- Search AI and
- Task AI
Zendesk reports that “in a few years,” 80% of customer interactions will be powered by AI – a significant leap from the 20% we see today. There really is no time to waste then. In the same context, the fact that the tech has gone mainstream so recently means new use cases will emerge all the time.
Why AI?
- Increased efficiency and productivity
- Innovate more and faster
- Improve customer and employee experience
- Impact revenue and margins positively
- Reduce costs
- Maintain and improve competitive position
- Manage risk, governance and compliance
General use cases:
The standard use cases for Generative AI are horizontal ones. These include:
- Automating things,
- Speeding up tasks like knowledge augmentation and management,
- Software development,
- Customer service and
- Recommendations and personalization
More specifically, and beyond the above bullet points below is a summary of likely use cases by industry.
Industry |
Broad Generative AI Utilities |
Healthcare |
– Medical image analysis and enhancement
– Clinical data synthesis and analysis
– Personalized treatment planning
– Drug discovery acceleration
– AI-assisted diagnostics and treatment planning |
Financial Services |
– Risk assessment and fraud detection
– Personalized financial advice generation
– Automated report and document creation
– Market trend analysis and prediction
– AI-driven compliance and regulatory reporting |
Manufacturing |
– Product design optimization
– Predictive maintenance
– Supply chain optimization
– Quality control enhancement
– AI-optimized energy management in production |
Retail & Ecommerce |
– Personalized product recommendations
– Customer behavior prediction
– Inventory management optimization
– Dynamic pricing strategies
– AI-powered visual search and product recognition |
Technology & Software |
– Code generation and optimization
– Automated testing and debugging
– User interface design
– Natural language processing for user interactions |
Media & Entertainment |
– Content creation and editing
– Personalized content recommendations
– Audience engagement prediction
– Virtual character and environment generation |
Education |
– Personalized learning content creation
– Automated assessment and grading
– Educational content summarization
– Adaptive learning path generation |
Real Estate |
– Property valuation modeling
– Virtual property tour generation
– Market trend analysis
– Client matching and recommendation |
Energy & Utilities |
– Energy demand forecasting
– Grid optimization
– Fault prediction and diagnosis
– Renewable energy output prediction |
Agriculture |
– Crop yield prediction
– Pest and disease detection
– Precision farming optimization
– Weather impact analysis |

As the AI landscape evolves, companies must remain agile, focusing on value creation while managing costs effectively.
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