Understanding the Economics of AI in 2025: A Guide for IT and Engineering Leaders
For many operational leaders, 2024 was year of AI exploration and POCs!. 2025 will be the year of productization and understanding the economics of AI. For many early AI investors, they are entering 'phase of digestion’ in 2025.
If you’re an engineering leader, your primary focus is likely on understanding the costs associated with building and operating AI products and features, as well as implementing AI tools to enhance productivity within your SDLC process. For cloud engineering leaders, the challenge is guiding peers on the real costs of running AI features while ensuring security, performance, and scalability. As an IT leader, your role involves managing IT budgets and procurement decisions to support operational functions. Meanwhile, functional leaders are responsible for proving ROI—whether by adopting systems that boost operational efficiency or leveraging AI-powered features to drive new revenue opportunities.
1. The Costs of Developing and Operating AI-Powered Product Features
When building AI features, the costs can vary significantly based on the approach you take. These could range from developing, training, and deploying your own models to leveraging third-party models hosted on your preferred cloud provider (e.g., AWS, Azure, Google Cloud).
Here are a few key cost considerations:
API Usage Costs: For fine-tuning, prompting, and inference through services like OpenAI, Anthropic, or similar platforms, you’ll need to calculate the per-use cost and scale it to your needs.
Cloud Hosting Expenses: Hosting AI models, inference engines, apps and agents on a cloud provider incurs costs for compute, storage, and network resources.
Data, Model Training and Inference Costs: If you’re building custom models, you’ll face expenses for gathering, cleaning, and preparing data, as well as the compute-intensive process of training, fine-tuning your model and accurate inference at scale.
Engineering Tooling Costs: If you are just into application development, you will need to understand costs of adopting co-pilots and other efficiency SDLC boosting Gen-AI tools(discussed in separate blog).
Understanding these components is essential to predict the total cost of ownership (TCO) for your AI initiatives.
2. The Role of IT in Supporting Other Functions
If you’re an IT leader, your role may involve enabling other functions to adopt AI tools effectively. This means modeling overall usage by functional teams to forecast demand, negotiate better contracts with vendors, and plan for future scalability. By analyzing usage patterns and volumes, you can ensure your organization gets the most value out of its investments.
3. Moving Beyond the Initial Excitement
AI is exciting—but enthusiasm alone cannot justify investment. It’s crucial to go beyond the initial hype and perform a thorough analysis of the economics of your AI initiatives. Ask yourself (and your team):
What is the long-term ROI of deploying these AI solutions?
Are there hidden costs, such as integration, training, or data migration?
How can we optimize costs without compromising on security, performance, or scalability?
Chances are, your finance team has already raised these questions to justify AI investments. By approaching AI with a solid understanding of its economics, you can make smarter, more sustainable decisions that benefit your organization.
We invite you to learn from our real-world experience in developing and executing AI strategies, and discover how we can support your organization's AI journey.