Companies Grapple with the Actual Cost Implications of AI Integration
In the rapidly evolving world of artificial intelligence (AI), the focus has often been on the direct costs of AI models. However, a closer look reveals a host of hidden costs that businesses must consider when deploying AI.
According to a recent report from our organization's Intelligence, the cost of deployment is the second biggest concern for 46.7% of those adopting generative AI, following only integration complexity. This concern is well-founded, as the hidden costs can collectively undermine the anticipated efficiency and return on investment (ROI) from AI if not anticipated and managed proactively.
Technical Debt and Ongoing Expenses
One significant hidden cost is technical debt, which refers to ongoing burdens from incomplete or rushed AI system development. Poor data quality, labeling errors, and overlooked evaluation or monitoring processes can lead to increased costs and reduced efficiency in the long run.
Infrastructure and Inference Costs
Unexpected increases in GPU usage, cloud compute, and autoscaling inefficiencies can cause infrastructure bills to spike dramatically, especially during traffic surges. For instance, as of early 2023, OpenAI's GPT-4 with an 8K context window costs $30 per million input tokens and $60 per million output tokens. However, these costs can be mitigated by the introduction of more cost-effective versions, such as GPT-4 Turbo, which costs 50% to 67% less.
Data-Related Costs and Risks
Licensing fees for data, compliance with regulations, and managing evolving data quality issues add to expenses and complexity. For example, Rohini Jain, CFO of Bill, advises businesses to utilize AI that is already embedded in platforms they use, such as invoicing, payments, or forecasting, for better ROI and more predictable costs.
Process Redesign and Change Management
AI integration often requires significant operational changes and retraining, incurring organizational overhead and trust-building efforts. Nicole DiNicola, global vice president of marketing at Smartcat, suggests that adopting AI doesn't have to be "all or nothing." She states that many platforms offer easy-to-use, low-cost options for organizations to start small and scale their adoption over time.
Security and Abuse Risks
AI systems introduce new attack surfaces and data vulnerabilities that require ongoing attention and investment. Addressing these risks is crucial for ensuring the security of sensitive business and customer data.
Governance and Compliance Overhead
Ensuring AI adheres to ethical, legal, and regulatory standards demands dedicated resources for oversight and control. This oversight is essential for maintaining trust and avoiding potential legal issues.
Evaluation and Debugging Complexity
Advanced AI like agentic systems require extensive testing, iteration, and debugging to reach production readiness. Many projects fail due to underestimated deployment complexity.
Integration with Existing Infrastructure
Customizations, API development, and secure data interfaces often significantly increase deployment costs beyond AI development itself.
Post-Deployment Monitoring and Maintenance
Sustaining an AI system’s performance through analytics and ongoing support generates recurring annual expenses.
Operational Inefficiencies in Related Workflows
Traditional procurement processes involve hidden costs like unauthorized spending and inactive vendor management, which AI can help uncover but initially require investment for automation solutions.
These hidden costs can collectively undermine the anticipated efficiency and ROI from AI if not anticipated and managed proactively. Addressing them requires multidisciplinary planning beyond just acquiring or developing AI models.
In summary, hidden AI deployment costs encompass technical, operational, data, security, and governance challenges as well as infrastructure expenses that extend well beyond the upfront price of AI models themselves. Recognizing and preparing for these expenses is critical for successful and sustainable AI adoption.
As the cost of AI models continues to decline, it is essential for businesses to stay informed about these hidden costs and plan accordingly to maximize the benefits of AI adoption.
- The hidden costs associated with AI deployment can significantly impact the anticipated efficiency and return on investment (ROI), according to a recent report.
- Technical debt, referring to ongoing burdens from incomplete or hurried AI system development, is one significant hidden cost businesses must consider.
- Unexpected increases in GPU usage, cloud compute, and autoscaling inefficiencies can cause infrastructure bills to surge, especially during traffic spikes.
- Licensing fees for data, compliance with regulations, and maintaining evolving data quality issues add to expenses and complexity in AI deployment.
- AI integration often necessitates significant operational changes and retraining, incurring organizational overhead and trust-building efforts.
- AI systems introduce new attack surfaces and data vulnerabilities that require ongoing attention and investment for security purposes.
- Ensuring AI adheres to ethical, legal, and regulatory standards demands dedicated resources for oversight and control, essential for maintaining trust and avoiding potential legal issues.
- Sustaining an AI system’s performance through analytics and ongoing support generates recurring annual expenses, making post-deployment monitoring and maintenance critical for long-term success in AI adoption.
