How Can CEOs Slash GenAI Costs Without Sacrificing Innovation?

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In the rush to adopt generative AI, companies are encountering an unforeseen obstacle: skyrocketing computing costs that risk stalling innovation and slowing down radical business changes.

A recent report from IBM’s Institute for Business Value (IBV) highlights the economic hurdles executives face amid the AI revolution. Titled “The CEO’s Guide to Generative AI: Cost of Compute,” the report reveals that average computing costs are projected to surge by 89% from 2023 to 2025. 

Notably, 70% of executives surveyed by IBM identify generative AI as a primary factor behind this spike. The effects are already rippling across industries, with every executive reporting that at least one generative AI project has been cancelled or delayed due to cost concerns.

While some CEOs may think GenAI’s strategic implications outweigh cost concerns, in reality, GenAI costs are deeply integrated into every stage of strategy, requiring CEOs’ ongoing attention to optimize its value. This blog discusses the hidden costs of adapting GenAI projects for enterprises. 

The Complex Landscape of Generative AI Costs

Inference Costs

Inference costs arise whenever a large language model (LLM) generates a response, using compute resources. For instance, generating a text completion with GPT-4 costs around $0.006 per 1,000 output tokens plus $0.003 per 1,000 input tokens. 

These costs can add up quickly, especially for companies producing large volumes of content. Options to reduce these costs include using smaller models, hosting open-source models, or optimizing the inference process. 

CEOs can guide their organizations to compare these methods, balancing performance and cost.

Fine-Tuning Costs

Fine-tuning customizes a GenAI model for specific tasks. Costs vary based on model size, data amount, and training rounds. 

For instance, fine-tuning 100,000 tokens over 3 training epochs with GPT-3.5 Turbo costs around $2.40. Techniques like “transfer learning” and “distributed training” can help reduce costs by reusing data or distributing tasks across multiple GPUs.

Prompt Engineering Costs

Effective prompt engineering improves GenAI responses but requires careful planning and investment. CEOs must decide between focusing on fine-tuning (more precise but costlier) or prompt engineering, based on the task’s demands and budget.

Cloud Costs

Beyond hosting, CEOs should evaluate the full cloud architecture needed for GenAI. In data-sensitive sectors like healthcare, local private cloud storage may be necessary. 

Many companies use a “lift-and-shift” approach to cloud migration, keeping legacy systems, which can increase GenAI costs. 

CEOs need to choose between public, private, and multi-cloud options that support GenAI without inflating costs.

Talent Costs

Building a skilled GenAI team is crucial, but a sudden talent rush can drive costs up. CEOs and HR leaders should create mid- to long-term plans, focusing on skills that support the strategic shifts GenAI brings. 

Internal training and role adjustments can also be cost-effective ways to build GenAI expertise.

Operational Costs

Machine Learning Operations (MLOps) streamline processes, reducing costs in model deployment and management. Automating these tasks reduces errors and improves model maintenance.

CEOs must consider elements like regular model retraining, data spread, and the need for ongoing support as part of the MLOps lifecycle.

Potential Hidden Costs

Infrastructure Overhaul

GenAI may require updating existing infrastructure, including powerful computing resources like GPUs or TPUs, which can be costly. CEOs must plan for these upgrades and ensure infrastructure flexibility for evolving GenAI demands.

Data Security

GenAI heightens data security needs, from data leakage prevention to defending against misinformation. CEOs should prioritize a clear security strategy, including data loss prevention and proactive employee training.

Ethical Considerations

GenAI can inadvertently reinforce biases in its training data, requiring investment in fair and transparent AI practices. CEOs must ensure that GenAI systems are developed responsibly, addressing biases and ethical concerns from the start.

Controlling Costs: A Strategic Approach for CEOs

Incorporate Cost Control in Decision-Making

CEOs can set a framework to guide cost-effective GenAI strategies by outlining decision roles, data requirements, and timelines. For example, a CEO-led committee could evaluate key GenAI projects, setting clear goals, review schedules, and benchmarks.

Monitor GenAI Costs Continuously

CEOs should use a comprehensive dashboard to track costs like model training, cloud usage, and employee expenses. Automated reports and alerts help CEOs stay updated on expenses and identify potential savings or adjustments.

Build a Skilled GenAI Team

CEOs, in collaboration with HR, should develop a GenAI talent plan, balancing new hires with internal training. Upskilling current staff in GenAI tools is often more cost-effective than recruiting entirely new teams.

The Bottom Line for CEOs

GenAI costs span inference, fine-tuning, prompt engineering, cloud usage, talent, and operations. CEOs must also account for hidden expenses, like infrastructure upgrades, data security, and ethical considerations.

Integrating cost control into decision-making, using monitoring dashboards, and building a skilled team will help CEOs maintain a balance between innovation and cost-efficiency for sustainable GenAI growth.

Gramener – A Straive Company, helps enterprises reduce GenAI adoption costs by offering tailored solutions, optimizing model efficiency, and enabling faster deployment through reusable frameworks, minimizing infrastructure and operational expenses.

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