Beyond The Buzz: The Real Cost Of Gen AI In Your Enterprise

By Sujatha S. Iyer

Moore’s Law states that technology gets cheaper as it is widely adopted, but can the same be said for generative artificial intelligence? A McKinsey report estimates gen AI could unlock $2.6 to $4.4 trillion in value annually. Yet, unlocking that potential isn’t cheap. The economic and operational realities of gen AI at scale remain largely unknown to decision-makers. So, is this "priceless" technology truly priceless?

Model Licensing And Infrastructure Costs

Enterprises face a trade-off between closed-source and open-source models. A closed-source model is priced per request. It can be customised to suit enterprise needs through prompt engineering and fine-tuning. However, this usage-based pricing can become expensive when multiple departments and use cases are involved, because request volumes are hard to predict.

Although open-source models avoid recurring licensing fees, they shift the burden towards infrastructure. Hosting these models requires cloud deployment, high-performance GPUs, storage, and ongoing database maintenance. These requirements add up to rigorous capital and operational costs.

These infrastructure costs are not trivial. According to IDC, enterprise spending on AI systems is expected to grow up to 27% every year through 2026. Much of this spending goes to infrastructure, which can make up half or more of the total bill. For gen AI alone, investments crossed $20 billion in 2024. Moreover, McKinsey estimates that by 2030, investments in global data centres could reach $6.7 trillion. An estimated 70% of this computing demand is attributed to AI.

Data Prep Costs

Planning before execution is crucial when money is involved. Before an AI model becomes enterprise-ready, the data it has to be trained on should be cleaned, labelled, de-duplicated, and structured. These efforts address data accessibility, compliance measures, and the removal of PII. This is a recurring process as AI is only as good as the data you feed it. According to McKinsey, many organisations have found that 60% to 80% of a data scientist's time is spent preparing the data for modelling, and the more time that goes into preparation, the higher the overall costs.

Middleware And Maintenance Costs

Middleware is the software glue that connects enterprise systems, such as APIs, databases, and applications. It enables gen AI to work with tools like ERP, CRM, and HRM applications and databases. But pay-per-call expenses also occur. For example, a gen AI chatbot that answers delivery questions must query the ERP system and to check billing, it has to connect with the CRM. Each of these calls comes at a cost, and the integration layers need to be built and continuously updated as systems evolve. 

Risk And Compliance Costs

Organisations face multifaceted gen AI deployment risks beyond initial implementation. Security vulnerabilities like data leakage and prompt injections can compromise sensitive information, and with regulations like the GDPR and HIPAA imposing strict rules on safeguarding sensitive data, organisations must take proper measures to protect infrastructure against these risks.

As real-world data changes, AI models gradually lose their edge, which is called model drifting. Where the AI model takes biased decisions and churns less accurate information, this affects the reputation of the enterprise. A small problem can snowball into system failures, frustrated customers, and hefty compliance fines. This highlights the need for governance and continuous oversight. 

Opportunity Costs

Gen AI is designed to make employees more productive, reduce costs, and handle routine workflows. However, integrating it into an existing tech stack can be challenging, especially for organisations that are not digitally mature. There’s also the risk of overutilising AI in situations where it adds little value. In some cases, enterprises might launch multiple pilots without a clear RoI, diverting resources away from projects with more predictable outcomes. If not managed carefully, the opportunity cost of chasing AI experiments can outweigh their initial benefits. 

Strategies For Cost-Effective Adoption 

To manage costs and maximise impact, enterprises can:

• Start Small: Focus on high-value, well-defined use cases before scaling.

• Plan Before Investment: Build both short-term and long-term RoI frameworks to evaluate outcomes.

• Adopt Hybrid Deployments: Blend self-hosted models with third-party gen AI services. This enables sensitive workloads to remain in-house while external APIs, which are faster and more efficient, can handle less sensitive workloads. This mix helps balance cost, flexibility, and control.

• Monitor Continuously: Set up frameworks to track API calls, infrastructure use, and drift-related retraining expenses.

The Gen AI Promise And Challenge

Gen AI may generate massive business value but unlocking it is not cheap. Behind big number headlines lie compute bills, compliance hurdles, and integration costs. Yet, enterprises that can define clear RoI goals upfront and deploy gen AI strategically are more likely to see benefits. In the end, the real question isn’t whether gen AI is priceless but whether your organisation can capture its value without losing sight of the very real costs that come with it. 

The author is head of AI security, ManageEngine, the enterprise IT division of Zoho Corp.

Disclaimer: The views expressed here are those of the author and do not necessarily represent the views of NDTV Profit or its editorial team.