The hidden cost of AI: Why businesses may be spending more than necessary?

By Ritu Mehrotra, Co-founder & CEO of Shunya Labs

As artificial intelligence moves from experimentation to large-scale business adoption, organisations are beginning to face a growing challenge: the rising cost of making AI work well. While companies are investing in models to boost productivity, automate tasks and improve customer experiences, the technology behind these applications can carry significant operational costs.

The real cost of AI is not limited to buying hardware or paying for cloud services. It also includes the computing power needed to process data, the time required to generate a response, the cost of correcting errors and the human effort involved when systems do not work as expected. When these inefficiencies occur thousands or even millions of times, they can add significantly to spending. The question for businesses is no longer just how powerful an AI model can be, but whether it can deliver reliable results quickly and at a reasonable cost.

The Infrastructure Costs behind AI
Creating and running AI systems requires significant infrastructure. This includes processors such as GPUs, cloud computing, data storage, network connectivity and electricity. Companies must also account for system integration, security, performance monitoring and ongoing maintenance.

For businesses using third-party AI tools, costs can rise with the number of requests. A service that seems inexpensive during a trial can become costly when it is used across departments, customer channels or markets. This means companies need to consider the total cost of ownership. They should look beyond the initial development cost or the price of an individual API call and account for the long-term expenses of operating the system.

Token Consumption: The Hidden Cost Multiplier
Another cost that is often overlooked is the number of tokens an AI system uses. AI models process text in units called tokens, and many services charge based on the number of input and output tokens processed.

Long prompts, extensive conversation histories, large documents and repeated instructions increase token usage. For example, a customer support chatbots that sends the entire conversation history to a model every time a customer asks a follow-up question may end up processing far more information than necessary.

The answer is not to make every prompt short. It is to ensure that the model receives only the information it needs. Better context management, targeted information retrieval and limits on unnecessary output can reduce token use without compromising response quality.

Does Every Task Need a Large AI Model?
A growing trend in business AI is the use of smaller, specialised models, including Small Language Models (SLMs). Large language models can perform complex tasks, but not every business application requires that level of capability.

Simple tasks such as sorting customer messages, extracting data from forms, routing support tickets or answering common questions can often be handled by smaller, focused models. Custom SLMs can be designed for specific business requirements. Depending on the task and deployment environment, they may require fewer computing resources, respond faster and run on local devices or edge systems.

Model size is not the only factor. Combining models with techniques such as retrieval-augmented generation (RAG) and knowledge-graph-based systems can help AI access relevant information, understand relationships between data points and generate more grounded answers without repeatedly processing large amounts of text.

One practical approach is to use specialised models for routine tasks and route complex or ambiguous queries to larger models. This can help balance cost, speed and accuracy rather than treating every interaction in the same way. Smaller models are not automatically more effective in every situation; their suitability depends on the task, architecture, hardware and quality requirements.

The Multilingual Voice AI Challenge
The cost of AI becomes especially clear in customer-facing tools such as Voice AI. In a linguistically diverse country like India, building effective voice support involves much more than recognising speech.

Customers may use different accents, mix languages or switch between Hindi and English, sometimes within the same sentence. A voice AI system typically has to recognise the speech, interpret the language and intent, generate an appropriate response, and convert that response into speech.

Errors at any stage can cause the system to misunderstand the customer, provide an incorrect answer or require the interaction to be repeated. Additional processing, longer wait times and transfers to human agents all add to the overall cost.

Specialised multilingual speech recognition, language-understanding and text-to-speech systems, supported by suitable AI architecture and reliable knowledge bases, can help businesses build voice systems that respond more accurately and efficiently. The right combination will depend on the languages, accents, use cases and operating conditions the system needs to support.

Measuring the Cost of Completing a Task
The biggest shift businesses need to make is in how they measure AI spending. Looking only at API fees or token use gives an incomplete picture. A more useful measure is the cost of completing a task successfully. A system with lower processing costs may ultimately be more expensive if it regularly produces incorrect answers, requires repeated attempts or needs human intervention.

Businesses should therefore assess how the system performs across the entire interaction. Relevant measures include accuracy, response time, token consumption, the frequency of repeated processing and the number of times a human needs to step in.

For example, an AI support system that resolves a customer’s problem in one interaction may offer greater value than a cheaper alternative that fails several times before transferring the customer to a human agent. This shifts the focus from the cost of generating an individual AI response to the overall business value of resolving the task successfully.

Building Efficiency without Compromising Performance
Using AI efficiently means selecting suitable models and designing the surrounding system carefully. Businesses can route tasks to appropriate models, cache frequently used data, improve information retrieval, compress models where appropriate and monitor computing usage. These measures can reduce unnecessary processing while keeping performance aligned with business needs.

Businesses should also ask whether every task requires the same level of intelligence. Routine jobs may be handled effectively by smaller or specialised models, while complex analysis or sensitive decisions may require more capable models and additional checks. Regular performance reviews can help identify wasted computing, reduce delays and ensure that cost optimisation does not come at the expense of accuracy or reliability.

From Models to Smarter AI
As AI becomes part of everyday business operations, efficiency will play a central role in determining its return. The next stage of adoption will require businesses to look beyond model size and raw computing power. It will depend on using systems grounded in relevant knowledge and designing AI around specific tasks and operating requirements.

Ultimately, the success of AI adoption will not be determined by how much computing power a company possesses, but by how effectively it is used. The real competitive advantage may lie in delivering accurate, reliable results at speed while minimising unnecessary computation and operating costs.