Should You Build Real Intelligence or Just Acquire It for a Price?

Artificial Intelligence may seem like a race. One where enterprises and industries are scrambling to build models that provide a reliable edge over competitors. To organisations that do not have in-house capabilities towards an AI-project, intelligence may also be perceived as a transaction. Hence the complexity of which model to buy? If intelligence is available for a price, how do we measure their efficiency?

With Large Language Model (LLM)-driven capabilities, there is a great deal of hope in unlocking operational efficiency for better decision-making. But there is a genuine pressure to find a tool that is "truly-intelligent" - because intelligence today is no longer optional or sustenance, it has become a question of survival.

What We Forget (Mostly) About LLMs?

Businesses are pouring resources into building in-house AI capabilities. But many are quickly realizing that deploying LLMs is not as seamless—or rewarding—as it seems. Many well-funded efforts have failed to scale, either collapsing under infrastructure demands or producing underwhelming results despite high costs. The truth is, the approach to building truly intelligent AI systems is often riddled with inefficiencies, misconceptions, and misplaced priorities.

Many organizations find themselves facing prolonged development cycles, unpredictable accuracy, and difficulty in operationalizing outputs across teams. The challenge isn’t in accessing LLMs—there are plenty of open-source and commercial options. The real problem lies in deploying them reliably, affordably, and securely.

A typical on-premises deployment of a high-performance LLM requires - GPU clusters with sustained energy draw; Significant DevOps and MLOps overhead; Data governance and compliance layers; Continuous monitoring to prevent model drift. Such integration of such models into enterprise workflows can take anywhere from 6 to 9 months. In many cases, models may never move past the experimental phase, leading to sunk costs and disillusionment.

AI Inside a Box

It is believed that LLMs as plug-and-play form could guarantee fast results. However, that could be a myth. And over time, organisations have also learnt that adding more SaaS or in-prem capability serves little purpose unless the core logic is not being solved. Many SaaS companies now market themselves as “AI-first” without truly embedding AI into the product’s core logic.

Many times, what is sold is hence an integration, not even a capability. We have witnessed a huge problem of mis-selling of AI - where the AI-stack is actually veneer rather than a native solution. This creates problems with integration, where APIs are hard to adapt to industry-specific workflows. Furthermore, security, data governance and lack of vertical contextualisation makes such models less accurate in resolving the enterprise problem. On the contrary, it accentuates what an AI tool has been considered for - intelligence that solves real problems. In simple words, is it even intelligent if it cannot learn and show the true business context from an environment?

Leveraging Simplicity for Industry-Specificity

There are tailored LLMs and verticalized templates come where an enterprise instead of dirtying hands at a DIY or relying on monolithic generic tools can start training domain-centric templates. Such models are available and can be tethered into familiar workflows. Such templates offer more than simplicity and reliability. The biggest USP is their contextual precision - they understand the business logic and can cut through domain jargon. Since such capabilities require smaller infra footprint, they support more predictable costs.

In fact, such AI-enabled applications in the B2B sector are projected to create over $2 trillion in value globally by 2030. This value will accrue to only those who want to build and adopt AI, but also to those who deploy and adapt to it intelligently.

Being Reliable Matters

AI is naturally a moat and hence every organisation wants to build a journey around acquiring the true potential of AI. But that doesnt mean one need not introspect on quality controls, and the aesthetics of integration. In fact, the bigger question around non-native enterprises is how do they squeeze an AI strategy around a complex architecture. Many organisations internally may have created multiple architectures, data-lakes, and even adopted analytical software and data-analysis capabilities. How does one leverage a faster AI capability? One that is like ChatGPT, but minus the limitations?

There are answers and solutions that offer simple interoperability, have templates for organisations to choose from and can fasten an enterprise's journey towards real intelligence. We have consistently been told that be it analytics or AI, they ought to be on-prem or expensive. But to a business user, nothing is more brilliant than a tool that shows insights, presents the reasons for it, and ultimately offers solutions running into simple contextual options. Organisations, a large number of them have realised that rather than acquiring intelligence as a commodity, it is reasonable to start a journey and move towards the podium.

-By Anil Nair, Co-founder, DeepSpot

(Disclaimer: The views expressed in this article are solely those of the author and do not reflect CyberMedia’s stance.)