The conversation surrounding Artificial Intelligence in the enterprise has fundamentally shifted. We have moved past the initial phase of experimentation, characterised by open-ended chatting and creative brainstorming, and entered the era of utility. With industry forecasting worldwide AI spending to top $1.5 trillion in 2025, the focus is no longer on what AI can say, but on what Agentic AI can do.
For business leaders and IT procurement teams, this transition from novelty to infrastructure presents a new, expensive paradox. While the potential for efficiency is undeniable, the marketplace has become crowded with solutions ranging from simple automation scripts to complex reasoning engines.
“The AI revolution presents the biggest opportunity yet for Indian SMEs, but also the most noise. We see our role as the signal in that noise. We are dedicated to ensuring that every business can find and deploy the right AI tools not just to compete, but to lead.” — Akash Nangia, Co-Founder, Techjockey.com
In this noisy environment, the greatest risk facing businesses is not failing to adopt AI, but rather overbuying it. Industry data reveals that average SaaS license utilisation hovers around 50%, meaning nearly half of paid-for software features sit unused. Much like purchasing a Ferrari to drive in bumper-to-bumper city traffic, many organisations are acquiring enterprise-grade, fully autonomous agents to solve problems that require only simple automation.
The result is a bloated technology stack, spiralling integration costs, and a return on investment that fails to materialise. To navigate this landscape effectively, decision-makers must learn to look past the marketing hype and strictly match the level of intelligence to the complexity of the business problem.
The Anatomy of the Overbuying Trap
The root cause of overbuying often lies in a misunderstanding of the different tiers of AI capability. In the rush to future-proof their operations, stakeholders often gravitate toward the most advanced solution available, assuming that more power equals better performance. However, in the world of AI agents, smarter often means slower, more expensive, and harder to control.
It is helpful to view the market through three distinct tiers of complexity. At the base level, there is Robotic Process Automation (RPA). These are rules-based systems perfect for high-volume, deterministic tasks like resetting passwords or filing invoices. They do not think; they follow a strict map.
The second tier consists of Copilots. These systems work alongside a human, drafting emails, summarising code, or organising data, but ultimately requiring a human to approve the output.
The third and most expensive tier is the Autonomous Agent. These systems can reason, plan, and execute multi-step workflows without supervision. They are powerful, but they are also computationally expensive and carry a higher risk of error.
The trap occurs when a business deploys a Tier 3 Autonomous Agent for a Tier 1 task. Using a large language model to answer static questions about business hours is an inefficient use of resources. It introduces the risk of hallucination, where the AI invents facts, into a process that demands 100% accuracy. True strategic procurement is not about buying the best AI; it is about right-sizing the AI to the specific job at hand.
The Job-to-be-Done Framework
Before evaluating software vendors, organisations must conduct a rigorous audit of the workflow they intend to automate. This audit should focus less on the desired outcome and more on the variability of the inputs.
If a workflow is repetitive and the data inputs are consistent, such as data entry between two legacy systems, standard automation is often superior to generative AI. It is faster, cheaper, and completely predictable. However, if the workflow involves high variability, such as parsing unstructured Request for Proposal (RFP) documents, interpreting customer sentiment, or synthesising market research, then the reasoning capabilities of an AI agent become necessary.
Furthermore, the risk tolerance of the workflow dictates the type of agent required. In creative industries, an AI that hallucinates or provides a novel, unexpected answer is a feature. In regulated industries like fintech or healthcare, it is a liability. For high-stakes environments, businesses must prioritise agents with strong grounding capabilities. Grounding ensures the agent draws its answers exclusively from a verified internal knowledge base, rather than its general training data, effectively placing a safety perimeter around its intelligence.
Specialization Over Generalization
A significant trend emerging in the Indian B2B market is the shift away from general-purpose models toward vertical-specific agents. Early adopters often made the mistake of trying to force a generalist model, like a standard version of GPT-4, to act as a specialised legal or medical expert. This requires immense effort in prompt engineering and rarely yields perfect results.
The smarter investment today is found in Small Language Models (SLMs) and specialised agents. An AI agent pre-trained specifically on Indian tax compliance or supply chain logistics will often outperform a massive, generalist model.
“The rapid shift from ‘what’ to ‘which’ is the clearest signal that SMEs are past curiosity and are actively seeking deployment. We want to change the perception that IT is expensive… It is actually affordable. If you see the ROI against it, it is worth spending on it.” — Arjun Mittal, Co-Founder, Techjockey.com
However, identifying these niche solutions requires looking beyond the most advertised brands. Decision-makers are better served by exploring the wider array of options available online and through aggregated marketplaces like Techjockey, where specific vertical agents can be compared side-by-side against generalist alternatives.
These specialised agents understand the jargon, the context, and the regulatory framework out of the box. They are typically more cost-effective to run because they do not carry the computational weight of knowing world history when they only need to know about inventory management.
The Integration Reality
Perhaps the most overlooked factor in choosing an AI agent is interoperability. An agent is only as useful as the systems it can access. A common frustration for businesses is deploying a sophisticated support agent that can converse fluently with customers but lacks the technical permission to actually resolve their issues in the CRM or ERP system.
If an agent acts as a silo, an isolated brain that cannot write to your database, its utility is capped at conversation. The most valuable agents in the Techjockey ecosystem are those with deep, pre-built integrations into existing business software. The ability to seamlessly hand off a complex issue to a human operator, complete with a full context summary, is also a critical integration feature. The human-in-the-loop protocol should be a seamless bridge, not a jarring disconnection.
A Phased Approach to Adoption
Finally, to avoid shelfware, businesses should reject the big bang deployment strategy. McKinsey’s 2025 AI report notes that while adoption is high, only about one-third of organisations have successfully scaled AI, largely because they attempt too much, too fast.
As we move deeper into AI Agents to Agentic AI tools, the novelty of AI is fading, replaced by a demand for tangible ROI. The winners in this new era will not be the companies with the most cutting-edge, expensive models.
They will be the organisations that view AI not as magic, but as a utility, applying the right amount of voltage to the right machine. By focusing on specialisation, interoperability, and prudent scaling, businesses can harness the true power of AI agents without falling victim to the hype.
