Reimagining Automation in the Mid-Market Sector — qBotica - July 14, 2025
By Staff Reports - July 14th, 2025
As generative AI reshapes the automation landscape, mid-market enterprises are under pressure to modernize—without the disruption or cost traditionally associated with large-scale transformation.
In this exclusive BizTechReports interview, Mahesh Vinayagam, CEO and Founder of qBotica, explains how combining generative AI with robotic process automation (RPA) is changing the game. Rather than ripping and replacing core systems, organizations are now layering conversational AI on top of existing platforms to deliver intelligent workflows that scale efficiently.
Vinayagam shares insights into how this automation-as-a-service approach is driving real ROI, elevating customer experience, and disrupting traditional BPO models. With a service-forward, tech-enabled model, qBotica is helping organizations move beyond cost containment to strategic enablement—without forcing massive IT overhauls.
NOTE: The feature below has been organized into the strategic, operational, financial contexts that emerged in the interview.
Here is what he had to say:
STRATEGIC IMPERATIVES
BTR: What’s driving the shift from traditional RPA to AI-enabled automation in the mid-market?
Vinayagam: Traditional RPA was always about driving efficiency through structured automation. You had a clearly defined process, typically repetitive and rule-based, and you trained bots to perform it faster and cheaper than people. That model still has value—but today’s environment demands more.
Mid-market organizations, in particular, are facing higher customer expectations, shorter product cycles, and more complex service delivery models. These firms can’t afford to scale linearly with headcount. They need to scale intelligently—with systems that adapt, learn, and augment human capability. That’s where generative AI comes in.
We’re seeing the convergence of AI and RPA because companies want automation that can handle variation, interpret unstructured data, and respond in real time. A quoting system that once processed a fixed set of inputs now needs to interpret emails, extract nuanced requirements, and generate custom responses—often in seconds. That’s not traditional RPA anymore; that’s intelligent automation.
More importantly, automation is no longer a support function. It’s becoming a strategic lever. In the past, automation was something IT did to reduce labor costs. Now it’s something the business wants to accelerate market responsiveness, improve customer satisfaction, and expand into new geographies. We’re turning reactive processes into proactive growth engines.
BTR: How do you see this affecting traditional outsourcing models?
Vinayagam: The impact is profound. For decades, business process outsourcing (BPO) was the go-to strategy for reducing operational cost—what I often call the “your mess for less” model. You’d take a function like accounts payable or customer service, offshore it to a provider, and lock in a three- to five-year contract. The provider would hire hundreds of people to do the work, and your costs would go down.
That model is still very much alive—but it’s being disrupted. What AI and automation have done is decouple scale from labor. You don’t need 100 people in a low-cost country anymore if you can deploy an intelligent agent that handles 80 percent of those interactions faster, cheaper, and without errors.
We’ve already seen traditional BPOs lose deals to automation-first firms. Clients are realizing they can achieve higher service levels, faster cycle times, and better data visibility without the complexity of managing offshore relationships or dealing with legacy SLAs. In many ways, AI-enabled automation is becoming the new outsourcing—but with far more agility and transparency.
OPERATIONAL IMPLICATIONS
BTR: How does AI change the way organizations approach process design?
Vinayagam: AI completely reorients the automation mindset. In the past, when we looked at a process, we asked: What are the steps? What are the inputs and outputs? What are the rules? And how can we script those rules into a bot?
Now we’re asking a very different question: How does the business actually want this experience to function? How can we make that interaction more fluid, more adaptive, and more useful for the customer or employee at the center of it?
Take a quoting function, for example. A logistics provider might get thousands of email requests each day for pricing on shipments. Some are simple. Others contain attachments, questions, or require clarification. With traditional automation, you’d filter out the easy ones and throw the rest to a human queue.
With AI, we can build an agent that reads the email, parses the request, identifies missing data, replies with clarification questions, and then—if appropriate—generates a quote or escalates it. It becomes an intelligent triage layer. That’s a fundamentally different operational model.
BTR: Where is this being applied most effectively?
Vinayagam: We’re seeing strong adoption in three categories: quote-to-cash, hire-to-retire, and case-to-resolution processes. These include quoting, customer onboarding, invoice processing, recruitment screening, benefits enrollment, and support ticket triage.
In one example, a healthcare staffing firm used our platform to automate initial candidate screening. Instead of having recruiters manually review hundreds of resumes, an AI agent now emails shortlisted candidates, conducts a conversational pre-interview, captures structured data, and escalates promising leads to human staff. That used to take hours per candidate. Now it’s done in minutes—at scale.
Another example is in order management for a mid-sized equipment finance company. Their clients submit all kinds of documents—purchase orders, equipment specs, financial disclosures. These are often non-standard, handwritten, or in multiple languages. Traditional OCR and RPA couldn’t handle that. But AI-powered document intelligence can. It extracts the right data, interprets context, and feeds it into underwriting systems in near real time.
FINANCIAL CONSEQUENCES
BTR: How quickly do mid-market firms see returns from automation investments?
Vinayagam: The economics are compelling. For most of our clients, if a process consumes more than $200,000 per year in labor cost—and many do—then we can deliver full payback within 12 months. In some cases, it's within a single quarter.
That kind of return changes the conversation. You’re no longer fighting for capital expenditure approval or making a five-year business case. You’re showing the CFO that this pays for itself, and then some, within the current fiscal cycle.
We also help clients quantify value beyond labor savings. For example, by increasing quote velocity, one client grew top-line revenue by nearly 15 percent in six months. Another reduced customer onboarding time from weeks to days, improving client retention and upsell opportunities.
BTR: Does this model change how automation is purchased?
Vinayagam: Absolutely. We’ve adopted an automation-as-a-service model. Instead of asking clients to buy licenses, hire consultants, and manage infrastructure, we bundle everything into a monthly subscription. That includes the AI layer, the automation workflows, the implementation, the support, and even the hosting.
This flattens the cost curve and removes friction from the buying process. Clients don’t need a massive upfront investment. They just need a qualified use case and a willingness to commit to outcomes over a two- to three-year term.
We’ve also built flexibility into our model. Clients can scale usage up or down. They can add languages, geographies, or use cases as their business evolves. It’s the Tesla model applied to automation—you subscribe to the capabilities you need, and everything else is already built in.
TECHNOLOGICAL IMPLEMENTATION
BTR: How does your approach fit into existing enterprise architectures?
Vinayagam: That’s one of the biggest advantages of this model. We don’t ask clients to replatform. Our AI and automation layers sit on top of existing systems—whether it’s SAP, Salesforce, QuickBooks, Oracle, or even Excel.
We use connectors and APIs to interact with those systems. Where APIs don’t exist, we use computer vision or email parsing to extract and act on data. This means we can deploy automation without disrupting existing processes or retraining entire departments.
In fact, one of our clients was still using a homegrown ERP system built in the early 2000s. We were able to automate key parts of their invoice processing workflow by treating the interface as a black box—using screen-scraping and AI-driven data entry rather than forcing a rewrite.
BTR: Is the AI really flexible enough to evolve with the business?
Vinayagam: Yes—and that’s what separates AI-first automation from traditional RPA. The models learn from experience. They improve accuracy over time. They identify failure points and suggest better approaches. And because we control both the automation engine and the conversational layer, we can continuously iterate without breaking downstream processes.
In the past, if you wanted to upgrade a workflow, it could take months. Now we can push updates, retrain models, or reconfigure responses in days—or even hours.
We’re also model-agnostic. We don’t lock clients into one LLM or one automation platform. Depending on the use case, we might use OpenAI, Claude, Google Gemini, or an internal model. That flexibility ensures that the technology always fits the task—not the other way around.
Closing Take:
AI-powered automation is redefining how mid-market companies modernize, scale, and compete. By combining intelligent interfaces with flexible workflow execution, this new model delivers real business value without the disruption of traditional transformation programs. For organizations ready to act, the tools—and the ROI—are already here.