Low-code and no-code platforms are reshaping enterprise operations, promising agility, scalability, and faster time-to-market. Balakrishnan Kavikkal, Co-founder and CEO of Autonom8 Inc., highlights how these transformative technologies are disrupting traditional implementation models, offering businesses an edge in navigating the complexities of automation. In a candid conversation, Kavikkal shares insights on the evolution of enterprise workflows, the integration of AI-driven tools, and the critical steps organizations must take to harness the full potential of hyper-automation while ensuring compliance, security, and workforce adaptability.
TAM: How do you see the rise of low-code/no-code platforms transforming the traditional developer ecosystem, and what steps are necessary to ensure these tools empower developers without compromising on quality and scalability?
Balakrishnan Kavikkal: Before starting Autonom8, I ran a company called ServYarn, a services company. While I made substantial profits, I always felt the services industry was ripe for disruption. Implementing solutions like Salesforce or ERP often revealed misalignment among stakeholders. Here’s how it typically unfolded:
A company forms a high-level committee to select a product. Major corporations pitch their solutions with much fanfare, creating a utopian belief that the chosen product will solve all problems. After months of deliberation, the product is handed off to consulting firms for implementation. Internally, the responsibility shifts from the board to operational teams like HR or finance. This is where the real issues begin.
Consulting firms thrive on project delays, billing monthly, while product companies disengage post-sale. Meanwhile, internal teams pressure consultants to customize the product to fit their needs, often without change management. A year or two later, the board questions the progress. Millions have been spent, compromises are accepted, and no one is satisfied. I experienced this frustration firsthand with CRM and ERP implementations, even with reputed products like Salesforce.
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The root cause, I realized, was a lack of alignment among stakeholders. Organizations need an approach similar to startups: start small with a clear minimum viable product (MVP), go live quickly, and iteratively adapt as requirements evolve. Instead of disrupting the entire organization, this approach focuses on achieving incremental success.
However, traditional products are rigid. They meet 70–80% of requirements but struggle with customization for large enterprises. Even minor changes require going back to the product’s design team, often overseas.
This insight inspired the idea of a low-code platform: a flexible solution where every feature is a component. Unlike traditional CRMs or ERPs, low-code allows users to assemble components quickly and make adjustments as needed. The platform empowers organizations to adapt their workflows in real-time, avoiding the pitfalls of lengthy implementations.
We also saw an opportunity to integrate emerging technologies like AI. Even before ChatGPT’s launch, we collaborated with OpenAI to incorporate large language models (LLMs) into our platform. This allowed us to develop a co-pilot feature where users could write in natural language, and the system would generate workflows automatically.
For example, we implemented a loan origination system—a high-volume workflow—within eight weeks, compared to over a year with traditional methods. Changes are made in minutes or hours, not days or weeks, transforming the implementation process.
In the last five years, this approach has proven highly effective. By combining low-code flexibility with AI-driven automation, Autonom8 offers a transformative solution for modern enterprises.
TAM: With Gen AI driving rapid innovation, concerns about automation-induced job displacement persist. How do you believe organizations can balance adopting Gen AI-driven solutions while fostering trust and addressing workforce anxiety?
Balakrishnan Kavikkal: I don’t have a definitive answer, and I tend to think differently from those who claim that jobs won’t be lost due to technological advancements. I believe job disruption is inevitable and significant. For example, just a couple of days ago, TCS announced its results, showing a revenue increase of a few billion dollars, yet they only added around 500–600 people. Compare this to five or ten years ago when similar growth would have required tens of thousands of new hires because revenue growth was directly tied to workforce expansion for tasks like coding.
The reality is that the nature of work is changing. The traditional mindset—starting a job, specializing in a specific skill, and retiring as an expert in that field—no longer applies. To stay relevant, the workforce must adapt, continuously learn, and pivot as needed. Opportunities still exist, but they require agility and the ability to acquire new capabilities.
Take Generative AI (GenAI) as an example. It’s enabling remarkable efficiencies. At my office, almost every engineer has a GenAI tool open; they’re not manually writing code anymore. The same shift is visible in other professions. Think about doctors—many patients now research their symptoms online before visiting a doctor, essentially seeking a second opinion rather than a primary one.
This type of disruption has always accompanied industrialization and automation. The difference now is that AI, particularly GenAI, is putting even traditionally “irreplaceable” jobs at risk. The only way forward is to remain adaptable, actively seek out new skills, and focus on building capabilities that ensure relevance in an AI-driven world.
Jobs in fields like contact centers, back-office operations, and even managerial roles involving data analysis and decision-making are increasingly vulnerable. For example, we support AI agents on our platform, and these agents can analyze data and make decisions just as effectively—if not more so—than human managers.
The automation projects we’re working on with banks and airlines illustrate this shift. It’s impressive, but it’s also unsettling to see how many roles AI can replicate or enhance. The pace of change is daunting, and it highlights the need for constant learning and adaptability in this new era.
TAM: Hyper-automated workflows are gaining traction in the banking sector. How do you envision these workflows evolving to address challenges such as compliance, fraud detection, and customer experience while maintaining operational efficiency?
Balakrishnan Kavikkal: Our team, including myself and the founders, has extensive experience in enterprise software. We’ve built and sold software for large corporations, which ingrained in us the importance of handling data, compliance, and security. When we developed our platform, even though it focused on low-code capabilities, security was a fundamental aspect of the core architecture. As a SaaS company, we understood that even a single data breach or security incident could jeopardize our entire business, particularly since our target customers are large enterprises, not small businesses. We aimed to show how automation could deliver value at scale for these organizations.
From the outset, data handling and security were non-negotiable priorities. Today, our platform in India complies with all of RBI’s regulatory requirements. Out of our 40 customers, around 25 are in the financial sector. These organizations frequently conduct rigorous third-party security audits—quarterly or at least annually. While minor areas for improvement are sometimes identified, there have been no critical issues with the platform.
Handling sensitive data is central to our operations. For example, we process approximately 5 million customer documents, including Aadhaar and PAN data, every month for multiple financial institutions. This volume of sensitive information requires meticulous attention to data security and architecture. Even now, as our customers continue to run penetration tests and other evaluations, ensuring data safety remains a top priority.
TAM: Video KYC has emerged as a pivotal innovation for financial institutions. What unique technologies or methodologies does Autonom8 employ to enhance security and efficiency in video KYC processes, and how do these advancements influence customer trust and regulatory compliance?
Balakrishnan Kavikkal: The core principles remain the same. There are systems within your control and others that operate within the broader internet ecosystem. For instance, customers interact with your platform through their devices and networks, which are outside your direct control.
Thankfully, today’s technologies are robust enough to ensure secure data transmission. We’re all familiar with internet banking and similar applications, where data is securely encrypted during transit and decrypted on the other end. Standards for handling data in transit, at rest, and during access are well-established, making it feasible to maintain stringent security measures without compromise.
These are globally accepted protocols, not proprietary standards, which ensures interoperability and reliability. For example, encryption standards dictate how data is secured during transmission, storage, and access. We adhere to the highest levels of these standards to ensure compliance and data safety. This includes ensuring data residency within the country, even when leveraging cloud solutions.
Major cloud providers like Google and AWS have made significant investments to align their platforms with regulatory requirements, such as those of IRDAI, RBI, and other agencies. These providers offer cloud environments that are already approved for secure data storage and transfer, making them reliable partners for compliance-driven businesses.
Our primary responsibility is to build secure applications and eliminate any possibility of data leaks. By strictly following these globally recognized standards, we ensure that our platform remains secure and compliant at all levels.
TAM: What are your views on the DPDP Act? How does it impact your company?
Balakrishnan Kavikkal: Yes, we’re evaluating some of these requirements, and our internal security team has been reviewing the relevant laws. Some of our customers have also raised these concerns, as they involve an additional layer of reporting and compliance related to handling customer data and securing customer consent for its use. From a technology perspective, I don’t believe it’s particularly challenging to implement these requirements.
At the end of the day, we act as an intermediary between the end consumer and the enterprise. The greater challenge will likely lie with the enterprises themselves. While we host multiple enterprises running their workflows on our platform, we maintain strict data segregation. For instance, if a customer applies for a loan with two different banks using our platform, we ensure their data is handled separately for each bank. Our core architecture is designed to guarantee that customer data remains under the control of the enterprise. In many cases, once a workflow is completed, the data is transferred back to systems within the enterprise’s control, not ours.
For enterprises, the upcoming regulations will introduce stricter controls on how they can use data. Large corporations, especially those with multiple verticals like banks and insurance companies, may face new constraints to prevent potential misuse of data. While there may be differing opinions on whether every aspect of the regulation is beneficial, from a technical standpoint, I don’t foresee any significant hurdles in implementing the necessary changes to comply with the Digital Personal Data Protection (DPDP) requirements.
TAM: As Gen AI becomes increasingly integrated into low-code/no-code solutions, how can platforms like Autonom8’s ensure they remain accessible to businesses of all sizes while continuing to push the boundaries of innovation in automation?
Balakrishnan Kavikkal: Today, most of our customers are medium and large enterprises, with only a few small businesses in the mix. The reason is straightforward. Platforms like Freshdesk or Zoho are primarily designed for small and medium enterprises (SMEs). For example, we ourselves use Zoho for our HR needs because, as a small company, it’s an easy and cost-effective solution that allows us to quickly get up and running without extensive product evaluation.
However, as an enterprise grows, its requirements become more complex. Larger organizations often need siloed environments, such as separate servers and tenants, for better control and security. Additionally, our platform has inherent costs associated with security and infrastructure, which may not be economical for very small customers. These factors naturally make our services more suited to medium and large enterprises.
The key advantage we offer lies in flexibility. Many off-the-shelf products, like those I mentioned earlier, work well for about 80–90% of use cases but are difficult to customize. Our core philosophy is different: we empower enterprises to automate their processes exactly how they want. This approach is especially valuable for large organizations with well-defined processes that need to be executed or modified in specific ways, rather than being forced to adapt to rigid, pre-built solutions.
While theoretically, any customer could benefit from our platform, in practice, the base costs of setting up a tenant, implementing security measures, and maintaining infrastructure make us a better fit for medium and large enterprises. For instance, while we rarely pitch for commoditized solutions like simple website chatbots, some of our customers still use our platform for such purposes. A notable example is the San Diego Zoo in the US, which relies on our platform to manage its chatbot for website support. However, for solutions that have become highly commoditized, we may not always be the best fit as a partner.
TAM: Advice for organisations looking to automate their workflows
Balakrishnan Kavikkal: There’s a lot of buzz in the tech world right now, and it’s reflected in my own sales pipeline. Everyone seems intrigued by the rapid changes happening globally—not just in technology, but also with broader events like Trump and other developments. However, a lot of time is being wasted because people are still in the experimentation phase. They’re trying things out but aren’t fully convinced about Gen AI yet. Concerns like hallucinations, data privacy, and whether large language models (LLMs) might provide incorrect or inappropriate responses dominate the conversation.
At the same time, there’s a growing realization that we’re in the midst of a massive disruption. Companies feel they need to explore this space, but there’s a hesitation in making concrete decisions. Vendors like us are bearing the brunt of this uncertainty, with everyone wanting to run proof-of-concepts (POCs) without committing to a clear direction.
In my view, a more effective approach would be to start with something manageable and within your control. For example, instead of focusing on customer-facing solutions where there’s concern about how a bot’s error might impact your brand, consider automating internal workflows. Back-office operations, decision-making processes, and business rules are ideal candidates for low-code platforms and Gen AI-driven automation. These can be implemented with much less risk and can yield quick, tangible results.
The pace of technological advancement today is astonishing. If you were to go on vacation for just two weeks without your devices, you’d return to a completely transformed landscape. OpenAI might have released a new version, and other players would have launched updates to catch up. It feels like we’re moving so fast that an avatar of you could be managing your work while you’re away.
Even for someone like me, with 30 years of experience in technology, it’s incredible to witness how quickly things are evolving. That’s why I believe it’s essential to start small. Automate a manageable process and build confidence, because eventually, there will be no choice but to embrace automation.
It’s a bit like buying laptops today. When I started my career, only a few people had access to the green-screen computers, and they were considered a luxury. Now, laptops are a basic necessity—you don’t even think twice about getting one when you need to get work done. Automation is heading in the same direction.
Very good