In our newest interview, we communicate with Santhosh Vijayabaskar, Director of Clever Automation & Course of Re-engineering in Monetary Companies. With years of experience in Robotic Course of Automation (RPA) and Clever Automation (IA), Santhosh shares his perspective on how these applied sciences have developed from easy automation instruments to drivers of enterprise-wide transformation. He delves into key methods for integrating AI with RPA, bettering operational effectivity, and overcoming frequent implementation pitfalls. Achieve insights on how automation can reshape workflows and improve enterprise outcomes on this informative dialogue.
As an knowledgeable in Robotic Course of Automation (RPA) and Clever Automation, how have you ever seen these applied sciences evolve, and what do you think about their most transformative impression on operational effectivity?
Within the early days, RPA was primarily used to automate easy, repetitive duties—primarily mimicking human actions in rule-based processes like information entry and step-by-step duties. It was an amazing software for fast wins however restricted in scope as a result of its dependence on structured information. As organizations started to scale their automation efforts, RPA shortly hit a ceiling when confronted with unstructured information or duties requiring extra advanced decision-making.
That’s the place Clever Automation (IA) stepped in, revolutionizing the house by combining RPA with AI applied sciences like Pure Language Processing (NLP), Machine Studying (ML), Pc Imaginative and prescient, and, extra just lately, Generative AI. IA allowed automation to evolve from a fundamental productiveness software right into a driver of enterprise-wide transformation. It’s not nearly automating duties anymore—IA permits corporations to reimagine total workflows.
For instance, in customer support, AI-driven chatbots can now deal with a wide range of buyer queries, whereas RPA works behind the scenes to replace CRM methods in real-time. This mix has decreased human intervention by as much as 60%, permitting staff to concentrate on extra strategic duties. In my expertise, the mixing of AI with RPA has led to operational value reductions of as much as 40%, whereas concurrently growing accuracy and compliance. It’s a game-changer as a result of it permits organizations to scale effectively with out having to scale their workforce in parallel.
In terms of Course of Excellence, what methodologies or frameworks do you imagine are simplest in driving sustainable effectivity enhancements by way of automation?
Course of excellence is about creating environment friendly, adaptable, and sustainable workflows. In my expertise, methodologies like Lean, Six Sigma, and Agile, when utilized with AI-driven automation, can ship long-lasting effectivity beneficial properties.
Lean is extremely efficient at eliminating waste and streamlining workflows. Easy instruments just like the 5 Whys and Worth Stream Mapping might help establish inefficiencies earlier than automation is even thought of. This ensures that we’re automating optimized processes, not damaged ones. As an illustration, I’ve seen Lean practices scale back pointless steps in a fintech course of by 25%, which in flip made automation much more impactful.
Six Sigma focuses on lowering variation and bettering high quality by way of a data-driven strategy. It’s vital to make clear that reaching a full Six Sigma (99.99966% effectivity) isn’t vital for each group. It’s extra about making use of its rules to succeed in a sigma degree that works in your objectives—whether or not that’s 4-sigma or 5-sigma. I typically use sig sigma instruments like SIPOC (Suppliers, Inputs, Processes, Outputs, Clients) and DMAIC (Outline, Measure, Analyze, Enhance, Management) in the course of the consulting section and all through this system to make sure that enhancements are measurable and sustainable.
Agile methodologies are important for dynamic enterprise environments. The iterative improvement strategy has persistently delivered quicker outcomes and higher stakeholder engagement in my tasks. By mixing these frameworks—Lean for waste discount, Six Sigma for consistency, and Agile for flexibility—automation initiatives result in sustainable, long-term effectivity enhancements.
Might you elaborate on the position RPA performs in reaching seamless integration between current enterprise processes and rising AI applied sciences?
RPA’s position as a bridge between conventional enterprise processes and rising AI applied sciences can’t be overstated. For a lot of organizations, particularly these with legacy methods that lack the flexibleness to combine AI options immediately, RPA serves as a vital middleman. I typically describe RPA because the “glue” that binds the previous with the longer term—permitting organizations to leverage the facility of AI and not using a full overhaul of their current infrastructure. Take legacy methods, for instance.
Many industries, significantly in banking, insurance coverage, and healthcare, depend on older methods which are secure however not designed to work with fashionable AI platforms. RPA can automate the interplay between these methods and newer applied sciences, resembling AI-based doc processing or buyer sentiment evaluation. I’ve seen circumstances the place bots are used to extract information from legacy methods, construction it in a usable format, and feed it into an AI engine for real-time decision-making. This allows organizations to unlock AI’s potential for predictive analytics, machine studying, and even pure language understanding without having to interchange their total infrastructure.
Past the technical integration, RPA additionally performs a crucial position in operationalizing AI fashions. AI’s power lies in its potential to investigate giant datasets and make selections based mostly on patterns, however it’s RPA that takes these selections and turns them into actionable workflows. As an illustration, in customer support, AI can predict the very best plan of action based mostly on historic information, however it’s the RPA bots that perform these actions, whether or not it’s sending follow-up emails, updating CRM information, or escalating circumstances to human brokers when vital. This seamless interplay between RPA and AI ensures that companies can leverage AI insights in actual time, driving extra environment friendly and clever operations.
What are the important thing indicators you utilize to evaluate the success of automation tasks, significantly by way of bettering operational effectivity and delivering measurable enterprise outcomes?
When evaluating the success of an automation mission, I take a look at a number of key indicators. The primary is course of time discount. How a lot quicker is the method being accomplished post-automation? In lots of the tasks I’ve led, course of occasions have been decreased by as a lot as 30-40%. For top-volume duties, this makes a considerable distinction.
Subsequent, I concentrate on error price discount. Automation ought to lower the chance of human errors, which, in industries like finance or healthcare, can result in pricey penalties. In a single monetary companies mission, we decreased errors in a crucial course of from 12% to beneath 1%, considerably bettering compliance and audit efficiency.
Monetary outcomes are, in fact, essential. I usually measure return on funding (ROI) over a 6-12 month interval. Most tasks I’ve labored on obtain optimistic ROI inside this timeframe, particularly when factoring in labor value financial savings and elevated accuracy.
Lastly, worker and buyer satisfaction are key. Automation ought to free staff from repetitive duties, permitting them to concentrate on higher-value work. Clients, alternatively, profit from quicker service. In a single mission, buyer satisfaction scores improved by 20% as a result of quicker response occasions enabled by automation.
Within the context of Clever Automation, how do you make sure that AI-driven processes stay adaptable to quickly altering enterprise environments?
To make sure AI-driven processes stay adaptable to quickly altering enterprise environments in Clever Automation, I concentrate on a number of key methods:
- Modular, microservices-based structure: This design permits elements like RPA bots, AI fashions, or analytics engines to be up to date or changed independently, with out disrupting the whole system.
- Steady studying and suggestions loops: AI fashions want common updates with new information to remain related. For instance, in a customer support software, the AI ought to regulate to new product interactions by studying from evolving buyer queries.
- AI governance framework: Establishing governance helps monitor and regulate AI efficiency according to enterprise objectives. Common A/B testing, situation evaluation, and opinions hold AI aligned with strategic aims.
- Human-in-the-loop strategy: Whereas AI can automate many processes, human oversight is crucial for high-risk duties. This stability ensures adaptability whereas sustaining management for refinement when vital.
Based mostly in your expertise, what are the frequent pitfalls corporations encounter when implementing RPA at scale, and the way can these be mitigated to realize course of excellence?
One of many largest pitfalls I’ve seen is failing to standardize processes earlier than automation. Inconsistent processes throughout departments can result in RPA breaking down or creating inefficiencies. The secret’s to make sure that processes are standardized and optimized upfront.
One other frequent problem is change administration. Staff can typically resist automation as a result of fears of job displacement. In my expertise, one of the simplest ways to mitigate that is to contain staff early within the course of, present coaching, and clearly talk how automation will improve their roles fairly than change them. Lastly, governance is crucial. With out sturdy governance, RPA can find yourself siloed, with totally different groups creating their very own automations. Establishing a Middle of Excellence (CoE) ensures that RPA efforts are aligned, scalable, and compliant with greatest practices.
How do you see the way forward for Robotic Course of Automation evolving with the growing integration of AI, and what improvements are you most enthusiastic about on this house?
The way forward for RPA is deeply intertwined with AI. Cognitive RPA, the place bots not solely observe guidelines but in addition be taught from information, will quickly grow to be the norm. This may permit bots to deal with extra advanced, decision-based duties. I’m significantly excited in regards to the potential of Generative AI in RPA workflows. Think about bots that not solely execute duties but in addition generate insights and even create new workflows based mostly on evolving enterprise circumstances.
Hyperautomation, the place RPA, AI, and analytics work collectively to automate end-to-end processes, is one other development I’m intently following. I’ve already seen AI-driven course of mining instruments establish inefficiencies that may then be automated utilizing RPA, leading to vital productiveness beneficial properties.
In your work, how do you make sure that automation initiatives preserve a human-centric focus, making certain that they complement fairly than change human decision-making?
In automation, my key precept is to increase human capabilities fairly than change them. A human-in-the-loop mannequin is crucial in making certain that automation helps, fairly than replaces, human decision-making. Automation ought to deal with routine, repetitive duties, permitting staff to concentrate on higher-value actions resembling strategic decision-making, problem-solving, and consumer engagement.
Within the monetary companies area the place I work, automation streamlines duties like information reconciliation or compliance reporting, however crucial selections—resembling approving giant transactions or managing portfolios—nonetheless require human judgment. AI can analyze information and supply insights, however associates should interpret these insights, making use of contextual information to make knowledgeable selections.
Equally vital is change administration. By involving staff early within the automation design course of, gathering their suggestions, and providing coaching, we might help them see automation as a software that enhances their work. This strategy fosters collaboration between people and machines, resulting in higher job satisfaction and improved outcomes.
Out of your perspective, how can organizations stability short-term beneficial properties in operational effectivity with the long-term strategic advantages of Clever Automation and AI?
Balancing short-term beneficial properties with long-term strategic worth is without doubt one of the largest challenges organizations face when implementing Clever Automation. Many corporations are tempted to concentrate on fast wins—automating low-hanging fruit that delivers quick value financial savings—however this strategy can restrict the long-term potential of automation. To attain true worth, organizations have to take a phased strategy that focuses on each tactical and strategic outcomes. Within the brief time period, corporations can prioritize automating routine duties that yield quick effectivity beneficial properties, resembling information entry, claims processing, or invoicing. These tasks present a fast ROI and assist construct momentum for future initiatives. Nevertheless, it’s essential to tie these short-term tasks to a broader automation roadmap that aligns with long-term enterprise objectives.
What recommendation would you provide to organizations seeking to embark on their automation journey, significantly in industries which are extremely regulated or face advanced compliance necessities?
For organizations in extremely regulated industries, resembling finance, healthcare, or insurance coverage, compliance ought to be a key consideration from day considered one of any automation mission. My recommendation is to start out by involving authorized and compliance groups early within the course of. Automation instruments, particularly in sectors with stringent laws, should be designed with transparency and auditability in thoughts. In my expertise, automating processes that deal with delicate information, resembling monetary transactions or affected person information, requires sturdy governance frameworks to make sure that regulatory necessities are met with out compromising effectivity. It’s additionally crucial to pick out automation platforms which have built-in compliance options, resembling audit trails, information encryption, and role-based entry management. These capabilities are important for making certain that automated processes stay compliant with business laws.
Moreover, organizations ought to think about implementing AI ethics and governance frameworks to make sure that their automation initiatives are each moral and compliant with evolving regulatory requirements. For corporations new to automation, my recommendation is to start out small, automate just a few key processes that supply quick advantages, after which develop from there. By specializing in high-impact areas and making certain that compliance is constructed into the inspiration of the automation technique, organizations can embark on a profitable automation journey whereas sustaining regulatory peace of thoughts.
