In our newest interview, we converse with Santhosh Vijayabaskar, Director of Clever Automation & Course of Re-engineering in Monetary Providers. With years of experience in Robotic Course of Automation (RPA) and Clever Automation (IA), Santhosh shares his perspective on how these applied sciences have advanced 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 widespread implementation pitfalls. Acquire 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 take into account 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 knowledge entry and step-by-step duties. It was an amazing software for fast wins however restricted in scope on account of its dependence on structured knowledge. As organizations started to scale their automation efforts, RPA rapidly hit a ceiling when confronted with unstructured knowledge or duties requiring extra complicated decision-making.
That’s the place Clever Automation (IA) stepped in, revolutionizing the area 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 primary productiveness software right into a driver of enterprise-wide transformation. It’s not nearly automating duties anymore—IA permits corporations to reimagine complete 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 techniques in real-time. This mix has lowered human intervention by as much as 60%, permitting workers to deal with extra strategic duties. In my expertise, the mixing of AI with RPA has led to operational price 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.
With regards to Course of Excellence, what methodologies or frameworks do you imagine are only in driving sustainable effectivity enhancements by 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 can assist determine 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 way more impactful.
Six Sigma focuses on decreasing variation and bettering high quality by a data-driven method. It’s vital to make clear that attaining a full Six Sigma (99.99966% effectivity) isn’t vital for each group. It’s extra about making use of its rules to achieve a sigma stage that works in your targets—whether or not that’s 4-sigma or 5-sigma. I usually use sig sigma instruments like SIPOC (Suppliers, Inputs, Processes, Outputs, Clients) and DMAIC (Outline, Measure, Analyze, Enhance, Management) through 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 growth method has persistently delivered quicker outcomes and larger 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.
May you elaborate on the function RPA performs in attaining seamless integration between current enterprise processes and rising AI applied sciences?
RPA’s function 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 techniques that lack the flexibleness to combine AI options instantly, RPA serves as a vital middleman. I usually describe RPA because the “glue” that binds the previous with the long run—permitting organizations to leverage the ability of AI with out a full overhaul of their current infrastructure. Take legacy techniques, for instance.
Many industries, significantly in banking, insurance coverage, and healthcare, depend on older techniques which can be steady however not designed to work with trendy AI platforms. RPA can automate the interplay between these techniques and newer applied sciences, equivalent to AI-based doc processing or buyer sentiment evaluation. I’ve seen instances the place bots are used to extract knowledge from legacy techniques, construction it in a usable format, and feed it into an AI engine for real-time decision-making. This permits organizations to unlock AI’s potential for predictive analytics, machine studying, and even pure language understanding with no need to switch their complete infrastructure.
Past the technical integration, RPA additionally performs a essential function in operationalizing AI fashions. AI’s power lies in its capability to investigate massive datasets and make choices primarily based on patterns, but it surely’s RPA that takes these choices and turns them into actionable workflows. As an illustration, in customer support, AI can predict the very best plan of action primarily based on historic knowledge, but it surely’s the RPA bots that perform these actions, whether or not it’s sending follow-up emails, updating CRM information, or escalating instances 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 employ to evaluate the success of automation tasks, significantly when it comes to bettering operational effectivity and delivering measurable enterprise outcomes?
When evaluating the success of an automation mission, I have 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 instances have been lowered by as a lot as 30-40%. For prime-volume duties, this makes a considerable distinction.
Subsequent, I deal with error price discount. Automation ought to lower the probability of human errors, which, in industries like finance or healthcare, can result in pricey penalties. In a single monetary companies mission, we lowered errors in a essential course of from 12% to under 1%, considerably bettering compliance and audit efficiency.
Monetary outcomes are, in fact, essential. I sometimes 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 price financial savings and elevated accuracy.
Lastly, worker and buyer satisfaction are key. Automation ought to free workers from repetitive duties, permitting them to deal with higher-value work. Clients, however, profit from quicker service. In a single mission, buyer satisfaction scores improved by 20% on account of quicker response instances 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 deal with 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 complete system.
- Steady studying and suggestions loops: AI fashions want common updates with new knowledge 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 in step with enterprise targets. Common A/B testing, situation evaluation, and evaluations maintain AI aligned with strategic aims.
- Human-in-the-loop method: Whereas AI can automate many processes, human oversight is essential for high-risk duties. This steadiness ensures adaptability whereas sustaining management for refinement when vital.
Based mostly in your expertise, what are the widespread pitfalls corporations encounter when implementing RPA at scale, and the way can these be mitigated to attain 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 bottom line is to make sure that processes are standardized and optimized upfront.
One other widespread problem is change administration. Workers can usually resist automation on account of fears of job displacement. In my expertise, one of the simplest ways to mitigate that is to contain workers early within the course of, present coaching, and clearly talk how automation will improve their roles reasonably than exchange them. Lastly, governance is essential. With out sturdy governance, RPA can find yourself siloed, with totally different groups creating their very own automations. Establishing a Heart of Excellence (CoE) ensures that RPA efforts are aligned, scalable, and compliant with finest 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 area?
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 knowledge, will quickly turn into the norm. This may permit bots to deal with extra complicated, decision-based duties. I’m significantly excited concerning 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 primarily based on evolving enterprise circumstances.
Hyperautomation, the place RPA, AI, and analytics work collectively to automate end-to-end processes, is one other pattern I’m carefully following. I’ve already seen AI-driven course of mining instruments determine inefficiencies that may then be automated utilizing RPA, leading to important productiveness beneficial properties.
In your work, how do you make sure that automation initiatives keep a human-centric focus, making certain that they complement reasonably than exchange human decision-making?
In automation, my key precept is to increase human capabilities reasonably than exchange them. A human-in-the-loop mannequin is crucial in making certain that automation helps, reasonably than replaces, human decision-making. Automation ought to deal with routine, repetitive duties, permitting workers to deal with higher-value actions equivalent to strategic decision-making, problem-solving, and shopper engagement.
Within the monetary companies area the place I work, automation streamlines duties like knowledge reconciliation or compliance reporting, however essential choices—equivalent to approving massive transactions or managing portfolios—nonetheless require human judgment. AI can analyze knowledge and supply insights, however associates should interpret these insights, making use of contextual data to make knowledgeable choices.
Equally vital is change administration. By involving workers early within the automation design course of, gathering their suggestions, and providing coaching, we can assist them see automation as a software that enhances their work. This method fosters collaboration between people and machines, resulting in larger job satisfaction and improved outcomes.
Out of your perspective, how can organizations steadiness 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 deal with fast wins—automating low-hanging fruit that delivers quick price financial savings—however this method can restrict the long-term potential of automation. To attain true worth, organizations must take a phased method 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, equivalent to knowledge entry, claims processing, or invoicing. These tasks present a fast ROI and assist construct momentum for future initiatives. Nonetheless, it’s essential to tie these short-term tasks to a broader automation roadmap that aligns with long-term enterprise targets.
What recommendation would you supply to organizations trying to embark on their automation journey, significantly in industries which can be extremely regulated or face complicated compliance necessities?
For organizations in extremely regulated industries, equivalent to finance, healthcare, or insurance coverage, compliance ought to be a key consideration from day one in all any automation mission. My recommendation is to begin by involving authorized and compliance groups early within the course of. Automation instruments, particularly in sectors with stringent rules, should be designed with transparency and auditability in thoughts. In my expertise, automating processes that deal with delicate knowledge, equivalent to 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 essential to pick automation platforms which have built-in compliance options, equivalent to audit trails, knowledge encryption, and role-based entry management. These capabilities are important for making certain that automated processes stay compliant with business rules.
Moreover, organizations ought to take into account 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 begin small, automate a couple of key processes that provide quick advantages, after which increase 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.