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 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 skilled in Robotic Course of Automation (RPA) and Clever Automation, how have you ever seen these applied sciences evolve, and what do you contemplate their most transformative affect on operational effectivity?
Within the early days, RPA was primarily used to automate easy, repetitive duties—basically mimicking human actions in rule-based processes like knowledge entry and step-by-step duties. It was an important instrument for fast wins however restricted in scope attributable to 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 fundamental productiveness instrument right into a driver of enterprise-wide transformation. It’s not nearly automating duties anymore—IA permits firms to reimagine whole 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 mixture has diminished human intervention by as much as 60%, permitting workers to concentrate on extra strategic duties. In my expertise, the combination of AI with RPA has led to operational value reductions of as much as 40%, whereas concurrently rising accuracy and compliance. It’s a game-changer as a result of it allows organizations to scale effectively with out having to scale their workforce in parallel.
Relating to Course of Excellence, what methodologies or frameworks do you imagine are handiest 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 will help 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 much more impactful.
Six Sigma focuses on lowering variation and bettering high quality by way of a data-driven method. It’s essential to make clear that reaching a full Six Sigma (99.99966% effectivity) isn’t mandatory for each group. It’s extra about making use of its rules to achieve a sigma degree that works in your objectives—whether or not that’s 4-sigma or 5-sigma. I usually use sig sigma instruments like SIPOC (Suppliers, Inputs, Processes, Outputs, Prospects) and DMAIC (Outline, Measure, Analyze, Enhance, Management) throughout the consulting part 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 method has persistently delivered sooner 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.
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 instantly, RPA serves as an important middleman. I usually describe RPA because the “glue” that binds the previous with the longer term—permitting organizations to leverage the ability of AI and not using a full overhaul of their current infrastructure. Take legacy methods, for instance.
Many industries, notably in banking, insurance coverage, and healthcare, depend on older methods which are secure however not designed to work with trendy 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 knowledge from legacy methods, 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 without having to exchange their whole infrastructure.
Past the technical integration, RPA additionally performs a essential position in operationalizing AI fashions. AI’s energy 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 most effective 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 circumstances to human brokers when mandatory. 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, notably when it comes to bettering operational effectivity and delivering measurable enterprise outcomes?
When evaluating the success of an automation venture, I have a look at a number of key indicators. The primary is course of time discount. How a lot sooner is the method being accomplished post-automation? In most of the tasks I’ve led, course of occasions have been diminished by as a lot as 30-40%. For prime-volume duties, this makes a considerable distinction.
Subsequent, I concentrate on error charge 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 providers venture, we diminished errors in a essential 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 constructive 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 workers from repetitive duties, permitting them to concentrate on higher-value work. Prospects, then again, profit from sooner service. In a single venture, buyer satisfaction scores improved by 20% attributable to sooner 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 parts 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 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 consistent with enterprise objectives. Common A/B testing, situation evaluation, and opinions hold AI aligned with strategic goals.
- 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 mandatory.
Primarily based in your expertise, what are the frequent pitfalls firms 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 secret’s to make sure that processes are standardized and optimized upfront.
One other frequent problem is change administration. Staff can usually resist automation attributable to 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 quite than change them. Lastly, governance is essential. With out sturdy governance, RPA can find yourself siloed, with completely 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 rising 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 study from knowledge, will quickly change into the norm. It will enable bots to deal with extra complicated, decision-based duties. I’m notably 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 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 intently following. I’ve already seen AI-driven course of mining instruments determine 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 keep a human-centric focus, making certain that they complement quite than change human decision-making?
In automation, my key precept is to increase human capabilities quite than change them. A human-in-the-loop mannequin is crucial in making certain that automation helps, quite than replaces, human decision-making. Automation ought to deal with routine, repetitive duties, permitting workers to concentrate on higher-value actions resembling strategic decision-making, problem-solving, and consumer engagement.
Within the monetary providers area the place I work, automation streamlines duties like knowledge reconciliation or compliance reporting, however essential choices—resembling 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 information to make knowledgeable choices.
Equally essential is change administration. By involving workers early within the automation design course of, gathering their suggestions, and providing coaching, we will help them see automation as a instrument 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 among the largest challenges organizations face when implementing Clever Automation. Many firms are tempted to concentrate on fast wins—automating low-hanging fruit that delivers fast value 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 quick time period, firms can prioritize automating routine duties that yield fast effectivity beneficial properties, resembling knowledge 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 supply to organizations seeking to embark on their automation journey, notably in industries which are extremely regulated or face complicated compliance necessities?
For organizations in extremely regulated industries, resembling finance, healthcare, or insurance coverage, compliance needs to be a key consideration from day one in every of any automation venture. 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, have to be designed with transparency and auditability in thoughts. In my expertise, automating processes that deal with delicate knowledge, 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 essential to pick out automation platforms which have built-in compliance options, resembling audit trails, knowledge 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 contemplate implementing AI ethics and governance frameworks to make sure that their automation initiatives are each moral and compliant with evolving regulatory requirements. For firms new to automation, my recommendation is to start out small, automate just a few key processes that supply fast 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.
