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, enhancing operational effectivity, and overcoming frequent implementation pitfalls. Acquire 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 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 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 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), Laptop Imaginative and prescient, and, extra not too long ago, 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 quite a lot 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 staff to give attention to 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 growing 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.
In the case of Course of Excellence, what methodologies or frameworks do you imagine are handiest in driving sustainable effectivity enhancements via 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 positive factors.
Lean is extremely efficient at eliminating waste and streamlining workflows. Easy instruments just like the 5 Whys and Worth Stream Mapping may also help determine inefficiencies earlier than automation is even thought-about. This ensures that we’re automating optimized processes, not damaged ones. For example, I’ve seen Lean practices cut back pointless steps in a fintech course of by 25%, which in flip made automation much more impactful.
Six Sigma focuses on decreasing variation and enhancing high quality via a data-driven method. It’s vital to make clear that attaining a full Six Sigma (99.99966% effectivity) isn’t obligatory for each group. It’s extra about making use of its rules to succeed in a sigma stage that works on your targets—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) through 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 growth method has constantly 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.
May you elaborate on the position RPA performs in attaining 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 techniques that lack the pliability to combine AI options straight, RPA serves as an important middleman. I typically describe RPA because the “glue” that binds the previous with the long run—permitting organizations to leverage the facility 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 are secure however not designed to work with fashionable AI platforms. RPA can automate the interplay between these techniques 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 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 while not having to exchange their complete infrastructure.
Past the technical integration, RPA additionally performs a vital position in operationalizing AI fashions. AI’s energy lies in its capability to investigate massive datasets and make choices primarily based on patterns, nevertheless it’s RPA that takes these choices and turns them into actionable workflows. For example, in customer support, AI can predict the perfect plan of action primarily based on historic information, nevertheless 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 obligatory. 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 when it comes to enhancing operational effectivity and delivering measurable enterprise outcomes?
When evaluating the success of an automation challenge, I take 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 instances have been lowered by as a lot as 30-40%. For top-volume duties, this makes a considerable distinction.
Subsequent, I give attention to error fee 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 challenge, we lowered errors in a vital course of from 12% to under 1%, considerably enhancing compliance and audit efficiency.
Monetary outcomes are, after all, 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 give attention to higher-value work. Clients, however, profit from sooner service. In a single challenge, buyer satisfaction scores improved by 20% as a result of sooner 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 give attention to 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 all the system.
- Steady studying and suggestions loops: AI fashions want common updates with new information to remain related. For instance, in a customer support utility, the AI ought to modify to new product interactions by studying from evolving buyer queries.
- AI governance framework: Establishing governance helps monitor and modify AI efficiency consistent with enterprise targets. Common A/B testing, state of affairs evaluation, and critiques preserve AI aligned with strategic aims.
- Human-in-the-loop method: Whereas AI can automate many processes, human oversight is vital for high-risk duties. This stability ensures adaptability whereas sustaining management for refinement when obligatory.
Primarily based in your expertise, what are the frequent 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 secret is 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, the easiest way to mitigate that is to contain staff early within the course of, present coaching, and clearly talk how automation will improve their roles quite than substitute them. Lastly, governance is vital. With out robust 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 additionally be taught from information, will quickly turn into the norm. It will enable 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 additionally generate insights and even create new workflows primarily based on evolving enterprise situations.
Hyperautomation, the place RPA, AI, and analytics work collectively to automate end-to-end processes, is one other development 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 positive factors.
In your work, how do you make sure that automation initiatives keep a human-centric focus, guaranteeing that they complement quite than substitute human decision-making?
In automation, my key precept is to increase human capabilities quite than substitute them. A human-in-the-loop mannequin is important in guaranteeing that automation helps, quite than replaces, human decision-making. Automation ought to deal with routine, repetitive duties, permitting staff to give attention to 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 vital choices—resembling approving massive 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 data to make knowledgeable choices.
Equally vital is change administration. By involving staff early within the automation design course of, gathering their suggestions, and providing coaching, we may also help 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 stability short-term positive factors in operational effectivity with the long-term strategic advantages of Clever Automation and AI?
Balancing short-term positive factors with long-term strategic worth is without doubt one of the largest challenges organizations face when implementing Clever Automation. Many corporations are tempted to give attention to fast wins—automating low-hanging fruit that delivers instant 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 brief time period, corporations can prioritize automating routine duties that yield instant effectivity positive factors, 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 targets.
What recommendation would you supply to organizations trying to embark on their automation journey, significantly 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 all any automation challenge. My recommendation is to begin 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 robust governance frameworks to make sure that regulatory necessities are met with out compromising effectivity. It’s additionally vital to pick automation platforms which have built-in compliance options, resembling audit trails, information encryption, and role-based entry management. These capabilities are important for guaranteeing that automated processes stay compliant with business laws.
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 just a few key processes that provide instant advantages, after which increase from there. By specializing in high-impact areas and guaranteeing that compliance is constructed into the muse of the automation technique, organizations can embark on a profitable automation journey whereas sustaining regulatory peace of thoughts.