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 widespread implementation pitfalls. Achieve insights on how automation can reshape workflows and improve enterprise outcomes on this informative dialogue.
As an professional 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 impression 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 information entry and step-by-step duties. It was an incredible device 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 rapidly 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 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 device 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 programs in real-time. This mix has decreased human intervention by as much as 60%, permitting staff to deal with extra strategic duties. In my expertise, the combination 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.
Relating to Course of Excellence, what methodologies or frameworks do you consider are best 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 features.
Lean is extremely efficient at eliminating waste and streamlining workflows. Easy instruments just like the 5 Whys and Worth Stream Mapping may help establish 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 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 enhancing high quality via a data-driven method. It’s essential to make clear that attaining a full Six Sigma (99.99966% effectivity) isn’t mandatory 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, Prospects) and DMAIC (Outline, Measure, Analyze, Enhance, Management) in the course of 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 persistently delivered quicker outcomes and better 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 present 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 programs that lack the flexibleness to combine AI options instantly, RPA serves as an important 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 present infrastructure. Take legacy programs, for instance.
Many industries, notably in banking, insurance coverage, and healthcare, depend on older programs which are steady however not designed to work with fashionable AI platforms. RPA can automate the interplay between these programs and newer applied sciences, akin to AI-based doc processing or buyer sentiment evaluation. I’ve seen circumstances the place bots are used to extract information from legacy programs, 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 with no need to interchange their total infrastructure.
Past the technical integration, RPA additionally performs a vital function in operationalizing AI fashions. AI’s energy lies in its capacity to research massive datasets and make selections based mostly on patterns, however it’s RPA that takes these selections and turns them into actionable workflows. For example, in customer support, AI can predict the most effective 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 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 utilize to evaluate the success of automation tasks, notably by way of enhancing operational effectivity and delivering measurable enterprise outcomes?
When evaluating the success of an automation challenge, 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 most of the tasks I’ve led, course of instances have been decreased by as a lot as 30-40%. For prime-volume duties, this makes a considerable distinction.
Subsequent, I deal with error charge 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 decreased errors in a vital course of from 12% to beneath 1%, considerably enhancing 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 price financial savings and elevated accuracy.
Lastly, worker and buyer satisfaction are key. Automation ought to free staff from repetitive duties, permitting them to deal with higher-value work. Prospects, then again, profit from quicker service. In a single challenge, buyer satisfaction scores improved by 20% as a result 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 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 in keeping with enterprise objectives. Common A/B testing, situation evaluation, and critiques hold 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 steadiness ensures adaptability whereas sustaining management for refinement when mandatory.
Primarily based 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 greatest 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 hot button is to make sure that processes are standardized and optimized upfront.
One other widespread 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 moderately than exchange them. Lastly, governance is vital. 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 area?
The way forward for RPA is deeply intertwined with AI. Cognitive RPA, the place bots not solely comply with guidelines but in addition study from information, will quickly turn into the norm. This can enable bots to deal with extra advanced, decision-based duties. I’m notably 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 based mostly on evolving enterprise situations.
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 establish inefficiencies that may then be automated utilizing RPA, leading to vital productiveness features.
In your work, how do you make sure that automation initiatives keep a human-centric focus, making certain that they complement moderately than exchange human decision-making?
In automation, my key precept is to increase human capabilities moderately than exchange them. A human-in-the-loop mannequin is important in making certain that automation helps, moderately than replaces, human decision-making. Automation ought to deal with routine, repetitive duties, permitting staff to deal with higher-value actions akin to strategic decision-making, problem-solving, and shopper engagement.
Within the monetary companies area the place I work, automation streamlines duties like information reconciliation or compliance reporting, however vital selections—akin to 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 selections.
Equally essential is change administration. By involving staff early within the automation design course of, gathering their suggestions, and providing coaching, we may help them see automation as a device that enhances their work. This method fosters collaboration between people and machines, resulting in better job satisfaction and improved outcomes.
Out of your perspective, how can organizations steadiness short-term features in operational effectivity with the long-term strategic advantages of Clever Automation and AI?
Balancing short-term features with long-term strategic worth is without doubt one of the greatest challenges organizations face when implementing Clever Automation. Many corporations are tempted to deal with fast wins—automating low-hanging fruit that delivers rapid price financial savings—however this method can restrict the long-term potential of automation. To realize true worth, organizations must take a phased method that focuses on each tactical and strategic outcomes. Within the quick time period, corporations can prioritize automating routine duties that yield rapid effectivity features, akin to 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 trying to embark on their automation journey, notably in industries which are extremely regulated or face advanced compliance necessities?
For organizations in extremely regulated industries, akin to finance, healthcare, or insurance coverage, compliance needs to be a key consideration from day one in every of any automation challenge. My recommendation is to start out 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 information, akin 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 vital to pick out automation platforms which have built-in compliance options, akin to audit trails, information encryption, and role-based entry management. These capabilities are important for making certain that automated processes stay compliant with trade rules.
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 corporations new to automation, my recommendation is to start out small, automate a couple of key processes that supply rapid advantages, after which increase from there. By specializing in high-impact areas and making certain 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.
