Exploring the Transformative Potential of Generative AI in the BPM Industry
The National Association of Software and Service Companies (NASSCOM) recently hosted a thought-provoking roundtable discussion in Mumbai, India. This event consisted of industry experts sharing their experiences and insights. The focus was on leveraging generative AI in the business process management (BPM) sector. Among many of the participants was our Mumbai site leader, Sonali Bhagwat. Sonali had the wonderful opportunity to represent DATAMARK at this event.
A Platform for Knowledge Exchange
The NASSCOM round table served as a valuable platform for professionals in the BPM industry to exchange ideas. Specifically, the discussion focused on the utilization of AI. Participants from dozens of companies shared their success stories, shedding light on diverse applications of this cutting-edge technology.
DATAMARK’s Journey with Generative AI
As a representative of DMi, Sonali shared the remarkable ways in which our organization has embraced generative AI. The utilization of AI has allowed DMi to deliver exceptional results for clients. One of the key areas where DATAMARK utilizes AI is in our contact centers, aligning with broader trends in AI customer experience improvement. Leveraging this kind of technology enables us to achieve significant advancements in call summarization, automated response generation, sentiment analysis, and predictive analysis, along with the development of chatbots and virtual assistants.
Training and Continuous Improvement
DMi also leverages generative AI for training purposes, combining the power of AI algorithms with manual intervention to ensure smooth operations and continuously improve performance. By analyzing data and feedback, the system identifies areas for improvement, providing valuable insights to agents and supervisors alike.
Expanding Possibilities
During the NASSCOM roundtable, participants also discussed the exciting potential of generative AI in settling insurance claims. Currently at DATAMARK, we automate the data entry process for insurance claims, and it is something that DATAMARK is looking to expand upon. By integrating AI technology, DATAMARK aims to enhance the claims settlement process further, streamlining it and improving overall efficiency.
How Generative AI Is Creating New Business Value Across Industries
Generative AI is transforming business when use cases are tied to clear operational goals and validated against metrics. In BPM, an AI application should demonstrate measurable business value across business functions by reducing average handle time, improving first contact resolution, and increasing accuracy and compliance. Structured pilots, A/B testing, and cost per interaction tracking help confirm that a generative AI model, AI agents, or other AI tools optimize business processes rather than add complexity.
Effective AI for business requires disciplined deployment. Implementing generative AI includes human review for high-risk actions, auditable prompts and responses, privacy controls for PII and PHI, and retrieval grounding so artificial intelligence systems cite approved sources. With this foundation, organizations can integrate generative AI into workflows, scale approved generative AI solutions, and manage model performance over time.
Generative AI applications deliver value when they address specific business challenges. Examples include knowledge retrieval for agents, automated quality reviews with explainable rationales, redaction and data classification for regulated data, and claim document triage with exception routing. These generative AI use cases, supported by governance and continuous monitoring, help generative AI in business improve decision quality and throughput while maintaining control. As enterprises continue adopting generative AI, a measurable framework ensures that the value of generative AI is realized in production, not just in prototypes.
Partner with DATAMARK to Unlock the Power of Generative AI
At DATAMARK, we believe that AI is not just a tool; it’s a transformative force in business process management. From intelligent automation to enhanced customer interactions, we are actively exploring how generative AI can deliver measurable value for our clients. If you’re ready to drive innovation, improve operational efficiency, and stay ahead in a rapidly evolving industry, we’re ready to collaborate. Visit our website to explore our AI-driven solutions. Follow us on LinkedIn for more insights on how we’re shaping the future of BPM through technology and expertise.
FAQs About Generative AI in Business
A large language model is an AI system trained on vast amounts of text to understand and generate human language. It is the foundation that makes generative AI tools useful in practice, enabling them to summarize calls, draft responses, answer questions, and engage in dialogue. At DATAMARK, the value we have seen from these models comes not just from the technology itself but from how carefully it is trained, grounded in relevant data, and governed to make sure outputs are accurate and compliant with client requirements.
One of the things that came through clearly at the NASSCOM roundtable was that organizations serious about generative AI are equally serious about how it is governed. Responsible AI means keeping humans in the loop for high-risk decisions, protecting sensitive data, maintaining auditability of prompts and outputs, and monitoring model behavior over time. At DATAMARK, we treat this not as a limitation on what AI can do but as the foundation that makes it possible to deploy AI confidently at scale and maintain client trust throughout.
Predictive AI looks at historical data to forecast what is likely to happen next. Generative AI goes a step further by producing new content or responses based on what it has learned. In our contact center work, we use both. Predictive analysis helps us understand where issues are likely to arise, while generative AI handles things like call summarization, automated response generation, and sentiment analysis. The two capabilities work well together, and understanding the distinction helps organizations apply each one to the right kind of problem.
Our experience has been that the organizations getting the most from generative AI started with a clear business problem rather than the technology. At DATAMARK, our AI work in contact centers grew out of specific operational goals: reducing handle time, improving response accuracy, and giving agents better real-time support. Starting with well-scoped use cases makes it easier to validate value early, build internal confidence, and develop the governance discipline needed before scaling further. Jumping to broad implementation without that foundation tends to create complexity rather than resolve it.
Training data quality is something we pay close attention to at DATAMARK, particularly given the regulated industries we work in. The outputs of a generative AI system are only as reliable as the data it was trained on. That means sourcing data that is accurate and representative, removing or anonymizing sensitive information before it enters the training pipeline, and documenting where data comes from so that model behavior can be audited when needed. Getting this right from the start saves significant effort later and is essential for maintaining performance in production environments where accuracy and compliance are not optional.
