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Regulated industries, such as healthcare and finance, often face significant challenges in adopting new technologies due to stringent regulations. These regulatory frameworks can impede AI integration, leading to outdated systems and processes.

A Harvard Business Review article highlights key challenges, including regulatory uncertainty, lack of transparency, bias risks, and stringent data privacy requirements. It states that many AI models operate as black boxes, making accountability difficult.  Additionally, compliance with laws like GDPR and HIPAA increases operational costs and slows adoption.

In the case of JP Morgan Chase, an analysis from the International Journal of Scientific Research and Engineering Trends found that AI helped the company navigate regulatory challenges by utilizing advanced technologies like fraud detection and risk management algorithms. Through tools such as the Katana Lens platform, the bank proactively manages risks and increases compliance by tracking over 600,000 rule changes annually. This system monitors regulatory shifts, enabling better risk oversight and ensuring alignment with evolving regulations.

Emerj Managing Editor Matthew DeMello recently spoke with Vrinda Khurjekar, VP of North Americas at Searce, and Paul Pallath, Applied AI Lead at Searce, on the ‘AI in Business’ podcast to discuss the challenges regulated industries face in adopting AI, especially in the context of compliance and risk management. They also highlighted how AI agents can autonomously adapt to business changes, driving agility and improving operational efficiency.

This article examines two critical insights from their conversation:

• Integrate autonomous AI agents for self-sustaining automation: Leveraging agentic systems to autonomously adapt to changes, reduce manual effort, and drive ROI through agile, continuous processes.

• Adopting a process-first mindset for AI integration: Optimizing AI for specific processes across the enterprise, not as a standalone solution, to drive broader adoption and business impact.

• Establishing responsible AI frameworks: Build trust with stakeholders and avoid ethical pitfalls by defining which tasks AI should handle autonomously and ensure transparency, accountability, and compliance.

Guest: Vrinda Khurjekar, VP of North Americas, Searce 

Expertise: Business Process Improvement, Automation, Artificial Intelligence

Brief Recognition: Vrinda Khurjekar is the VP of Americas at Searce, where she has played a key role for the past 14 years, contributing to the company’s growth from a small 20-member team to a global tech consulting partner with over 1,300 employees. She is also a core member of Searce’s global executive team. Vrinda earned her Master’s Degree in Computer Science from Pune University, India.

Guest: Paul Pallath, VP of Applied AI, Searce

Expertise: Generative AI, Digital Transformation, Machine learning

Brief Recognition: Dr. Paul Pallath is the VP of Applied AI at Searce. He holds a Ph.D. from IIT Delhi, India, and has previously worked with leading companies such as Vodafone India, SAP, and Levi Strauss. An industry-recognized thought leader in both AI and Data, Paul blends technical expertise and operational excellence with business acumen.

Integrate Autonomous AI Agents for Self-Sustaining Automation

Vrinda and Paul open the conversation by discussing the challenges of regulated industries adopting new technologies. Vrinda highlights how strict regulations slow down tech adoption, leaving industries like healthcare and finance with outdated systems, unrealized benefits from past transformations, and a shortage of skilled talent. 

Paul adds that while digital-native companies rapidly innovate, traditional firms struggle between the fear of missing out on advancements like AI and regulatory hurdles. The growing gap between them leads some business leaders to hesitate, or worse, rush adoption without fully understanding compliance requirements.

Vrinda explains the evolution of automation from business process outsourcing (BPO) to RPA and now AI. She tells the story of how BPO focused on cost reduction through outsourcing processes, while RPA brought efficiency by hard-coding repetitive tasks, such as reducing a 30-minute task to 15 minutes. However, RPA lacked flexibility — if data formats changed, the automation process could fail:

“In an RPA world, you would have a piece of invoice paper or a scanned copy, and you were hard coding to say that you know what the date will be on the right-hand side top, and the signature will be on the left side bottom. You would have to find those at a pixel level to automate it. So if, for some reason, the date was on the left side top, the model would fail.”

– Vrinda Khurjekar, VP of North Americas at Searce

Conversely, AI fundamentally transforms automation by understanding processes contextually, not just following rigid rules. For example, unlike RPA, AI can interpret invoice variations without failure, making automation more adaptable and efficient. The shift also makes it easier to measure ROI, as AI delivers more comprehensive, intelligent automation.

Expanding on Vrinda’s history of BPO and RPA, Paul highlights how the advent of large language models in the last few years and emerging agentic AI systems today have revolutionized automation. Unlike RPA, which relies on fixed, static coding and requires manual updates when processes change, AI agents can autonomously plan, reason, and adapt to evolving business needs. 

These agents communicate like humans and collaborate with other AI agents to develop coherent plans and execute tasks while integrating seamlessly with business applications. As businesses evolve — much like living organisms — AI agents can detect and adjust to changes in processes, systems, or integration layers. 

These agentic capabilities, in turn, drive holistic, dynamic, self-sustaining automation processes throughout the enterprise, reducing the need for constant human intervention and freeing teams to focus on strategic tasks rather than troubleshooting process changes.

Adopting a Process-First Mindset for AI Integration

Vrinda notes that companies typically fall into two categories in the early stages of AI adoption: those already experimenting with AI and those unsure where to start. 

She goes on to explain that companies adopting AI to use new technology often fail to realize its full potential. The key issue is treating AI as a standalone solution rather than integrating it strategically into business processes. AI might not transform every step of a process but can optimize specific ones, potentially eliminating or altering others. 

Without this process-focused approach, AI projects risk becoming isolated experiments that don’t address real business problems or achieve widespread adoption. AI initiatives must be tied directly to solving tangible, everyday business challenges for successful implementation.

While AI technology may be complex, Paul adds that its primary goal is to simplify business processes, enabling faster decision-making and better customer service. By adopting a “process-first” mindset, organizations can identify operational pain points and ensure AI is implemented to make existing processes more efficient and relevant.

He insists such a process-first approach is especially critical in regulated industries like healthcare, life sciences, and banking, where balancing innovation with compliance is essential.

Paul then highlights three key benefits of process-first thinking:

• Regulatory Compliance: By analyzing processes early, companies can identify potential compliance risks before AI is integrated, avoiding regulatory pitfalls.
• Operationalizing Governance: Process-first thinking helps embed AI within existing governance frameworks, ensuring transparency, traceability, and auditability.
• Building Trust and Driving Adoption: Involving stakeholders throughout the process fosters trust and encourages widespread adoption, which is crucial for AI to deliver real business value. Without this trust, many AI implementations fail to gain traction.

“Taking a process-first approach ensures that everyone involved—whether they interact with, use, or develop AI—plays a role in its integration across the business. This approach not only embeds AI into existing systems, but also brings stakeholders along in the journey, fostering trust and confidence in the changes ahead while enabling faster adoption.

Actual value creation comes from adoption. By prioritizing processes, organizations can address essential considerations across industries, particularly those operating within regulatory frameworks.”

– Paul Pallath, VP of Applied AI at Searce

Establishing Responsible AI Frameworks

Ultimately, Vrinda asserts the importance of cross-departmental collaboration and local decision-making when transforming business processes. She explains that in a scenario like a hospital revamping its patient experience, involving the front office and departments like insurance and inventory is crucial since their processes are interconnected. 

Building alignment across these departments from the beginning is key. Additionally, she stresses the importance of empowering teams closest to the customer journey, as they can provide insights into the actual processes and help drive more effective, locally informed decisions. Her bottom-up approach helps ensure the transformation aligns with real-world needs and nuances.

In turn, Paul emphasizes defining what tasks AI should handle autonomously and what should remain under human oversight, advocating for a responsible AI framework. Using healthcare and banking as examples, Paul underscores AI’s ethical challenges due to its lack of empathy and understanding of social norms.

He notes that AI may identify urgent clinical needs in healthcare but lacks the emotional intelligence to prioritize patients based on age or other human factors. In similar scenarios, he warns that AI could perpetuate biases inherent in historical data in banking, leading to potentially unfair outcomes.

Paul then concludes that building trust through transparency, accountability, and auditability in AI processes is essential. He quickly stipulates that such capabilities require establishing clear AI use and data policies, ensuring the public and stakeholders trust the technology.

Finally, Paul points out that while regulation is necessary, it should not stifle innovation. He suggests that industries engage proactively with regulators to shape balanced, effective AI regulations.