Digital product building has entered a new phase. For years, many teams treated artificial intelligence as a layer added on top of an existing experience. A chatbot answered questions. A model powered a recommendation widget. Those features were helpful, but they rarely changed the core way work got done. Today, the highest impact products are being designed differently. Intelligence is moving inside the workflow, closer to the moments where decisions happen and value is created. The next step is even bigger: products built around agents that can reason, plan, and act, while learning from outcomes over time.
Sandeep Shivam is a product and technology leader with nearly two decades of experience building AI-driven solutions in the financial sector. As Associate Director for the Touchless Lending platform at Tavant, he leads innovation in borrower and lender experience, intelligent automation, and advanced decision systems for enterprise-scale customers. He is a Fellow of BCS, a Distinguished Fellow of the SCRS, an IEEE Senior Member, and a member of the Forbes Technology Council, known for shaping practical and responsible AI across complex digital ecosystems. Sandeep has delivered keynote talks at conferences across the United States and has served as a reviewer, judge, and session chair for journals and research conferences. His work is published through Forbes, the Mortgage Bankers Association, HousingWire, and other industry platforms. He is passionate about engineering AI native products that create value and advocates innovation that improves outcomes for businesses and communities.
The Challenge: Moving Beyond AI as an Add-On Feature
The early wave of AI adoption was mostly optional. Teams focused on accuracy, user interface placement, and whether the feature "fit" into the product. Large language models (LLMs) and Retrieval Augmented Generation (RAG) changed the equation. When a system can retrieve trusted knowledge at the right moment, it can do more than answer. It can guide a process, reduce effort, and make the experience faster and more consistent. That is the bridge between AI-enhanced products and AI-native products.
In an AI native product, intelligence is not a feature. It is the operating layer. The system observes context, proposes the next step, validates inputs, and improves through feedback. This shift demands a leadership mindset change. When AI is an add-on, product teams can optimize for isolated success. When AI is embedded into workflows, teams must think in systems: inputs, decisions, handoffs, guardrails, and learning loops.
In agentic products, the product itself behaves like a participant in the workflow. It operates with a degree of autonomy, it searches for missing information, it weighs tradeoffs, and it collaborates with humans. That requires stronger governance, clearer accountability, and success metrics that include trust and reliability, not just engagement.
A Three-Stage Journey: From Conversation to Autonomous Action
Sandeep Shivam's work in highly regulated mortgage lending demonstrates how AI capabilities evolve through distinct stages. Mortgages are highly regulated, which means every step must remain explainable, auditable, and compliant with lender policy and GSE requirements. That reality shaped a three-stage journey that illustrates the transformation from AI that talks, to AI that checks, to AI that drives the workflow.
Stage 1: The Add-On Assistant
The first stage was an add-on assistant. The safest entry point was a policy-grounded chatbot that could answer borrower questions, such as how dependents are defined or what documentation is generally required. It reduced confusion and improved support, but it did not change the workflow. Borrowers still uploaded incomplete documents, reviews still happened later, and the cycle time remained largely the same.
"The safest entry point for regulated industries is establishing trust through conversational AI that operates within clear policy boundaries," explains Sandeep Shivam. "It improves support and reduces confusion, but the workflow itself remains unchanged. This stage is about building foundation and user confidence."
Stage 2: Intelligence Embedded in the Workflow
The second stage embedded intelligence directly into the workflow. When borrowers submit documents against lender conditions, delays often come from simple mismatches: wrong borrower, wrong year, missing pages, or the wrong document type. Evaluating documents upon upload enables instant feedback. If a condition required the most recent two months of bank statements, the product could confirm whether the file was a bank statement, whether it belonged to the borrower, and whether the dates were current.
This reduced back and forth because errors were corrected in real time, not days later. Still, the system was reactive. It validated what arrived, but it did not actively drive the file toward readiness.
"This verification layer transforms the user experience by eliminating delays," Sandeep Shivam notes. "What previously took days of back-and-forth now happens instantly. But we're still responding to what users give us, not actively orchestrating what needs to happen next."
Stage 3: AI Native Model with Autonomous Agents
The third stage moved to an AI native model with an always-present agent. In this design, the agent does more than validate documents. It extracts signals, applies policy context, and determines what is needed next to make the file decision ready. If a bank statement shows a large deposit, the agent does not wait for an underwriter to find it later. It flags the issue early, asks for a letter of explanation, and if the borrower indicates it was a gift, the agent can generate a compliant gift letter and guide the borrower through execution.
This left-shift underwriting work by pulling clarifications forward, reducing downstream conditions, and shortening end-to-end cycle time while preserving auditability.
"In the AI-native stage, the product becomes a guided system that actively drives toward completion," Sandeep Shivam emphasizes. "The agent reasons about what's needed, orchestrates next steps, and collaborates with humans all while maintaining full compliance and auditability. This is where AI transforms from a tool into an intelligent participant in the workflow."
The Leadership Mindset: Systems Thinking for AI-Native Products
The journey from conversational AI to autonomous agents requires more than technical implementation—it demands a fundamental shift in how product leaders think about AI. When AI is an add-on, teams can optimize features in isolation. When AI becomes the operating layer, success requires systems thinking.
"Teams must consider the entire system: how inputs flow, where decisions happen, how handoffs occur, what guardrails protect outcomes, and how learning loops drive continuous improvement," Sandeep Shivam explains. "In agentic products, you're not just building features you're designing an intelligent participant that operates with measured autonomy."
This shift impacts governance and accountability. Success metrics expand beyond traditional engagement to include trust, reliability, and outcome quality. Teams must establish clear boundaries for AI decision-making, ensure explainability for regulated environments, and create feedback mechanisms that enable continuous learning without compromising compliance.
Sandeep Shivam and the Future of AI-Native Products
As Associate Director at Tavant, Sandeep Shivam brings deep expertise in building AI-driven solutions that operate at enterprise scale in highly regulated environments. His work spans the full spectrum of intelligent automation: from natural language interfaces that improve borrower experience, to verification systems that accelerate operations, to autonomous agents that orchestrate complex multi-step workflows.
Sandeep Shivam's contributions extend beyond implementation to thought leadership in the AI community. As a Fellow of BCS, Distinguished Fellow of the SCRS, and IEEE Senior Member, he has delivered keynote talks at conferences across the United States and served as a reviewer, judge, and session chair for journals and research conferences. His insights, published through Forbes, the Mortgage Bankers Association, HousingWire, and other industry platforms, consistently advocate for innovation that creates real value for businesses and communities.
"The lesson is simple: the journey moves from AI that talks, to AI that checks, to AI that drives the workflow," Sandeep Shivam reflects. "Each step increases impact without breaking compliance, and the AI native stage turns the product into a guided system that improves through structured feedback and policy grounded decisions."
The Practical Path Forward: When to Use Which AI Approach
A final point matters for leaders. Not every problem needs an agent. Many teams rush to agents because it sounds advanced, but the smarter move is to start with the workflow. If the task is repetitive and inputs are structured, rule-based automation can be the best answer. AI earns its place when inputs are unstructured, and the work requires extraction, summarization, or synthesis. AI agents make sense when the workflow requires multiple steps, changing context, and judgment, and when the system must act, not just respond.
"Understanding when to apply each AI paradigm is critical," Sandeep Shivam notes. "Conversational AI establishes trust and handles unstructured questions. Verification systems eliminate friction in defined processes. Autonomous agents orchestrate complex workflows that require reasoning and adaptation. The key is matching the AI capability to the workflow complexity and business value at stake."
For organizations seeking to evolve from AI-enhanced to AI-native products, the three-stage framework Sandeep Shivam demonstrates provides a practical roadmap. Start with conversational foundations that build user trust. Embed verification into workflows to eliminate friction and accelerate processes. Progress to autonomous agents when workflows demand orchestration, reasoning, and adaptive decision-making.
"We're at the beginning of a transformation in how organizations approach database performance," Sandeep Shivam concludes. "Intelligence is moving inside workflows, closer to the moments where decisions happen and value is created. The next step is even bigger: products built around agents that can reason, plan, and act, while learning from outcomes over time. This isn't about replacing human expertise it's about amplifying it, enabling organizations to scale intelligence across entire workflows while maintaining the trust and reliability that regulated industries demand."
As enterprises continue to navigate digital transformation, the ability to effectively leverage AI across the full spectrum from conversation to verification to autonomous action becomes increasingly critical. With leaders like Sandeep Shivam demonstrating practical implementation in complex, regulated environments, AI-native products are poised to reshape how organizations create value in the digital economy.
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