The Agent Universe: From Automation to Autonomy

Abstract

The panel explored how AI‑driven “agentic” automation is reshaping banking, financial services, and wealth‑management workflows. Ruchika described Netwest’s large‑scale deployment of AI‑enabled tools that boost productivity while preserving regulatory compliance. Dr. Diksit recounted his three‑decade journey building national‑scale digital systems, emphasizing the need for strict governance, explainability, and a human‑in‑the‑loop approach. Sanjeev illustrated how AI augments wealth‑advisor–client relationships, arguing that the real competitive edge lies in up‑skilling (or “rewiring”) staff to become AI‑experts. The discussion concluded with audience questions on data security, AI‑driven credit processes, and the evolving role of physical branches in an increasingly virtual financial ecosystem.

Detailed Summary

• The moderator opened with light‑hearted remarks about Delhi traffic and the late arrival of panelist Sanjeev.

• He outlined three thematic pillars for the discussion:

  1. Enterprise reality – turning hyper‑automation concepts into production‑ready AI agents.
  2. Architecture, governance, and trust – the technical and regulatory scaffolding required for safe agentic solutions.
  3. Future‑ready workforce – reskilling and cultural change needed to realize the promised productivity gains.

• He invited Ruchika to start the conversation.

2. Ruchika (Netwest Group) – AI‑Led Hyper‑Automation in a Global Bank

2.1 Business Impact

• AI is positioned as a trust‑building catalyst that deepens customer relationships.

• Netwest has deployed Microsoft Copilot and internal LLM tools to 60 000 employees.

• Customer‑facing outcomes:

  • Retail banking: “Quora” (an intelligent assistant) handles 12.9 M interactions for 20 M customers; AI now supports 4‑to‑11 end‑to‑end journeys.
  • Commercial & Institutional: 100 % of complaint handling is AI‑summarized and supported.
  • Private banking & wealth: Relationship managers receive AI augmentation, boosting productivity by 30 %.
  • Engineering: 12 000 developers use AI tools; roughly 35 % of code is AI‑generated.

• Overall, AI frees staff from repetitive tasks so they can focus on human‑centric activities (money‑management, life‑event advice).

2.2 Governance & Explainability

• Regulatory pressure demands model auditability (“why did a model decide”).
• Netwest instituted an AI & Data Ethics Code of Conduct and mandatory training (≈ 58 000 employees completed).
• Leadership involvement: a Head of Responsible AI (2024) and a Chief AI Research Officer (2023) embed governance at every layer.
• The bank follows a four‑step governance loop: policy → training → audit trails → continuous feedback.

2.3 Workforce Enablement

• Skill‑building programs (partnered with University of Edinburgh and Tier‑1 Indian institutes) certify staff on responsible AI.
• Emphasis on leadership engagement to ensure AI ethics percolates throughout the organization.

3. Dr. Pankaj Diksit (Signet) – Architectural Foundations & Human‑in‑the‑Loop

3.1 Career Snapshot & Lessons Learned

• 28 years of experience: Indian Army (colonel) → IBM → GST national platform → CTO of GEM → Chief AI Officer at Signet.
• Highlighted early hallucination issues in AI engines (e.g., fabricated citations) that taught him the importance of curated, verified knowledge bases.

3.2 Core Architectural Principles for AI‑Enabled Hyper‑Automation

Principle	Explanation
Zero‑Trust Security & Data Fabric	All data pipelines enforce strict identity verification; data is siloed per client (“multi‑tenant”) and encrypted.
Compliance‑by‑Design	Governance controls are baked into every FGLT layer (Framework‑Governance‑Logic‑Technology).
Modularity & API‑First	Systems are tightly defined yet loosely coupled through APIs, allowing rapid integration while preserving auditability.
Explainability & Audit Trails	Every AI decision must be traceable; logs record which model component produced a result.
Hybrid Human‑AI Loop	AI handles high‑volume, low‑risk tasks; a threshold‑based risk delegation routes borderline cases to humans for final approval.

3.3 Application to Regulated Workflows

• Litigation Management: AI drafts responses to GST notices; humans approve content before filing.
• KYC & Tax Returns: AI pre‑processes data; a final human check ensures 100 % accuracy required for regulatory filings.
• Hybrid Computation Model: Combine Excel (trusted numerical engine) with AI for context extraction and user interaction.

3.4 ROI Perspective

• AI alone can be costlier than human processing for certain compliance tasks (up to 7×).
• Value is derived from risk reduction, error elimination, and speed (e.g., instant motor‑insurance quotes reduced from 30 min to 30 sec).

4. Sanjeev – Wealth‑Management Perspective; Reskilling vs. Re‑wiring

4.1 AI as “Friend or Foe”

• AI is not a mere tool; it is an agentic assistant that can amplify human capabilities.
• The Indian IT labour advantage is shifting: “AI will replace those who don’t adopt it.”

4.2 Productivity Gains Through AI

• Automation of repetitive tasks (data entry, research, portfolio analysis) frees advisors to focus on relationship‑building.
• Sanjeev’s own AI EA (Executive Assistant) schedules meetings, summarizes calls, and follows up automatically.
• Example: AI‑generated portfolio analysis now processes 500 portfolios per day (potentially 200 000) with error rates dropping to 0.001 %.

4.3 Return‑on‑Investment (ROI) Outlook

• Compliance‑critical processes justify higher AI spend because they prevent costly errors.
• Investment in people‑centric AI training (“Beyond” program) cultivates internal AI champions who can create bots in weeks, dramatically reducing hiring costs.

4.4 Skill Development: Reskilling vs. Re‑wiring

• Ruchika argues for both up‑skilling (training on AI ethics, coding, and responsible use).
• Sanjeev contends that rewiring—adopting a mindset of continuous self‑learning and hands‑on experimentation—is the true differentiator.
• Both agree that AI fluency is now a core competency for all roles, not just data scientists.

5. Audience Q&A

Question	Respondent(s)	Key Points
Data‑security & breach protection in FinTech	Ruchika (Netwest)	Emphasised data‑strategy partnership with AWS & Accenture, zero‑trust guardrails, data democratization to enable AI while protecting client data.
Segregating client data (multi‑tenant security)	Dr. Diksit	Stressed zero‑trust, siloed data, encryption, and strict access controls to preserve trust.
Bottlenecks for AI‑driven credit disbursement	Dr. Diksit & Sanjeev	Identified high‑cost AI for tasks that can be handled by RPA, need for limited data exposure, and the necessity of physical human presence for trust.
Future of physical branches vs. virtual AI	Sanjeev	Argues that physical presence will remain vital for high‑trust interactions, especially sales and relationship roles; AI will augment but not replace human touch.
Balancing AI cost vs. benefit	Dr. Diksit	Recommend a hybrid AI/RPA model, exposing only minimal data to AI agents and leveraging inexpensive RPA for deterministic tasks.

The moderator closed the session, directing participants to his LinkedIn for follow‑up answers and announcing the next session.

Key Takeaways

• AI‑led hyper‑automation is already at production scale in large banks, touching millions of customers and generating measurable productivity gains (e.g., 30 % higher relationship‑manager output, 35 % AI‑generated code).
• Governance, explainability, and zero‑trust security are non‑negotiable for regulated financial institutions; dedicated roles (Head of Responsible AI, Chief AI Research Officer) are now commonplace.
• Hybrid human‑AI workflows – using risk‑based thresholds to decide when a human must review AI output – balance speed with compliance.
• Architectural best‑practice: modular, API‑first design, data fabric, and compliance‑by‑design enable rapid iteration while preserving auditability.
• Reskilling vs. re‑wiring: both are required. Formal training builds a baseline of responsible AI knowledge; continuous, hands‑on experimentation rewires mental models to treat AI as a collaborative partner.
• ROI is derived more from risk mitigation and service acceleration than from outright cost reduction; AI is justified where it prevents errors, speeds up customer interactions, or unlocks new revenue streams (e.g., instant insurance quotes).
• Physical human presence remains essential for trust‑heavy stages of the customer journey (wealth‑advice, sales, compliance verification), even as AI automates back‑office functions.
• Data strategy is the backbone of AI – a robust, secure, and democratized data foundation is critical to enable trustworthy agents without exposing client data across tenants.

These insights illustrate how Indian financial institutions are leveraging scale, talent, and digital infrastructure to pioneer an agent‑centric future where autonomous software agents augment – rather than replace – human expertise.

See Also:

• beyond-the-cloud-the-sovereign-ai-moment
• ai-for-fraud-prevention-and-financial-inclusion-in-bfsi
• democratizing-ai-resources-in-india
• enterprise-adoption-of-responsible-ai-challenges-frameworks-and-solutions
• scaling-trusted-ai-for-8-billion
• ai-innovators-exchange-accelerating-innovation-through-startup-and-industry-synergy
• shaping-secure-ethical-and-accountable-ai-systems-for-a-shared-future
• ai-beyond-moonshots-a-playbook-for-many
• ai-driven-enforcement-better-governance-through-effective-compliance-services
• building-ai-for-bharat-from-innovation-to-outcomes