GuideJul 2026 · 11 min read
AI Investment Due Diligence: Defensible Moats
A practical guide to assessing defensible AI moats in regulated finance, from evidence interoperability to inference economics and governance.
Read guideENGAGE · TEAM
Abhay Chrungoo
Managing Director
Experience25+ years building systems in complex, high-stakes environments.
LinkedInAbhay is Managing Director of Bugni Labs and leads its work in systems engineering for the AI-native enterprise, the layer where agentic, rules-based and policy-based systems meet the operational reality of regulated enterprises. He works hands-on, leading teams where the bar is set not by what the technology can do but by what a regulator will accept. Over more than 25 years building web-scale systems for regulated enterprises, he has put AI into the decisions they are held accountable for, including financial-crime detection, screening, customer risk and credit, and built systems that stay governable, observable and reliable as scale and complexity grow. He developed Bugni's AI-Native Engineering practice, in which human engineers and AI systems work as one team, with governance and evidence built into how systems are made. Today his teams ship agentic systems into financial-crime detection and investigation at major UK banks.
What Abhay writes about
Written from the vantage point of someone who has watched enterprise AI programmes stall. The recurring threads are why pilots never become platforms, where governance gaps open as adoption scales, how to judge a vendor or a defensible moat, and what AI-native engineering actually changes about the way teams build. Several pieces are pattern work on modernising core banking without a rewrite.
25 pieces · 16 Perspectives · 1 Field Note · 8 Guides
PerspectiveJun 2026 · 7 min read
Autonomous business models need control
Autonomous business models only work when decision evidence, domain boundaries, and rollback are engineered into the operating model.
Read perspectiveGuideJun 2026 · 13 min read
Build a defensible AI MoatMap
A practical guide to mapping defensible AI moats through domain logic, evidence, governance, provider independence, and reversibility.
Read guidePerspectiveJun 2026 · 8 min read
GenAI's trough is a governance gap
The GenAI trough shows that financial institutions need governed AI platforms, not another round of isolated pilots.
Read perspectivePerspectiveJun 2026 · 8 min read
AI obsolescence is architectural
AI obsolescence is caused by tight coupling, weak governance, and delivery systems that cannot absorb model churn.
Read perspectiveGuideJun 2026 · 13 min read
AI copilots need engineering control
A guide to enterprise AI copilots for regulated engineering teams, covering context, governance, verification, security, and rollout.
Read guidePerspectiveJun 2026 · 4 min read
The Strangler Fig Playbook for Core Banking
Core banking modernisation works when the new platform grows around live operations, proving each boundary before legacy capability is retired.
Read perspectiveGuideMay 2026 · 14 min read
AI Copilot Enterprise: Developer's 2026 Buyer's Guide
A practical buyer's guide for enterprise AI copilots in regulated engineering teams, covering selection criteria, integration, governance, rollout, and FAQ.
Read guidePerspectiveMay 2026 · 3 min read
Why Agentic AI Initiatives Fail and What Survivors Share
Agentic AI initiatives survive when teams constrain autonomy, encode governance into runtime paths, and measure production behaviour before scale.
Read perspectiveField NoteMay 2026 · 3 min read
Choosing an AI Engineering Partner in Financial Services
Financial services teams need AI partners who can leave governed systems behind, not black-box dependency or slideware.
Read field notePerspectiveMay 2026 · 9 min read
Model interoperability as regulatory expectation: a stress-exit pattern for AI in financial services
The case for model interoperability in financial services: abstraction layer, regression tests, and tested stress-exit plans, on existing banking precedent.
Read perspectivePerspectiveApr 2026 · 8 min read
Beyond monolithic AI controls: a classification framework for financial services
A four-tier framework (Assistive, Augmented, Semi-autonomous, Peer) for proportionate AI governance in financial services, with controls matched to each autonomy tier.
Read perspectivePerspectiveApr 2026 · 4 min read
The AI Adoption Trap Between Proof of Concept and Production
Enterprise AI pilots stall when teams optimise for impressive demos before they build the operating model that production adoption requires.
Read perspectivePerspectiveMar 2026 · 4 min read
The Regulated Enterprise Has No Room for AI That Cannot Explain Itself
Regulated enterprise AI only compounds when every decision can be traced, challenged, reversed, and explained to the people accountable for it.
Read perspectivePerspectiveMar 2026 · 9 min read
The Pathfinder Bottleneck
Most organisations begin AI adoption with a few pilots. By the time those pilots deliver results, the landscape has already moved. What enterprises need is a continuous stream of pathfinders.
Read perspectivePerspectiveFeb 2026 · 8 min read
Enterprise AI Needs Two Speeds
Enterprise AI adoption requires two concurrent velocities: strategic architecture that holds for years, and tactical experimentation that delivers insight in weeks. Most organisations only have one.
Read perspectivePerspectiveJan 2026 · 3 min read
Engineering Pole Reversal
Many of the heuristics that guided software engineering for decades are starting to flip in value. The effect is analogous to geomagnetic reversal: the forces remain the same, but the direction flips. Compasses calibrated to the old orientation point the wrong way.
Read perspectivePerspectiveJan 2026 · 4 min read
Microservices in Banking: Patterns That Avoid Distributed Monoliths
Microservices help banks only when service boundaries follow business domains, event records, and operational ownership rather than deployment fashion.
Read perspectivePerspectiveNov 2025 · 6 min read
AI Copilot ROI Enterprise: Hidden Engineering Costs
AI copilot ROI enterprise calculations often flatter the tool and hide the engineering work that follows. The return only holds when the platform absorbs the cost.
Read perspectivePerspectiveNov 2025 · 9 min read
AI-Native vs AI-Assisted Development: What the Distinction Actually Means for Engineering Teams
Most teams calling themselves AI-native have bought better autocomplete. The actual distinction is not about which model writes the code. It is about where in the delivery process human judgement still lives, and what is left for it to do.
Read perspectiveGuideOct 2025 · 20 min read
CIO's Playbook for Enterprise AI Adoption: Board to Production
Master enterprise AI adoption with this CIO playbook: secure board buy-in, build strategies, implement AI-native systems, and deliver production value in regulated industries like finance.
Read guideGuideOct 2025 · 16 min read
What Is AI-Native Engineering? A Complete Guide for 2026
A guide to AI-native engineering: governed lifecycle design, agent-ready delivery, cost choices, and regulated enterprise operations.
Read guideGuideOct 2025 · 13 min read
Third-Wave GTM for Financial Services Tech Firms
Third-wave GTM in financial services: how agent-to-agent transactions reshape sales, what agent-ready software must do, with implementation framework and FAQ.
Read guideGuideOct 2025 · 13 min read
How to Evaluate an AI Platform Vendor in Financial Services
A decision framework for selecting AI platform vendors in financial services, with evidence, governance, and operating-risk criteria.
Read guidePerspectiveOct 2025 · 10 min read
Build vs Buy for Enterprise AI
Compare building in-house AI solutions versus buying from vendors for enterprises. Review costs, timelines, pros, cons, stats, and top platforms to decide.
Read perspective