Associate Partner, McKinsey & Co · Senior Principal, QuantumBlack

Turning Complex AI
Ecosystems into Production Outcomes.

Senior technology leader trusted to architect, deliver, and scale enterprise AI systems in demanding environments.

Capabilities

What I Solve

The hard problems. The ones that stall when complexity meets ambition.

01
AI from Pilot to Production
Most enterprises can run a pilot. Few can scale it. Designing the architecture, governance, and organizational capability that takes AI from controlled experiment to governed production system—with measurable outcomes on the other side.
02
Complex Ecosystem Delivery
Enterprise AI rarely fails on the model. It fails on the ecosystem: misaligned stakeholders, legacy systems, governance gaps, and competing priorities. Leading delivery where complexity is the environment, not the exception.
03
Enterprise Architecture & Platforms
Designing scalable data platforms, AI workflows, agentic systems, and operating models that support production AI today and adapt to what's coming. Architecture decisions made with delivery, not just elegance, in mind.
04
Decision Intelligence Systems
Forecasting, optimization, anomaly detection, and digital twins—connecting advanced analytical capability, both traditional ML and GenAI, to the decisions that actually move the business. Systems built for operational use, not executive dashboards.
Delivery Profile

Trusted in Complex Environments

◆ Leadership
Cross-functional from C-suite to engineering
Aligning executives, architects, data teams, risk, and operations toward shared delivery outcomes.
◆ Delivery
Production systems, not prototypes
9+ years focused on getting AI from controlled environments into governed, monitored, production-grade deployment.
◆ Breadth
Global enterprise delivery experience
Complex programs across geographies, regulatory environments, and organizational structures.
◆ Technical
Architecture judgment, not just oversight
Deep technical fluency across traditional ML, GenAI, agentic systems, data platforms, and enterprise architecture.
◆ Value
ROI-driven transformation focus
Every engagement oriented toward measurable business outcomes—cost, speed, revenue, decision quality. Not experimentation for its own sake.
◆ Execution
Bridges strategy, architecture, and delivery
Rare combination of executive alignment, technical depth, and hands-on delivery discipline in a single operator.
9+
Years of McKinsey & QuantumBlack leadership
$500M+
Transformation impact enabled through AI programs
Global
Enterprise programs across industries and geographies
Hands-On
Still building and experimenting with frontier systems
Domain Focus

Active Capabilities

A focused set of areas where deep expertise meets sustained investment.

Agentic AI Systems
Enterprise AI Operating Models
Memory Architectures
Scalable GenAI Platforms
Decision Intelligence
Optimization Engines
Digital Twins
AI Governance at Scale
Semantic Layers & Knowledge Systems
Forecasting & Anomaly Detection
Responsible AI
Data Platform Architecture
Career

Experience

Fifteen-plus years of progressive leadership across analytics, data, and AI delivery in large, complex organizations.

Current
McKinsey & Company
Associate Partner
Current
QuantumBlack, AI by McKinsey
Senior Principal
Prior
Metro / Makro Cash & Carry
Manager, Data Mining
Prior
dunnhumby
Data Solutions Manager
Prior
Target
Analyst
Prior
HSBC
Business Analyst
Operator Intelligence

Featured Insights

Delivery 7 min read
Why Most Enterprise AI Programs Fail After the Pilot
The demo worked. The pilot delivered. And then momentum died. This failure pattern follows a consistent, diagnosable script—and it's not a technology problem.
Read article
Agentic AI 7 min read
Why Memory Systems Will Differentiate Enterprise AI Agents
Most enterprise AI deployments are stateless by design. The next frontier isn't smarter models—it's persistent, contextual memory that makes agents genuinely useful across sessions.
Read article
Delivery 7 min read
The Real Bottleneck in Enterprise AI Is Execution
Everyone has a strategy. What separates the organizations capturing value from those still producing slides is execution—an organizational and leadership capability, not a technical one.
Read article
Let's Connect
Always Happy to Connect

Whether to discuss enterprise AI, exchange perspectives on what's working in production, or just compare notes — always happy to have a substantive conversation.

Delivery Portfolio

Where Complex Technology
Meets Real Delivery

Fifteen-plus years of enterprise delivery across AI transformation, data platforms, decision systems, and operating model design. Engagement details are confidential; the nature of the work is not.

01
Enterprise AI Transformation
Turning fragmented AI initiatives into coordinated, scaled operating capability. From governance design and sequencing to delivery execution—building the foundation that makes AI programs durable rather than episodic. Includes standing up AI COEs, prioritization frameworks, and production deployment programs that span multiple business units.
Strategy Governance Execution COE Design
02
Production GenAI Systems
Moving generative AI from controlled experiments into governed, monitored production workflows. Designing the architecture, testing frameworks, and oversight mechanisms that make GenAI trustworthy at scale. Includes agentic system design, RAG pipelines, memory architectures, and integration with enterprise data and process layers.
GenAI Agentic Systems RAG Production
03
Data Foundations for AI
Building the data platforms, semantic layers, and governance structures that make AI reliable in production. Addressing the gap between analytics-ready and genuinely AI-ready data infrastructure. Includes data product design, lakehouse architecture, data quality frameworks, and lineage systems that support governed AI deployment.
Data Platform Semantic Layer Governance Lakehouse
04
Decision Intelligence
Forecasting, anomaly detection, optimization, and simulation—connecting advanced analytical capability to the decisions that move the business. Designing systems that make decision-making faster, more consistent, and measurable. Includes both traditional ML and GenAI approaches, and hybrid architectures where each plays to its strengths.
Forecasting Optimization Digital Twins Traditional ML
05
AI Operating Models
Designing the organizational structures, governance frameworks, and ways of working that allow AI to scale beyond isolated teams. COE design and implementation, hub-and-spoke talent models, prioritization and portfolio management, and value measurement systems that sustain executive commitment to AI investment over time.
Operating Model COE Talent Architecture Value Tracking
06
Transformation Rescue & Scale-Up
Helping stalled or struggling AI programs regain momentum. Diagnosing what's actually broken—governance, ownership, architecture, change management—and building a recovery path that creates durable progress rather than renewed activity. Includes program assessment, stakeholder realignment, and execution recovery.
Program Recovery Diagnosis Stakeholder Alignment Execution
Approach

How the Work Gets Done

Delivery discipline applied to technically complex, organizationally demanding environments.

01 · Diagnosis
From clarity to momentum
Every engagement starts with a clear diagnosis of what's actually constraining progress—not the presenting problem, but the structural root cause. Technical problems get technical solutions. Organizational problems get organizational intervention. The distinction matters.
02 · Orchestration
Cross-functional delivery leadership
Enterprise AI delivery requires aligning functions that don't naturally align: business, engineering, data, risk, legal, and operations. The leadership capability that makes this work—stakeholder alignment, sequencing, change management—is as important as the technical work.
03 · Production
Outcomes over activity
Every engagement is oriented toward measurable outcomes in production: systems that run, decisions that improve, organizations that are more capable at the end than the beginning. Not demos. Not strategy decks. Not pilots that never graduate.
Always happy to connect
If you're working on something in this space and want to exchange perspectives, compare notes, or just have a substantive conversation — reach out.
Operator Intelligence

Perspectives from the
Delivery Side of Enterprise AI

What actually works in production—and why most programs struggle to get there. Earned perspectives, not commentary.

Delivery April 2026
Why Most Enterprise AI Programs Fail After the Pilot
The demo worked. The pilot delivered. And then momentum died. This failure pattern is more common than the industry admits—and it follows a consistent, diagnosable script that has nothing to do with the model.
7 min read Read →
Agentic AI April 2026
The Case for the Single Orchestrating Agent
Most enterprise agent programs are over-architected before they've proven anything. One strong, well-instrumented agent that reliably executes multi-step work is worth more than a network of agents that can't be trusted.
7 min read Read →
Architecture April 2026
What Production-Grade Agent Systems Actually Require
The demo worked. Everyone agreed the technology was ready. Then the production question came up—and the answers got vague. Reliability, observability, governability, and recoverability are not optional.
6 min read Read →
Delivery March 2026
Governance Patterns That Don't Slow Delivery
The most common objection to AI governance is that it slows delivery. This is understandable and almost always wrong. Good governance is a delivery accelerator—the organizational trust that gets AI into production faster.
5 min read Read →
Architecture March 2026
Why Architecture Matters More Than Prompts
Enterprise AI spent two years focused on prompts. In production, the limiting factor is almost never the prompt quality. It's the data architecture, memory architecture, monitoring architecture—all the unglamorous infrastructure around the model.
6 min read Read →
Delivery February 2026
The Real Bottleneck in Enterprise AI Is Execution
Everyone has a strategy. What separates the organizations capturing value from those still capturing slides is execution—an organizational and leadership capability, not a technical one. This is the actual shortage.
7 min read Read →
AI Strategy February 2026
What Leaders Underestimate About Scaling AI
Getting the first AI system into production is hard. Scaling AI across an enterprise is a different kind of hard—the challenges don't scale linearly. They compound in ways that catch most organizations off guard.
7 min read Read →
Delivery January 2026
How to Rescue a Stalled AI Program
Stalled AI programs are more common than the industry admits. The pattern is recoverable—but recovery requires honest diagnosis, not more technology investment. Most diagnoses are looking in the wrong place.
6 min read Read →
Data Platforms December 2025
The Data Foundation Problem No One Talks About
The problem that delays more AI programs than any technical limitation, and receives a fraction of the attention it deserves. AI-ready and analytics-ready are not the same thing—and the gap is larger than most organizations expect.
6 min read Read →
Operating Models November 2025
From COE to Capability: Building AI Operating Models That Last
Most enterprises respond to AI's moment by creating a Center of Excellence. Some build genuine capability. Many build innovation theater. The difference is almost entirely a function of design, accountability, and what the COE is held responsible for.
7 min read Read →
Delivery October 2025
Complexity Is the Real Requirement
Enterprise AI is hard not because the technology is hard—but because the environments in which it has to work are genuinely complex. That complexity is not a problem to be solved. It's the operating environment.
6 min read Read →
Agentic AI September 2025
Why Memory Systems Will Differentiate Enterprise AI Agents
Most enterprise AI deployments are stateless by design. The next frontier isn't smarter models—it's persistent, contextual memory that makes agents genuinely useful across sessions, workflows, and organizational contexts.
7 min read Read →
Agentic AI August 2025
Tool Judgment Is Becoming More Important Than Tool Access
The bottleneck in enterprise AI isn't the number of tools available to an agent. It's whether the agent can judge when not to use them, how to chain them correctly, and when human escalation is the right call.
5 min read Read →
Architecture July 2025
The Return of Semantic Layers in the Agent Era
Semantic layers were considered solved infrastructure. With the rise of LLM-based agents querying enterprise data, they're back—and more important than ever. Here's why they matter and how they need to evolve.
6 min read Read →
Operating Models June 2025
The Talent Architecture of an AI-Native Enterprise
Building an AI-native enterprise isn't just a technology challenge. It requires a deliberate talent architecture—new roles, new incentives, and a different relationship between data scientists, engineers, and business leaders.
8 min read Read →
Technical Journal

The Build Journal

Selective experiments, prototypes, and system notes from staying close to the frontier. Not polished thought leadership—raw signal from hands-on work with emerging technology.

April 2026 Memory Systems
Agentic AI
Testing memory patterns across modern agent frameworks
Comparing episodic, semantic, and procedural memory patterns across major agent frameworks. The core tension: how much context to persist vs. how much to re-derive at runtime. More persistence means better continuity but higher latency and cost; more re-derivation means cleaner state but agents that feel amnesiac after short gaps. Building a lightweight evaluation harness to compare approaches on realistic multi-session enterprise tasks. Early signal: most frameworks optimize for single-session performance and treat cross-session continuity as an afterthought.
April 2026 Knowledge Systems
RAG · Knowledge Graphs
Building private AI wiki systems from unstructured folders and notes
End-to-end pipeline from raw unstructured documents—PDFs, markdown notes, email threads, meeting transcripts—into a queryable, agent-accessible private knowledge system. Exploring chunking strategies (fixed-size vs. semantic), embedding model choices, and hybrid retrieval (dense + sparse) for precision on domain-specific queries. The hard part isn't ingestion—it's freshness. Building an incremental re-indexing layer that detects when source documents change and updates only affected chunks, without full re-ingestion. Also experimenting with graph-based knowledge structures alongside vector search to handle entity relationships that flat retrieval misses.
March 2026 Optimization
Traditional ML · Decision Intelligence
Comparing optimization-first scheduling engines vs. LLM planning approaches
Running a head-to-head between constraint-based solvers and language model planners on a realistic resource scheduling problem with mixed hard and soft constraints. LLMs are surprisingly competitive on soft constraint handling and produce far better natural-language explanations. But they degrade badly as hard constraints multiply—they start forgetting constraints mid-plan in ways that are hard to detect without systematic validation. Hybrid architecture looks most promising: LP/MIP solver handles hard constraint satisfaction, LLM handles user-facing explanation and soft constraint ranking. Neither alone is the answer.
March 2026 Ontologies
Knowledge Engineering · Graph Memory
Exploring enterprise ontology evolution and graph memory patterns
How do you keep a knowledge graph current as business domains evolve? Products get renamed. Org structures change. Definitions drift. Experimenting with LLM-assisted ontology curation—using models to detect when incoming documents introduce terminology that conflicts with or extends the existing graph. Also testing automated drift detection: flagging nodes whose connected context has shifted significantly over a rolling time window. Working on a lightweight proposal-and-approval workflow for ontology changes that doesn't require dedicated knowledge engineers.
February 2026 Toolchains
Local AI · Agent Workflows
Hands-on experiments with local AI toolchains and agent workflows
Running capable quantized models locally for private, low-latency agent tasks. Evaluating practical trade-offs against cloud APIs across four dimensions: privacy (local wins clearly), cost at scale (local wins after break-even), latency (local wins for short context), and capability ceiling (cloud still ahead, gap narrowing). Tool-use reliability is the biggest gap—smaller local models still struggle with consistent structured output under complex chaining. Careful prompt engineering and explicit state management compensate for a surprising amount of raw capability difference.
February 2026 Observability
Enterprise AI · Governance
Designing enterprise-grade agent observability and control layers
What does production-grade observability look like for autonomous agents running consequential enterprise tasks? Standard software observability doesn't translate cleanly to non-deterministic systems. Building a prototype that captures structured decision traces, tool call logs with full parameter and response capture, escalation records, and anomaly flags. Key design challenge: making traces human-readable without losing fidelity. Using a two-layer approach—structured JSON trace for machine processing, LLM-generated plain-English summary for human auditors. Next step: integrating with a human-in-the-loop approval workflow for flagged decisions.
January 2026 Hybrid ML
Traditional ML · GenAI Integration
Prototyping hybrid ML + GenAI pipelines for structured prediction tasks
Testing a pattern that's becoming more relevant as GenAI matures: using traditional ML models as guardrails and validators for GenAI outputs, rather than treating them as alternatives. A gradient boosting model trained on historical outcomes flags GenAI-generated recommendations that fall outside the distribution of historically valid decisions. The combination catches hallucinations and out-of-distribution outputs more reliably than using a second language model as a checker—and is more explainable to business stakeholders. Also experimenting with using GenAI to generate features and structured context that improve traditional ML performance on sparse or unstructured input domains.
Public & Advisory

Speaking on the Future
of Enterprise AI

Available for select conferences, podcasts, panels, leadership forums, and private executive sessions where the conversation is substantive.

Topics

What I Speak On

All topics grounded in real enterprise delivery—not theoretical frameworks or vendor talking points.

01
Agentic AI in the Enterprise: What's Real, What's Not
02
How Fortune 500 Firms Are Actually Adopting AI
03
AI Beyond Pilots: What Actually Scales
04
Memory Systems and the Next Wave of AI Agents
05
AI Operating Models for Large Enterprises
06
Decision Intelligence & Digital Twins in Practice
07
Separating AI Hype from Durable Business Value
08
Enterprise Architecture for the Agent Era
Formats
Keynotes Panel discussions Fireside chats Podcast conversations Executive offsites Leadership briefings Advisory sessions Board presentations
ST
Sanchit Tiwari
Associate Partner, McKinsey & Company · Senior Principal, QuantumBlack
Sanchit Tiwari is a senior enterprise technology delivery leader with over fifteen years of experience helping large organizations turn complex AI programs into production outcomes. He serves as Associate Partner at McKinsey & Company and Senior Principal at QuantumBlack, AI by McKinsey.

His work spans generative AI, agentic systems, decision intelligence, digital twins, and the operating models required to take AI from isolated pilots to governed, production-grade programs at scale.

Known for bridging technical depth with executive communication, Sanchit brings a practitioner's perspective to every conversation—grounded in hands-on delivery across industries and geographies.
Request a Speaking Inquiry

Share brief details and Sanchit will follow up within a few business days.

About

Sanchit Tiwari

Sanchit Tiwari works where AI ambition meets execution reality. He helps large organizations move from pilots and fragmented initiatives to scalable production systems with measurable business value.

He combines executive alignment, architecture judgment, and delivery discipline—the combination that determines whether AI programs create lasting capability or expensive artifacts.

He has led enterprise-scale programs spanning generative AI, agentic systems, traditional ML, advanced analytics, digital twins, decision intelligence, scalable data platforms, and AI operating models. His work is cross-functional by nature: aligning business leaders, engineering teams, data organizations, risk functions, and operations toward shared production outcomes.

Known for translating complexity into momentum, Sanchit operates at the intersection of strategy and execution. He is as comfortable in an architecture review as in an executive steering committee—and understands that the two conversations have to connect for programs to succeed.

He maintains hands-on technical fluency—actively experimenting with frontier systems, prototyping architectures, and staying close to what's actually emerging—so that his strategic and delivery judgments are grounded in current technical reality, not abstracted from it.

Quick Profile
◆
Current Role
Associate Partner
McKinsey & Company
◆
Also
Senior Principal
QuantumBlack, AI by McKinsey
◆
Location
Greater Chicago Area
United States
◆
Focus
Enterprise AI Delivery
Production Systems · Architecture · Operating Models
◆
Experience
15+ Years
Analytics, Data & AI Delivery Leadership
Expertise

Areas of Deep Work

Fifteen years of progressive investment across a focused set of interconnected domains.

Enterprise AI Delivery
End-to-end leadership of large-scale AI programs—from strategy and architecture through governed production deployment and adoption.
Agentic & Generative AI
Architecture and delivery of agentic systems, LLM-based workflows, RAG pipelines, and GenAI platforms for enterprise production use cases.
Traditional ML & Statistical Modeling
Deep experience with regression, classification, time series forecasting, clustering, and optimization—the methods that still power high-stakes production decisions.
ML + GenAI Integration
Designing hybrid architectures where traditional ML and generative AI each play to their strengths—combining precision, explainability, and flexibility in the same system.
Decision Intelligence
Combining ML, optimization, and business logic to create systems that support faster, more consistent, and more autonomous decisions at scale.
Data Platform Architecture
Scalable, AI-ready data infrastructure: lakehouses, semantic layers, data products, lineage systems, and real-time pipelines designed for production AI.
AI Operating Models
Organizational structures, governance frameworks, and ways of working that enable AI to scale beyond isolated programs into enterprise-wide capability.
Digital Twins & Simulation
Digital twin architectures for operational planning, scenario analysis, continuous optimization, and decision support in complex operational environments.
AI Governance & Responsible AI
Risk frameworks, model risk management, explainability standards, audit architectures, and compliance design for production AI in regulated environments.
Contact

Let's Have a
Real Conversation

If you're working on enterprise AI and want to exchange perspectives, discuss what's actually working, or just connect — feel free to reach out. All messages read personally.

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Delivery Leadership
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Location
Greater Chicago Area, United States
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