Delivering AI, Data & Digital Transformation Programs Predictably

Program and Project Leader with 15+ years of experience delivering complex, enterprise-scale programs through disciplined governance, execution leadership, and delivery management.

Proven track record leading application development, data platforms, analytics, generative AI, cloud modernization, and regulatory transformation initiatives using Agile, Hybrid, and Waterfall delivery models.

Delivered initiatives with high delivery risk and significant executive visibility, where successful outcomes depended on strong governance, alignment across business and technology stakeholders, and disciplined execution.

Connect on linkedin.com/in/ishariff

Areas of Expertise

Program leadership expertise spanning AI, applications, data, cloud, regulatory and delivery transformation initiatives—from strategy and investment planning through implementation, operational readiness, and business value realization.

Represenative Programs

U.S. Regulator-Driven AML Platform Delivery

Led delivery of a $12M enterprise AML platform supporting unusual transaction referrals, financial crimes case management, regulatory reporting, and enterprise rollout across 1,100+ branches and lines of business.

Scope included front-end micro applications, Oracle FCCM case management, APIs, batch integration, data lifecycle management (ingestion, curation, reporting and data quality) on Azure data platform, change management, training, SIT/UAT, and production release.

Generative AI Text-to-SQL Platform

Led delivery of a Generative AI proof of concept, prototype, and MVP enabling business users to query enterprise data using natural language.

Scope included Azure OpenAI GPT-4o, Databricks SQL Warehouse, Azure Data Lake, metadata-driven prompting, SQL validation, SELECT-only controls, enterprise security, and governed access to AML investigation data.

Customer 360 & Customer Risk Rating Transformation

Led delivery of an enterprise customer identity and risk-rating integration initiative to create a unified customer risk profile across AML and financial crimes platforms.

Scope included Enterprise Customer ID integration, Oracle FCCM ( Financial Crimes and Complaince Management) and TM ( Transaction Monitoring) , data governance, survivorship rules, reconciliation, archival, duplicate suppression, SIT/UAT, production deployment, operational transition, and audit controls.

Wealth Fractional Trading Product Launch

Led end-to-end delivery of a fractional trading capability across web, mobile, core banking, APIs, batch processing, and Azure data platforms.

Scope included 13 applications, 20 workstreams, mainframe and AS400 enhancements, securities APIs, ETL pipelines, reporting, reconciliation, integration testing, and production release.

Digital Banking Platform & Cloud-Native Delivery

Led delivery of a $10M Kubernetes-based private cloud and digital banking platform supporting microservices, APIs, PaaS capabilities, web/mobile applications, and the PCF MasterCard launch.

Scope included API gateway integration, single-page applications, Hadoop-based data migration, legacy system integration, multi-vendor delivery, and production release.

Enterprise Platform Modernization & Cloud Migration

Led an $11M multi-year platform modernization and private cloud migration program across legacy applications, databases, middleware, infrastructure, and security services.

Scope included refactoring and migrating 30+ Java and .NET applications, consolidating 60 databases into three enterprise data platforms, and establishing repeatable migration patterns, reference architectures, and delivery governance.

Acquisition & Enterprise Integration Program

Led application, data, infrastructure, and operating model integration initiatives as part of $20M credit card platform acquisiton and enterprise integration progam.

The program involved integrating acquired application portfolios, data platforms, analytics capabilities, and supporting infrastructure into enterprise business and technology ecoysytem.

Scope included data warehouse modernization, master data management, analytics, enterprise reporting, infrastructure deployment across multiple data centres, and coordination of business, technology, vendor, and PMO stakeholders across multiple workstreams.

Predictable Delivery Playbook

Playbook Move: Early Architecture Alignment

Situation
A non-negotiable delivery deadline.
Multiple systems.
Multiple teams.

You are tasked with building a complex technology solution composed of multiple interconnected systems.
There is no room for delivery failure.

Default Move
Most programs respond by building a detailed schedule first.
Tasks are assigned.
Effort is estimated.
Milestones are set.
Execution begins.
Everything stays green—right up until integration testing. Then reality hits: the system does not work. Overnight, the program goes from green to red, and the deadline slips out of reach.

What Went Wrong
The schedule was finalized and execution started before the end-to-end architecture was aligned.
Without early architecture alignment:
• Teams optimize their own component, but no one owns the full end-to-end system
• Integration defects appear during SIT and UAT
• Data definitions conflict across systems
• Rework hits every downstream milestone
• Delays compound, and costs spiral beyond expectations
By the time executives discover delivery risk, it is too late—recovery is expensive, and timelines are already compromised.

Playbook Move
Get Early Architecture Alignment
Treat architecture alignment as the first execution milestone—not a technical side activity.
Do not finalize the delivery schedule until the end-to-end solution is aligned across business and technology teams.
Schedule confidence without architecture validation is false confidence.

Execution Rules
Rule 1: Time-Box It
Set a two-week architecture alignment window.
Time-boxing creates urgency and forces decisions.

Rule 2: Align Technology Leaders First
Bring together architects, system owners, development leads, data leads, and integration owners.
Align on:
• System boundaries
• Integration patterns
• Data ownership
• Security, privacy, and audit requirements
The goal is not perfection.
The goal is shared understanding of the end-to-end system.

Rule 3: Bring Business Leaders in Early
Do not keep architecture discussions inside technology.
Bring business leaders in early so critical assumptions are challenged before the build starts.
This reduces late-stage surprises and improves decision speed when trade-offs are needed.

Rule 4: Build the Schedule After Alignment
Do not start with a detailed task-level plan.
Start with a time-boxed delivery model:
• POCs
• System demos
• MVP releases
• Validation checkpoints
Build execution around validated architecture—not assumptions.

Closing Principle
Complex programs do not fail because the schedule was wrong.
They fail because the schedule was built before the solution was understood.
Schedules do not create delivery confidence.
Validated architecture does.

Playbook Move: Validate the architecture before committing to the schedule

A non-negotiable deadline.
Multiple teams.
Multiple vendors.
A complex architecture aligned on paper.

Default move: Most programs move straight into build.

The schedule looks green.
Stakeholders agree.
Delivery begins.

Then reality hits.

A platform constraint was missed.
An integration assumption was wrong.
A vendor dependency slips.

The architecture worked in workshops.
It fails in production.

The program shifts from delivery to recovery.

Playbook move: Validate architecture with a POC.

Treat it as the first real delivery checkpoint.

Use it to:

• Validate production constraints
• Align with current requirements
• Turn multiple teams into one delivery team — often the biggest predictor of delivery success

Complex programs rarely fail because of poor planning.
They fail because critical assumptions were never tested.

No-Surprise Delivery Principle: Validate the architecture before committing to the schedule.

Playbook Move: Always have a working systems and demo it regularly

Everything was green—until QE tried to start testing.
Systems didn’t work together.
Test data wasn’t ready.
Dependencies were never planned.
QE lost weeks before testing even began.
The program shifted from delivery to recovery.

Predictable Delivery Playbook: No-Surprise Delivery of Complex Programs
How you got here
A non-negotiable deadline.
Multiple systems.
Multiple teams.
Architecture was aligned.
The architectural POC was complete.
Development started.

Default move: Development teams build in silos and then hand over the build for QE to begin testing.

Playbook move
Do not wait for SIT to prove the system works end-to-end.
Instead, throughout the development phase, ensure you always have an end-to-end working system—and demo it regularly to your stakeholders.
Use demos to bring QE, business users, change management, and executives into delivery early.
PowerPoint reports convey delivery dates.
Demos make sure you can deliver against those dates—they facilitate stakeholder engagement and downstream planning.

No-Surprise Delivery Principle
During development, make sure you always have an end-to-end working system—and demo it regularly.

AI Delivery Point of View

Investments get you to the AI table: Successful AI project execution ensures you’re not on the menu.

🔥 If you’re not at the AI table, you’re on the menu.

Many organizations understand this.
Executive attention and growing AI spend attest to it.

Yet even after securing “a seat at the table,” many may still end up on the menu.

Why?

Because awareness and investment don’t close the AI execution gap.

What’s missing is a structured, repeatable way to deliver AI initiatives that produce business outcomes—and can be operated safely.

This is where the PMI – CPMAI framework makes the difference.

⚙️ Purpose-built for data-centric, learning-based AI, it extends traditional and agile delivery with an iterative, phase-based approach, embedding trustworthy AI throughout the project lifecycle.

💡 Investments get you to the AI table.
✅ Successful AI project execution ensures you’re not on the menu.

AI projects require an execution framework aligned with thier characterstics

Planning for certainty in systems built on uncertainty is one of the first AI execution gaps I described in my previous post.

Closing this gap requires an execution framework designed for probabilistic, data-driven systems.

A common reflex to AI delivery challenges is to rely on stricter AI governance controls.
AI governance is essential—but bolting it onto delivery models designed for deterministic software does not close the execution gap.

What does close the gap is extending traditional and agile delivery with lifecycle discipline built for AI—explicitly accounting for data dependence, probabilistic behavior, and how performance evolves over time.

This is exactly what PMI-CPMAI does.
It builds on existing delivery practices to provide a fit-for-purpose execution framework for AI projects.

Instead of forcing AI into software-era assumptions, it adapts execution to how AI systems actually behave.

Investment gets organizations to the AI table.
Execution assumptions decide whether value is realized—or lost.

AI projects can’t be delivered as traditional software projects

A key reason AI investments don’t translate into outcomes is simple—but often missed.

AI projects are executed as if they were traditional software projects.

The planning assumption is carried over:
Same input.
Same output.
Predictable behavior.

But AI systems don’t work that way.

They learn from data, behave probabilistically, and change as conditions change.

When AI delivery models assume software-style determinism, execution plans struggle because they are built on incorrect assumptions about how AI systems behave.

This is one of the first AI execution gaps:
planning for certainty in systems built on uncertainty.

Investment gets organizations to the AI table.
Execution assumptions decide whether value is realized—or lost.

AI Transformation & Adoption

• Microsoft 365 Copilot
• Generative AI
• Agentic AI
• AI-assisted software development and delivery
• AI governance, adoption, and value realization

Application, Data & Platform Modernization

• Traditional and Scaled Agile delivery across the enterprise technology stack
• Application development and modernization initiatives
• Data lifecycle management including ingestion, curation, analytics, machine learning, and Generative AI
• Cloud migration and modernization
• Enterprise data and analytics platforms

Enterprise Transformation & Regulatory Programs