An Engagement Manager’s Guide to AI-Driven Projects & Team Enablement
- Patty Greer
- Jul 30
- 3 min read
Updated: Jul 31
I don’t usually consider myself a “writer” – but I became inspired to write a follow-up to my earlier article, [When AI Becomes Both a Bridge and a Backbone: An Engagement Manager Perspective] The project had two tightly coupled objectives:
1. Build a business application to manage document-related workflows from start to finish.
2. Enrich and migrate 20,000 legacy documents into that system with searchable, structured metadata.
What motivated me to write this follow-up was a desire to capture what I’d do differently – not from theory, but from experience in a real world, fast paced project. I wanted to focus on how these lessons could help me, and other Engagement Managers, better support developers in future AI initiatives.
If that sounds like something you could use too, then read on. This one’s for us.
Start With Broad and Representative Sampling
What I learned:
Starting with a small set of 10 documents didn’t expose the full data complexity (e.g., handwritten notes, multi-date formats, poor scans).
What developers need:
Early exposure to all types of data edge cases.
Time and resources to handle unpredictable data structures.
Action for me as EM:
Champion broad, upfront sampling so developers can design resilient parsing and extraction logic.
Plan early technical spikes focused solely on data complexity & exploration.
Flexibility in Scope and Requirements
What I learned:
Requirements shifted as new document variations emerged. Fixed scopes didn’t hold up & became impractical.
What developers need:
A safe space to adapt without penalty.
Clarity on evolving priorities and shifting success criteria.
Action for me as EM:
Bake flexibility explicitly into sprint planning and estimates.
Normalize change – a new user story isn’t failure: it’s good discovery.
Set expectations with stakeholders that evolving scope is part of AI work, not a deviation from it.
Balance Vision With Implementation Constraints
What I learned:
With no data scientists, developers navigated complex edge cases, unexpected formats on their own – from MVP features to decoding messy documents.
What developers need:
Clear translation of architectural vision into day-to-day tasks
Early alignment on what counts as “realistic” edge case (e.g., what to do when dates are scribbled in margins).
Action for me as EM:
Be a translator between strategy and execution.
Capture and document edge-cases early for smoother development.
Early UI Alignment Drives Developer Success
What I learned:
Mockups weren’t just visual design guides — they defined how extracted data would be interpreted and displayed.
What developers need:
Clarity on how data flows into the UI.
Confidence in how fields will be displayed so they can build better APIs and back-end logic.
Action for me as EM:
Initiate cross-functional workshops early to co-create UI mockups. (when possible - it’s big win!)
Use mockups to validate extraction and data formatting logic with the team.
Guardrails and Fallback Logic
What I learned:
AI indexing failed on certain edge cases and needed human-in-the-loop fallback. We needed human fallback paths when confidence was low.
What developers need:
Defined lines between automation and manual review handling.
A structure for handling uncertainty (in this case it was implementing human-in-the-loop workflows and error reporting)
Action for me as EM:
Partner with developers to define confidence scoring (certainty of AI decisions) and fallback error handling workflows.
Bring identified edge-case scenarios into technical design discussions early on
Plan for Continuous Lifecycle Ownership
What I learned:
The AI Model didn’t stop at migration or go-live - it powered real-time app features. This required ongoing tuning and support.
What developers need:
A plan for maintaining, any retraining, and evolving AI.
Clear post-launch ownership & capacity support.
Action for me as EM:
Build lifecycle planning into the project/product roadmap.
Clarify roles and expectations for long-term AI support.
The Checklist I’ll Carry Forward:
☐ Broad, early data diversity sampling completed & edge cases documented
☐ Scope flexibility built into sprint plans and estimates
☐ Architectural goals translated into clear, practical tasks
☐ Edge case handling documented and shared
☐ UI/UX mockups reviewed with dev team early
☐ Data display requirements clearly defined
☐ Mission-critical fields prioritized and accuracy thresholds set
☐ Confidence scoring and fallback logic implemented
☐ Human-in-the-loop review processes defined
☐ Lifecycle support plan and post-launch roles documented
☐ Resources allocated for ongoing model updates and maintenance
The Checklist I’ll Carry Forward:
This project reminded me that AI isn’t a plug-and-play solution. It’s a living system – shaped by people, data, and context – and its success depends on continuous alignment and adaption.
As Engagement Managers, we aren’t just here to manage timelines and deliverables. We’re here to guide complexity with clarity, align stakeholders, and ensure the team can move forward with confidence. If my lessons help you do that even a little better – then mission accomplished!
[1] Developer-focused insights in this article were reviewed and validated by Alex Madryga, (alex.madryga@acceleratedfocus.com) whose thoughtful feedback ensured these recommendations reflect practical, real-world developer needs.
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