COReady
Content assistant
Campaign copy, brand alignment, and creative briefs
AI implementation · Marketing operations
Turning individual needs into role-based AI assistants.
I led a discovery-driven program to understand how different team members work, define useful AI opportunities, and build personalized Claude assistants supported by testing, documentation, and shared governance.
- My contribution
- Program design, discovery, assistant development, and rollout
- Initial builds
- Seven role-based workflows completed or in refinement
- Implementation model
- Discover, define, build, test, refine, and govern
- Core platform
- Claude with Smartsheet and shared documentation
The assistant portfolio
One implementation model. Personalized support for each role.
ROLE-BASED SUPPORT
Personalized assistants
Built around responsibilities, recurring work, and approved source material.
OPTesting
Operations assistant
Workflows, reporting, and process documentation
INReady
Insights assistant
Research, synthesis, and customer pattern analysis
EVRefining
Events assistant
Planning, vendor coordination, and follow-up
Company ownershipShared tools stay with the organizationDocumented standardsInstructions and source materials are reviewableFeedback loopReal use shapes each refinement
7initial role-based builds completed or refining
1:1discovery and feedback with team members
6repeatable stages in the implementation model
1shared governance structure
01
The challenge
Access to AI did not give the team a clear path to adoption.
Different roles relied on different information, recurring tasks, and decision-making processes. A generic assistant could not reflect those differences or give people enough context to make AI part of their regular work.
Without a structured rollout, team members would be left to experiment independently, shared knowledge could remain tied to individual accounts, and leadership would have limited visibility into how assistants were created and maintained.
The needMove the team from a blank prompt window to practical assistants built around real responsibilities, approved information, and repeatable workflows.
02
The approach
Start with the person and the work—not the platform.
I met individually with team members to understand their responsibilities, repetitive work, reference material, and desired support.
I translated each conversation into proposed assistant functions and sent them back for confirmation before building.
I created role-specific projects with instructions, business context, working standards, and supporting materials.
Team members reviewed and used their assistants, then shared feedback that shaped the next version of each workflow.
03
The system
Personalized assistants supported by one repeatable implementation model.
Customize the workflow, not just the name
Every assistant reflects a defined set of responsibilities, source materials, and recurring tasks.
Build with team members
Users confirm the need, review instructions, test the assistant, and participate in refinement.
Keep shared tools with the company
A company-managed structure protects shared assistants and institutional knowledge when roles change.
Design for review and maintenance
Security, permissions, documentation, feedback, and manager review are part of implementation—not an afterthought.
Use cases in the portfolioContent and campaigns · Workflow and reporting · Customer and product insights · Technical communications · Tradeshow planning · Budget processing
04
The progress
The team moved from general AI access toward structured, role-based adoption.
The foundation, discovery process, governance approach, and first group of personalized assistants are in place. The rollout remains active, with additional users, measurement, shared templates, and final reporting still planned.
- Seven initial role-based workflows built or refining
- Use cases confirmed through individual discovery
- Testing and feedback included in the build cycle
- Shared ownership and documentation established
- Approved use-case library planned for broader reuse
- Adoption and productivity measurement planned
The strongest AI assistant does not begin with the technology. It begins with understanding the person, the work, and the problem worth solving.
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