AI Strategy &
Transformation
Find practical opportunities, assess build-versus-buy options, and connect technology choices to organizational priorities.
Desirée Clark
AI strategy.
Grounded in
real-world execution.
I connect organizational priorities, technology decisions, and the systems people use every day—bringing hands-on engineering experience to AI transformation.
Business perspective.
Technical depth.
Technical depth.
01 / The perspective
A bridge between
what’s possible
and what’s practical.
The most useful AI work starts with a business problem, not a technology demo.
I’m an AI strategy and enablement professional with a software development management background and hands-on experience building and deploying AI-powered business systems. I connect organizational priorities to practical technology decisions, implementation plans, and team adoption.
I lead stakeholder discovery, build-versus-buy evaluations, and workflow design, with a focus on accountable, maintainable systems. Building the systems myself informs the decisions I help teams make—and the training, documentation, and support they need to adopt them.
Connecting business, technical, and operational stakeholders—from discovery through handoff.
02 / Areas of expertise
Strategy with the depth
to make it happen.
From the first business question
to the systems and people behind it.
AI Strategy &
AI Systems
Engineering
Build AI-enabled workflows and integrations with clear boundaries, human review, and thoughtful testing.
AI Adoption &
Enablement
Make systems usable through documentation, training, and adoption support grounded in how teams work.
Business Systems
Architecture
Connect data, tools, and processes into maintainable systems with clear ownership and reliable handoffs.
Responsible AI &
Governance
Bring data security, risk awareness, and human accountability into the design of AI-enabled operations.
Cross-Functional
Collaboration
Translate between business and technical stakeholders to build shared understanding and workable decisions.
03 / Selected initiatives
Business challenges.
Thoughtful systems.
A selection of work connecting
strategic decisions to implementation.
Review Sentiment & Dealer Intelligence Hub
Turn dealership review data into decision-support views for client services, with Gemini sentiment classification and Hub integration.
Explore the approach
The challenge
Client services needed usable views of review sentiment, recurring praise and complaints, and trends across dealerships.
My contribution and decisions
Built Review Sentiment, a Yext-integrated analytics spoke, and contributed shared UI components, a resizable insight drawer, and spoke integration to the DIOS Hub.
Delivery and results
Supported ingestion snapshot: 250,000+ reviews across 350+ dealerships. This is ingestion volume, not an AI analysis count or adoption measure. Authenticated end-to-end publishing was verified in development; Hub work was a contribution to the wider platform.
Chatbot Build vs. Buy Analysis
Compare rules-based, SaaS AI, and custom AI approaches against cost, control, governance, and maintenance.
Explore the approach
The challenge
A department wanted a website chatbot to reduce repetitive email inquiries and help visitors find answers, but needed a clear view of the options, setup effort, and cost.
My contribution and decisions
Structured the landscape into three tiers and evaluated each against cost, control, data governance, and maintenance.
Delivery and results
A budget-aligned recommendation, side-by-side cost model, and risk-weighted comparison delivered to leadership. The original analysis also flagged an existing institutional chatbot as an option to investigate.
Multi-Source AI Agent Workflow
Consolidate calls, email, and internal discussion into coordinated task updates, client briefs, and stakeholder summaries.
Explore the approach
The challenge
Client activity was spread across meeting transcripts, email threads, and internal discussions, requiring manual review and translation into Asana tasks.
My contribution and decisions
Designed and delivered AI workflows across three businesses at Varia Global, translating stakeholder needs into coordinated workflows across Asana, Slack, Google Workspace, and client communications.
Delivery and results
Enabled daily AI workflows across 10 client accounts and trained a nontechnical operator to run them independently. Runbooks, reusable training materials, hands-on instruction, and office hours supported adoption, with approval gates and structured-output validation before system updates.
Explore all initiatives · Read the engineering documentation
04 / Leadership perspective
Technology moves fast.
Purpose keeps it grounded.
Principles that shape how I approach
AI within an organization.
The business question comes first.
Understand the process, the friction, and the decision that needs to improve. Then ask whether AI is the right tool.
Adoption is part of the architecture.
A solution needs more than a working model. It needs clear expectations, usable workflows, and people who understand their role.
Useful systems outlast the demo.
Make ownership, review, documentation, and maintenance explicit. Sustainable systems are designed for the teams that inherit them.
05 / Responsible AI
Progress requires
judgment.
Responsibility belongs inside the system—from the first design decision to the way it is used every day.
Keep human accountability clear.
Use human approval gates before business-system updates and validate structured AI output before execution.
Respect data and context.
Use role-based access, data-governance decisions, and separate service accounts and secrets to control how information moves.
Make limitations visible.
Use workflow and regression testing, document boundaries and operating steps, and make maintenance and handoff part of delivery.
06 / Let’s connect
A conversation is
a good place to start.
I welcome conversations about AI leadership, strategy, transformation, and the practical work of turning ideas into systems people can use.
Connect on LinkedIn (opens in a new tab)