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.
My work sits at the intersection of strategy, systems, and people. I identify practical opportunities for AI, translate them into clear technology decisions, and design the workflows that make those decisions usable.
Having built AI-powered business systems myself, I bring an implementation perspective to the conversation: what to build, what to buy, where people need control, and how teams can adopt and maintain a solution.
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.
Choosing the right path to AI.
Chatbot Build vs. Buy Analysis
Evaluating chatbot approaches through the lens of cost, ownership, maintenance, and data governance.
Explore the approach
The challenge
Give leadership a clear way to evaluate chatbot options against business needs and budget.
The approach
Compare rules-based, SaaS, and custom AI approaches. Make ownership, ongoing maintenance, and data governance explicit alongside costs.
The output
A cost-informed recommendation and decision framework, with tradeoffs made explicit for leadership review.
Turning fragmented information into coordinated action.
Multi-Source AI Agent Workflow
Connecting client communications to task updates, client briefs, and stakeholder summaries—with people in control.
Explore the approach
The challenge
Bring information spread across communication and work-management tools into a coherent operational workflow.
The approach
Build context from multiple sources, use focused AI agents to process information, and check for duplicates before proposed updates proceed.
The output
Task updates, client briefs, and stakeholder summaries, with human approval gates before changes reach business systems.
A stronger foundation for business decisions.
Ad Data Reporting Automation
Connecting paid-media performance and lead attribution in a repeatable, shared reporting foundation.
Explore the approach
The challenge
Connect advertising performance and lead information in a consistent structure for reporting and analysis.
The approach
Use a repeatable ingestion pipeline, separate client datasets, and consistent campaign mapping to support a shared reporting model.
The output
A shared data foundation feeding dashboards and AI-assisted analysis.
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.
Define where AI can assist, where people approve, and who owns the final decision.
Respect data and context.
Consider access, privacy, and appropriate use before connecting information to a model.
Make limitations visible.
Test the workflow, document its boundaries, and give teams a way to review and question the output.
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)