Becoming an AI Expert: Lessons from Amanda Van Den Elzen’s Rise

Artificial intelligence is moving from buzzword to everyday business tool, and professionals who know how to use it well are suddenly in high demand. Figures like Amanda Van Den Elzen, highlighted for establishing herself as an expert in the use of AI, show what’s possible when curiosity meets disciplined practice. You don’t need a PhD or a big-city tech job to build credible AI skills. With a clear roadmap, you can become the go-to AI person in your company or community, just as Amanda has in hers.

From AI Curiosity to Local Expert: What Amanda Represents

The mention of Amanda Van Den Elzen as an emerging expert in the use of AI highlights a broader shift: artificial intelligence is no longer reserved for Silicon Valley labs or giant tech firms. Professionals in everyday cities and industries are learning to harness tools like large language models, image generators, and automation platforms to solve practical problems.

While specific details of Amanda’s work remain local to her community and coverage, her recognition reflects a familiar pattern: someone experiments with AI, applies it to real workflows, helps others understand it, and gradually becomes the trusted person everyone turns to with AI questions. You can follow a similar path by combining structured learning, hands-on experimentation, and a commitment to sharing what you learn.

What It Really Means to Be an “AI Expert” Today

“AI expert” can sound intimidating, but in most organizations it doesn’t mean designing new algorithms from scratch. It usually means being skilled at applying existing AI tools responsibly and effectively.

Practical AI Expertise vs. Academic AI Research

• Academic AI: Builds new models, writes research papers, and focuses on theory, mathematics, and novel techniques.
• Applied AI: Uses existing models and platforms (like ChatGPT, Claude, or image generators) to solve real-world problems in business, education, marketing, operations, and more.

Most professionals aiming to mirror Amanda’s kind of expertise are on the applied side. They focus on understanding what modern tools can and cannot do, how to integrate them into daily work, and how to communicate their value.

Core Capabilities of a Modern AI Specialist

• Translating messy business problems into AI-friendly tasks.
• Designing high-quality prompts and workflows for language and image models.
• Evaluating AI outputs critically and improving them through iteration.
• Understanding basic data privacy, ethics, and risk mitigation.
• Training colleagues and stakeholders to use AI tools responsibly.

Foundations: Skills You Need to Build First

You don’t have to be a programmer to become known for AI in your niche, but you do need some foundational skills.

1. Comfort with Data and Digital Tools

AI is data-driven, so basic data literacy is invaluable. That includes:

• Working with spreadsheets and dashboards (e.g., Excel, Google Sheets).
• Understanding what structured vs. unstructured data means.
• Recognizing the limits of your data and where it might be biased or incomplete.

2. Clear Communication and Domain Knowledge

Experts like Amanda become trusted because they understand both AI and the specific domain they operate in—whether that’s education, marketing, operations, or another field. Clear communication helps you:

• Explain AI concepts in plain language for stakeholders.
• Gather requirements from non-technical colleagues.
• Document AI-powered workflows so others can follow them.

3. Ethical Awareness and Responsible Use

As soon as you start influencing decisions with AI, you also take on responsibility. At minimum, you should understand:

• Why AI sometimes produces incorrect or fabricated information.
• How to avoid sharing sensitive or confidential data with external tools.
• The importance of human review in high-stakes decisions.

Mastering Prompting: Your Primary Lever for AI Quality

For many business-oriented AI experts, prompt design is the central skill. Good prompts turn generic AI tools into tailored assistants that behave consistently and produce reliable results.

Elements of an Effective Prompt

1. Role: Tell the AI who to act as (e.g., “You are a marketing strategist for a small local business”).
2. Goal: Specify what you actually want (e.g., “Draft a 4-week email campaign outline for new customers”).
3. Context: Add background (industry, audience, constraints, tone, length).
4. Format: Define how the answer should be structured (bullets, table, outline, step-by-step plan).
5. Evaluation: Ask the AI to check its work (e.g., “List any assumptions you made and potential risks”).

Designing Real-World AI Workflows

What sets recognized experts apart is not just that they can get a single good answer from an AI tool, but that they can design repeatable workflows. These workflows save time and can be taught to others.

Example: Turning a Manual Process into an AI Workflow

Imagine a local professional who spends hours per week on email drafting and routine documentation. A practical AI workflow might look like:

• Use AI to generate first-draft responses based on templates and past answers.
• Refine tone, length, and clarity via iterative prompting.
• Save finished drafts as reusable prompt snippets for future work.

By documenting such a workflow and sharing it internally—through a short guide, lunch-and-learn session, or training—you begin building the same kind of reputation that someone like Amanda holds in her own circles.

Aligning AI with Business Value

Recognized AI practitioners stand out because they focus on value, not novelty. They pick use cases where AI produces measurable benefits.

High-Impact Use Cases for Everyday Organizations

• Customer communication: Drafting emails, FAQs, and support replies that humans then review.
• Content creation: Producing outlines, first drafts, and variations for blogs, social media, and newsletters.
• Process documentation: Turning calls or meetings (via transcripts) into clear procedures and checklists.
• Data summarization: Condensing lengthy reports, surveys, or research into executive summaries.

Linking AI Projects to Clear Outcomes

When you can point to outcomes like “saved three hours per week for each team member” or “increased response speed to customer inquiries,” your role naturally shifts from “tech enthusiast” to “trusted AI advisor.” This is often how local experts gain recognition in the first place.

Choosing the Right AI Tools for Your Context

Another part of becoming an AI expert is knowing which tool to use when. You don’t have to master everything; you need a reliable toolbox and a sense of each tool’s strengths.

How to Build Your Own Path to AI Expertise

If you’re inspired by people like Amanda Van Den Elzen and want to become “the AI person” in your own ecosystem, a deliberate path will get you there faster.

Step-by-Step Roadmap

1. Pick your domain: Decide where you want to excel (e.g., education, marketing, operations, finance).
2. Learn the basics: Spend a few weeks understanding how modern AI models work at a high level and what they can do.
3. Experiment daily: Commit to using an AI tool for 15–30 minutes each workday on real tasks.
4. Capture wins: Document specific examples where AI saved time, improved quality, or enabled something new.
5. Teach others: Offer internal sessions, write simple guides, or host informal Q&As.
6. Stay current: Follow a small number of reliable AI news sources or newsletters and update your workflows as tools improve.

Sharing Knowledge: The Bridge from User to Expert

One of the clearest signs that someone has become an AI expert in their environment is that people seek them out for help. That usually follows a period of intentional knowledge sharing.

Practical Ways to Share Your AI Expertise

• Internal workshops: Short sessions on “5 AI shortcuts for our team” can rapidly raise your profile.
• Practical guides: Create one-page checklists or screenshots showing how to complete common tasks with AI.
• Local talks: Speak at community meetups, professional associations, or local colleges about applied AI.
• Mentoring: Offer to pair up with a colleague each month to help them integrate AI into their workflow.

Managing Risks and Setting Boundaries

With growing influence comes responsibility. Experts who earn lasting trust balance enthusiasm with caution.

Key Risk Areas to Address

• Accuracy: Always keep a human in the loop for editing and final decisions.
• Privacy: Avoid feeding confidential or regulated data into external AI tools unless governance is in place.
• Bias: Be alert to biased outputs, especially in hiring, lending, or other sensitive contexts.
• Transparency: When AI influenced a result, say so—especially in customer-facing or public work.

Final Thoughts

Amanda Van Den Elzen’s recognition as an expert in the use of AI reflects a broader opportunity: dedicated professionals in any region, from large tech hubs to smaller cities, can become indispensable by learning to apply AI thoughtfully. You don’t need to invent new algorithms; you need to understand the tools, align them with real problems, and help others benefit from them safely.

By developing strong prompting habits, designing reliable workflows, connecting AI projects to tangible business outcomes, and sharing what you learn, you can chart a similar path. Over time, that consistency turns curiosity into capability—and capability into the kind of practical AI expertise that organizations and communities increasingly rely on.

Editorial note: This article was inspired by coverage of Amanda Van Den Elzen’s work with AI and her emergence as a recognized expert in its use. For more context, see the original reporting at Green Bay Press-Gazette.