AI in Design: Practical Workflows, Ethics and Portfolio Tips from Agency Leaders

How today’s creative teams use AI in design to speed research, multiply outputs and protect craft — without losing the human soul of design

This guide distills insights from a Watkins College of Art at Belmont University lecture on AI in design, featuring two industry professionals. 

AI isn’t replacing designers — it’s amplifying them. That was the throughline from two seasoned creative leaders who recently spoke with Watkins College of Art at Belmont University students about best practices for AI in design –– practical ways to streamline workflows, protect creativity and keep humans at the center.  

Their message: learn, unlearn and relearn fast, use AI tools where it frees time and improves outcomes, and keep humans at the center — because AI doesn’t have a soul. You do. 

How to Use AI for Design 

Below are practical ways to use an AI design workflow, with ethical guardrails and portfolio advice for students and emerging professionals as they prepare for AI and the future of the design industry.  

1) Use AI for Design Research (Move Faster, Ask Better Questions) 

Best practice: Treat AI as a strategic research assistant. Use it to map a subject’s background, surface talking points and generate first-draft interview guides or creative briefs. 

Why it matters: Hours of prework can shrink to minutes with an AI creative workflow, letting teams spend more time on concept and craft. 

• For example, some teams use structured-prompt tools that automatically generate interview questions from a person’s public talks or published work. 

Pro tip: Start with a structured prompt (role, goal, audience, constraints) and ask AI to return sources you can verify. 

2) Prep & Plan with Clarity 

Best practice: Use AI to turn raw research into a short brief: problem, audience, insight, success criteria, deliverables. 

Why it matters: Clear inputs lead to better outputs — human and machine. 

Pro tip: Ask AI to generate 3–5 alternative problem statements and choose the one that best frames the creative challenge. 

3) Concept with Breadth, Decide with Taste 

Best practice: Rapidly explore directions — visual metaphors, headline angles, mood boards — then curate. 

Why it matters: AI generated design expands option space; designers still decide what’s right. 

Pro tip: Prompt for constraints (brand voice, accessibility needs, production realities) to avoid “any-style” ideas you can’t deliver. 

 4) Edit and Produce More Efficiently 

Best practice: Lean on AI-enabled tools for tedious tasks: syncing audio/video, rough cuts, transcript pulls, layout variations, background cleanup. 

• For example, AI-enabled video editors can detect who is speaking and automatically switch to that camera angle — cutting hours off the editing timeline. 

Why it matters: Minutes saved on mechanics become hours you can re-invest in story, pacing and polish. 

Pro tip: Keep a human in the loop. AI is great at first passes; people are great at timing, taste and nuance. 

5) Multiply Your Content from a Single Core Asset 

Best practice: Atomize one longform piece (talk, case study, video) into shorts, carousels, blog snippets and email copy — with AI accelerating the slicing. 

• Some content teams upload a full video to an AI tool that clips highlights and even predicts which segments will perform best on social. 

Why it matters: Same team, dramatically more distribution through design automation. 

Pro tip: Set a reusable template: 10–15s clips, a 60–90s cut, 3 quote cards, a “4 things we learned” carousel, one blog, one email. 

6) Distribute with Intention 

Best practice: Use AI-assisted scheduling and subject-line testing, but keep channel strategy human: audience, timing, cadence, purpose. 

Why it matters: Ai tools help you ship more; human strategy helps you ship what matters. 

Pro tip: Pre-write alt text and captions with accessibility in mind — then have a human review for clarity and tone. 

7) Measure, Learn, Refine 

Best practice: Ask AI to summarize campaign performance and suggest hypotheses, but validate insights with your team. 

• For example, AI-powered dashboards can highlight which design variations drive higher engagement and suggest what to test next. 

Why it matters: Continuous improvement beats one-off “wins” when it comes to AI in design. 

Pro tip: Track both efficiency (time saved, rounds reduced) and effectiveness (reach, completion rate, conversion, qualitative feedback). 

Ethical Best Practices for AI in Design 

Using AI responsibly as a designer is just as important as using it efficiently. Ethical best practices for AI in design ensure your work builds trust with clients, protects intellectual property and keeps human creativity at the core of the process. 

• Be transparent when it counts. Disclose AI graphic design use (or other uses) to clients where ownership, likeness rights or editorial integrity are in play. 

• Respect IP and likeness. Avoid style mimicry of living artists; do not use AI generation or use images you cannot legally license. 

• Credit humans. If AI accelerates work, credit the people making decisions and adding craft. 

• Don’t let design automation speed lower standards. “Good enough” is fine for ephemeral, low-risk placements — but not for brand-defining work. 

• Portfolio integrity. Label which elements were AI-generated design and explain your AI creative workflow process and decisions. 

Student Portfolio Tips for Integrating AI in Design 

Employers want to see not only what you created, but how you created it. By clearly explaining how AI supported your work — and where your own design decisions made the difference — you’ll showcase both technical fluency and creative judgment. 

• Show your thinking. Include a brief “how we used AI tools” panel: problem > process > your judgment calls. 

• Demonstrate restraint. Pair an AI-accelerated design draft with a fully crafted final to show human taste and finishing skills. 

• Own the brief. Hiring managers care that you solved the right problem for the right audience more than that you used the latest AI tools for designers. 

• Speak to collaboration. Note where you coordinated with writers, producers, clients or legal on ethics and rights. 

Try it Out: A Playbook to Prepare for AI 

Preparing for AI doesn’t have to be overwhelming. This step-by-step playbook gives designers a practical way to experiment, refine and build confidence using AI in design workflows. 

1. Map the workflow (research > concept > produce > atomize > distribute > measure). 
2. Circle the friction points (repetitive, time-intensive, error-prone). 
3. Pilot one tool per friction point on a low-risk project. Document time saved and quality impact. 
4. Share wins weekly in a short team huddle so practices spread. 
5. Set simple policies (disclosure when AI is used, rights checks, accessibility, review gates). 
6. Keep learning. Tools will change. Your judgment is the durable skill.  

As one agency leader put it, “AI doesn’t replace creativity; it amplifies it. Keep humans in the loop — and keep the soul in the work.” 

By following these best practices for AI in design, creatives can work faster, smarter and more responsibly — while building portfolios that show both innovation and integrity. 

Editor’s note: These practices synthesize insights on AI in graphic design education shared during a Watkins College of Art Creative Professionals Lecture Series on “AI in Design,” featuring Nashville agency leaders Mark Scrivner (Snapshot / Creative Assembly) and Robert Froedge (Lewis Communications). The guidance above generalizes their advice for broad use in creative teams.