Design Lead for Granting

When I joined the Grants product area at Benevity, design had been historically minimal — the experience was developer-led with limited patterns, low evidence infrastructure, and inconsistent alignment with broader company standards. What began as a single team with no design leadership quickly expanded into four cross-functional product teams with ambitious delivery targets.

In this context, I established design capability, mentored a growing team, and built systems that enabled sustained quality, clarity of decision-making, and scalable delivery beyond individual effort.

The Challenge

• Grants lacked structured design processes, reusable patterns, and product design leadership.
• Teams shipped features without measurable evidence trails or shared decision frameworks.
• UX outcomes varied widely, increasing rework risk and cognitive load across multiple teams.
• Planning and release processes were cumbersome, and long-range strategic delivery lacked design voice and coordination.

This environment created decision debt, delivery inefficiencies, and slowed innovation velocity, especially as the scope expanded.

My role

​I operated at a systemic and strategic level across four primary areas of influence: 

• ​Establishing foundational design capability and structure
• Scaling decision quality through AI and evidence processes
• Enabling autonomy for designers and contractors
• Bridging the gap between platform, product, and UX maturity

Here, my goal was not individual features, it was to reduce decision debt, streamline reuse, and embed quality into how teams operate.

What I Actually Built

Design Scaling & Alignment System

• Aligned the Grants experience with broader company patterns to reduce fragmentation.
• Introduced reusable page patterns and component use rules where none existed.
• Elevated design maturity in an environment previously developer-led without slowing engineering throughput.

Platform & Self-Serve Enablement

• Led UX for key self-serve and platform initiatives based on recurring client needs.
• Consolidated custom solutions into scalable configurations to reduce maintenance overhead.
• Enabled teams to deliver flexibility without proportional increases in development cost.

Design QA & Governance

• Introduced a design QA practice as a formal part of release processes.
• Ensured accessibility and interaction quality review before shipping.
• Reduced downstream rework and surfaced accountability for product quality.

AI Adoption & Evidence-Based Decision Infrastructure

• Drove AI adoption within and beyond my team to accelerate research synthesis and ideation.
• Implemented tools like NotebookLM to shorten synthesis cycles by 5–10× while retaining traceability.
• Coached designers and stakeholders on responsible AI experimentation.
• Led adoption of Pendo to improve product measurement and data maturity.

Organizational Impact

• Accelerated innovation - AI workflows reduced synthesis and ideation times dramatically.
• Improved decision quality​ - Clearer evidence trails combining qualitative research and analytics.
• Increased autonomy​ - Designers and contractors gained ownership of process and outcomes.
• Reduced delivery risk - Alignment and QA baked quality into release pipelines.
• Enabled scale in constrained environments​ - Systems replaced dependency on individual effort.

What Having Me Means

✨ Progress without compromising quality​ - Teams move faster with clear guardrails.
✨ Less debate, clearer defaults​ - Decisions rest on evidence and reusable systems.
✨ Reduced cognitive load​​ - Complex work becomes manageable and repeatable.
✨ AI as a leverage tool, not a distraction - Amplification, not replacement.
✨ ​Systems that continue working when I’m not in the room - The true mark of leverage.

AI & the Future — Human-Centered Adoption

I was an early internal advocate for AI adoption, sharing insights from industry events and embedding practical experimentation into everyday team workflows, which led to measurable improvements in throughput and confidence in decision-making.

I use AI with curiosity, not blind trust, tools can be wrong, and human judgment remains essential. AI accelerates many workflows, but it isn’t always faster. Knowing when not to use it matters as much as knowing when to lean in.

At UXDX 2025 in New York, I felt a shared tension across attendees... excitement paired with anxiety. What consistently helped was play. Treating AI as something to explore, rather than fear, made people more willing to engage with it. I saw the same pattern at Benevity: curiosity lowered resistance and helped adoption feel safer and more human.

Closing Reflection

AI accelerates expectations and compresses timelines, but sustainable impact comes from strong systems, clear accountability, and evidence-based decisions not sheer speed. Never this. My focus has been on helping teams move faster without overloading individuals, balancing tools with thoughtful judgment.

Let's talk

You can send me an email at [email protected] if you're interested to learn more about me, or connect with me on LinkedIn.

Talk to Anne

Credits to Storyset for their vector illustrations that made this page look lot better.