Measuring the Impact of AI: How Associations Can Evaluate the True Value of AI Solutions

Artificial Intelligence (AI) is rapidly gaining traction across associations, offering new ways to boost efficiency, enhance member engagement, and uncover valuable insights. But once you’ve implemented an AI solution—whether it's a knowledge assistant like Betty, a recommendation engine, or automated content creation—how do you determine if it's truly delivering value?

Evaluating AI’s impact requires a thoughtful approach that goes beyond basic usage stats. Here’s a framework to help associations assess AI’s real contribution.

Look at Both Internal and External Value

AI creates value in two primary ways: for your staff (internal) and for your members (external).

Internal value may include:

• Reducing staff time spent answering repetitive questions
• Streamlining routine workflows
• Surfacing data-driven insights to guide strategy and decision-making

External value is member-facing and might involve:

• Providing faster, more accurate answers to member questions
• Creating more personalized, intuitive experiences
• Recommending relevant content, resources, or events
• Expanding global access to knowledge, enabling members from different regions, time zones, or languages to engage with association resources around the clock

By breaking down barriers of time, geography, and language, AI tools like Betty can make an association's knowledge more accessible to members worldwide—an increasingly important benefit as associations grow their international reach.

A Leap Forward for AI Agents

AI agents rely on a continuous stream of information: user input, internal decisions, external API responses, and evolving goals. With traditional context limits, memory was a bottleneck, requiring engineered solutions like vector stores or memory databases. Now, agents can operate with far more continuity and autonomy.

Larger context windows mean:

• Fewer hallucinations due to better grounding in the full context.
• More coherent behavior over long tasks (e.g., writing a full report, planning an event, or completing multi-stage forms).
• Simpler architectures that rely less on fragile memory modules and more on direct reasoning.

This capability positions LLMs as not just chatbots but as full-fledged task-driven assistants that can rival traditional software workflows.

Focus on What AI is Helping People Do Better

Start by identifying the processes AI is transforming:

For staff, AI can:

• Automate responses to common member inquiries
• Summarize meeting notes or transcripts
• Recommend content for newsletters or communications
• Draft outlines or initial versions of reports, articles, or proposals
• Highlight emerging member needs based on inquiry trends

For members, AI may:

• Offer 24/7 access to timely, personalized support—no matter where in the world they are
• Suggest learning opportunities or upcoming events tailored to their interests
• Simplify complex tasks or application processes

By understanding these shifts, you can pinpoint where to track improvements—whether that's saved time, improved satisfaction, or better outcomes.

Measure More Than Just Usage

While it’s tempting to rely on simple activity counts—like number of interactions or clicks—these metrics alone can be misleading. More interaction doesn’t always equal more value.

Ask deeper questions:

• Was the issue resolved? Did the AI provide a complete, helpful answer?
• What happened next? Did the member continue deeper into your resources, or disengage?
• Were they satisfied? Did the interaction leave the member feeling supported?

And as AI supports more global engagement, consider:

• Are international members engaging more easily?
• Is content being accessed at times that suggest global usage?
• Are language barriers being reduced through AI-powered support?

Metrics That Offer Real Insight

Here are some meaningful metrics to track:

• Interaction volume: How many members engage with the AI tool?
• Resolution rate: How often does AI successfully answer questions?
• Time to resolution: How quickly do users receive the information they need?
• Behavioral follow-up: After interacting with AI, do users explore more resources or drop off?
• Geographic reach: Are members across different regions using the tool?
• User feedback: Gather both explicit feedback (e.g., thumbs up/down) and analyze language used during interactions for frustration or satisfaction signals.

Remember: A short, successful session that resolves a member’s question immediately can be far more valuable than a longer, drawn-out interaction.

 
Listen Closely to Feedback

User feedback is one of the richest sources of insight. Even simple mechanisms—quick surveys, thumbs up/down buttons, or open-ended prompts—can reveal whether your AI tool is meeting expectations.

Additionally, pay attention to conversational signals:

• Are users expressing gratitude ("Thanks, that helped!")?
• Are they repeating themselves or rephrasing the same question multiple ways?
• Do interactions reveal frustration or confusion?

These qualitative insights often point to opportunities for fine-tuning AI performance.

The Bottom Line

Measuring AI’s value isn’t just about counting interactions. It’s about understanding what your members and staff are trying to achieve—and how well AI is supporting those goals.

Importantly, AI opens the door to global knowledge access, helping associations serve an increasingly diverse, worldwide membership. By combining usage data, behavioral patterns, and user feedback—including signals of international and multilingual engagement—associations can develop a comprehensive view of AI’s real-world impact. And with that knowledge, you’ll be positioned to continually refine your AI tools, maximize their effectiveness, and deliver even greater value to your members and staff—wherever they are.