Now, the report shows you what changed. Figuring out why takes a bit more…well, legwork. Maybe some members missed their renewal reminders, or this year’s annual conference moved to a location that was harder for members to get to. Or it could be a little of both (plus a few things nobody’s noticed yet!).

Before you fall down a rabbit hole of what-ifs, it helps to have a clear path forward. So, how do you get from the numbers to a well-rounded decision that accounts for both the what and the why? That’s where data-informed decision-making comes in!

In this article, we’ll look at what changes when your organization becomes intentional about being data-informed. We’ll also cover how your technology can speed up the process and what to consider before bringing new insights to your board. Let’s get right to it!

Key Takeaways

• Data-informed decisions use evidence alongside context and judgment, giving organizations more to work with than numbers alone.
• A supportive technology platform helps teams access the information they need to weigh their options and plan their next steps.
• A clear and consistent process helps teams build this habit by guiding what they investigate and how they assess the results of their decisions.

Data-Informed vs. Data-Driven: What’s the Difference?

Before we get into what being “data-informed” actually is, you might be wondering how it differs from a more familiar term: data-driven. While the two are often used interchangeably, there’s a useful distinction we can draw!

According to Marvel (2023), a purely data-driven approach simply lets the data determine the decision, while a data-informed one considers it alongside experience and context to guide human judgment.

For member-based organizations, that second definition usually fits better. Your team knows things a report can’t show (like a chapter that recently lost its volunteer lead or a policy change that affected one license type). These details may sit outside your data, but they can change what the numbers mean!

For example, suppose engagement dropped in a chapter. Acting on the decline alone might prompt your team to try a discount or a new email campaign. Knowing the volunteer lead stepped down gives you something more specific to investigate: whether fewer local activities contributed to the drop. That could point toward, say, recruiting a new lead before launching a promotion.

As West (2019) explains, organizations should strike a balance between data analysis and their team’s expertise to make good decisions!

How Data-Informed Decisions Show Up for Member-Based Organizations

For many teams, being data-informed means making it a habit to understand what changed before deciding how to respond. To put that habit into practice, start with these steps:

• Ask what the number means: Check who’s affected and whether the change is part of a longer, more nuanced trend.
• Look for context: Speak with members and staff about what might explain the findings.
• Weigh your options: Consider which response best fits the evidence and what your team has learned.
• Pick a next step you can measure: Decide what to track and how frequently to review and update the results.

Let’s go back to that renewal dip from earlier and see how each approach might play out:

• A purely data-driven response based on those figures alone might be to increase renewal outreach in that region, then track whether the rate improves.
• A data-informed team would also speak with members and staff before choosing a response. If those conversations revealed that renewal reminders hadn’t reached some members, the next step could be to check contact details and follow up directly with those affected.

So, why does the latter approach tend to work well? Member-based organizations serve people, and people’s choices don’t always fit neatly into a report. A data-informed approach leaves room for the context your staff and volunteers bring, while keeping decisions grounded in evidence. The data guides the conversation, and your team makes the call.

Your Technology Helps You Get There

Of course, making sense of member data can take time, and member-based organizations are often stretched thin. In NTEN’s 2025 Data Empowerment Report, over 79% of organizations identified limited time as a challenge when collecting data (Abazajian, 2025).

Don’t worry, though; your technology can lend a hand! The right reporting tools can help your team find answers without waiting on a specialist, so more of their time goes toward the decision itself. Look for features like:

• Custom reports: These let your team pull the information a question needs, like renewal rates by membership type or course completions by region.
• Scheduled reports: These deliver key numbers to your inbox at regular intervals, making changes easier to spot.
• Segmentation: This groups members by shared characteristics or activity to show who a decision affects.
• Leadership dashboards: These display the figures your board cares about in one place, making updates quicker to prepare.
• Surveys: These gather member feedback to help explain the why behind a number.

These tools tend to work best when they draw from the same information. A natively all-in-one platform (like Vocalmeet’s!) can bring membership management, learning, events, payments, and reporting into a single modular system, so every report draws from a single pool.

Putting the Data to Work: From Insight to Board Decision

Before your next board meeting, try to keep these considerations in mind:

• Start with the question: Identify what your board needs to decide, like “Should we adjust our fee structure for early-career members?” Then, pull the relevant data.
• Check the numbers: Confirm the date range and check for any gaps. If something seems surprising, ask a staff member (who’s familiar with the situation) to review it!
• Share the story behind the numbers: Bring in relevant feedback and context to help your board understand the recommendation, including what the evidence supports and what’s still uncertain.
• Plan how you’ll assess the outcome: Decide which results to track and how often to review them. For example, if you adjust fees for early-career members, check their renewal rate each quarter and compare it with the same period last year.

Conclusion

Whether it’s a monthly course enrollment summary or feedback from a post-event survey, your data and reports will keep landing. Being data-informed lets you transform your findings into something that moves your organization forward with greater purpose.

So, take a moment to ask what the numbers suggest and how you might respond. Then consult your team, who understand members’ needs and experiences, and decide how you’ll measure the result before you act. You may be surprised by how much more focused your board conversations become!

Sources

Abazajian, K. (2025). 2025 Data empowerment report discover how nonprofits collect, manage, use, and govern their data. An NTEN report. https://word.nten.org/wp-content/uploads/2025/07/2025-Data-Empowerment-Report.pdf

Marvel, D. (2023, March 20). Data as inspiration, not just information. Northwestern Site. https://imcprofessional.medill.northwestern.edu/blog/data-as-inspiration-not-just-information

West, A. (2019, April 22). Data-driven decision making for not-for-profit organizations. The CPA Journal. https://www.cpajournal.com/2019/04/22/data-driven-decision-making-for-not-for-profit-organizations/

Meanwhile, your new program coordinator is searching through old documents and emails to understand what needs to happen before event registration opens. Is there early-bird pricing? Do members get first access before it opens to everyone else?

In both of these scenarios, the information likely already exists. The challenge here is finding it. That’s where Organizational Intelligence comes in, which we’ll cover in today’s article. Let’s get started!

What is Organizational Intelligence?

Organizational Intelligence is a feature that turns your existing knowledge into practical, accessible guidance.

Think about how much sheer data your organization has built up over the years (and manages daily!): membership policies, program details, operating procedures, internal documents, and even staff expertise. Now, that’s a lot.

An Organizational Intelligence module brings all of this together into a shared knowledge base. Then, using its related AI assistant, members and staff can simply ask questions in everyday language and receive guidance based on what they’re authorized to see.

Users don’t even need to know where anything is stored; the assistant retrieves it for them. (Though, if you’d like, it can also point people to where the information lives. It’s all a matter of configuration.)

But what does this actually look like in practice? Let’s walk through a few scenarios.

How Organizational Intelligence Can Help Your Organization

1. It Makes Member Support More Accessible

Let’s go back to the member who’s looking for your eLearning courses. Without navigating multiple pages or contacting staff, they could simply ask your organization’s AI assistant: “What professional development programs are available to me?”

Using your approved information, the assistant outlines options and guides them to relevant resources.

The same approach works for other everyday questions like:

• What benefits come with my membership?
• How does the renewal process work?
• Where can I find upcoming webinars?

For members, this creates a much simpler starting point for getting answers and exploring what their organization offers. For staff, it means less time spent on repetitive inquiries.

2. It Helps Staff Find the Information They Need

Now, let’s look at things from the staff perspective: Imagine you’ve just taken over your organization’s annual conference. You’ve worked on the event before, but you’re not exactly sure what needs to happen before workshop registration opens.

Normally, you might dig through old documents, search your inbox, check the shared drive, or message the person who handled it last year. With an Organizational Intelligence module, you could just ask your internal AI assistant!

Using approved procedures and organizational knowledge, the assistant can:

• Lay out the relevant steps
• Explain established practices
• Identify who to contact for further clarification

This can be particularly useful during onboarding, role changes, busy event seasons, or for teams that need consistent guidance on recurring tasks. And if a question isn’t covered or needs a decision from an actual person, it can point you to the right department!

3. It Preserves Process Expertise When Responsibilities Change

Of course, not everything your organization knows is written down. Long-time staff members often carry valuable insight into how things work, why certain processes exist, which vendors and partners to contact for what, and how unusual situations are handled.

But what happens when they retire or step away for an extended period? No need to worry! Organizational Intelligence can carry that know-how forward, pairing documented procedures with reviewed staff input. That way, years of practical experience stay within reach for whoever steps in next.

Putting Organizational Intelligence Into Action

You can try a simple example right here! See the little blue orb with the speech icon in the bottom-right corner of this page? Click it and ask a question about something on our website.

That’s VM Aurora, our helpful assistant, and an example of an Organizational Intelligence module in action. Give it a try and see how a simple question can help you find the information you’re looking for!

Our journey even reached an important milestone in 2026, when Vocalmeet received a Gold Stevie® Award for Company of the Year at the Stevie Awards for Technology Excellence. It was a proud moment for our team and a recognition of the years of work and persistence that brought us there.

We’ve shared plenty of ideas and lessons along the way. But one story we haven’t told in much detail is, well, our own! So, in this (slightly more personal) article, we’re stepping away from our usual advice and insights. Instead, we’re taking a look back at how Vocalmeet became the company it is today.

We’ll look back at our early years, including the challenges we faced and the turning points that brought us to where we are now. Ready to hear our story? Let’s get started.

Our Early Years

The seeds of what would eventually become Vocalmeet were planted in 2009, when we recognized a challenge that needed a better solution.

At the time, it was common to use separate systems for nearly everything: one for learning, another for events and membership management, and others for payments, communications, and reporting.

That fragmented setup was normal. And, for a while, we accepted it too. But as we worked more closely with organizations and saw the problems that arose (especially in their day-to-day operations), we started asking a rather interesting question: Why not combine them? 

This was the start of a much larger, more ambitious pursuit of an “all-in-one” platform. And we were truly curious! So, we tested the waters and asked more questions, each more specific than the last. We worked through all the what-ifs and possibilities, and even talked to many people about it.

At one point, we faced doubts about whether an all-in-one platform was too risky or too difficult to make work in practice. But we kept moving forward. With every new question, we got a little closer to an answer we could act on! Eventually, we knew there was no turning back.

The Shift, The Challenges, and Everything in Between

Of course, moving from an idea to reality wasn’t simple. But then again, what ambitious idea comes without a few challenges?

And we weren’t afraid of challenges. In fact, we welcomed them! This only fueled us to brainstorm and listen more closely to our clients. Onwards and upwards, we told ourselves.

Eventually, the pieces started coming together, and we committed to the shift. By the time Vocalmeet officially took shape in 2016, we released the first version of our all-in-one platform, consolidating learning, membership, events, and payments in a single place.

2024: Nothing But Up

The beginning of 2024 marked one of our biggest steps forward yet. After years of building and expanding our platform, we launched version 2 of our all-in-one platform: VM Uplink.

This release was such a major milestone in Vocalmeet’s evolution. Built from the ground up, Uplink brought together much of what we had been working toward for years. It gave us a better, stronger foundation for a consolidated platform offering membership, learning, certification, events, data, automation, and other crucial systems.

Of course, our approach had always been natively all-in-one. With VM Uplink, though, we were able to bring more of that original vision together than ever before. What’s more, we kept improving our platform.

As artificial intelligence (AI) developed, we also began expanding our own AI capabilities, including functionality designed to support workflow automation, member and learner engagement, assessment integrity, and course promotion.

With VM Uplink, organizations could now manage all of their vital operations in the same platform, including:

• Association Management Systems
• Member Management Systems (including renewals)
• Customer relationship management (CRM)
• Event Management Systems (including event and conference registration)
• Learning Management Systems (including course delivery and registration, assessments and exams, certification, and continuing education (CE) tracking)
• AI proctoring
• Reporting and Analytics
• eCommerce and accounting
• Agentic Automation

We knew these developments could be especially valuable for member-based organizations balancing growing programs and member needs with limited time and resources. That had always been part of what drove us: finding better ways to make complicated processes work together easily.

We couldn’t be prouder of how far VM Uplink has come. What started as an ambitious idea more than a decade ago has become something we could finally see working at a much larger scale.

Built for What’s Next

Now in 2026, we’re asking ourselves: Where do we go from here? This exact question has followed us throughout Vocalmeet’s story. And in a world where technology can change remarkably quickly, the answer can be…well, tricky.

Still, we’ve never found that uncertainty discouraging. If anything, it gives us more to explore! AI, for example, is a big part of that conversation today, and we’ve continued developing AI-enabled capabilities as the technology evolves and as we learn more about where it can genuinely support our clients.

Our core value lies in standing with our clients and helping them do better. At Vocalmeet, we never stop exploring ideas, no matter how seemingly simple or wild.

We’ll continue to listen to our clients, make data-informed decisions, grow as conversations expand, and keep building for what’s next. That’s what we did more than a decade ago, it’s what we’ve done in the decade since, and it’s what we’ll continue to do in the decades forward!

We hope you’ll join us on our journey.

But then there are the requirements that get specific. Like, really specific. One organization may have a unique approval process. Another may need certain information checked or routed differently before a member can move forward.

When it comes to meeting those needs, you’ll usually encounter two familiar options: configuration and customization. So, what’s the difference, and which one does your organization actually need?

In this article, we’ll look at when each approach makes sense and how greater flexibility can help when your organization’s processes don’t fit neatly into a box. Let’s get started!

Key Takeaways

• Configuration adjusts a platform’s settings and preferences without changing its core code. When configuration can’t meet an organization’s needs, customization may be needed to modify the code.
• In practice, configuration is often easier than customization. The right approach depends on your organization’s needs and the platform’s capabilities.
• Customization can be more complex, often requiring additional development, testing, maintenance, and vendor involvement.
• The more configurable a platform is, the more requirements an organization may be able to meet without custom development.

What’s the Difference?

Before comparing the two, it helps to first define exactly what configuration and customization are. Eyjolfsson (2023) describes configuration as “adjusting the settings and preferences of an existing software application to meet the specific needs of a user or an organization, without changing its core code.”

Customization, on the other hand, goes a bit further. It usually “involves making changes to the software’s core code to add new features, change its behavior, or integrate it with other applications” (Eyjolfsson, 2023).

In practice, it’s often easier to configure than to customize. But does that mean configuration always wins? Not necessarily. Sometimes, an organization may need customization because the functionality simply isn’t available within the existing platform!

That being said, we generally still recommend configuration over customization when possible. As Arena Marketing (2017) puts it, “configuration requires no changes to the software source code; this one distinction has powerful business ramifications that have driven many companies to move far away from customizable toolkits.”

In the end, the right approach depends on what your organization actually needs and what the platform is capable of doing. So, rather than dwelling on which one is “better,” let’s look at how each works in a realistic scenario:

The Configured Way

Let’s say a professional regulatory body manages an annual renewal process for several different membership or license types. Most members follow the same general steps, but the details vary.

For example, one group may need to answer additional questions, while another has to upload supporting documents. On top of that, certain applications may require staff to review items first before approving.

Depending on a platform’s capabilities, an organization can configure its processes using tools that already exist within the system. For instance, staff members could:

• Add the necessary fields to the renewal form
• Determine which questions appear for different member types
• Set permissions
• Create approval steps
• Build reports to track outstanding renewals

The organization gets a process that reflects how it operates without changing the underlying software. That’s what makes it a configuration solution: you take the existing building blocks the platform gives you, then arrange them to meet your organization’s requirements.

The Customized Way

Now, say you’re a professional regulatory body with a far more specific renewal process. In our example, your members hold one of two license types, and each must complete a competency assessment during renewal. What happens next depends on their license type and their score.

First, all renewing members take the competency test.

• A License A member who scores between 0 and 50 must first complete a learning path, while a License B member with the same score must complete a different learning path. They must complete their respective learning paths before proceeding to renew their membership.
• Members scoring 51 to 75 must complete a single course, with the exact course depending on their license type. They must complete their respective courses before proceeding to renew their membership.
• Finally, those who score 76 and above move directly to renewal.

In this case, the platform now has to:

• Evaluate several variables together
• Determine which combination applies to each member
• Assign the appropriate requirements
• Decide what happens once those requirements are complete

At that level of complexity, the workflow may exceed what the platform’s existing configuration tools can support. Custom programming may be required to follow a highly specific set of rules unique to its renewal process. In this case, customization might be the right option!

The Potential Complications of Customization

As you can see, customization certainly has its place. Unfortunately, it can also get complicated, fast. Plus, custom functionality usually comes with more moving parts…and the more things move, the trickier things can get!

This can mean requiring additional:

• Development: Custom functionality has to be designed and built specifically for your organization, usually by a developer (or even an entire development team!).
• Testing: That specific functionality then needs to be checked carefully to make sure it works as intended without affecting other parts of the platform.
• Maintenance: Sometimes, it may need ongoing attention as your processes change or the underlying platform evolves.
• Vendor involvement: Changes that might otherwise be handled internally may require a developer or vendor to step back in, especially when that functionality needs to be updated later.

And, if the custom functionality is part of a Software as a Service (SaaS) platform, too much customization can also mean that your organization may no longer get the same SaaS benefits!

This is because it’s no longer the same infrastructure getting the same updates across the board; it’s become too much of its own instance, so updates have to be done manually. This can require extra resources (and cost!).

According to Orbit Analytics (2020), the “limitations of software customization often lead to long-term complications that organizations fail to anticipate.” Those complications can become more noticeable as the platform evolves, particularly if custom functionality requires ongoing upkeep.

Now, we’re really not trying to scare you away from customization. It still plays an important role when it’s truly called for! The key is being thoughtful about what truly needs to be custom-built and what could be handled through configuration instead.

Flexible Platforms for More Capable Configuration

If it sounds like there are no solutions, we get it; this can sound like a lot. But there’s actually a way to reduce the need for customization: by choosing a platform with greater configurability. 

The more flexible a platform’s configuration tools are (like Vocalmeet’s!), the more requirements an organization may be able to accommodate without custom development.

Let’s go back to our customization scenario from earlier. Remember the regulatory body whose renewal process depended on multiple variables? A highly configurable platform may offer another option: breaking the workflow into smaller steps!

First, staff could configure memberships to be split into License A and License B members.

Next, they could run reports on the competency assessments and divide members into groups by score:

• License A
  • 0-50
  • 51-75
  • 76+
• License B
  • 0-50
  • 51-75
  • 76+

These groups could then be assigned their relevant learning or renewal statuses. For the groups that require completion of a learning path or course, running reports on these items would give staff lists of who else is currently eligible for renewal.

Note that some manual work may be required in the configured version (like periodically exporting a report and then uploading it elsewhere to create a group). However, the majority of the process remains automated, and no costly customization is required. So, your best option really just depends on how flexible you are with your process and where you want to put your resources!

Conclusion

When it comes down to it, while it’s easy to be dazzled by a software’s many bells and whistles, the important factor is flexibility. That flexibility can come from your organization’s ability to adapt its workflows, your vendor’s configuration options, or a combination of both. If your processes can’t be tweaked and your platform’s configurations can’t meet them where they’re at, then customization is likely the way to go.

Talk it through with your technology provider and think about what will be easiest for your team to manage over time. Whichever route you take, your goal is simple: your technology should always make things better for the people who use it!

Sources

Arena Marketing. (2017, October 12). Why more companies chose configurable over customizable solutions. Arena. https://www.arenasolutions.com/blog/why-more-companies-chose-configurable-over-customizable-solutions/

Eyjolfsson, F. (2023, October 3). Configuration vs. customization: The right approach for your software. Academy.Broadcom.Com. https://academy.broadcom.com/blog/valueops/software-configuration-versus-customization

Orbit Analytics. (2020, May 6). Why software customization challenges can hurt your business. Oracle EBS Reporting, BI & Analytics Software Solutions | Ad Hoc Reporting. https://www.orbitanalytics.com/blog/software-customization-challenges/

But what if your member-based organization’s data is scattered across multiple platforms? In that case, AI may struggle to identify and resolve your real issues. Sometimes, it can even make the confusion worse!

Luckily, there’s a solution to that, and that’s exactly what we’re exploring in today’s article. We’ll be looking at how your organization can maximize its AI usage potential for accurate reporting with strong data, connected systems, and the right technology platform. Let’s dive right in!

Key Takeaways

• To truly harness the power of AI, it needs accurate, complete, and connected data to draw from.
• Fragmented systems can lead to duplicate records, missing context, and unreliable AI outputs.
• A single source of truth (SSOT) helps organizations make better decisions by keeping member, learning, event, payment, and engagement data in one place.
• An all-in-one platform can help create the connected foundation AI needs to support smarter, more reliable operations.

The New Reality: AI as Opportunity for Member-Based Organizations

When you think of AI, you likely think of the word “efficient”, a word that’s music to any organization’s ears. Charles-Edwards (2025) formally defines efficiency as “achieving goals and sustaining high performance with minimal waste, whether of time, effort, or resources. Tell me: who doesn’t want that?

For organizations with so much on their plate, this couldn’t ring any more true. Your teams often manage so many things left and right (member services, learning programs, events, payments, reporting, communications, and more!) all at once. When work depends on manual processes, you can imagine how small tasks can quickly pile up and become major time drains.

This is exactly where AI can offer meaningful support. Generative tools can help your team with everything from everyday administrative tasks like brainstorming and writing to more decision-driven responsibilities like research and early-stage problem-solving (Stanford Graduate School of Business, 2026).

But then…there’s a catch: AI needs meaningful data to work with. If your information is incomplete, outdated, or stored across disconnected systems, AI may produce results that are inaccurate or even downright irrelevant.

Fragmentation: A Barrier to AI’s Powers

We already know that the more complete the context you give and the more specific your prompts are, the more effectively AI can assist you. So, when we’re talking about a fragmented system, you can see how that’s problematic: “complete” and “fragmented” are opposites!

Say you use one system for membership, another for learning, and entirely different platforms for events, email marketing, and reporting. You may find that basic integrations do the job well enough. However, standard integrations only allow limited data sharing.

Mazurkiewicz (2023) explains that while your tools may work perfectly well individually, getting them to work together can be challenging. If your software can’t connect smoothly, your organization quickly runs into a separate systems problem.

So, why would AI find this troublesome? Because AI is only as good as the data that fuels it. And, with a fragmented setup, member-based organizations can become trapped in data silos, leading to inefficiencies, inaccuracies, and even security risks.

When AI Hallucinates

IBM (2023) defines AI hallucination as a phenomenon in which a generative AI perceives nonexistent patterns, creating “outputs that are nonsensical or altogether inaccurate.”

For example, let’s say your organization’s platform automatically tracks member engagement through a point system. You can then identify the lowest-point users and reach out to remind them of member benefits they may be missing or courses they may be interested in.

In a connected environment, the score can automatically draw from various actions and touchpoints: membership status, page views, renewal history, course completions, event attendance, payment activity, communication records…you name it!

But what if you’re dealing with fragmented systems? In this case, AI may only see part of that story. It might, for instance, flag an active learner as a disengaged member because their learning activity sits in a separate LMS. Or it might miss a member who has stopped attending events because event data lives on another platform.

What’s even more problematic is that AI doesn’t necessarily tell you, “Hey, I don’t think I have enough information to make an accurate decision here.” Worse, it can even sound completely confident when delivering an inaccurate result!

Solving Fragmentation with a “Single Source of Truth” Approach

If separate systems are the problem, then the natural solution would be, well, their opposite. Enter: the Single Source of Truth (SSOT)!

An SSOT is a centralized system where your organization’s information is stored, updated, and accessed consistently. That means you’re seamlessly connecting all your data in a single environment. That data can include:

• Membership
• Learning
• Events
• Payment
• Certification
• Communication
• Engagement

As a result, your team has a clearer, more accurate view of what’s happening across the organization so they “can remove questions about inconsistent data formats, reliability, and timeliness” (Queiroz et al., 2024).

For AI, this matters even more. An SSOT gives AI better, stronger context; instead of looking at one isolated record, it can work from a full view of the member journey, from registration or renewal status and course progress to event attendance and even payment history.

That makes AI support more practical, helping your teams with the bigger-picture questions faster:

• Which members completed the required training but haven’t renewed?
• Which members may benefit from outreach based on recent activity?
• Which learners need a reminder before their certification deadline?

As LawVu’s Sarah Barker (2025) says, “[b]y building your AI adoption strategy around a single source of truth, you can create an accurate, secure, and self-sustaining data ecosystem that will only deepen in value as it grows.”

Where an All-in-One Platform Can Help

We’ve established that SSOT is the right approach, but the catch is that you also need the right technology. Sure, it’s okay to work with integration and stitch systems together. But, sometimes, you may run into limitations. That’s why we recommend a natively all-in-one platform.

This type of platform (like Vocalmeet’s!) lets your critical information live in a single environment. It’s built from the ground up as one modular solution that brings all your systems together. There’s no need to hope separate systems will play nice, or pray that an integration doesn’t suddenly deprecate; everything just works. 

This gives your organization the benefit of centralized workflows and gives AI the technology foundation it needs to work from cleaner data with fewer system gaps and more reliable context!

Conclusion

AI may be the exciting next step, but its advantage becomes even stronger when the foundation behind it works together! So, start with that structure: centralize your data and create one reliable place for information to live. You’ll be surprised by how much smoother things can run when your information works together, and how much easier it becomes to make confident, data-driven decisions.

Sources

Barker, S. (2025, May 4). The secret to successful AI adoption: Building a single source of truth. LawVu. https://lawvu.com/articles/the-secret-to-successful-ai-adoption-building-a-single-source-of-truth/

Burnham, K. (2026, April 22). How AI is reshaping workflows and redefining jobs . MIT Management Sloan School. https://mitsloan.mit.edu/ideas-made-to-matter/how-ai-reshaping-workflows-and-redefining-jobs

Charles-Edwards, H. (2025, March 21). Organisational efficiency – what does it really take? Helen Charles-Edwards. https://hceconsulting.co.uk/perspectives/organisational-efficiency-what-it-really-takes

IBM. (2023). What are AI hallucinations? In IBM. https://www.ibm.com/think/topics/ai-hallucinations

Stanford Graduate School of Business. (2026, April 13). How AI is reshaping the future of work. Stanford Graduate School of Business; Stanford University. https://www.gsb.stanford.edu/exec-ed/difference/how-ai-reshaping-future-work

Queiroz, M., Tallon, P., & Coltman, T. (2024). Data value and the search for a single source of truth: What is it and why does it matter? Proceedings of the … Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2024.795

That’s where AI proctoring comes in. In this article, we’ll look at how this practical feature works and how member-based organizations can use it for online assessments while reducing the amount of hands-on monitoring required. Let’s dive right in!

Yes, Humans Are Still In The Loop!

Before we get into how AI proctoring works, we’ll first reassure you that yes, humans are still kept in the loop here. You aren’t delegating the final decision to AI, especially when it comes to something as high-stakes as an exam.

Think of AI proctoring as an extra (helpful!) set of eyes during an online exam. It monitors the session and flags activity that may require a closer look later by a real person. These instances can include unusual behavior or actions that don’t match the rules set for your specific assessment, like having multiple people on-screen at once or disappearing from the webcam’s view entirely.

The keyword here is flags. A flag doesn’t automatically determine that the test taker has done something bad or wrong. Instead, it allows a human reviewer to examine a particular moment or activity and make the call, much like they would during live proctoring! This process helps save time and effort.

Scenario

A test taker looks away from the screen several times during the exam. Here, AI will flag each of those moments for review. Then, that same test taker also gets up and moves around the room three or four times. Will AI flag that, too? Yes, of course.

Do these actions automatically mean the person should fail? Not necessarily. The AI system will recognize that behavior as suspicious and record it so that when it comes time for the reviewer to make the final decision, the activity can be traced and evaluated.

Straightforward, right? This setup leaves repetitive, tedious monitoring to AI while leaving the final judgment with the human proctor.

Conclusion

With AI proctoring, reviewers no longer need to keep their eyes glued to a screen for hours at a time. Testing becomes easier to manage and more flexible to schedule, while teams can focus on the precise moments that actually need attention. Less hands-on monitoring, more flexibility, and better use of administrator time. That’s a win-win-extra win!

If you’re using AI for every single task, though, then…well, you can keep reading. In some situations, it’s worth pausing to think about whether you’re using AI responsibly. Using it to draft an email? Sure, that’s no problem. Putting together a newsletter? Go ahead!

What about analyzing sensitive member data or making recommendations that could affect someone’s access, eligibility, or opportunities? Now, that’s a different scenario.

That’s where an AI Code of Conduct comes in. It provides guidance on how to use AI responsibly and ethically, in ways that align with your organization’s expectations. In this article, we’ll explore what an AI Code of Conduct is and why it matters to your organization. Let’s get started!

Key Takeaways

• An AI Code of Conduct is a list of ways your organization has agreed to use AI, with clear boundaries and limitations.
• It can reduce risks around sensitive member information, inaccurate outputs, and inconsistent decision-making.
• Human review remains important, especially when AI is used for higher-risk tasks that affect members.
• Shared guidelines make it easier for teams to use AI responsibly and consistently across the organization.

What Is an AI Code of Conduct?

An AI Code of Conduct is a set of guiding principles that ensure AI technology is used responsibly, through a safe, secure, and humane approach (Coursera, 2024). For organizations, it sets clear boundaries and limitations as to how and when to use AI.

For example, an AI code of conduct may explain:

• What kinds of AI tools can be used
• What information can and can’t be entered into public AI systems
• When human review is required
• How AI-generated content should be checked
• How bias, fairness, and accessibility should be considered
• Who approves AI use for higher-risk tasks
• What to do if an AI-related concern comes up

And, depending on your workflow, that’s just the tip of the iceberg. The key point here is that, without a code of conduct, everyone may end up making their own call about what’s okay and what isn’t.

Hicklen (2025) explains that teams “may operate in silos and make independent decisions about AI adoption that conflict with broader business strategies or ethical considerations.” In practice, that can look different across your organization. One team may stick to basic, low-risk tasks like brainstorming, while another uses AI to draft member-facing content.

You may think, “Okay, well that’s not too bad”, but you can start running into some real trouble when it involves sensitive and/or confidential information. Consider: what if someone thoughtlessly uploads internal notes or member information? Yikes!

People end up making unsupervised judgment calls because there isn’t a clear list of do’s and don’ts. So, in the absence of shared guidance, each person on your team may end up with a slightly different idea of what responsible AI use actually is.

Why Member-Based Organizations Need One Now

That’s why an AI Code of Conduct matters, especially for organizations. Think about it: you often manage a wide range of information. From member profiles, payment details, certification records, to continuing education activity, event participation, complaints…the list goes on! The University of Delaware (2026) points out, “[a]s AI systems learn from vast amounts of data, protecting your personal information becomes more critical than ever”.

Naturally, members expect their organization to protect their information and make decisions with care. If AI is being used behind the scenes, those expectations still apply. The key player here is trust.

1. You Protect Your Organization’s Sensitive Information

For a long time, we’ve all been setting our rules and standards around what we call data privacy. However, as Gomstyn and Jonker (2024) point out, “data privacy predates AI, and the way people think about it has evolved” now that AI has come into the picture.

As with any data privacy concern, AI privacy issues often begin with the usual culprits of collecting sensitive information (without consent) and then using it. But this time, there’s an added consideration: how that data is entered into, processed by, or used within an AI system.

Let’s do a little reflection exercise here for a moment: When was the last time you paused and thought about the information you’re feeding to AI tools?

For instance, maybe your organization has just finished collecting data from a recent survey. Now, should a team member simply paste the survey responses into an AI tool to summarize their key themes? Well, that’s going to depend on your agreed-upon protocol!

Maybe your organization does allow this, but only if the data has been preprocessed; that is to say, it’s been reviewed, anonymized (where needed), and handled in accordance with your organization’s privacy standards.

Again, this all depends on what your organization has agreed is acceptable. The more sensitive and high-value the information you’re using is, the more precaution and thought you’ll likely need to dedicate when crafting the rules and limitations of its use.

2. You Avoid Confidently Wrong AI Outputs

Earlier this year, we hosted an online webinar called “Beyond the Hype: What Today’s Systems Can’t Do,” where we underscored one very important point: AI tools typically generate likely answers rather than verified results. What’s more alarming is that it can sound confident even when it’s completely wrong!

IBM (2023) calls this an AI hallucination: a phenomenon in which a generative AI perceives nonexistent patterns, creating outputs that are nonsensical or altogether inaccurate.

What’s worse, “[w]hen an AI says something that seems a bit fishy, users may not be as skeptical as they should be because the AI asserts the answer with confidence, even when that confidence is unwarranted” (Bittel 2025).

So, how does an AI Code of Conduct help in a situation where AI gets it wrong? It all boils down to setting limitations: how much AI can contribute to a work and what the review process looks like.

Let’s say a staff member asks an AI tool to look at a member’s continuing education records and confirm whether they appear to meet the requirements for certification renewal (again, whether or not this is allowed depends on the model and limitations of your AI use, as this type of information could be sensitive and could potentially violate data privacy laws if fed to a public AI system).

The AI confidently says yes, but it has actually just overlooked a missing requirement or relied on an outdated policy. If no one verifies the answer, the organization could give the member incorrect information about their eligibility!

An AI Code of Conduct can help set clear standards for how AI-generated work should be reviewed before it’s shared, published, or used in critical decisions (like those that affect members!). Additionally, it can further explain how that review should be carried out based on your organization’s established process.

3. You Set a Standard for Consistent AI Use

Because AI is so accessible and convenient, people often want to use it wherever and whenever they can. Now, we’re not here to police anyone about how much they can use AI in their workflow! Instead, we simply want to warn people of the issues that can arise when people start making unsupervised decisions about how a tool is used.

With an AI Code of Conduct, you give everyone on your team the same starting point. Instead of deciding what’s appropriate on a case-by-case basis, you can refer to your set rules for how AI should be used across the organization.

For example, your marketing team may use AI to brainstorm social media posts, while your education team uses it to summarize course feedback, and your membership team uses it to analyze trends in member data.

As you can imagine, each department’s tasks can vary considerably. Thankfully, the expectations surrounding them can be quite similar! What’s important is that everyone knows which tools are approved, what information can be entered, when human review is required, and who to ask when they’re unsure.

Conclusion

We’re living the AI reality right now. For many organizations, its growing role in day-to-day work likely isn’t going anywhere anytime soon. That makes it worth deciding what responsible use actually looks like for your team; an AI Code of Conduct is what gives your team a shared set of rules to follow. Protect your data, and your members’ trust: consider drafting your own AI Code of Conduct today!

Sources

Bittel, J. (2025). AI chatbots remain confident — even when they’re wrong. In Carnegie Mellon University . https://www.cmu.edu/news/stories/archives/2025/july/ai-chatbots-remain-confident-even-when-theyre-wrong

Coursera . (2024). AI ethics: What it is and why it matters. In Coursera. https://www.coursera.org/articles/ai-ethics

Emtrain. (2025). Ethics and AI: Building trust in the workplace. In Emtrain. https://emtrain.com/blog/ethics-and-compliance/ethics-and-ai-building-trust-in-the-workplace/

Gomstyn, A., & Jonker, A. (2024). Exploring privacy issues in the age of AI. In IBM. https://www.ibm.com/think/insights/ai-privacy

Hicklen, H. (2025). Don’t have a company AI policy? How it’s impacting your team. In Clutch. Clutch. https://clutch.co/resources/ai-policy-impacts-team

IBM. (2023). What are AI hallucinations? In IBM. https://www.ibm.com/think/topics/ai-hallucinations

University of Delaware. (2026). Privacy in the age of AI: Why it matters more than ever | secure UD threat alerts. Udel.Edu. https://sites.udel.edu/threat/2026/01/28/privacy-in-the-age-of-ai/

July 23, 2026

Vocalmeet Inc. has been named the winner of a Gold Stevie® Award in the Company of the Year category.

For more than a decade, Vocalmeet has worked with some of North America’s largest member-based organizations to develop technology that helps associations, regulatory bodies, and unions deliver professional education online, issue credentials, support compliance, and serve large learner communities.

A More Cohesive Approach to Technology

Many organizations rely on a learning management system (LMS) for course delivery, a customer relationship management (CRM) system for member data, and separate tools for exams, certification, payments, events, reporting, and communications. This can create duplicate records, inconsistent user experiences, and additional administrative work.

Vocalmeet’s platform, VM Uplink, addresses this fragmentation through a modular all-in-one solution. Organizations can select the capabilities they require and add modules over time, allowing for phased implementation and future expansion.

VM Uplink covers the full learning journey, from registration through assessments, exams, certification, and continuing education tracking. Its artificial intelligence (AI)-enabled capabilities assist with course promotion, workflow automation, reporting, learner engagement, and assessment integrity.

Connecting these processes helps improve program visibility, strengthen record accuracy, and reduce manual administration by up to 60%.

This breadth and practical value were noted by the Stevie® Awards judges:

“Vocalmeet has built a comprehensive learning platform that unifies LMS, CRM, certification, events, and AI-enabled capabilities,” one judge commented. “Strong growth, large-scale deployments, and a focus on reducing system fragmentation demonstrate meaningful value for professional associations and certification-driven organizations.”

Demonstrated Impact

Vocalmeet’s clients range from organizations with several thousand members to those serving millions of users.

The significance of this work also stood out to the judges:

“Vocalmeet delivers an exceptional entry with its VM Uplink platform,” one wrote. “Backed by magnificent deployment metrics, including supporting 2.8 million users and issuing 525,000 certifications, this submission perfectly demonstrates scalability and massive real-world educational impact. Brilliant work.”

Achievement Through Collaboration

“Receiving a Gold Stevie® Award for Company of the Year is a meaningful moment for everyone at Vocalmeet,” said Dr. Laurelle Jno Baptiste, Chief Implementation Officer at Vocalmeet. “Our team has spent years listening closely to organizations that manage complex education, certification, and compliance programs. Their needs have shaped VM Uplink and pushed us to keep improving the platform as their priorities and challenges evolve.”

“This recognition belongs to our team and to the clients who’ve trusted us with such important work,” she added. “Their insight, collaboration, and ambition continue to influence the technology we build. We’re grateful to grow alongside them.”

Vocalmeet will continue investing in private cloud infrastructure, data security, and practical tools that make complex education programs easier to manage and access.

About Vocalmeet

Vocalmeet provides learning, membership, event, automation, reporting, and data management technology for member-based organizations across Canada and the United States. Its flagship platform, VM Uplink, helps organizations manage professional education, certification, and member operations within a single environment.

Contact us today to learn more.

About the Stevie® Awards

The Stevie® Awards, widely recognized as the world’s premier business awards, are nicknamed the Stevies, derived from the Greek word stephanos, meaning “crowned.” The Stevie Awards are conferred through nine programs: the Asia-Pacific Stevie Awards, the German Stevie Awards, the Middle East & North Africa Stevie Awards, The American Business Awards®, The International Business Awards®, the Stevie Awards for Great Employers, the Stevie Awards for Women in Business, the Stevie Awards for Technology Excellence, and the Stevie Awards for Sales & Customer Service.

The Stevie Awards receive more than 12,000 nominations each year from organizations in more than 70 nations. Honoring organizations of all types and sizes and the people behind them, the Stevies recognize outstanding performances in the workplace worldwide. Learn more about the Stevie Awards at http://www.StevieAwards.com.