AI maturity in associations and nonprofits isn't a one-and-done scenario. Checking the box off with AI adoption pilots just doesn't cut it.
For associations and nonprofits, AI transformation is defined and tied to one very simple thing - delivery. Whether member-driven or mission-driven, orgs can only define themselves as AI-mature if they've leveraged AI within every corner of their operations to deliver, and continue delivering, on their promise.
Yes, operational efficiency plays a huge role in influencing productivity, driving revenue, reducing staff time, and relegating more administrative and otherwise repetitive tasks to automation. But, in the end, the most mature organizations get to tout that AI has influenced real decision-making on programming, new member models, member engagement, legislative movements, etc. - all factors that contribute to driving value to constituents.
While every org is going to carry a different flavor, there are standard signals that indicate an association or nonprofit is AI mature.
AI maturity path
Before we dive into the signals, let's take a look at the common maturity path for associations and nonprofits ⇓
AI-curious: Your org is aware, open, and interested in exploring AI concepts. There's a great deal of conversation around the topic but little action behind it. Your staff may be dipping their toes in ChatGPT for research at most.
AI-exploratory: Your org is launching and piloting disparate AI tools without a plan or connectivity. And there may still be a balance of AI champions, AI-indifference, and AI nay-sayers in the bunch.
AI-operational: Your org-wide functions are fueled by AI, your staff bandwidth has opened considerably based on AI efficiencies, they're invested and teamed up with AI, and there are basic AI policies and governance in place.
AI-strategic: Your org is consistently and seamlessly collecting trends and business intelligence in a way that positions you to make short-term and long-term business decisions based on your mission and strategy.
AI-transformational: Your org is continually shifting and delivering on the promise that serves up membership or mission-based outcomes. While there's never a 'target reached' scenario, you're demonstrating value.
It's a step, not a leap (like ever)
AI maturity in associations and nonprofits is no longer just about adopting new technologies, or being open to the possibilities. It's about looking at transformation through the least self-serving lens. The peak of AI maturity isn't measured by what your organization gets. It's measured by what your constituents get once your org at its strongest sustainable capacity.
Take a look at the visual above and think about where you are today. It's important that you as an organization assess your current state. And yes, within your assessment, you're going to focus on the data and the culture and the technology when it comes to the iterative steps. But in the end, the first question you need to ask yourself is:
Are we living up to our promise, our value, and our mission?
The answer will most likely always be 'no' because no organization can remain static in their promise of value. There are so many other driving factors that force change and evolution. So your follow-up question is now:
Are we holistically leveraging AI in a way that we can get there?
If the answer is 'no', which for most organizations - and companies in general - it is, then you can start taking stock of where you're truly at.
Are you watching AI webinars, having routine conversations and meetings around AI, purchasing some ad hoc AI tools to try out? And maybe getting experimental in the process? Well then, you're more than likely dancing in the AI curious or AI exploratory phase. Yes, this can feel demoralizing because you've put a lot of work to get where you are today with AI. But not to worry, every small step is significant in getting you down the path. Let's look at key AI maturity dimensions for orgs.
Note that these signals won't necessarily be linear in nature. Because of the complexity of AI transformation, the process will likely be curlicue - and that's completely fine as long as you manage expectations and keep moving.
Centralized data and tech stack
At the heart of AI maturity lies robust data integration and activation. This means orgs move beyond siloed, fragmented data systems into centralized, governed platforms that enable real-time insights and automated workflows. Rather than stitching together AI-based point-solutions, orgs need to consolidate their data into umbrella tech where AI and data is woven into every thread of functionality.
What maturity looks like:
In the end, if you're treating your data as a trust asset, ensuring it's clean, ethically managed, and governed, you're in the zone.
Cultivated cultural infusion
True cultural acceptance is measured by empowerment and investment. Your professional staffers need to understand:
The different types of AI and their use cases
To get your teams empowered and invested, you need to institute standard change management practices. That is, you establish transparency, collaboration, a safe space, and a continuous learning environment, both product-based and principle-based.
What maturity looks like:
Leaders model healthy AI use themselves
Ultimately, if your staff understands AI's direct impact on mission or member delivery, and their eyes are opening to AI aptitudes, you can achieve cultural AI maturity.
Solidify governance and policies
The most important aspect of AI adoption is that staff needs to truly understand the value and start opting into AI on their own. Governance is a necessity. And it helps to get everyone focused and applying it in a universal way. But, "forced" adoption is still a sign of immaturity when it comes to AI.
A well established formal AI governance framework is visible at the board and leadership level, and covers explicit policies around ownership, goals/metrics, processes, practices, ethics/privacy, and compliance/risk.
You see that this governing framework doesn't intrude on innovation. It's merely meant to get everyone on a universal system so that curiosity is nurtured without risk.
What maturity looks like:
This level of governance is especially critical for associations and nonprofits where reputation, safety, and thought leadership is everything.
Scaled operations
When I talk about AI government frameworks and bodies, I'm not referring to AI-governed hypnosis. The framework is there to put parameters in place, and ensure security for missions and safety for members.
It does not put AI in lock-up.
True scalability comes from a culture of innovation and stewardship. It's important that teams continuously tinker, innovate, and find better ways to run operations using AI. AI isn't always a trickle down effect—the ones on the floor can also determine what makes sense, and then multilaterally fit in within the framework.
Scaled ops is when AI is embedded in the fabric of day-to-day operations (marketing, member services, events, publications, certification, foundation). It also means maturity scorecards and heatmaps are leveraged so that orgs can benchmark progress, find holes, and guide spend.
When teams of five look like teams of 50, that's the true testament of AI scalability. Some examples include when:
Conference sessions automatically become content currency
What maturity looks like:
Human-AI cooperatives
During the 'cultural infusion' process (see above), education is focused on what AI can do for you. When it comes to human-AI collabs, education tends to shift to what AI can't do for you so that you can remove the blind spots and own the output.
AI responsibility and sustainability is achieved when well-balanced human-AI collaboration models are in place. These are partnerships where humans remain the stewards, and AI remains executionary—carrying out decisions, and augmenting outputs based on staff's expertise and defined escalation paths.
Achieving and sustaining AI isn't just about technical infrastructure - it's equally focused on data literacy and continuous innovation amongst all stakeholders. All users need to understand the ingredients (both data and prompts) to 'direct and respect' the nature of their AI working partners. And yes, this translates into the agentic world as well. Staff still needs to build, configure, and manage their agents with a human lens.
Understanding AI biases and weaknesses is a necessary part of the educational process. It's about understanding where you stop and where AI begins. Mature associations and nonprofits lean on AI for analysis and recommendations, but take on the ultimate responsibility for member- and mission-based outcomes.
What maturity looks like:
Human–AI collaboration is and has always been about creating efficiencies, increasing productivity, and just doing better with less. Beneath the underbelly of all of this lies accountability, sustainability, and reputational integrity.
Let HighRoad be your AI trust fall
Not sure where to begin on your path to AI maturity? Trust in HighRoad and HubSpot. Since HubSpot's backbone is built on AI, orgs that leverage it are naturally en route to hitting critical AI maturity benchmarks.
First, HubSpot's AI-powered CRM brings orgs' data currency into one consolidated platform. Then since associations and nonprofits tend to work in niche tech, HighRoad's bi-directional, real-time data integration, Spark takes it from there. It connects association AMS', CRMs, and CDPs (single source of truth) with HubSpot (single source of action) to amalgamate the data required for scalable, reliable AI operations.
Spark then takes it a step further by integrating behaviorally-based association tech (think LMS', EMS, etc.) directly into HubSpot.
Finally, HighRoad offers year-round services to ensure proper implementation, onboarding, support, adoption, and ongoing optimization. This helps minimize the risk of orgs falling back on AI fragmented tools or even worse...pilot purgatory.
Whether you're on HubSpot or not, book time with us to learn more.