AI will create both. And an early example is Practivated.
Every organization I work with pays for software. Every one of them wants success. Those two things are not the same purchase—and the gap between them is costing the sector more than it knows. Here's how we break it down.
97% Of Tech Budgets Go To Software.
The Hidden Cost of Software That "Works"
Success Is a Behavior, Not a Metric
Practice Is the Engine. Not Training. Not Information.
The Unicorn: Change That Doesn't Cost Energy
Recognition Is Not Soft. It's Fuel.
Success First Software Is Now Possible With AI
And 3% Goes To Humans
Every tool that takes more than it gives is a drain your mission can't afford
I've spent years doing forensic analyses of how organizations actually invest in technology. Not what they say they invest in—what the receipts show. One particularly self-aware nonprofit let me parse over 2,000 transactions across 14 years and more than 100 systems. The result: 97% of their investment went toward making software work. 3% went toward making humans successful.
They're not an outlier. They're typical.
Today we get software with a side of success. What we need is success with a side of software. The future of AI is going to be software that's creating success—and every leader I work with is carrying the invisible debt of having invested in the wrong thing: a sector running on depleted energy, drowning in tools that function perfectly, developing no one.
Every tool that takes more than it gives is a drain your mission can't afford
Every person on a development team eventually learns the same lesson: keeping the system fed is just part of the job. The CRM needs its records. The database needs its inputs. The dashboard needs its data. The software is working—and the humans are working for it.
This is the extractive model. It trains organizations to be right about the wrong things. We measure what software can capture—dates, dollars, activity logs—and those easy metrics gradually become proxies for success, even when they tell us nothing about whether the people doing the work are getting more capable, more confident, or more effective.
Gartner's research confirms what the receipts show: 70% of digital transformation initiatives fail. Not because the software doesn't function, but because the human side of the equation was never resourced. Implementation gets funded. People don't.
The hidden cost isn't the subscription fee. It's the energy debt. Every tool that requires humans to serve it draws from the same finite pool organizations need to actually change. In a sector saturated with initiatives, mandates, and pivots, that debt compounds quietly—until leaders find themselves knowing exactly what needs to change and unable to make it happen.
It's why I stopped implementing Salesforce. The ratio was too lopsided. Too much going in. Not enough coming back.
Practivated is proof that it doesn't have to be that way.
Fundraiser behavior is the primary influencer of donor action or inaction.
My friend Mallory Erikson has trained over 60,000 fundraisers. Her foundational insight—the one that eventually led her to build Practivated—is deceptively simple: fundraiser behavior is the primary influencer of donor action or inaction. Not the ask amount. Not the campaign strategy. Not the CRM. The behavior of the human in the room.
She grounds this in B.J. Fogg's behavioral model: behavior happens when motivation, ability, and prompt align at the same moment. Fundraisers generally have the motivation. Most have absorbed the frameworks. What's consistently missing is the practiced ability—the kind that only comes from doing the hard thing enough times that the nervous system stops treating it like a threat.
That gap—between understanding and execution—is not a training problem. It's a behavior problem. Software that logs activity cannot see it. Workshops that explain frameworks cannot close it. And more technology that measures outputs instead of human growth will not solve it.
Success, in this frame, is a fundraiser doing something they couldn't bring themselves to do before. Walking into a conversation they would have avoided. Making an ask they would have softened. Holding a pause they would have filled. That's behavior change—and it's the only metric that actually moves missions forward.
What simulation data reveals about how behavioral change actually happens
Fundraisers don't need more information. They need practice—the kind that mirrors the real world, safely.
The State of Donor Conversations report shows what happens when they get it. Fundraisers who completed more than one simulation improved an average of 5 points. When they reviewed feedback and immediately applied it in their next attempt, scores jumped 10–15 points—not from a new framework, but from doing it again, differently, in a safe environment.
Anders Ericsson's research on deliberate practice is unambiguous: expertise is not innate. It's the product of structured repetition with immediate feedback. What's been missing is infrastructure—a place to practice high-stakes human conversations without paying the cost of failure in real relationships.
Simulation provides that. Stephen Porges' Polyvagal Theory explains why it works neurologically: when the nervous system detects safety, the prefrontal cortex stays online for complex social engagement. Fear of a real donor saying no shuts down exactly the cognitive flexibility needed to navigate the conversation well. Practice in simulation recalibrates that response—not theoretically, but physiologically. The fundraiser who can't make the ask isn't lacking willpower. Their nervous system is running a protection protocol. Remove the perceived threat. The behavior changes.
Why the real constraint was never time or money—it was disruption
Every organizational change initiative has an energy cost. Workshops require attendance and vulnerability. New systems require learning curves. Performance reviews that suddenly emphasize different metrics trigger threat responses. People comply outwardly, resist inwardly, and the change never becomes embodied.
This is change saturation—when disruption exceeds an organization's capacity to absorb it. And the sector is living inside it. Leaders know what needs to change. But creating more change creates more disruption, which drains the energy required to make change stick. It's a physics problem, not a motivation problem. You cannot pour more in than the container holds.
Practivated solves a different problem than most technology. It doesn't add disruption—it absorbs it. Fundraisers practice when they have capacity, at a pace their nervous systems can integrate. No scheduling conflicts. No performance anxiety. No mandate from leadership. The platform meets people where they are and builds behavioral change that doesn't require a change initiative to sustain.
What makes this the unicorn isn't efficiency. It's alignment. Practivated addresses a problem that's already disrupting fundraisers—the gap between knowing and doing—while being easy enough to use that it doesn't create new disruption to solve it. The result is change without the energy cost of change. That's what sector leaders have been trying to manufacture through strategy for decades.
Why deficit-based accountability drains the capacity you need most
Traditional accountability depletes energy. Deficit-hunting—tracking missed targets, flagging gaps, surfacing what's not working—draws from the same finite pool organizations need to sustain behavioral change. Cooperrider and Srivastva's Appreciative Inquiry research is unambiguous: organizations grow in the direction of what they study. Measure error elimination, get defensiveness. Measure skill acquisition, get momentum.
Practivated applies this directly. Leaders can see a fundraiser's behavioral indicators improve across sessions—concretely, specifically, in the skills that matter. They can name it. The fundraiser who pushed into a harder simulation and stretched their capacity gets recognized for that, not penalized for the temporary dip in score.
The conversation shifts from "you're not performing" to "you improved 15 points after applying feedback—let's build on that." The first framing triggers a threat response. The second triggers motivation. One drains the tank. The other fills it.
Recognition-based accountability isn't a management philosophy. It's an energy strategy. In a sector running on depleted reserves, it may be the only accountability model that works.
What it means when AI creates capacity instead of consuming it
Mallory didn't build Practivated to replace fundraising coaches. She built it because the Fogg model demands infrastructure: motivation and knowledge aren't enough if the prompt and practiced ability aren't there for the moment that matters. Practivated is that infrastructure—AI configured to leave fundraisers more capable after every interaction than they were before.
Not AI that processes faster. Not AI that generates reports. AI that develops humans—building the behavioral capacity that makes everything downstream more effective. Benjamin Bloom's research showed that one-on-one tutoring produces results two standard deviations above standard instruction. Practivated delivers that at scale, for every fundraiser in the organization.
It does this through four mechanisms. Behavioral shaping through simulation builds skill where it lives—in the body, not the mind. Adaptive coaching through Coach Tivy tailors feedback to each individual based on what actually works across thousands of interactions. Physiological safety creates the neurological conditions for real change without triggering resistance. And recognition-based accountability keeps the energy equation positive—building momentum instead of spending it.
This isn't incremental improvement. It's a category change: technology designed on the premise that human success is the metric, and that the tool's job is to serve that—not the other way around.
The tools we choose now will determine whether we build capacity or deplete it
The 97% of investment in software with only 3% on success wasn't a story about one organization. It was a mirror held up to decades of investment patterns across the sector. We've been funding software performance and hoping human performance would follow as a byproduct. It hasn't. And in a sector already at the edge of its capacity to absorb change, that gap isn't just inefficiency—it's a mission risk.
The sector doesn't need better fundraising software. It needs more successful fundraisers.
That's the investment thesis Mallory built Practivated around. And it's the thesis sector leaders need to bring to every technology decision they make.
The State of Donor Conversations report shows what developmental technology actually delivers: measurable behavioral growth, stronger fundraiser confidence, and change that integrates into culture without disrupting it. It's grounded in 370 real simulations and 106 practicing fundraisers—data that tracks not just what happened, but how humans improved.
The question isn't whether AI will transform the social sector. It will. The question is whether you'll invest in AI that depletes your organization's capacity to change—or AI that builds it.
(PS. Just gotta give B.J. Fogg a shoutout for being a legend in behavioral design. The man was prompt engineering before prompting was even a thing. Run don't walk if you see space in one of his workshops or bootcamps.)