Rethinking AI Adoption: From Functionality to Fulfilment
- Nichola Wolfe
- Jun 4
- 5 min read
Is Your AI Stuck at the Bottom of Maslow’s Pyramid?
AI tools are coming onto the market thick and fast but why are so many still failing to stick?
Across industries, we’ve seen the same pattern: a bold investment in AI tools, followed by disappointing engagement, low adoption, and unmet expectations. And with AI budgets continuing to surge, the cost of missed adoption is growing just as fast. Gartner reports that up to 80–90% of AI projects fail to deliver real business value.
The instinctive response? Tweak the product. Upgrade the tech. Improve the UI.
But maybe we need to ask some deeper questions first:
Are we seeking to solve the right problems with AI?
Is AI being positioned to solve problems that actually matter to people?
What do employees care enough about to want to change how they work?
Adoption rarely fails because the technology isn’t good enough. It fails when the value doesn’t feel personally relevant, when people can’t see how it helps them move forward.
The opportunity lies in the alignment. When individual motivation connects with organisational purpose, that’s when AI truly lands.
Productivity: A Solution in Search of a Problem?
The default lens for AI tends to be productivity. Faster. Smarter. More efficient. But what if productivity isn’t the problem people are trying to solve? These are all rational, business-aligned goals. But if you’ve ever tried to introduce an AI product to a team, you’ll know this truth: Just because something improves productivity doesn't mean people will use it.
That’s because productivity might not be the problem they actually care about solving.
We rarely stop to explore:
What do employees actually value?
What are they already incentivised to care about?
How does this tool help them succeed and not just perform?
The Use Case Shift: AI Is Climbing Maslow’s Hierarchy
According to a recent Harvard Business Review article, GenAI use cases are trending upward, from tasks like document drafting and research, toward more human-centred needs like therapy, emotional support, and life planning. In short, use cases have shifted from technical and functional to emotive over the past year.
In 2024, the top use cases were classic productivity functions: generating ideas, editing text, and searching. By 2025, the top three are:
Therapy/companionship
Organising life
Finding purpose
AI isn’t just a work tool anymore; it’s becoming a self-actualisation tool, something people rely on to help progress in life and work. People are moving from doing more to being more.
The AI Motivation Hierarchy: A New Way to Think About What People Want
If we borrow from Maslow’s hierarchy of needs and adapt it to the modern workplace, we can see how AI usage is evolving upwards, toward fulfillment and meaning.
Here’s how that looks:
Level | What People Want | How AI Can Deliver It |
|---|---|---|
🏔️ Self-Actualisation | To do meaningful work, shape ideas, and feel purposeful | Helps you lead with impact, innovate, and make strategic decisions |
🚀 Progress & Recognition | To grow, get noticed, and advance professionally | Elevates how you show up—credible, forward-thinking, prepared |
🧠 Mastery & Autonomy | To feel in control, build skills, and make decisions with confidence | Gives you the tools to solve problems independently and with clarity. |
😌 Relief & Support | To reduce friction, remove stress, and stay on top of things | Cuts admin, simplifies work, and keeps you organised |
⚙️ Functionality & Access | To get basic things done quickly and reliably | Summarises meetings, drafts content, finds information on demand. |
But, most AI products are still pitched at the base of this hierarchy: “Do your tasks faster. Find your files. Write your emails.” Useful, yes but rarely motivating. Adoption and real opportunity occurs when we climb higher toward progress, recognition, and ultimately, purpose.
The Adoption Disconnect: When AI Is Framed for the Organisation but Misses the Individual
When adoption stalls, it’s often because the product was pitched in organisational language, not human terms. Let’s look at how most AI products are pitched and why that might be where adoption starts to fall apart:
Functional Outcome | What People Actually Want |
Be more productive | Feel progress and achievement |
Automate tasks | Gain headspace and reduce cognitive load |
Work faster | Regain control over time and priorities |
Write smarter | Sound credible and feel confident |
Access more data | Make decisions they trust and can justify |
So what does this mean?
Functional outcomes help the organisation.
Human outcomes drive adoption.
Even That’s Not Enough: Where's the Incentive?
Even if we align with a person’s deeper motivators, one question still remains:
What’s the actual incentive to adopt this product today?
If using it doesn’t, help them succeed visibly, save them from a daily frustration or increase their credibility, confidence, or capacity, it won’t matter how brilliant the tool is. The emotional return just won’t be worth the effort. Motivation is personal but when it’s met, the organisation benefits too.
Experience Strategy: The Missing Link in AI Adoption
This is where experience strategy makes a real difference. Instead of starting with what the AI can do, we start with the lived experience of the person we’re asking to use it.
What’s the friction in their day-to-day?
What are they trying to become — in their role, their team, or their career?
How could this tool move them meaningfully closer to that?
The best AI tools make people feel more confident, more capable, and more in control. And this is where traditional change approaches often fall short. AI is more than a process shift; it’s a mindset shift. The old playbooks of communication plans and training decks don’t cut it anymore. What’s needed now is empathy, narrative, and purpose.
Because when people feel like a tool is helping them grow—not just perform—they’re far more likely to adopt it.
In the below table lets step through some examples of how a traditional change approach and an experience strategy approach differ.
Traditional Change Approach | Experience Strategy Approach |
Focus on process and compliance | Focus on people's motivations and behaviours |
Roll out tool features and training | Connect tool to real user goals and frustrations |
Communicate "what's coming" | Co-create purpose and meaning behind the change |
Measure uptake | Measure confidence, credibility and felt value |
Assumes resistance and plan mitigation | Assumes opportunity and designs for engagement |
Change as a one-time event | Change as an evolving relationship between tool and user |
AI adoption isn’t just about helping people learn the tool. It’s about making them want to use it because it supports who they want to become. At the heart of an experience strategy approach is this simple but powerful idea: To enable adoption, you need to understand who the user is becoming. This requires an acknowledgment that AI isn’t just a capability shift, it’s a mindset and identity shift.
So Where Do We Go From Here?
Too often, organisations focus on getting people to want the products they've already built—rather than building products people actually want. That’s the shift. Not just in design, but in how we position, pitch, and enable adoption. To take an experience strategy approach sone next steps you can consider taking are:
Rethink your pitch: are you selling output, or solving a problem people actually care about?
Rethink your lens: are you focused on process, or on purpose?
Rethink your adoption strategy: are you asking people to change, or giving them a reason to?
I help organisations take a more human, experience-led approach to AI adoption grounded in purpose, behaviour, and what truly motivates people. Because in a world full of tools, the only ones that matter are the ones people choose to keep.
If you would like help with AI adoption at your organisation, let's connect.
