# I Built the Hiring System I Needed — Then Realized It Wasn't Just for Me

After recovering from cancer, I spent ten years volunteering to support cancer patients and their caregivers.

It was meaningful work. I had experienced cancer myself, and I understood how much a little support, practical guidance, and human connection could mean to someone navigating that experience.

But I hadn't stopped being a professional.

Throughout those years, I continued updating my skills. I had built a career spanning more than 25 years in UX research, product strategy, design, and digital transformation. I wanted to return to that work.

As I explored the industry again, I discovered something interesting.

The tools had evolved. The language had changed. Job titles, methodologies, and professional conventions were different.

But many of the underlying capabilities I'd developed throughout my career were still relevant.

I could still research complex problems, understand user needs, design systems, define requirements, and translate ideas into workable solutions.

I hadn't lost those skills.

What I hadn't anticipated was how difficult it would become to convince employers that I still had them.

When your career history becomes a barrier

I've always been good at writing résumés. I've helped other people with theirs, and I knew how to present my professional experience.

Getting interviews wasn't necessarily the problem. I secured interviews with established organizations, completed assessments, and performed well in technical and aptitude tests.

Yet I kept encountering rejection.

Sometimes I was considered overqualified because of my previous experience.

Other times, I was considered underqualified because of my career gap.

How could I be both?

My age was another barrier I experienced. I also encountered questions about whether I would fit into an organization's culture.

It became increasingly difficult to understand what employers were actually assessing.

Was it my ability to do the work?

My previous job titles?

My career chronology?

My age?

Or my ability to give a particular answer to a particular interviewer?

Those aren't interchangeable things.

And after years of trying to reenter my profession, I could see that the problem was much bigger than having a well-written résumé.

Returner programs offered hope, but the process still didn't work for me

I eventually realized that I needed to look at career returner programs.

The idea was encouraging.

Here were organizations acknowledging that talented, experienced people could have significant gaps in their careers and still have enormous value to contribute.

But the programs I encountered had their own limitations.

Many didn't seem designed for older professionals with substantial previous experience.

I found myself going through standardized assessments, aptitude tests, coding tests, and interviews that didn't always reflect the work I was actually capable of doing.

I did well in those assessments.

But performing well didn't necessarily translate into an opportunity.

Some interviews were conducted by recruiters who seemed to be working through a checklist rather than exploring the relevance of my experience. I often struggled to communicate the breadth of what I had done in terms that fit the questions being asked.

I could explain how to solve a complex problem, but that didn't always help when someone was looking for a specific keyword or a particular career sequence.

The disconnect was frustrating.

I didn't need an employer to pretend my career gap didn't exist.

I needed a process capable of understanding what I'd done, what I'd learned, and what I could contribute now.

After five years of searching, I began questioning whether the conventional hiring process would ever give me a realistic route back into my career.

And then I started asking a different question.

What if I designed the platform that could have hired me?

I had spent decades researching users, defining requirements, designing digital experiences, and solving complex systems problems.

Why not apply those same capabilities to the problem I was experiencing?

What would a hiring process look like if we designed it around the work itself?

What if employers had to define what they actually needed someone to accomplish before asking people to apply?

What if candidates could demonstrate relevant capability through realistic work, rather than depending on their résumé or their performance in an interview?

What if we could distinguish between someone having a particular credential and someone being capable of performing a particular responsibility?

And what if AI could help us understand that evidence without becoming another opaque system deciding who deserved an opportunity?

I wanted to build the platform that would help me get a job or restart my career.

That was the beginning of Adonea.ai.

I started applying my professional experience to the problem, developing the conceptual architecture, research, workflows, and governance principles for a different kind of opportunity system.

But as I worked through the design, something became increasingly clear.

My experience was only one version of a much broader problem.

It was never just about ageism

Ageism and career gaps were the barriers that initially drove me to create Adonea.ai.

But as I explored conventional recruitment more deeply, I encountered many other ways in which people could be overlooked.

Educational pedigree.

Gender. Race. Accent.

Disability and neurodivergence.

Unconventional career paths.

Transferable skills acquired outside formal employment.

People changing careers.

People returning after caregiving, illness, or other life events.

People who are highly capable but do not perform particularly well in conventional interviews.

And people whose experience simply doesn't translate neatly into the categories that recruitment systems expect.

These are not identical experiences. The barriers vary, and so do the accommodations and changes people may need.

But there is a common problem underneath many of them.

We have built hiring systems that rely heavily on proxies for capability rather than evidence of capability itself.

A degree can tell us something about someone's education.

A job title can tell us something about their previous employment.

Years of experience can tell us how long they occupied a particular role.

An interview can tell us how they respond to questions in a particular setting.

All of that information can be useful.

But none of it, on its own, establishes what someone can actually do.

And when those signals become rigid requirements, capable people can find themselves excluded before their relevant abilities are properly considered.

I began to understand that building something that would have helped me was not enough.

We needed to rethink the process itself.

Now AI is adding another barrier

AI is changing work, and learning to use it can create new opportunities.

I use AI extensively in my own work. It helps me research, explore ideas, test assumptions, and develop Adonea.ai.

I see enormous potential in it.

But I am also concerned about how quickly AI experience is becoming another requirement in the labor market.

We are already seeing advice telling workers to develop AI literacy, learn particular tools, and update their résumés accordingly.

Learning matters. Keeping our capabilities current matters.

But access matters too.

Not everyone has worked for an employer that provides AI tools, training, or opportunities to experiment.

Not everyone has the financial resources to purchase subscriptions or take courses.

And someone who has been outside the conventional workforce may not have had the same opportunities to develop or demonstrate AI-related experience.

Does that mean they cannot learn?

Does it mean their existing capabilities have become irrelevant?

Of course not.

Yet we risk creating another version of the same problem.

An employer adds AI experience to a job description. A screening system searches for those keywords. Someone with substantial transferable capability is filtered out because they cannot demonstrate previous access to a particular tool.

Previous opportunity becomes the evidence required to obtain future opportunity.

We haven't solved the problem.

We've added another box.

If AI is going to transform work, it should also help us create more accessible ways for people to demonstrate what they can do, including their ability to learn and apply new technologies.

AI literacy should expand people's opportunities.

It shouldn't become another proxy used to deny them one.

What we're building with Adonea.ai

Adonea.ai is not intended to be another applicant tracking system.

I'm not interested in building a faster way to process résumés, rank candidates, or automate rejection.

The approach we're developing begins before anyone applies for a job.

An employer defines the actual work: responsibilities, deliverables, necessary capabilities, working conditions, constraints, and what successful outcomes would look like.

People can then provide relevant evidence of capability or demonstrate what they can do through realistic work simulations.

The evidence matters.

Not simply where someone worked, which institution they attended, or whether their career followed a conventional path.

Our architecture is designed to distinguish between claimed, documented, and demonstrated evidence, while acknowledging uncertainty rather than converting everything into an artificial score.

AI can help make connections between work requirements and relevant evidence. It can explain why an opportunity may be relevant and help identify where additional evidence might be needed.

But it should not decide that one human being is inherently better than another.

We are designing the system without comparative candidate rankings, aggregate match scores, or automated rejection. Our proposed selection process also includes equal-chance consideration within appropriately qualified pools.

Human decision-makers retain responsibility for consequential decisions.

The aim is not to remove people from the employment process.

It is to make that process more transparent, more understandable, and more capable of recognizing human capability.

These are the principles behind the system we're developing. They will need to be tested and refined with workers and employers, not simply assumed to work because the architecture makes sense.

The future of work needs more than faster hiring

Looking back, I sometimes think about how differently my own journey might have unfolded if a system like this had existed.

What if I could have demonstrated how I would approach an actual UX research or product strategy challenge?

What if an employer could have seen how I analyzed a problem, structured the work, considered users, and developed a solution?

What if my career gap had been treated as part of my history rather than an obstacle that overshadowed my capabilities?

I don't know whether any particular employer would have hired me.

But I would have welcomed the opportunity to be considered through a process that made my capabilities visible.

That's what I wanted to create.

And the more I developed Adonea.ai, the more I realized that this wasn't just about helping older professionals return to work.

It was about people who don't fit neatly into the boxes.

People whose talents are difficult to express through conventional credentials.

People who have the ability to learn, adapt, contribute, and do meaningful work but struggle to gain access to the opportunity to demonstrate it.

AI is making this challenge more urgent, but it didn't create the underlying problem.

We've been overlooking human capability for a long time.

I created Adonea.ai because I wanted to build a hiring system that could have helped me.

I'm developing it for everyone whose capabilities deserve to be seen beyond the limitations of their résumé, credentials, age, or career history.

Because the future of work shouldn't depend on how neatly someone fits into the past.