The fertility industry has spent the last decade heavily focused on growth through consolidation. Large clinic groups have expanded quickly through acquisitions, new locations and international roll-ups, all with the expectation that scale would naturally improve operational performance and EBITDA over time.
But many groups and medium to large sized clinics are now running into the same problem. Owning more clinics does not automatically create operational leverage. Because underneath the group structure, many fertility clinics still operate with fragmented workflows, disconnected systems, inconsistent reporting structures and years of local operational processes layered on top of each other.
So while the ownership structure has consolidated, the operational infrastructure often has not. This becomes visible very quickly once groups try to answer relatively simple questions across the organisation.
Which locations are performing best operationally?
Where do we have the highest conversion to treatment?
Which workflows correlate with stronger outcomes?
Where is revenue leakage happening?
Why do reporting numbers differ across clinics?
Which locations are underutilised?
How do staffing models compare?
Which protocols are driving the strongest results across demographics?
What is the actual price of an IVF cycle?
In many clinics and networks, answering these questions still requires weeks of exports, spreadsheets, BI work and manual reconciliation across systems. Because the underlying operational structure was never designed for connected intelligence across the network.
But to be honest, in most clinics and networks, they will never be able to answer questions like this properly, no matter how much time they spend. Not always because the data does not exist, but because the structure underneath it makes reliable analysis almost impossible.
And this is where we think the next major EBITDA opportunity in fertility sits. Not (just) in acquiring more clinics, but in finally operationalising the data clinics already generate every single day.
Fertility clinics sit on enormous amounts of operational and clinical data. Patient journeys, treatment protocols, embryology, cryostorage, finance, payments, outcomes, cancellations, utilisation, staffing patterns and workflows across years of treatment history.
The problem is that most clinics are still structured around systems designed primarily to store records, not understand operations.
Over time this creates fragmentation everywhere. Different naming conventions. Different appointment structures. Different workflow logic between clinics. Different reporting definitions. Local operational workarounds that made sense historically, but make standardisation and benchmarking extremely difficult later. This usually did not happen because clinics wanted complexity. It happened because legacy systems forced teams to build logic into naming conventions instead of into the actual infrastructure.
A very good example is appointment structures. One fertility clinic may have multiple ways of describing what is operationally the same ultrasound scan (we have seen up to 80 different names for an ultrasound). Another may structure blood tests differently between locations. Often these structures were created simply to make downstream workflows easier for finance teams, reporting teams or local operational processes.
The problem is that once operational logic lives inside naming conventions instead of structured data models, the system slowly loses its ability to understand the clinic consistently. The same thing happens in labs, cryostorage, finance and outcomes over time.
Eventually fertility clinics end up with large amounts of data, but very little operational consistency underneath it. And that becomes expensive.
Because inefficiency in fertility rarely comes from one dramatic issue. It comes from thousands of small inefficiencies across workflows, reporting, staffing, utilisation, billing, patient progression and visibility across the organisation.
This is also why many fertility groups are becoming increasingly interested in structured longitudinal data models and operational intelligence layers. Once operational and clinical events sit inside one connected structure, groups can finally benchmark properly across clinics, identify bottlenecks earlier, understand operational variation and make decisions based on live operational realities instead of retrospective reporting exercises.
We do not even need to talk about AI yet, but just to make the point clearly: this is where AI also becomes genuinely useful. Not as a chatbot layered on top of fragmented systems, but as an interface into structured operational intelligence.
Because once the data foundation is structured correctly, clinics can start asking much more meaningful questions directly across the organisation. Not just what happened, but why it happened, where operational variation exists, what drives stronger outcomes, what impacts utilisation, where leakage occurs and which workflows scale best.
We believe this will define the next phase of fertility consolidation. The winners over the next decade will probably not simply be the groups that acquire the most clinics. They will be the networks and fertility clinics that operationalise the data already sitting inside their organisations.
Because fertility clinics are already sitting on the goldmine. Most just cannot access it properly yet.
At wawa, this is one of the core infrastructure problems we have spent years focused on solving. Not just building another fertility EMR, but building structured operational intelligence across the entire patient journey.
The interesting part is that once the foundation is structured correctly, many things the industry currently sees as “future AI” suddenly become very achievable. If this is something your clinic or network is thinking about, we are always happy to exchange perspectives.
