Healthcare leaders face a tough choice when it comes to adopting AI. Should they build custom solutions internally using foundational models like ChatGPT and Claude, or should they partner with specialised healthcare AI platforms? This question is central as AI becomes increasingly critical for healthcare operations, and CX specialist Ushur is delving into both sides of the coin.
Recent data shows that while 79% of healthcare organisations use AI, 42% have abandoned most of their AI initiatives due to cost and implementation difficulties.
Moreover, 67% of software projects fail because of incorrect build-versus-buy decisions, highlighting the high stakes involved.
The financial scale of healthcare AI is enormous, with investments reaching $32.3bn in 2024 and projected to grow to $208.2bn by 2030. These figures underscore how this decision will shape competitiveness in the healthcare sector for years to come.
Building AI internally is appealing due to promises of high returns and control. Healthcare AI projects can yield an average 147% ROI over three years, with some reporting returns as high as 791% when factoring in productivity gains.
The allure of full control over customised AI models is strong, with 61% of healthcare organisations pursuing partnerships still maintaining internal development efforts. However, initial timelines often balloon into multi-year projects that cost millions more than anticipated.
The hidden costs of internal AI development go well beyond upfront estimates. Basic healthcare AI minimum viable products (MVPs) cost between $150,000 and $200,000, while more comprehensive solutions range from $500,000 to $1m.
Recruiting the specialised talent needed to develop these systems can cost between $1.8m and $3.85m in the first year alone.
Senior AI engineers focused on healthcare command salaries above $200,000 annually, with additional premiums for regulatory expertise.
Operational expenses such as cloud infrastructure and ongoing compliance maintenance also add significantly to total costs.
More critically, healthcare organisations often underestimate the time required: urgent operational challenges demand immediate AI solutions, but internal projects commonly take 12 to 24 months or longer—rendering the AI obsolete before deployment.
The regulatory landscape further complicates internal AI builds. Healthcare AI must comply with extensive FDA approvals, which require months of documentation and can cost up to $500,000. HIPAA compliance demands continuous safeguards, and recent guidelines also require monitoring for bias, adding to auditing and reporting burdens.
Integrating general foundational models like ChatGPT and Claude, which lack built-in healthcare compliance features, increases complexity and risk.
Partnerships with specialised healthcare AI platforms present an attractive alternative. They enable organisations to deploy solutions within 3 to 6 months, compared to up to two years for internal builds, and reduce costs by 60-80%.
These platforms offer pre-built compliance frameworks, regulatory expertise, and domain-specific optimisations that would take years to replicate internally. Platform-based projects have higher success rates—between 80-90%—compared to internal efforts at 30-40%.
A strategic comparison framework highlights key differences: building internally requires $1.8-3.85m investment, 12-24 months to value, and heavy talent demands; hybrid build-and-buy approaches fall in the middle; while buying or partnering involves the least upfront costs, shortest timelines, and minimal specialised hiring.
Success depends on organisation size, technical maturity, and risk appetite.
The emerging best practice for healthcare leaders is a hybrid strategy—start with proven platforms for quick wins, then gradually build internal capabilities. This approach balances speed and control while allowing clinical teams to focus on patient care rather than infrastructure challenges.
Ultimately, the path forward lies in making data-driven, pragmatic AI decisions aligned with strategic goals and capabilities. Healthcare organisations that embrace specialised partnerships are poised to unlock faster, safer, and more effective AI outcomes—enabling superior patient care and operational efficiency in an increasingly competitive landscape.
Read the full blog from Ushur here.
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