Pilot to Platform: AI Becomes Healthcare’s Growth Engine

AI is fast becoming healthcare’s growth engine, according to McKinsey, whose outlook on US healthcare through 2026 and beyond signals that technology is now the sector’s primary driver of performance.

The consulting giant expects health services and technology to remain the fastest-growing segment, with software platforms increasingly central to how providers and payers operate in a complex, data-rich environment.

Generative AI and machine learning are the catalysts, automating workflows, strengthening connectivity and turning data into actionable insight.

Accelerating AI adoption

The economic backdrop is forcing the pace. Industry EBITDA as a share of national health expenditures has fallen from 11.2% in 2019 to 8.9% in 2024, compressing margins amid higher utilisation and reimbursement pressure.

Investment is concentrating where digital leverage is greatest – health services and technology, specialty pharmacy and non‑acute care – as organisations use AI to outsource complexity and reengineer processes.

In effect, the conversation has moved from pilots to platforms, with AI embedded across payer operations, provider performance and care delivery redesign.

The adoption filter is tightening as well.

“Adoption will favour solutions that are measurable, implementable and reduce burden – especially in under-resourced settings where funding programs may accelerate upgrades,” says Brian Litten, Co‑Founder and Managing Partner at Saltgrass.

Strategically, healthcare leaders must operate at two speeds: near‑term resilience and long‑term reinvention. AI sits at the centre of both agendas.

In the short run, it compresses administrative costs, stabilises operations and mitigates workforce strain. Over the longer horizon, it enables new care and pricing models, more precise population health management and truly interoperable ecosystems.

The next phase of US healthcare will not be defined by spending growth alone but by smarter systems, where AI is the connective tissue turning operational pressure into durable productivity and better outcomes.

Michael Dreher, Advisory Board Member at SiMLQ, underscores the operational imperative: “AI is no longer theoretical. From prior authorisation and revenue cycle to workforce optimisation and supply chain execution, healthcare is finally seeing technology applied where it matters most – removing friction from complex, manual workflows that drain efficiencies and strain morale.”

Where AI is landing now

For payers, McKinsey projects that recovery after 2027 will hinge on the adoption of new care models, optimised pricing, industry partnerships and AI‑enabled back‑end transformations.

That vision is already materialising through straight‑through claims processing that cuts cycle times and administrative cost, predictive modelling that sharpens risk adjustment and machine learning that detects fraud, waste and abuse while optimising operations in areas like contact centres and network management.

Data‑driven pricing and product design are becoming competitive necessities as regulatory landscapes and membership dynamics shift.

Providers face parallel headwinds – volatile labour costs, mounting uncompensated care and evolving reimbursement models – but a pathway to margin recovery is emerging as more uninsured individuals transition into employer-sponsored coverage.

Sustained improvement will still depend on disciplined cost management and targeted technology adoption.

Beyond pilots: AI as infrastructure

What is changing most is not just the list of AI use cases, but the role AI plays in the enterprise. Models are evolving from standalone pilots into embedded infrastructure that orchestrates data across fragmented systems, meets interoperability requirements and integrates into existing workflows without imposing disruptive change management.

The emphasis is squarely on outcomes that can be measured – lower cost per transaction, fewer denials, shorter days in accounts receivable, improved throughput and higher staff satisfaction.

In this environment, the winners will be those who build trust at clinical and operational depth.

McKinsey’s research points to vendors and partners that can demonstrate clinical‑grade accuracy and safety, integrate fluently with core platforms and data standards, provide transparent and auditable models under robust governance and link capabilities to hard ROI with clear, repeatable metrics.