Shaping the healthcare future with AI

Why AI matters for Providers

The healthcare industry is among the pioneers when it comes to running generative AI (Gen AI) pilot projects. However, the sensitive nature of the industry means that going from proof-of-concept (POC) to implementation can take time. For Providers, early wins are emerging in areas such as automated clinical documentation, ambient listening for patient encounters, and AI-driven decision support, which are critical to improve clinician productivity and patient outcomes.

Imagine a near future where a Provider’s Gen AI system listens during a patient visit, auto-generates structured clinical notes, and suggests evidence-based treatment options, all while reducing administrative time for physicians. Building this ecosystem requires deep Gen AI integration and reimagined workflows, but the payoff is significant: better care quality, improved clinician satisfaction, and enhanced patient experience.

Beyond cost savings: Driving quality, efficiency & growth

In healthcare and life sciences, cost is rarely the primary driver or measure of success for new technology adoption. The true benchmark is whether it improves care quality, clinician efficiency, and patient experience. While cost savings may follow, they remain secondary. This dynamic makes healthcare especially ripe for disruption through Generative AI. Despite persistent challenges such as data silos, incompatible systems, and a lack of standardization, innovation in Gen AI continues to accelerate, underscoring the industry’s commitment to meaningful transformation.

A recent report on the State of AI in 2025 by McKinsey & Co. finds that organizations that are seeing the greatest impact from AI often aim to achieve more than cost reduction from these projects. “While most respondents report that efficiency gains are an objective of their organizations’ AI use, high performers are more likely than others are to say their organizations have also set growth and/or innovation as an objective of their AI efforts,” it says.^[1]

Running and scaling AI projects isn’t easy. As Providers and Payers experiment with Gen AI to improve operations, many find it hard to move beyond pilot projects. Today, only about 10 to 15% of POCs make it to full production. The ones that do often deliver outsized returns, especially when processes are reimagined through an AI-first approach. This isn’t always possible, though. In healthcare, the stakes are much higher, with people’s lives being impacted, making doctors and clinicians more cautious. That’s why explainability, quality, and trust must be at the core of every Gen AI solution built for healthcare professionals.

The same McKinsey study found that while more organizations are running at least one Gen AI project (compared to last year), the majority are still at pilot stages. “88 percent report regular AI use in at least one business function, compared with 78 percent a year ago. But at the enterprise level, the majority are still in the experimenting or piloting stages,” it found.^[1]

Operational transformation: Reimagining Provider workflows with Gen AI

Providers

Providers can unlock significant value by applying Gen AI to both clinical and non-clinical workflows:

• Clinical documentation automation: Ambient listening solutions can help capture patient-provider conversations and auto-generate accurate notes, reducing burnout and freeing time for patient care.
• Decision support & care coordination: AI-driven systems can analyze EHR data and clinical guidelines to recommend personalized treatment plans and flag care gaps.
• Patient engagement & virtual care: Intelligent virtual assistants handle post-discharge check-ins, appointment scheduling, and FAQs, delivering empathetic, human-like interactions.

The AI-powered virtual care avatar can be trained to display high levels of human-like empathy, resulting in a more satisfying interaction for the patients as well. AI augments, not replaces, clinical judgment. Care providers remain the final authority, ensuring transparency, safety, and trust.

Payers

There is a large amount of overhead and back-and-forth between Providers submitting bills and invoices and the Payer approving or denying them. Payer organizations can use AI-powered advanced decision-support systems to analyze claims patterns and predict potential denials, reducing administrative burden and improving Provider relationships. They can leverage AI to streamline prior authorization by auto-generating decision summaries and ensuring alignment with CMS guidelines. Also, AI-driven insights can be used to close care gaps and enhance CAHPS scores, improving member experience and Stars ratings.

MedTech

A lot of us are already using wearables to track various health parameters. The next step is to connect them back into the electronic health records (EHR) system. This may be a few years into the future, but when that happens, the healthcare data becomes a living system, which, once integrated with the EHR, can support real-time decision making and transform care experience. This will enable proactive interventions and personalized care.

Administrative efficiency

Beyond clinical workflows, Gen AI can streamline revenue cycle management, coding, and claims submission, reducing errors and freeing staff from repetitive tasks. These operational gains translate into more time for patient-facing activities and improved financial performance.

The road ahead: Building a future-ready Provider ecosystem

Before investing in Gen AI, Providers should assess technology maturity and identify workflows that can be automated versus those requiring reimagination. Align initiatives with interoperability standards and quality metrics to ensure compliance and measurable ROI.

AI offers an unprecedented opportunity to reimagine and transform care. The next wave of healthcare innovation will not be defined by who adopts AI fastest, but by who reimagines processes that truly impact clinicians, patients, and operational efficiency.

References

1. The state of AI in 2025: Agents, innovation, and transformation (mckinsey.com)