The following is a guest article by Makesh Bharadwaj, CEO, Healthcare, MedTech & Life Sciences at Sutherland Global

Imagine this: before a patient walks into the exam room, an AI chatbot has already answered their insurance questions and secured pre-authorizations. During the visit, real-time notetaking tools quietly capture every detail, freeing the provider to focus fully on care. By checkout, an AI system has assigned accurate billing codes and scheduled the patient’s next appointment — no delays, no confusion.

AI tools also improve operational efficiency and accuracy on the provider side. From faster record-sharing between providers to automated discharge instructions and insurance coding, it frees up administrative staff to focus on higher-value work. This includes reducing the 13 hours a week that physicians and staff currently spend on prior authorizations — a task that 71% of physicians expect to be one of AI’s most valuable applications. This scenario is not hypothetical — these tools are already in action across healthcare organizations today. But without clear safeguards in place, their promise can quickly turn into a detrimental risk, opening the door to compliance failures, data breaches, reputational damage, and loss of patient trust.

The opportunity to streamline administrative workflows and improve care using AI is here. But in order to fully harness its benefits, your organization must approach it with strong governance, ethical intent, and transparent execution from day one. 

The Operational Stakes of AI in Healthcare

While AI is advancing on the clinical side — from diagnostics to imaging — adoption has been even faster in administrative workflows. More than 70% of healthcare organizations are exploring or implementing AI solutions to streamline operations, make administrative functions more efficient, and improve patient experiences.

Yet adoption alone does not guarantee success. Poorly governed AI exposes organizations to three critical risks:

• Compliance Gaps – HIPAA and privacy laws were built around human use, not the scale and speed of AI; without strict data access protocols and audit trails, even routine applications risk falling into regulatory gray zones
• Security Vulnerabilities – In April 2025 alone, healthcare breaches surged 371% month-over-month; a single ransomware incident can both endanger patient safety and cost an organization millions in fines and recovery expenses
• Erosion of Trust – Many patients remain cautious about AI in healthcare, especially when its role is unclear; without human oversight and transparent disclosure, organizations risk losing the very confidence that drives patient engagement

The stakes are clear: AI can either be a competitive advantage or an expensive vulnerability. What makes the difference is governance.

3 Steps for Responsible AI Adoption

The true value of AI is unlocked through the responsible operationalization of AI. Here are three proactive steps your organization can take to guide how AI is used, secured, and overseen. 

Establish a Security Council

Every healthcare organization adopting AI should form a cross-functional security council with leaders from legal, compliance, IT, clinical operations, and data governance. This team is responsible for setting clear policies around AI’s evaluation and management within your organization.

This includes defining acceptable use cases, establishing review processes, and setting parameters for data access and model validation. Just as important, those policies must be communicated across departments so every team knows how and why AI is being used.

With technology and regulations evolving, this council should also regularly review and update policies to make sure your organization stays compliant and aligned with patient expectations.

Use a Closed AI Model

AI tools that rely on patient data come with a heightened risk of security breaches or misuse. To prevent this, your organization should prioritize closed models — systems that keep data contained within your infrastructure and restrict third-party access.

Closed models help ensure PHI is not shared externally, used to train commercial algorithms, or stored on public platforms. Access is limited to authorized personnel, maintaining your organization’s compliance with HIPAA and internal privacy protocols.

For example, a hospital network using a closed model can safely deploy AI tools for claims processing or insurance code validation without risking patient data leaving its ecosystem. This maintains tighter control over sensitive data and stronger assurance that it stays protected.

Keep Humans in the Loop

While AI can assist with clinical and administrative tasks, final authority should always lie with qualified professionals. Human oversight at every stage of the patient journey is essential to maintaining accuracy, accountability, and clinical integrity throughout your organization.

It is also key to sustaining patient trust. Providers who explain clearly where AI is applied and how it benefits patients — whether by reducing billing errors or giving them more face time with their clinicians — help build stronger trust and engagement.

Set the Standard for Trusted AI Use

AI is streamlining documentation, accelerating billing, and reducing administrative overhead, creating measurable gains for both patients and providers. When deployed thoughtfully, it lowers costs and frees up teams to focus on what matters most: delivering high-quality healthcare.

But without oversight, those gains can quickly unravel. Security breaches and broken patient trust are only a couple of the possible consequences of moving forward without establishing strong AI guidelines.

As AI adoption accelerates across healthcare, the most forward-looking organizations will not just adopt new tools — they will define thoughtful frameworks to direct them. Now is the time to advance AI responsibly, so that operational integrity and exceptional patient care can move forward together.

About Makesh Bharadwaj

Makesh Bharadwaj is the CEO at Sutherland’s Healthcare Practice, where he leads the charge in accelerating digital transformation across payers, providers, med-tech, and life sciences clients. With over 30 years of experience in business and technology transformation, mergers and acquisitions, consulting, operations, and service delivery, Makesh has a proven track record of driving digital transformation and business growth in the healthcare, med-tech, and life sciences sectors.