Healthcare is not a discretionary service; patients seek care when they need it – often urgently. From a provider’s standpoint, demand frequently exceeds capacity. Appointment schedules are fully booked weeks in advance, clinical staff is stretched thin, and care delivery remains largely schedule-driven rather than need-driven. This operational mismatch between patient urgency and system capacity creates delays in diagnosis, treatment and follow-up – directly affecting outcomes, clinician workload and patient satisfaction.
At the same time, providers are facing financial pressure. Healthcare costs continue to rise, driven by labor shortages, wage inflation, supply expenses, technology investments and regulatory requirements. While policy efforts such as the 2022 No Surprises Act and measures to limit Medicare spending growth and pharmaceutical prices aim to protect patients, they also compress provider margins. Reimbursement pressures – combined with growing administrative complexity – challenge the fiscal sustainability of hospitals, health systems and physician groups. Providers must simultaneously reduce the cost of care delivery, maintain compliance, invest in digital capabilities and improve clinical outcomes.
How can AI help healthcare providers address these challenges?
From documentation and revenue cycle optimization to clinical decision support and access management, AI has the potential to improve efficiency, enhance care coordination, reduce administrative burden and support better patient outcomes.
Both technology suppliers and healthcare organizations must design AI-enabled operating models while they navigate regulatory complexity, manage risk and align clinical, financial and patient experience objectives. Healthcare providers need to focus not on technology for technology’s sake, but on practical, scalable solutions that strengthen resilience and deliver measurable value across the healthcare ecosystem.
Time and Cost as Primary Dimensions in Healthcare
Healthcare is related to time sensitivity, which defines risk, cost and patient experience. Time is the primary factor in emergency care, urgent care and even non-time-sensitive care.
Emergency care involves life-threatening conditions that require immediate intervention; while triage systems work reasonably well, emergency care is extraordinarily expensive and often followed by reimbursement and coverage disputes.
Urgent care for both acute and ambulatory aspects need fast diagnosis and treatment, and then coordination with primary/specialty care. ICU beds are also in short supply and expensive and this is where friction and delays often emerge.
Non-time sensitive care, such as chronic conditions or planned procedures, allows flexibility but still suffers long waiting time that can allow conditions to worsen.
Costs as the Other Dimension
High costs affect not only the healthcare industry but also the broader economy and individual consumers. In addition to improving time efficiency, healthcare providers must find strategies to reduce the cost of care throughout their organizations, from operational efficiency to technology adoption.
Here are four cost optimization opportunities for healthcare organizations:
1. Supply Chain and Purchased Services
Healthcare providers face rising costs for medical supplies, pharmaceuticals, equipment and outsourced services. This creates an opportunity to reduce expenses through better supplier management, standardization and demand planning. By improving visibility into purchasing patterns, consolidating vendors and re-negotiating smarter contracts, healthcare providers can dramatically lower unit costs and reduce waste. AI-enabled digital tools and analytics give healthcare providers real-time visibility into their supply chain, how fast they are being used and where shortages or excess inventory may occur.
By connecting inventory systems with usage data from departments such as operating rooms, pharmacies and patient units, providers can see demand patterns more clearly and plan replenishment more accurately. This helps prevent overstocking, which ties up cash and leads to waste from expired supplies, as well as shortages that can disrupt care or force expensive last-minute purchases.
2. Revenue Cycle Efficiency (Cost + Cash Flow):
Revenue cycle operations directly affect both costs and cash flow for healthcare providers. Inefficiencies in areas such as billing /coding, claim denials, slower collections and manual processes increase administrative costs and delay payments or result in underpayments.
AI can help streamline billing, coding and claims management through automation and better data integration. Improving revenue cycle efficiency helps providers get paid faster, reduce rework, lower staffing costs and improve overall financial stability while maintaining compliance. For example, in revenue cycle management (RCM) billing, AI can automate charge capture by identifying billable services directly from clinical records, ensuring that no revenue is missed. AI can predict which claims are likely to be denied based on historical patterns and payer behavior. AI also connects data across electronic health records (EHRs), billing systems and payer platforms, creating a unified view of the revenue cycle.
3. Administrative and Clinical Operations:
Digital technologies, automation and AI offer significant cost and productivity opportunities across clinical and administrative areas. Automation can reduce manual work in scheduling, documentation, billing and supply management, allowing staff to focus on higher-value tasks.
AI can support demand forecasting, clinical decision support and operational planning, helping reduce unnecessary tests, readmissions and inefficiencies. When implemented thoughtfully, these technologies help control costs while improving care delivery and patient experience.
4. Governance:
Strong governance and accountability are critical to managing healthcare costs effectively. Providers have an opportunity to build a cost-aware culture where leaders and frontline teams understand how decisions impact financial performance. AI can clarify ownership of budgets, create transparent reporting and improve quality and timeliness of performance metrics to improve accountability at all levels.
With a well-managed strategy realization office and project management office (SRO/PMO) that uses AI in cost management on a daily basis, organizations are better positioned to sustain long-term financial health and invest in strategic priorities.
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The Rise of the AI-Informed Patient
AI tools now enable patients to analyze preliminary symptoms. Patients now arrive with AI-enabled insights, symptoms analysis, preliminary diagnosis generated by tools like ChatGPT or similar platforms. More than 40 million Americans use ChatGPT daily to ask question about healthcare. In the U.S., the findings point toward growing reliance on AI tools as patients deal with access challenges, administration complexity and rising costs.
How Clinicians Are Responding
Primary care physicians are often booked weeks in advance. When a patient’s regular physician is unavailable, they are often redirected to another provider who is unfamiliar with the patient’s medical history. As a result, clinicians are turning to AI-powered research platforms such as Open Evidence to stay current with the latest medical studies, guidelines and clinical trials. These tools quickly summarize large volumes of peer-reviewed research and highlight what is most relevant to a specific condition or patient case.
Clinicians are improving how they capture and use patient lifestyle data such as diet, exercise, sleep, stress levels, smoking and social factors, even though they are not FDA approved as electronic medical record (EMR) inputs. Digital intake forms, patient portals, wearable devices and remote monitoring tools allow patients to share this information more consistently and accurately. AI and automation help organize and summarize this data outside the EMR, reducing the manual documentation burden.
To improve medication accuracy and adherence, clinicians are relying on integrated e-prescribing systems, medication reconciliation tools and digital monitoring solutions. These systems help ensure the right medication, dose and timing by automatically checking for drug interactions, allergies and duplications. Remote monitoring tools, smart pill dispensers and patient apps help track whether medications are taken as prescribed and flag potential issues early.
Where AI Can Make a Difference
Beckers Health report points out the rising adoption of AI tools among healthcare professionals. Sixty-six percent of U.S. physicians reported using AI for at least one use case in 2024, up from 38% the year prior. Nearly half of U.S. nurses reported using AI weekly. The most common uses of AI include documentation, administration work and diagnostic support. AI is becoming crucial in navigating a complex healthcare system.
The most promising areas for AI to meaningfully transform clinical care include:
Pre-visit symptom analysis: AI-driven tools can help patients articulate concerns and narrow down diagnostic possibilities, making patient-physician interactions more focused and productive.
Clinical decision-support platforms: these tools can equip physicians with evidence-based insights including scans, symptoms and historical analysis that allow them to plan and prepare for the patient care and more-positive outcomes.
Diagnostic and treatment workflows: AI can analyze and streamline inputs from radiology, labs and pharmacy to reduce turnaround time in seeing patients.
Non-clinical AI: this includes revenue cycle management, claim automation supply chain and inventory management, operation and workflow management, documentation, etc. AI also enhances patient access through chatbots and virtual assistants, reduces manual documentation and administrative work and supports governance, risk and compliance by identifying inefficiencies and potential issues early. AI in non-clinical areas lowers costs, improves efficiency and enhances the patient experience while allowing clinical teams to focus more on care delivery.
Clinical AI analyzes patient data such as medical history, lab results, imaging and vital signs to help clinicians identify risks, suggest possible diagnoses and recommend evidence-based treatment options. This supports faster and more accurate decision-making at the point of care.
The value of business process outsourcing (BPO) for healthcare providers is in the reduction of administrative burden and improved efficiency in areas such as revenue cycle management (RCM), call center operations and digital patient engagement. By offloading these labor-intensive and rules-driven tasks to specialized BPO teams, providers reduce internal staffing pressure, lower operating costs and improve cash flow. Chatbots and AI-enabled virtual assistants further enhance BPO value by handling routine inquiries 24/7, such as appointment reminders, payment questions and claim status checks.
The Healthcare Provider as an Autonomous Enterprise
In terms of organizational structure, healthcare providers typically operate in silos. Functions such as radiology, pathology and pharmacy work independently with their own set of efficiency technologies and tools, while clinicians and physicians work in a different set of software applications and hospital administration and facilities still others. The concept of a healthcare provider as an autonomous enterprise addresses these challenges by breaking down silos and enabling a more connected, intelligent operating model.
AI can bring this all together in the following ways:
Agentic AI can use its contextual awareness to tie different departments together to reference multiple aspects of patient care.
AI can tie together policies and procedures that differ from department to department to create a uniform standard to enable seamless interactions within a healthcare provider system.
AI can play a major role in governance, ensuring programs and projects are synchronized to ensure continuous workflows.
AI-enabled modernization of legacy systems helps break down silos by integrating disparate platforms into a unified, low risk and cost-efficient operating environment.
Role of AI in Clinical Practices
AI’s impact is already visible in many clinical support specialties that rely on data and pattern recognition. In radiology, deep-learning systems are being integrated into daily workflows for image analysis. AI algorithms can detect abnormalities on imaging with radiologist-level accuracy. For example, an FDA-cleared, computer-assisted detection system – which uses neural networks to scan chest X-rays and flag AOI (areas-of-interest) findings in the lungs, abdomen, ortho and other specialties – is becoming increasingly prevalent among radiology departments, clearly reducing turn-around time and leading to a much more timely data flow for the specialty physicians.
AI is also transforming cancer screening within radiology/oncology. A 2025 Nature Medicine study found that, when radiologists in a national screening program used an AI assist, breast cancer detection rates were 17.6% higher than with human review alone, without increasing false recalls. In other words, AI helped find more tumors without generating extra alarms. AI can also be applied to CT and MRI: algorithms now automate tasks like volumetric measurements, lesion detection and tissue characterization across modalities.
In cardiology, AI tools are increasingly used for both imaging and signal analysis. Echocardiography, for instance, is now routinely assisted by AI: algorithms automatically segment heart chambers, quantify left ventricle ejection fraction (LVEF) and strain and assess valve function. In cardiac CT, AI systems can automatically score coronary artery calcium and identify high-risk plaque. In pathology, AI-powered image analysis helps detect cancer cells and grade tumors on digitized slides. In fact, a growing number of algorithms are under evaluation or FDA review.
In pharmacogenomics and therapeutics, AI is used to personalize dosing (e.g., by analyzing a patient’s genetic profile along with clinical data: age, weight and organ function to determine how they will metabolize a drug), predict drug interactions (e.g., by evaluating how different medications may interact within a specific patient)and in silico drug discovery (e.g., by simulating how drugs interact with biological targets using computational models rather than relying solely on lab experiments).
Even in specialties like dermatology and ophthalmology, consumer and clinical AI tools can screen images for common diseases, e.g., diabetic retinopathy detection. Although not exhaustive, these examples show that AI is penetrating nearly every facet of clinical care – from diagnosis (radiology, pathology, cardiology) to treatment planning (precision medicine, dosing) to ongoing disease monitoring (remote sensors).
AI’s Role in Improving Efficiency in Clinical Operations
AI’s role is now expanding into higher-level clinical functions that require complex decision-making. By reading clinical notes and insurance criteria, these systems can predict whether a patient’s diagnosis and plan of care meet the payer’s requirements.
The following are two examples of AI in clinical operations:
Symptom analysis and pre-diagnostic support: AI-powered symptom checkers and chatbots are maturing as frontline triage tools. In the U.S. alone, a Journal of Medical Internet Research study shows 80-90% of patients find symptom checkers useful across age groups. This pre-diagnostic support can increase telehealth visits with positive outcomes and flag urgent cases faster for follow-up visits.
Workflow optimization in healthcare ancillaries: AI and automation are streamlining diagnostic workflows in the following fields:
Radiology departments face high demand and staffing shortages. AI triage tools can automatically flag urgent cases immediately after scans so that radiologists can prioritize them. Automated reporting tools and structured templates reduce dictation time and speed up report delivery. For clinicians, this means faster access to imaging results, fewer follow-up calls, and clearer, more consistent reports that support quicker clinical decisions.
Labs also deploy AI-powered instruments and robotics for tasks like automated microscopy and blood analysis, which reduce manual errors. Over time, these systems reduce turnaround time and free staff from routine sorting.
Pharmacy workflows are complex and highly regulated, involving medication ordering, verification, dispensing and monitoring. Workflow optimization can include automating order checks for allergies, interactions and dosing accuracy. AI and rules-based systems help prioritize high-risk medications and flag potential issues early. Inventory optimization ensures critical drugs are available when needed while reducing waste from expired medications. For clinicians, optimized pharmacy workflows lead to faster medication turnaround, fewer clarifications and greater confidence that patients are receiving the right medications at the right time.
AI’s Role in Administrative Operations
Administrative overhead is a major challenge in healthcare. AI and robotic process automation (RPA) are transforming revenue cycle and back-office operations, automating decisions and handling routine transactions. Claim denials cost hospitals billions and require endless hours of appeals. AI can solve these types of denials by validating data before submission. AI-based coding and RPA bots can translate clinical documentation into billing codes much faster and more accurate than humans.
AI Adoption in the Payer Segment
U.S. payer organizations are rapidly adopting AI for claims verification and auto-adjudication, increasing speed and consistency in payment decisions. Payers are increasingly using AI to reject non-clean claims to minimize initial payouts, which drives more appeals and resubmissions from hospitals and physician billing teams. As a result, “clean claims” have become critical, elevating the importance of upstream processes – particularly enrollment and provider data management (PDM) – to support auto-adjudication and reduce friction.
Given all this, technology suppliers are prioritizing high-volume, rules-heavy workflows in cases where generative AI (GenAI) can deliver measurable ROI. These include automating document verification (e.g., extracting and validating data from clinical notes, prior authorization requests and eligibility documents), reducing administrative costs in back-office functions and accelerating claims intake and adjudication. We expect broader adoption of GenAI over the next 12-24 months as organizations shift from pilots to scaled deployments, driven by improvements in model accuracy, better integration with payer and provider systems and clearer compliance guardrails for handling protected health information (PHI). Early outcomes typically include faster turnaround times, lower error rates and increased straight-through processing, with KPIs focused on claim cycle time, cost per claim and exception rates.
A likely near-term scenario includes AI-to-AI interactions that maintain human-in-the-loop oversight for final decisions and exception handling. In the context of health claims, agentic AI moves beyond simple rules or decision-support to execute parts of the workflow, effectively acting like a software claims processor. Such systems use generative and deep-learning models to handle complex exceptions, continually learn new patterns and collaborate across tasks. Use cases in the agentic AI, however, remain for now restricted largely to non-clinical areas.
Payer organizations would be wise to incorporate agentic AI into operational governance, auto-adjudication, prior authorization, member services and claims operations with outcome-based SLAs (including accuracy, latency, containment rate and customer satisfaction), human-in-the-loop guardrails and continuous monitoring for drift and bias.
AI’s Impact on BPO and Contact Center Models
Both GenAI and agentic AI are disrupting contact center and BPO processes, especially for prior authorization and claims-related interactions.
Payer organizations are moving toward fully automated or AI-supported workflows in these areas, reshaping operating models, staffing mixes and SLAs. Many organizations are moving from traditional headcount/FTE pricing to outcome- or subscription-based models. Key KPIs are also changing. Instead of just call volume or handle time, payers now track automation rate, first-contact resolution, denial rate and member satisfaction under AI handling. Some of the benefits that AI can bring to a contact center include enhanced customer experience, increased agent productivity, more precise intent understanding and call routing, improved data-driven insights and better cost reduction.
Today, the contact center has become a central hub for AI deployment, particularly in customer support and service improvement. Technologies such as natural language processing, machine learning, sentiment analysis, transcription and translation are automating routine tasks, enhancing intent recognition and call routing, and providing real-time agent assistance. These capabilities improve customer experience through faster, more accurate responses, increase agent productivity by reducing handle time and administrative burden and unlock deeper data-driven insights from unstructured interactions. While cost reduction remains a key driver, AI also elevates the strategic value of the contact center by improving service quality and operational efficiency.
What Is an Autonomous Enterprise?
The autonomous enterprise model offers a path forward. In this approach, systems anticipate risks, self-correct within defined policy guardrails and organize actions across clinical, operational and financial platforms, all while humans retain oversight and accountability. Examples include predictive detection of patient deterioration with automated escalation, self-remediating IT systems to protect EHR uptime, revenue cycle platforms that resolve routine exceptions and dynamic staffing or supply chain adjustments based on real-time demand.
Unlike task-based automation, autonomy enables coordinated, cross-system decision-making that reduces manual handoffs, lowers administrative burden, improves compliance and strengthens resilience. By starting with stable IT foundations and expanding autonomy into clinical and business workflows, healthcare organizations can improve patient outcomes, reduce clinician burnout and create a sustainable, AI-ready operating model without giving up human control over critical decisions.
What Healthcare Providers Should Look for in a Technology Supplier
Technology suppliers are critical for AI-driven healthcare transformation. They help enable preparedness by embedding AI into pre-visit preparation and post-visit follow up. They can deploy AI-powered systems that collect structured symptom data directly from patients before appointments to help identify diagnostic pathways, highlight inconsistency or missing information. This also helps give clinicians a clear understanding of patient concerns, reducing time spent on basic fact-finding and improving diagnosis focus.
Here are some functional areas healthcare providers can look for support from technology providers:
1. Revenue cycle automation
Prior authorization intake and follow-up
Medical necessity checks
Coding, charge capture and chart abstraction
Denial classification and prevention
2. Administrative workflow automation
Scheduling, referral management and call centers
Patient access and intake
Credentialing and provider onboarding
3. Clinical documentation and EHR optimization
Ambient clinical documentation
Chart prep and post-visit documentation
Inbox and message management
Redundant order and documentation workflows
AI tools can synthesize patient data across labs, imaging and notes into concise summaries. This reduces cognitive load and compensates for missed context, particularly when patients are seen by providers unfamiliar with their history. Beyond clinical insights, AI also can support operational readiness. Technology suppliers can deploy models that match patient urgency with appropriate clinician roles, identify cases suitable for advanced practice providers or nurse-led care. Technology suppliers can implement AI to analyze visit outcomes and patient data to recommend evidence-based follow ups, identify patients at risk and trigger workflows for nurses and care co-coordinators.
It is not possible to transform healthcare through standalone tools and AI. Its impact depends on how well it is designed, governed and operationalized within complex clinical environments.
Most importantly, the buy-in from clinicians is still some time away as they get more confident in AI’s outcome analysis. Technology suppliers should therefore be equally responsible for acting as a bridge between AI innovation and real-world care delivery.
What Healthcare Leaders Should Do Next
The healthcare industry is undergoing immense pressure to improve patient outcomes, reduce operational costs and provide better access and personalized care. This is where AI steps in, transforming everything from diagnostics to administrative processes. Across clinical care and pharmaceutical innovation, AI is unlocking new opportunities to improve outcomes, expand access and accelerate discovery.
The following examples illustrate key areas where this impact is most evident:
There is a rise in deployment of AI-powered tools like imaging systems, (along with ECG analysis, smart stethoscope expanded screening programs, etc.) especially in healthcare areas with limited medical resources.
Early detection of disease remains one of the most significant opportunities where AI in healthcare can deliver measurable value.
Personalized medicine can result in treatment plans that are tailored to diagnoses/test results and ensure better outcomes. Precision medicine allows doctors to use AI to move beyond generalized treatment protocols toward more individualized care models. By combining genomics, clinical data, imaging and real-world evidence, AI supports more accurate patient stratification and therapy selection.
In drug discovery and development, there is growing adoption of AI across the pharmaceutical value chain as organizations seek to reduce development timelines, improve trial success rates and manage rising R&D costs. AI-driven platforms are being used for target identification, molecule screening, trial design optimization and patient recruitment.
AI in healthcare is being brought to rural and underserved communities, ensuring that its benefits are not limited to high-income countries or large health systems. However, no matter where it is deployed, realizing value at scale will depend on data interoperability, clinician trust and the ability of health systems to integrate these capabilities into existing clinical workflows rather than deploying them as stand-alone innovations.
While early results are promising, sustained impact will require stronger governance models, regulatory alignment and closer collaboration between technology suppliers and clinical research organizations (CROs) to translate algorithmic insights into measurable business and clinical outcomes.
The Sourcing and Transformation Process
The sourcing and operating model should be deliberately designed to emphasize healthcare-specific outcomes, balancing cost controls, access, timely care and regulatory assurance while delivering demonstrable economic value. Healthcare providers need to define the AI value thesis, prioritizing a balanced portfolio across non-clinical and clinical domains. They must then quantify benefits, costs, risks and dependencies. When a sourcing roadmap is tied to clear business and clinical outcomes, a healthcare provider can more easily create a shortlist of right-fit vendors, best-of-breed AI platforms and AI-enabled BPOs.
ISG helps healthcare organizations design outcome-based RFPs and negotiate commercial terms that link fees to measurable results. We help organizations align governance and controls and integrate AI into healthcare systems for the greatest, long-lasting success. Contact us to find out how we can help you.
About the authors
SG Anand
Anamika Sarkar
Anamika Sarkar works as a Manager in ISG. She has close to 11 years of experience in research across various industries and geographies. At ISG, Anamika helps Manufacturing enterprises understand the latest technology trends, strategy, and innovation.
