Automation has become a defining force in the customer experience. Between the chatbots that answer our questions and the recommendation systems that shape our choices, AI-driven tools are now embedded in nearly every interaction. But the latest wave of so-called “agentic AI”—systems that can plan, act, and adapt toward a defined goal—promises to push automation even further.
“Every single person that I’ve spoken to has at least spoken to some sort of GenAI bot on their phones. They expect experiences to be not scripted. It’s almost like we’re not improving customer experience, we’re getting to the point of what customers expect customer experience to be,” says vice president of product management at NICE, Neeraj Verma.
For businesses, the potential is transformative: AI agents that can handle complex service interactions, support employees in real time, and scale seamlessly as customer demands shift. But the move from scripted, deterministic flows to non-deterministic, generative systems brings new challenges. How can you test something that doesn’t always respond the same way twice? How can you balance safety and flexibility when giving an AI system access to core infrastructure? And how can you manage cost, transparency, and ethical risk while still pursuing meaningful returns?
These solutions will determine how, and how quickly, companies embrace the next era of customer experience technology.
Verma argues that the story of customer experience automation over the past decade has been one of shifting expectations—from rigid, deterministic flows to flexible, generative systems. Along the way, businesses have had to rethink how they mitigate risk, implement guardrails, and measure success. The future, Verma suggests, belongs to organizations that focus on outcome-oriented design: tools that work transparently, safely, and at scale.
“I believe that the big winners are going to be the use case companies, the applied AI companies,” says Verma.
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Generative AI has the potential to transform the finance function. By taking on some of the more mundane tasks that can occupy a lot of time, generative AI tools can help free up capacity for more high-value strategic work. For chief financial officers, this could mean spending more time and energy on proactively advising the business on financial strategy as organizations around the world continue to weather ongoing geopolitical and financial uncertainty.
CFOs can use large language models (LLMs) and generative AI tools to support everyday tasks like generating quarterly reports, communicating with investors, and formulating strategic summaries, says Andrew W. Lo, Charles E. and Susan T. Harris professor and director of the Laboratory for Financial Engineering at the MIT Sloan School of Management. “LLMs can’t replace the CFO by any means, but they can take a lot of the drudgery out of the role by providing first drafts of documents that summarize key issues and outline strategic priorities.”
Generative AI is also showing promise in functions like treasury, with use cases including cash, revenue, and liquidity forecasting and management, as well as automating contracts and investment analysis. However, challenges still remain for generative AI to contribute to forecasting due to the mathematical limitations of LLMs. Regardless, Deloitte’s analysis of its 2024 State of Generative AI in the Enterprise survey found that one-fifth (19%) of finance organizations have already adopted generative AI in the finance function.
Despite return on generative AI investments in finance functions being 8 points below expectations so far for surveyed organizations (see Figure 1), some finance departments appear to be moving ahead with investments. Deloitte’s fourth-quarter 2024 North American CFO Signals survey found that 46% of CFOs who responded expect deployment or spend on generative AI in finance to increase in the next 12 months (see Figure 2). Respondents cite the technology’s potential to help control costs through self-service and automation and free up workers for higher-level, higher-productivity tasks as some of the top benefits of the technology.
“Companies have used AI on the customer-facing side of the house for a long time, but in finance, employees are still creating documents and presentations and emailing them around,” says Robyn Peters, principal in finance transformation at Deloitte Consulting LLP. “Largely, the human-centric experience that customers expect from brands in retail, transportation, and hospitality haven’t been pulled through to the finance organization. And there’s no reason we cannot do that—and, in fact, AI makes it a lot easier to do.”
If CFOs think they can just sit by for the next five years and watch how AI evolves, they may lose out to more nimble competitors that are actively experimenting in the space. Future finance professionals are growing up using generative AI tools too. CFOs should consider reimagining what it looks like to be a successful finance professional, in collaboration with AI.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
In July 2024, a botched update to the software defenses managed by cybersecurity firm CrowdStrike caused more than 8 million Windows systems to fail. From hospitals to manufacturers, stock markets to retail stores, the outage caused parts of the global economy to grind to a halt. Payment systems were disrupted, broadcasters went off the air, and flights were canceled. In all, the outage is estimated to have caused direct losses of more than $5 billion to Fortune 500 companies. For US air carrier Delta Air Lines, the error exposed the brittleness of its systems. The airline suffered weeks of disruptions, leading to $500 million in losses and 7,000 canceled flights.
The magnitude of the CrowdStrike incident revealed just how interconnected digital systems are, and the extensive vulnerabilities in some companies when confronted with an unexpected occurrence. “On any given day, there could be a major weather event or some event like what happened…with CrowdStrike,” said then-US secretary of transportation Pete Buttigieg on announcing an investigation into how Delta Air Lines handled the incident. “The question is, is your airline prepared to absorb something like that and get back on its feet and take care of customers?”
Unplanned downtime poses a major challenge for organizations, and is estimated to cost Global 2000 companies on average $200 million per year. Beyond the financial impact, it can also erode customer trust and loyalty, decrease productivity, and even result in legal or privacy issues.
A 2024 ransomware attack on Change Healthcare, the medical-billing subsidiary of industry giant UnitedHealth Group—the biggest health and medical data breach in US history—exposed the data of around 190 million people and led to weeks of outages for medical groups. Another ransomware attack in 2024, this time on CDK Global, a software firm that works with nearly 15,000 auto dealerships in North America, led to around $1 billion worth of losses for car dealers as a result of the three-week disruption.
Managing risk and mitigating downtime is a growing challenge for businesses. As organizations become ever more interconnected, the expanding surface of networks and the rapid adoption of technologies like AI are exposing new vulnerabilities—and more opportunities for threat actors. Cyberattacks are also becoming increasingly sophisticated and damaging as AI-driven malware and malware-as-a-service platforms turbocharge attacks.
To prepare for these challenges head on, companies must take a more proactive approach to security and resilience. “We’ve had a traditional way of doing things that’s actually worked pretty well for maybe 15 to 20 years, but it’s been based on detecting an incident after the event,” says Chris Millington, global cyber resilience technical expert at Hitachi Vantara. “Now, we’ve got to be more preventative and use intelligence to focus on making the systems and business more resilient.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
During Black Friday in 2024, Stripe processed more than $31 billion in transactions, with processing rates peaking at 137,000 transactions per minute, the highest in the company’s history. The financial-services firm had to analyze every transaction in real timeto prevent nearly 21 million fraud attempts that could have siphoned more than $910 million from its merchant customers.
Yet, fraud protection is only one reason that Stripe embraced real-time data analytics. Evaluating trends in massive data flows is essential for the company’s services, such as allowing businesses to bill based on usage and monitor orders and inventory. In fact, many of Stripe’s services would not be possible without real-time analytics, says Avinash Bhat, head of data infrastructure at Stripe. “We have certain products that require real-time analytics, like usage-based billing and fraud detection,” he says. “Without our real-time analytics, we would not have a few of our products and that’s why it’s super important.”
Stripe is not alone. In today’s digital world, data analysis is increasingly delivered directly to business customers and individual users, allowing real-time, continuous insights to shape user experiences. Ride-hailing apps calculate prices and estimate times of arrival (ETAs) in near-real time. Financial platforms deliver real-time cash-flow analysis. Customers expect and reward data-driven services that reflect what is happening now.
In fact, having the capability to collect and analyze data in real time correlates with companies’ ability to grow. Business leaders that scored company in the top quartile for real-time operations saw 50% higher revenue growth and net margins, compared to companies placed in the bottom quartile, according to a surveyconducted by the MIT Center for Information Systems Research (CISR) and Insight Partners. The top companies focused on automated processes and fast decision-making at all levels, relying on easily accessible data services updated in real time.
Companies that wait on data are putting themselves in a bind, says Kishore Gopalakrishna, co-founder and CEO of StarTree, a real-time data-analytics technology provider. “The basis of real-time analytics is—when the value of the data is very high—we want to capitalize on it instead of waiting and doing batch analytics,” he says. “Getting access to the data a day, or even hours, later is sometimes actually too late.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.It was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Agentic AI is coming of age. And with it comes new opportunities in the financial services sector. Banks are increasingly employing agentic AI to optimize processes, navigate complex systems, and sift through vast quantities of unstructured data to make decisions and take actions—with or without human involvement. “With the maturing of agentic AI, it is becoming a lot more technologically possible for large-scale process automation that was not possible with rules-based approaches like robotic process automation before,” says Sameer Gupta, Americas financial services AI leader at EY. “That moves the needle in terms of cost, efficiency, and customer experience impact.”
From responding to customer services requests, to automating loan approvals, adjusting bill payments to align with regular paychecks, or extracting key terms and conditions from financial agreements, agentic AI has the potential to transform the customer experience—and how financial institutions operate too.
Adapting to new and emerging technologies like agentic AI is essential for an organization’s survival, says Murli Buluswar, head of US personal banking analytics at Citi. “A company’s ability to adopt new technical capabilities and rearchitect how their firm operates is going to make the difference between the firms that succeed and those that get left behind,” says Buluswar. “Your people and your firm must recognize that how they go about their work is going to be meaningfully different.”
The emerging landscape
Agentic AI is already being rapidly adopted in the banking sector. A 2025 survey of 250 banking executives by MIT Technology Review Insights found that 70% of leaders say their firm uses agentic AI to some degree, either through existing deployments (16%) or pilot projects (52%). And it is already proving effective in a range of different functions. More than half of executives say agentic AI systems are highly capable of improving fraud detection (56%) and security (51%). Other strong use cases include reducing cost and increasing efficiency (41%) and improving the customer experience (41%).
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Artificial intelligence is fundamentally reshaping how the world operates. With its potential to automate repetitive tasks, analyze vast datasets, and augment human capabilities, the use of AI technologies is already driving changes across industries.
In health care and pharmaceuticals, machine learning and AI-powered tools are advancing disease diagnosis, reducing drug discovery timelines by as much as 50%, and heralding a new era of personalized medicine. In supply chain and logistics, AI models can help prevent or mitigate disruptions, allowing businesses to make informed decisions and enhance resilience amid geopolitical uncertainty. Across sectors, AI in research and development cycles may reduce time-to-market by 50% and lower costs in industries like automotive and aerospace by as much as 30%.
“This is one of those inflection points where I don’t think anybody really has a full view of the significance of the change this is going to have on not just companies but society as a whole,” says Patrick Milligan, chief information security officer at Ford, which is making AI an important part of its transformation efforts and expanding its use across company operations.
Given its game-changing potential—and the breakneck speed with which it is evolving—it is perhaps not surprising that companies are feeling the pressure to deploy AI as soon as possible: 98% say they feel an increased sense of urgency in the last year. And 85% believe they have less than 18 months to deploy an AI strategy or they will see negative business effects.
Companies that take a “wait and see” approach will fall behind, says Jeetu Patel, president and chief product officer at Cisco. “If you wait for too long, you risk becoming irrelevant,” he says. “I don’t worry about AI taking my job, but I definitely worry about another person that uses AI better than me or another company that uses AI better taking my job or making my company irrelevant.”
But despite the urgency, just 13% of companies globally say they are ready to leverage AI to its full potential. IT infrastructure is an increasing challenge as workloads grow ever larger. Two-thirds (68%) of organizations say their infrastructure is moderately ready at best to adopt and scale AI technologies.
Essential capabilities include adequate compute power to process complex AI models, optimized network performance across the organization and in data centers, and enhanced cybersecurity capabilities to detect and prevent sophisticated attacks. This must be combined with observability, which ensures the reliable and optimized performance of infrastructure, models, and the overall AI system by providing continuous monitoring and analysis of their behavior. Good quality, well-managed enterprise-wide data is also essential—after all, AI is only as good as the data it draws on. All of this must be supported by AI-focused company culture and talent development.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
As brands compete for increasingly price conscious consumers, customer experience (CX) has become a decisive differentiator. Yet many struggle to deliver, constrained by outdated systems, fragmented data, and organizational silos that limit both agility and consistency.
The current wave of artificial intelligence, particularly agentic AI that can reason and act across workflows, offers a powerful opportunity to reshape service delivery. Organizations can now provide fast, personalized support at scale while improving workforce productivity and satisfaction. But realizing that potential requires more than isolated tools; it calls for a unified platform that connects people, data, and decisions across the service lifecycle. This report explores how leading organizations are navigating that shift, and what it takes to move from AI potential to CX impact.
AI is transforming customer experience (CX). Customer service has evolved from the era of voicebased support through digital commerce and cloud to today’s AI revolution. Powered by large language models (LLMs) and a growing pool of data, AI can handle more diverse customer queries, produce highly personalized communication at scale, and help staff and senior management with decision support. Customers are also warming to AI-powered platforms as performance and reliability improves. Early adopters report improvements including more satisfied customers, more productive staff, and richer performance insights.
Legacy infrastructure and data fragmentation are hindering organizations from maximizing the value of AI. While customer service and IT departments are early adopters of AI, the broader organizations across industries are often riddled with outdated infrastructure. This impinges the ability of autonomous AI tools to move freely across workflows and data repositories to deliver goal-based tasks. Creating a unified platform and orchestration architecture will be key to unlock AI’s potential. The transition can be a catalyst for streamlining and rationalizing the business as a whole.
High-performing organizations use AI without losing the human touch. While consumers are warming to AI, rollout should include some discretion. Excessive personalization could make customers uncomfortable about their personal data, while engineered “empathy” from bots may be received as insincere. Organizations should not underestimate the unique value their workforce offers. Sophisticated adopters strike the right balance between human and machine capabilities. Their leaders are proactive in addressing job displacement worries through transparent communication, comprehensive training, and clear delineation between AI and human roles. The most effective organizations treat AI as a collaborative tool that enhances rather than replaces human connection and expertise.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
In a market flooded with AI promises, health care decision-makers are no longer dazzled by flashy demos or abstract potential. Today, they want pragmatic and pressure-tested products. They want solutions that work for their clinicians, staff, patients, and their bottom line.
To gain traction in 2025 and beyond, health care providers are looking for real-world solutions in AI right now.
Solutions that fix real problems
Hospitals and health systems are looking at AI-enabled solutions that target their most urgent pain points: staffing shortages, clinician burnout, rising costs, and patient bottlenecks. These operational realities keep leadership up at night, and AI solutions must directly address them.
For instance, hospitals and health systems are eager for AI tools that can reduce documentation burden for physicians and nurses. Natural language processing (NLP) solutions that auto-generate clinical notes or streamline coding to free up time for direct patient care are far more compelling pitches than generic efficiency gains. Similarly, predictive analytics that help optimize staffing levels or manage patient flows can directly address operational workflow and improve throughput.
Ultimately, if an AI solution doesn’t target these critical issues and deliver tangible benefits, it’s unlikely to capture serious buyer interest.
Demonstrate real-world results
AI solutions need validation in environments that mirror actual care settings. The first step toward that is to leverage high-quality, well-curated real-world data to drive reliable insights and avoid misleading results when building and refining AI models.
Then, hospitals and health systems need evidence that the solution does what it claims to do, for instance through independent-third party validation, pilot projects, peer-reviewed publications, or documented case studies.
Mayo Clinic Platform offers a rigorous independent process where clinical, data science, and regulatory experts evaluate a solution for intended use, proposed value, and clinical and algorithmic performance, which gives innovators the credibility their solutions need to win the confidence of health-care leaders.
Integration with existing systems
With so many demands, health-care IT leaders have little patience for standalone AI tools that create additional complexity. They want solutions that integrate seamlessly into existing systems and workflows. Compatibility with major electronic health record (EHR) platforms, robust APIs, and smooth data ingestion processes are now baseline requirements.
Custom integrations that require significant IT resources—or worse, create duplicative work—are deal breakers for many organizations already stretched thin. The less disruption an AI solution introduces, the more likely it is to gain traction. This is the reason solution developers are turning to platforms like Mayo Clinic Platform Solutions Studio, a program that provides seamless integration, single implementation, expert guidance to reduce risk, and a simplified process to accelerate solution adoption among healthcare providers.
Explainability and transparency
The importance of trust cannot be overstated when it comes to health care, and transparency and explainability are critical to establishing trust in AI. As AI models grow more complex, health-care providers recognize that simply knowing what an algorithm predicts isn’t enough. They also need to understand how it arrived at that insight.
Health-care organizations are increasingly wary of black-box AI systems whose logic remains opaque. Instead, they’re demanding solutions that offer clear, understandable explanations clinicians can relay confidently to peers, patients, and regulators.
As McKinsey research shows, organizations that embed explainability into their AI strategy not only reduce risk but also see higher adoption, better performance outcomes, and stronger financial returns. Solution developers that can demystify their models, provide transparent performance metrics, and build trust at every level will have a significant edge in today’s health-care market.
Clear ROI and low implementation burden
Hospitals and health systems want to know precisely how quickly an AI solution will pay for itself, how much staff time it will save, and what costs it will help offset. The more specific and evidence-backed the answers, the better rate of adoption.
Solution developers that offer comprehensive training and responsive support are far more likely to win deals and keep customers satisfied over the long term.
Alignment with regulatory and compliance needs
As AI adoption grows, so does regulatory scrutiny. Health-care providers are increasingly focused on ensuring that any new solution complies with HIPAA, data privacy laws, and emerging guidelines around AI governance and bias mitigation.
Solution developers that can proactively demonstrate compliance provide significant peace of mind. Transparent data handling practices, rigorous security measures, and alignment with ethical AI principles are all becoming essential selling points as well.
A solution developer that understands health care
Finally, it’s not just about the technology. Health-care providers want partners that genuinely understand the complexities of clinical care and hospital operations. They’re looking for partners that speak the language of health care, grasp the nuances of change management, and appreciate the realities of delivering patient care under tight margins and high stakes.
Successful AI vendors recognize that even the best technology must fit into a highly human-centered and often unpredictable environment. Long-term partnerships, not short-term sales, are the goal.
Delivering true value with AI
To earn their trust and investment, AI developers must focus relentlessly on solving real problems, demonstrating proven results, integrating without friction, and maintaining transparency and compliance.
Those that deliver on these expectations will have the chance to help shape the future of health care.
This content was produced by Mayo Clinic Platform. It was not written by MIT Technology Review’s editorial staff.
As healthcare faces mounting pressures, from rising costs and an aging population to widening disparities, forward thinking innovations are more essential than ever.
Accelerator programs have proven to be powerful launchpads for health tech companies, often combining resources, mentorship, and technology that startups otherwise would not have access to. By joining these fast-moving platforms, startups are better able to rapidly innovate, enhance, and scale their healthcare solutions, bringing transformative approaches to hospitals and patients faster.
So, why are healthcare accelerators becoming essential to the evolution of the industry? There are key reasons why these programs are reshaping health innovation and explanations how they are helping to make care more personalized, proactive, and accessible.
Empowering growth and scaling impact
Healthcare accelerator programs offer a powerful combination of guidance, resources, and connections to help early-stage startups grow, scale, and succeed in a complex industry.
Participants typically benefit from:
Expert mentorship from seasoned healthcare professionals, entrepreneurs, and industry leaders to navigate clinical, regulatory, and business challenges
Access to valuable resources such as clinical data, testing environments, and technical infrastructure to refine and validate health tech solutions
Strategic support for growth including investor introductions, partnership opportunities, and go-to-market guidance to expand reach and impact
Speeding up innovation
Accelerators help startups and early-stage companies bring their solutions to market faster by streamlining the path through one of the most complex industries: healthcare. Traditionally, innovation in this space is slowed by regulatory hurdles, extended sales cycles, clinical validation requirements, and fragmented data systems.
Through structured support, accelerators help companies refine their product market fit, navigate compliance and regulatory landscapes, integrate with healthcare systems, and gather the clinical evidence needed to build trust and credibility. They also open doors to early pilot opportunities, customer feedback, and strategic partnerships, compressing what could take years into just a few months.
By removing barriers and accelerating critical early steps, these programs enable digital health innovators to reach the market more efficiently, with stronger solutions and a clearer path to impact.
Connecting startups with key stakeholders
Today, many accelerator programs are developed by large healthcare organizations that are driving change from within. These accelerator programs are especially beneficial to startups since they have strong partnerships with hospitals, pharma companies, insurance providers, and regulators. This gives startups a chance to validate their ideas in real-world settings, gather clinical feedback early, and scale more effectively.
Many accelerators also bring together people from different fields; doctors, engineers, data scientists, and designers, encouraging fresh perspectives on persistent problems like chronic disease management, preventative care, data interoperability, and patient engagement.
Breaking barriers to global expansion
Healthcare accelerator programs act as gateways for international digital health companies looking to enter the U.S. market, often considered one of the most complex and highly regulated healthcare landscapes in the world. These programs provide tailored support to navigate U.S. compliance standards, understand payer and provider dynamics, and tailor offerings to meet the needs of U.S. patients and care delivery models.
Through market-specific mentorship, strategic introductions, and access to a robust health innovation ecosystem, accelerators help international startups overcome geographic and regulatory barriers, enabling global ideas to scale and make an impact where they’re needed most.
Building the future of healthcare
The role of healthcare accelerator programs extends far beyond startup support. They are helping to redefine how innovation happens, shifting it from isolated efforts to collaborative ecosystems of change. By bridging gaps between early-stage technology and real-world implementation, these programs play a critical role in making healthcare more personalized, preventative, and equitable.
As the digital transformation of healthcare continues, accelerator programs will remain indispensable in cultivating the next generation of breakthroughs, ensuring that bold ideas are not only born, but brought to life in meaningful, measurable ways.
Spotlight: Mayo Clinic Platform_Accelerate
One standout example of this innovation-forward approach is Mayo Clinic Platform_Accelerate, a 30-week accelerator program designed to help health tech startups reach market readiness. Participants gain access to de-identified clinical data, prototyping labs, and guidance from experts across clinical, regulatory, and business domains.
By combining Mayo Clinic’s legacy of clinical excellence with a forward-thinking innovation model, the Mayo Clinic Platform_Accelerate program helps promising startups to refine their solutions and prepare for meaningful scale, transforming how care is delivered across the continuum.
Finding value in accelerator programs
In a time when healthcare must evolve faster than ever, accelerator programs have become vital to the industry’s future. By supporting early-stage innovators with the tools, mentorship, and networks they need to succeed, these programs are paving the way for smarter, safer, and more connected care.
Whether tackling chronic disease, reimagining patient engagement, or unlocking the power of data, the startups nurtured in accelerator programs are helping to shape a more resilient and responsive health system, one innovation at a time.
This content was produced by Mayo Clinic Platform. It was not written by MIT Technology Review’s editorial staff.
Over the past 20 years building advanced AI systems—from academic labs to enterprise deployments—I’ve witnessed AI’s waves of success rise and fall. My journey began during the “AI Winter,” when billions were invested in expert systems that ultimately underdelivered. Flash forward to today: large language models (LLMs) represent a quantum leap forward, but their prompt-based adoption is similarly overhyped, as it’s essentially a rule-based approach disguised in natural language.
At Ensemble, the leading revenue cycle management (RCM) company for hospitals, we focus on overcoming model limitations by investing in what we believe is the next step in AI evolution: grounding LLMs in facts and logic through neuro-symbolic AI. Our in-house AI incubator pairs elite AI researchers with health-care experts to develop agentic systems powered by a neuro-symbolic AI framework. This bridges LLMs’ intuitive power with the precision of symbolic representation and reasoning.
Overcoming LLM limitations
LLMs excel at understanding nuanced context, performing instinctive reasoning, and generating human-like interactions, making them ideal for agentic tools to then interpret intricate data and communicate effectively. Yet in a domain like health care where compliance, accuracy, and adherence to regulatory standards are non-negotiable—and where a wealth of structured resources like taxonomies, rules, and clinical guidelines define the landscape—symbolic AI is indispensable.
By fusing LLMs and reinforcement learning with structured knowledge bases and clinical logic, our hybrid architecture delivers more than just intelligent automation—it minimizes hallucinations, expands reasoning capabilities, and ensures every decision is grounded in established guidelines and enforceable guardrails.
Creating a successful agentic AI strategy
Ensemble’s agentic AI approach includes three core pillars:
1. High-fidelity data sets: By managing revenue operations for hundreds of hospitals nationwide, Ensemble has unparallelled access to one of the most robust administrative datasets in health care. The team has decades of data aggregation, cleansing, and harmonization efforts, providing an exceptional environment to develop advanced applications.
To power our agentic systems, we’ve harmonized more than 2 petabytes of longitudinal claims data, 80,000 denial audit letters, and 80 million annual transactions mapped to industry-leading outcomes. This data fuels our end-to-end intelligence engine, EIQ, providing structured, context-rich data pipelines spanning across the 600-plus steps of revenue operations.
2. Collaborative domain expertise: Partnering with revenue cycle domain experts at each step of innovation, our AI scientists benefit from direct collaboration with in-house RCM experts, clinical ontologists, and clinical data labeling teams. Together, they architect nuanced use cases that account for regulatory constraints, evolving payer-specific logic and the complexity of revenue cycle processes. Embedded end users provide post-deployment feedback for continuous improvement cycles, flagging friction points early and enabling rapid iteration.
This trilateral collaboration—AI scientists, health-care experts, and end users—creates unmatched contextual awareness that escalates to human judgement appropriately, resulting in a system mirroring decision-making of experienced operators, and with the speed, scale, and consistency of AI, all with human oversight.
3. Elite AI scientists drive differentiation: Ensemble’s incubator model for research and development is comprised of AI talent typically only found in big tech. Our scientists hold PhD and MS degrees from top AI/NLP institutions like Columbia University and Carnegie Mellon University, and bring decades of experience from FAANG companies [Facebook/Meta, Amazon, Apple, Netflix, Google/Alphabet] and AI startups. At Ensemble, they’re able to pursue cutting-edge research in areas like LLMs, reinforcement learning, and neuro-symbolic AI within a mission-driven environment.
The also have unparalleled access to vast amounts of private and sensitive health-care data they wouldn’t see at tech giants paired with compute and infrastructure that startups simply can’t afford. This unique environment equips our scientists with everything they need to test novel ideas and push the frontiers of AI research—while driving meaningful, real-world impact in health care and improving lives.
Strategy in action: Health-care use cases in production and pilot
By pairing the brightest AI minds with the most powerful health-care resources, we’re successfully building, deploying, and scaling AI models that are delivering tangible results across hundreds of health systems. Here’s how we put it into action:
Supporting clinical reasoning: Ensemble deployed neuro-symbolic AI with fine-tuned LLMs to support clinical reasoning. Clinical guidelines are rewritten into proprietary symbolic language and reviewed by humans for accuracy. When a hospital is denied payment for appropriate clinical care, an LLM-based system parses the patient record to produce the same symbolic language describing the patient’s clinical journey, which is matched deterministically against the guidelines to find the right justification and the proper evidence from the patient’s record. An LLM then generates a denial appeal letter with clinical justification grounded in evidence. AI-enabled clinical appeal letters have already improved denial overturn rates by 15% or more across Ensemble’s clients.
Building on this success, Ensemble is piloting similar clinical reasoning capabilities for utilization management and clinical documentation improvement, by analyzing real-time records, flagging documentation gaps, and suggesting compliance enhancements to reduce denial or downgrade risks.
Accelerating accurate reimbursement: Ensemble is piloting a multi-agent reasoning model to manage the complex process of collecting accurate reimbursement from health insurers. With this approach, a complex and coordinated system of autonomous agents work together to interpret account details, retrieve required data from various systems, decide account-specific next actions, automate resolution, and escalate complex cases to humans.
This will help reduce payment delays and minimize administrative burden for hospitals and ultimately improve the financial experience for patients.
Improving patient engagement: Ensemble’s conversational AI agents handle inbound patient calls naturally, routing to human operators as required. Operator assistant agents deliver call transcriptions, surface relevant data, suggest next-best actions, and streamline follow-up routines. According to Ensemble client performance metrics, the combination of these AI capabilities has reduced patient call duration by 35%, increasing one-call resolution rates and improving patient satisfaction by 15%.
The AI path forward in health care demands rigor, responsibility, and real-world impact. By grounding LLMs in symbolic logic and pairing AI scientists with domain experts, Ensemble is successfully deploying scalable AI to improve the experience for health-care providers and the people they serve.
This content was produced by Ensemble. It was not written by MIT Technology Review’s editorial staff.
