Olive AI’s Rise, and Fall in Healthcare: What Went Wrong?

Last Updated on March 18, 2025

Olive AI was once the poster child of healthcare tech innovation—fast-growing, VC-backed, and full of bold promises to revolutionize operations with cutting-edge AI solutions. For Gen Z entrepreneurs chasing big ideas and building disruptive apps, Olive’s journey was a masterclass in ambition. But what looked like a rocket ship soon hit turbulence.

From scaling too fast to product misalignment and internal chaos, Olive AI’s rise and fall offers powerful lessons on what it really takes to build sustainable, AI-driven ventures. In this breakdown, we dive into the hype, the peak, and ultimately, the downfall of Olive AI—unpacking where things went wrong and what founders can learn from it.

If you’re crafting your own startup story, this is more than a postmortem—it’s a reality check on growth, vision, and execution in the AI era. Because success isn’t just about launching fast—it’s about building smart, staying focused, and knowing when to pivot.

Introducing Olive AI

Olive AI was a healthcare automation company that used artificial intelligence (AI) and machine learning (ML) to automate administrative tasks in hospitals and other healthcare organizations. Olive AI’s platform promised to revolutionize the healthcare industry by streamlining critical tasks and improving operational efficiency. By automating administrative tasks, Olive AI could help healthcare providers reduce costs, improve revenue cycles, and provide a better patient experience.

Its platform was designed to handle a wide range of tasks, including:

• Claims processing: Olive AI’s platform could automate the claims processing process, from submitting claims to payers to following up on denials. Its claims processing solution could help hospitals reduce the time it takes to submit and process claims, which can lead to faster reimbursement from payers.
• Prior authorizations: Olive AI could automate the prior authorization process, which is required by many payers for certain procedures and medications. It could help hospitals reduce the administrative burden of obtaining prior authorizations, which can free up staff to focus on other tasks.
• Patient access: Olive AI could help patients schedule appointments, pre-register for appointments, and complete other tasks related to accessing care. It could help patients schedule appointments more easily and efficiently, which can improve the patient experience.

Also read: How AI Chatbots Benefit Healthcare Services

Rise of Olive AI

Olive AI helped users in a number of ways, including:

Reduced Administrative Costs

Administrative costs account for nearly 25% of total hospital expenditures in the U.S., according to a study published in JAMA. Olive AI helps reduce these costs by automating repetitive administrative tasks, such as claims processing, insurance verification, and patient registration.

For example, Cleveland Clinic, one of the largest medical centers in the U.S., partnered with Olive AI to automate their claims processing. As a result, they saved approximately $1.2 million per year and significantly reduced manual labor hours.

By eliminating inefficiencies, hospitals and clinics can redirect financial resources toward improving patient care, hiring skilled professionals, and upgrading medical equipment. For Gen Z entrepreneurs looking to enter healthcare startups, adopting AI-driven automation can lower operational costs and improve overall business efficiency.

Improved Revenue Cycles

On average, hospitals in the U.S. face denial rates of 10-15% on insurance claims, leading to delayed payments and revenue loss. Olive AI improves revenue cycles by automating claim submissions, detecting potential denials before submission, and accelerating reimbursement processes.

A study by Olive AI found that its claims processing solution could help hospitals speed up revenue cycles by an average of 10 days. For example, AdventHealth, a nonprofit health system, integrated Olive AI into its billing process and saw a 30% decrease in claim denials.

For healthcare entrepreneurs, faster revenue cycles mean better cash flow, reduced dependency on loans, and a more sustainable business model. AI-driven revenue cycle management ensures timely payments, helping startups scale operations without financial bottlenecks.

Better Patient Experience

Patient satisfaction is a key factor in the success of healthcare businesses. Long wait times, difficulty in scheduling appointments, and administrative inefficiencies often frustrate patients. Olive AI enhances the patient experience by automating appointment scheduling, pre-registration, and billing.

A study by Olive AI found that its patient access solution reduced the time it takes for patients to schedule an appointment by an average of 20 minutes. For example, Baptist Health, a leading healthcare provider, implemented Olive AI’s appointment scheduling system and saw a 40% reduction in call center wait times.

This level of efficiency is crucial for Gen Z healthcare entrepreneurs launching telemedicine startups like Zocdoc’s business model or AI-powered health services. By integrating AI-driven scheduling and automated patient engagement tools, startups can attract and retain more patients, leading to higher satisfaction and business growth.

Denial Management

Claim denials cost U.S. hospitals $262 billion annually, according to the American Hospital Association. Olive AI helps healthcare providers proactively address denial issues by analyzing patterns in claim rejections, identifying root causes, and suggesting corrective measures.

For instance, Mercy Health, a nonprofit healthcare system, reduced denial rates by 22% within the first year of using Olive AI. By implementing AI-driven denial management, hospitals recover millions in lost revenue and reduce the burden on administrative staff.

For healthcare startups, automated denial management can prevent cash flow interruptions, allowing them to stay financially stable and expand services. AI-driven tools also ensure compliance with insurance regulations, reducing legal risks associated with claim disputes.

Medical Billing Automation

Medical billing is one of the most complex aspects of healthcare administration. Billing errors contribute to 80% of medical bill inaccuracies, causing payment delays and patient dissatisfaction. Olive AI automates medical coding, charge capture, and claim submission, reducing errors and improving billing efficiency.

For example, Mount Sinai Health System adopted Olive AI for medical billing automation and saw a 25% reduction in billing errors, leading to faster reimbursements and improved revenue integrity.

For Gen Z entrepreneurs launching AI-powered healthtech platforms, automated billing solutions simplify financial operations, minimize errors, and improve patient trust. AI ensures accurate coding and documentation, preventing revenue losses due to misfiled claims.

Clinical Documentation Improvement (CDI)

Clinical documentation errors contribute to $11 billion in unnecessary healthcare spending annually, according to a report by Becker’s Hospital Review. Olive AI enhances Clinical Documentation Improvement (CDI) by identifying and correcting documentation inaccuracies in real time.

For example, Intermountain Healthcare implemented Olive AI’s CDI solutions and improved documentation accuracy by 15% within six months, leading to fewer claim denials and higher reimbursement rates.

For healthcare startups, AI-powered CDI ensures that electronic health records (EHRs) remain accurate, reducing compliance risks and enhancing patient care quality. Gen Z entrepreneurs can leverage CDI technology to build smart healthcare platforms that ensure error-free patient records, streamlined workflows, and improved medical decision-making.

Olive AI’s platform was designed to be scalable and flexible, so it could be used by healthcare providers of all sizes, from small clinics to large hospital systems. Olive AI also offered a variety of training and support resources to help healthcare providers get the most out of its platform.

In 2021, Olive executives said the company’s enterprise AI was in place at more than 900 hospitals in over 40 U.S. states, including more than 20 of the top 100 U.S. health systems.

Also read: Blockchain In Healthcare: 10 Best Use Cases

Fall of Olive AI

Olive experienced many of the same headwinds as other organizations — including shifts in the industry landscape, evolving customer expectations and challenging market conditions. The shutdown move came after a prolonged financial struggle and the inability to secure additional funding. But several important factors contributed to Olive AI’s failure, including:

Overpromising and Underdelivering

Olive AI positioned itself as a revolutionary solution for healthcare automation, claiming to reduce administrative costs by 5X. However, these ambitious promises were largely based on rough estimates rather than concrete data. Many hospitals and healthcare systems that adopted Olive AI expecting significant savings were disappointed when the projected cost reductions did not materialize.

For example, a major Midwest hospital system signed a multi-million-dollar contract with Olive AI, anticipating substantial savings in claims processing and billing. However, internal reviews found that cost reductions were closer to 10-15% instead of the promised 500% improvement. The hospital eventually terminated its contract early, citing misaligned expectations and lack of real savings.

Additionally, Olive AI often exaggerated its automation capabilities, claiming that it could completely eliminate manual tasks. In reality, many customers found that the software still required human intervention for verification and troubleshooting, leading to frustration and operational inefficiencies. This mismatch between expectations and reality led to declining trust among healthcare providers, making it difficult for Olive AI to retain customers.

Lack of Focus

Olive AI’s rapid expansion was one of its biggest downfalls. The company aggressively pursued growth, onboarding over 900 hospitals and healthcare organizations within a short period. However, this expansion came at the cost of scalability and product refinement.

One major issue was that Olive AI failed to tailor its offerings to different types of clients. Large hospital networks and small healthcare clinics have vastly different needs, yet Olive AI attempted to provide a one-size-fits-all solution. As a result, many smaller clients struggled with the complexity of the AI tools, while larger clients found the automation insufficient for their extensive operations.

For instance, a 250-bed community hospital in Florida signed up for Olive AI expecting seamless integration with its existing systems. However, the implementation process was riddled with technical issues, and the AI failed to meet the specific administrative needs of the hospital. Ultimately, the hospital opted out of using the technology and returned to its previous workflow, citing a lack of practical benefits.

Instead of prioritizing client retention and long-term satisfaction, Olive AI focused on acquiring new customers to impress investors. This led to poor service quality and misaligned product development, which eventually hurt the company’s reputation.

Bad Customer Experience

A key factor in Olive AI’s decline was its failure to provide adequate customer support. Clients frequently reported long response times, lack of proactive assistance, and a poor understanding of the product’s capabilities.

For example, a large healthcare provider in California implemented Olive AI to streamline prior authorizations but found that the system frequently made errors in processing insurance claims. When the company reached out for support, Olive AI’s team took weeks to respond, and even when they did, the solutions provided were often generic and ineffective.

In another case, a hospital system in New York reported that Olive AI’s solutions failed to integrate smoothly with their existing electronic health records (EHR) system. After multiple failed attempts to fix the issue, the hospital requested that Olive AI shut down its service, stating that it had become more of a liability than an asset.

Poor customer support also led to contract terminations and declining renewals. Many healthcare providers felt misled by Olive AI’s marketing claims and decided to switch to other automation solutions that offered better support and reliability.

Lack of Transparency

One of the biggest criticisms against Olive AI was its lack of clear progress tracking and transparency regarding cost savings. Unlike traditional software vendors that provide detailed performance metrics and reports, Olive AI failed to give clients a clear picture of their return on investment (ROI).

For example, a large hospital chain in Texas requested a detailed breakdown of how much money Olive AI had actually saved them. Instead of a precise report, the hospital received broad estimates with no supporting data. When pressed for specifics, Olive AI’s team could not provide verifiable proof of the promised savings, leading to growing skepticism among clients.

Additionally, Olive AI did not keep an internal log book of progress toward automation goals unless a client specifically requested it. This lack of documentation made it difficult for customers to track improvements, identify inefficiencies, or make data-driven decisions.

A former Olive AI client, who worked as a CFO of a hospital system in Illinois, publicly criticized the company for its lack of accountability. In a report, they stated:

“We were promised automated cost savings and improved efficiency, but after a year, we had no measurable impact. When we asked for reports on ROI, we received vague responses and no hard data. We ultimately decided to end our contract with Olive AI.”

This lack of transparency eroded customer trust and made it difficult for Olive AI to retain long-term contracts, further accelerating its decline.

Financial Fallout and Mass Layoffs

As dissatisfaction grew, Olive AI struggled to maintain its revenue streams. Clients started terminating contracts, which led to a sharp decline in cash flow. By mid-2022, Olive AI had burned through more than $800 million in funding but failed to achieve sustainable profitability.

Facing mounting financial pressure, Olive AI laid off nearly 450 employees, which accounted for 35% of its workforce. The layoffs primarily impacted engineering, sales, and customer support teams, further worsening the company’s ability to address client concerns.

Investors, once optimistic about Olive AI’s potential, began losing confidence. In a 2023 financial review, venture capital analysts labeled Olive AI as one of the biggest AI healthcare failures in recent years. The company, once valued at $4 billion, saw its valuation plummet as clients and investors pulled out.

Lessons for Gen Z Entrepreneurs

The failure of Olive AI serves as an important lesson for Gen Z entrepreneurs looking to build AI-driven businesses:

1. Avoid Overpromising – AI is a powerful tool, but setting unrealistic expectations can lead to loss of credibility. Transparency in capabilities and results is key.
2. Prioritize Customer Experience – Rapid growth is meaningless if customers are dissatisfied. Ensuring proper support, training, and continuous improvements is critical.
3. Focus on a Niche – Expanding too quickly across multiple customer segments can dilute product effectiveness. Specializing in a well-defined market leads to better solutions and customer loyalty.
4. Ensure Transparency – Clients need clear, verifiable data to measure AI’s effectiveness. Regular reports, performance tracking, and open communication are essential for maintaining trust.
5. Sustainable Growth Matters – Growing too fast without solid foundations can lead to financial instability. Entrepreneurs should focus on scalability, retention, and profitability rather than just increasing customer numbers.

Overpromising and underdelivering, rushed product growth, bad customer support, and a lack of transparency were some of the contributing factors. Once a rising star, its inability to scale effectively serves as a stark reminder of the challenges facing startups in the healthcare arena. Due to budget restraints, Olive sold its clearinghouse and patient access business units to Waystar and its prior authorization business unit to Humata Health. Olive AI was not the only healthcare startup to close recently due to budgetary restraints. Pear Therapeutics and Babylon Health also closed their doors in recent months.

Key Insights

Realistic Goal Setting

It’s essential to set achievable goals. The overpromising and underdelivering by Olive led to frustration and dissatisfaction among customers. It damaged Olive AI’s reputation and made it difficult for the company to attract new customers. Overpromising and underdelivering can undermine trust and credibility. Honesty and transparency should be at the core of your product’s value proposition. Remember, it’s not just about what you promise but what you can deliver.

Customer-Centric Approach

However good your product might be, if your product approach is not customer-centric, you are supposed to go down. Understanding your customers’ unique needs is pivotal. Olive AI’s customers reported that the company was not proactive in addressing issues and often lacked knowledge of its own product’s capabilities. Businesses should provide excellent customer service and be responsive to their customers’ needs. Businesses must also tailor their solutions to meet the unique pain points of their customers. A deep understanding of customer needs and a commitment to meeting them should guide product development and customer relations.

Also read: How AI in CRM Enhances Customer Experience and Boosts Sales

Transparency & Accountability

Detailed progress tracking and transparency regarding outcomes and cost savings are vital. Olive AI’s failure to keep a clear log of its progress towards achieving estimated outcomes or cost savings hurt its reputation. Customers should have a clear picture of the value they’re receiving. And as a business, its your duty to show your value to them.

Balanced Growth and Focus

Scaling too quickly without a clear focus can strain your resources, hinder development, and result in a lack of scalability. Olive AI tried to do too much too soon, which spread its resources thin and led to a lack of focus. Businesses should focus on their core competency and build a deep understanding of their customers’ needs first. Achieving balance is crucial.

Start with Value proposition

In an economy that values money more than we did a couple years ago, it is more important than ever to have a clear understanding of the value you are creating and to design a platform that enables that value to grow. Rather than simply growing something and then trying to achieve value later, it is essential to start with a value proposition and build a platform around it.

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Conclusion

Olive AI’s rise and fall offer valuable lessons for entrepreneurs, investors and businesses in the tech and healthcare industries. Companies can learn from Olive AI’s mistakes to build resilient products and organizations that can withstand the challenges of a rapidly evolving market.

Despite the closure of 3 healthcare unicorns, the fact that funding of digital health startups using AI reached $10 billion in 2021 and $3 billion through the first half of 2022 — compared with a three-year funding total of $6 billion from 2017 to 2019 — underscores the potential of this rapidly growing sector. However, the cautionary tale of Olive AI highlights the importance of companies focusing on building long-term value, rather than prioritizing short-term growth at all costs.