Key Takeaways
• Privacy concerns and outdated systems pose the biggest technical challenges – 75% of insurance experts worry most about privacy, while disconnected IT systems make smooth integration tough
• People issues matter more than tech problems – Success hinges more on staff than gadgets, yet bosses don’t prioritize helping workers adapt even though 64% of CEOs know it’s crucial
• Unclear rules slow things down – 11% of insurance firms feel ready to comply with GenAI regulations making them hesitant to roll it out in different areas
• Scaling through platforms beats scattered trials – Companies need unified control systems and standard setups instead of endless isolated tests to get real returns
• Working with users builds crucial trust – People of all ages are trusting Enterprise AI health info less so developing things together is key for success
The numbers tell one thing, but the situation on the ground feels different.
Over 70% of healthcare groups are working to implement or experiment with GenAI. However, the healthcare payer industry seems stuck in what some call “implementation purgatory,” where big plans fail to meet basic execution.
Healthcare payers face a critical decision point. A majority are either working with outside vendors (59%) or developing their own tech in-house (24%), yet many remain stuck in testing phases. Generative AI holds the most promise for improving clinician productivity, but these organizations are struggling to get the basics right.
The stakes have never been clearer. GenAI might bring in $1 to $1.5 trillion by 2027 through care delivery, boosting clinical work, and simplifying administration. This chance feels pressing as big players like CVS Health, UnitedHealth Group, and Humana saw profit drops of 5.2%, 6.2%, and 14.2% in the last quarter of 2023.
This guide highlights common mistakes healthcare payers encounter when using GenAI, explains why such challenges stick around, and offers tested ways to overcome them. Many healthcare leaders recognize the need to get implementation right—over 70% prioritize data concerns like security, privacy, compliance, and quality. Yet, they often miss hidden obstacles that block smooth integration into everyday workflows.
The issue isn’t about technology. Strategy plays a big role too.
7 GenAI Hurdles Holding Healthcare Payers Back
Even though generative AI in healthcare gains attention most payers struggle to integrate it. Studies reveal 91% of business leaders worry about challenges in adopting the technology. The gap between aiming high and actually acting on it is not shrinking, it is growing.
1. Concerns about data security and privacy
Worries over privacy dominate conversations about GenAI. Around 75% of insurance workers see data privacy as their main concern. Healthcare payers handle massive amounts of private patient data making them prime targets for hackers. Protecting personal health information requires advanced methods like anonymization and pseudonymization. Teams trying to implement this technology often balance the need to use data with the responsibility to safeguard it.
2. Navigating regulations and managing risks
11% of insurance companies feel ready to handle current and future GenAI regulations. The rules keep changing. HIPAA rules, state-level privacy laws, and new Enterprise AI policies make navigating the regulatory scene tough. It gets worse with no national guidelines in place to manage the development of non-medical Enterprise AI tools. Many companies choose careful strategies, which slows their adoption of Enterprise AI.
3. Integration with legacy systems
Most healthcare payers still rely on old legacy systems that make it hard to work with GenAI. These outdated setups bring many challenges. Few experts now know how to handle the coding for these older systems. Maintaining them uses up a lot of time and money. Adding new technologies to such systems doubles the cost. Moving data between mismatched formats also risks messing up data quality.
4. Workforce adaptation and change management
Businesses often overlook people in the process. Executives place little focus on improving employee skills at 63 percent, taking care of worker concerns at 60 percent, and handling role changes at 57 percent. Meanwhile, IBM’s research shows that 64 percent of CEOs think the success of GenAI relies more on employees than on technology. The situation gets tougher, with 35 percent of employees needing to learn new skills or get retrained within three years.
5. Explainability and transparency requirements
The way GenAI works is often like a “black box,” causing issues in healthcare where understanding decisions is essential. Being open and clear goes past meeting legal rules. It is key to gaining trust among patients, providers, and other parties involved. If explainable AI is missing, healthcare payers may end up with tools that doctors and patients refuse to use even if the tools work well .
6. Tracking ROI and understanding value
Achieving visible returns is still tough even after large investments. Short-term financial benefits dominate traditional ROI models, which overlook the bigger picture of GenAI’s future potential. Companies face challenges setting healthcare-specific KPIs and weighing direct benefits like lower costs with less obvious ones like happier patients. Many businesses focus on single Enterprise AI projects often ignoring the gray area around uncertain returns.
7. Expanding beyond pilot programs
The numbers make it clear: 83% of healthcare leaders try GenAI in pre-production setups, but less than 10% put money into systems capable of full-scale rollout. Payers fall into endless testing traps that deliver no real change. This happens because of isolated strategies, a lack of unified Enterprise AI leadership, and not spending enough on the digital tools needed to scale up operations.
Want to overcome these challenges? Use our self-assessment framework to find your specific roadblocks and decide what steps to take next.
Why do Challenges Stick Around in the Healthcare Payer Industry?
Healthcare payers deal with problems that go beyond just technology. These issues are not just small tech problems. They are rooted in how the industry has been built.
Disconnected IT systems and isolated platforms
Healthcare data is like islands—close but separated by wide gaps. This mess of systems isn’t random. It’s built into the structure.
Payers use broken systems where disconnected data is everywhere. A lot of them depend on old claims data often months out of date, not matching up with updated EHR or data from patients. This problem is costly. Disorganized data wastes resources and drives up healthcare expenses. Experts estimate around 20-25% of U.S. healthcare spending $1 trillion, gets wasted this way.
Trying to build GenAI tools on messy data is like trying to build a house while your building materials are scattered all over. It can be done, but it’s extremely slow and difficult.
No consistent frameworks exist to guide Enterprise AI development
The way Enterprise AI is governed today feels like scattered weather patterns. There’s some order, but no unified system to steer the whole climate. Standard enterprise governance systems often fall short when it comes to tackling issues unique to Enterprise AI like data accuracy biased algorithms, or risks during implementation.
Although more than 100 Enterprise AI governance frameworks exist (over 100), most are too focused on certain industries or outdated as new technologies emerge. They also struggle to uphold ethical standards while keeping public accountability in check. Instead of solving problems, this piecemeal approach leaves payers confused as they work to adopt GenAI solutions.
Resistance to change and job loss fears
Healthcare’s long-standing roles and highly structured workforce make change hard to accept. Many employees push back against new tools sticking with what they know even when newer tech could make their work much more efficient.
This pushback happens because of cognitive biases like status quo bias and loss aversion. Workers tend to worry more about learning new skills or losing their jobs than about the benefits of new technology. If companies ignore these human concerns even advanced GenAI systems will not succeed.
Uncertainty around GenAI regulations
The rules governing GenAI tools remain unclear. GenAI adoption faces a lot of regulatory uncertainty. Right now, “there remain open questions on the approach to regulating GenAI-enabled products that may fall within FDA’s jurisdiction.” This lack of clarity makes healthcare payers hesitant.
GenAI features, like producing different results from the same inputs and evolving , lead to tricky regulatory issues. To comply, payers face higher risks when using GenAI tools in places with different Enterprise AI and patient data regulations.
Moving ahead involves solving these foundational problems, not just focusing on technical fixes.
From Roadblocks to Results: Tested Strategies to Put GenAI into Action
To make GenAI work, you need more than just good ideas. Our experience with health insurance companies shows clear approaches that get real results, helping organizations move past endless testing and start making a real difference.
Creating a Safe Base for GenAI
Safety isn’t something you add on later—it’s the foundation of getting GenAI to work well. Begin with tough data rules that do more than just tick boxes. Put in place strict ways to use less data and advanced methods to hide identities that go way beyond just removing names. Think about using techniques like k-anonymity and differential privacy to keep patient info safe [19].
Set up zero trust access controls that check identity and give specific permissions based on roles. This method lowers the chance of exposure while allowing GenAI to access the data it needs.
GenAI Framework That Follows Regulations
Create a team from different departments with clear power to make decisions about both creating and using GenAI [20]. This isn’t a group that just talks—it’s a center that takes action. Put your energy into valuable uses that don’t put key business functions at risk. Set clear limits and create ways to get feedback when mistakes happen to stop them from happening again [20].
The aim: to follow rules ahead of time, which helps roll out GenAI faster instead of slowing it down.
Connecting with Old Systems That Gets Results
Big changes often fail. Smart additions work better. Let GenAI tools look at and understand old code—this speeds up updates up to 100 times compared to old ways [21]. Build APIs and middle software to link old systems with new apps [22].
Sometimes it’s best to “go slow to go fast”—taking time to make detailed plans helps things grow later [23]. This step-by-step approach cuts down risks while building up speed.
Change Management and Skill Development
Follow the ADKAR model (Awareness, Desire, Knowledge, Ability Reinforcement) to make transitions work [24]. Start with test runs, turn findings into lessons then grow step-by-step. For every $1 you spend on tech, put $5 into change management to boost skills and usage [23].
Success hinges more on people than tech—invest with this in mind.
Transparency and Explainability Requirements
Set basic explainability rules for all Enterprise AI models. Write down data sources and reasons behind outputs [25]. Set up systems that watch inputs and responses in real-time to spot PHI patterns, attempts to trick the system, and policy breaks [19].
Trust grows through openness, not through complexity.
ROI Measurement Framework
Look beyond short-term money metrics to grasp GenAI’s full potential to add value:
- Better clinical outcomes
- More efficient operations
- Improved patient/member experiences [2]
Set up clear KPIs with checkpoints to track progress as you put things into action [2]. Getting returns in healthcare takes time—but you can’t wait to start measuring.
How to Scale Across the Organization
Use a platform-based approach that makes infrastructure and development processes the same across the board [23]. Get our eBook to check out ways to roll out GenAI in healthcare. Make cloud integration and data access your top priorities to back up wide-ranging Enterprise AI abilities [10].
To go from pilot to production, you need infrastructure that can grow, governance that can change, and teams that can get things done. Companies that get these things right overcome roadblocks and unlock the full power of GenAI.
The Future of GenAI in Healthcare Payer Organizations
GenAI is picking up speed for healthcare payers. Experts think the global Enterprise AI market for healthcare payers will hit $3.58 billion by 2030 [26]. This shows how payers are changing their operations, how they talk to members, and how they coordinate care.
This growth isn’t just about new tech. It points to a big change in what gives companies an edge.
New trends in GenAI tools made for payers
The payer-specific GenAI ecosystem has gone beyond simple automation. Companies now split their approach: 59% team up with outside vendors for tailored solutions, while 24% create their own tools [27]. This mix shows a growing market where specialized tools tackle specific payer problems.
Enterprise AI models built for the unique aspects of payer data are now the norm. These models work through huge amounts of messy healthcare data, find useful insights, and streamline complex tasks [28]. What’s the outcome? GenAI solutions will cut admin costs by 30% for most health insurers with over a million members by 2027 [28].
The chance to succeed is obvious. Companies that create special skills now will grab a bigger share of the profits as the field grows.
People’s trust and teamwork in using GenAI
Trust decides if GenAI will catch on. People’s doubt about health info from GenAI has grown in all age groups: young adults (30% up from 21%) and older folks (32% up from 24%) show the biggest jumps [29].
Trust-building needs co-design methods where consumers play an active role in GenAI creation [1]. This teamwork makes sure solutions tackle real problems while boosting credibility. Seasoned consumers offer views from patients, caregivers, and communities that algorithms can’t produce.
The takeaway? Tech alone doesn’t create trust. Teamwork does.
Getting ready for ongoing model tweaks and changes
GenAI models are living systems that need constant care. These models’ performance shifts over time and the same inputs can lead to different outputs [4]. This lack of consistency creates problems for clinical uses where reliability is a must.
Innovative payers have an influence on “algorithmovigilance”, a method to keep watch similar to monitoring drug safety [4]. Some groups create protected LLMs within local health systems guarding them from outside shifts that might cause drift [1].
Taking care of models isn’t a choice. It’s a way to stay ahead.
Our Enterprise Deployment & Scaling and Optimization & Governance services help payers build GenAI systems that last and change with the times.
Getting Past Implementation Roadblocks
The trillion-dollar chance isn’t just talk. It’s here now.
Most healthcare payers can’t escape the pilot phase; they struggle to grow their projects, refuse to give up on failed methods, and find themselves stuck between goals and action. The industry can’t afford this deadlock. Not when profits are going down and competition is getting tougher.
The seven roadblocks we’ve spotted aren’t just tech problems. They’re big-picture decisions. Keeping data safe, following rules, and working with old systems are real headaches, but how people adapt and resist change can make or break things. Companies that tackle both sides at once gain a lasting edge.
Tomorrow’s winners in healthcare will be those who build adaptable well-governed foundations now. These firms can adjust to new business models, rule changes, and market shifts. Using platforms to grow beats scattered test runs every time. Having a standard setup lets you try things out, roll them out, and make them better fast.
Healthcare payers stand at a crossroads. Those who tackle roadblocks to implementation with a plan will reap GenAI’s promised benefits. Those who stick to piecemeal approaches will lag further. The divide between front-runners and stragglers will grow.
Reach out to Torsion for expert help on your GenAI path if you’re set to go beyond tests to company-wide rollout.
Winning needs balance: new ideas with rule-following, tech with people skills, quick gains with lasting worth. Putting GenAI to work is about changing how healthcare payers run, choose, and give value to members.
The groups that get this difference will guide healthcare into its next chapter.
