5 Big Myths about AI in Health Plans (and the Truth Behind Them)
When it comes to artificial intelligence, skepticism is natural, especially in an industry as complex and risk-sensitive as healthcare. With a mix of hype, misunderstanding, and headlines that often oversimplify what AI actually is, health plans may find it difficult to separate what’s real from what’s noise.
Let’s break down five of the most common AI myths, and uncover the truths that health plans need to know.
With any new tech comes skepticism. Let’s clear up some common AI myths:
Myth #1: AI will replace health plan staff
Reality: AI supports staff by handling repetitive, process-centric tasks and learns from human interventions. It does not replace the people who do the thinking.
From prior authorization intake to claims triage and member outreach, AI can take over portions of the time-consuming work that doesn’t require human judgment - freeing up your team to focus on higher-value activities like PA or claims issue resolution, complex care coordination, and provider support.
Think of AI as an assistant, not a replacement. It works 24/7, never gets tired, and scales instantly without adding headcount.
Myth #2: AI is always right
Reality: AI is powerful, not perfect. It makes predictions based on patterns in data, and its accuracy depends on input quality, model design, and ongoing oversight.
Health plans must have robust governance in place to review how AI models are performing, where they're making decisions, and what unintended bias or errors may emerge over time.
Responsible AI requires human oversight, compliance checkpoints, and explainable logic. Therefore, oversight and governance are essential.
Myth #3: AI is too complex to use
Reality: You don’t need to overhaul your systems to benefit from AI. Many health plans start small, deploying AI to one area -such as automating claims triage or segmenting member outreach, or flagging risky claims - and scale from there.
The best AI solutions aren’t additional heavyweight solutions or platforms. They integrate seamlessly with your existing systems and scale with your organization’s needs.
Start where the pain is. Solve a single workflow challenge. Then scale up from a position of success and clarity.
Myth #4: AI is just a fancy chatbot
Reality: While chatbots are often the most visible example of AI in healthcare, they barely scratch the surface.
AI is behind:
Risk adjustment predictions
Prior Authorizations
Care gap identification
Fraud, waste, and abuse detection
Real-time decision support in prior authorization
Smart claims processing
Automated provider directory maintenance
Chatbots simulate conversation. AI agents take action, make decisions, and orchestrate work across systems, delivering real operational value.
Myth 5: AI can’t be trusted
Reality: AI is only as trustworthy as its design, governance, and oversight. With the right guardrails in place: fair data practices, explainability (also referred to as “interpretability”), compliance frameworks, and bias monitoring, AI can be secure, transparent, and ethically aligned.
Trust grows when AI augments - not replaces - human decision-making, and when stakeholders understand how and why it's being used.
AI that’s built for healthcare, by healthcare experts, is far more trustworthy than generic solutions with no industry grounding.
Bottom Line:
AI is not a silver bullet, but it’s also not science fiction. When approached with strategy, transparency, and a focus on real operational outcomes, AI becomes a powerful lever for improvement.
The key isn’t to blindly adopt it - it’s to adopt it intelligently, with a partner who knows your business.
Looking Ahead:
We’ve reached the final post in our AI 101 series, but we’re just getting started.
Ready to see where AI can make the biggest impact in your health plan? Start with Discovery AI Solution from healthplans.ai - a fast, secure, and low-lift way to uncover where time is being lost, processes break down, and automation delivers the most value.
Let’s talk