GoHealth’s AI-First Approach
In health insurance innovation, the transformation doesn’t come from automating what already exists but by imagining what is newly possible.
Just ask Vice President of Product Management Pritesh Patel. He works at GoHealth, a health insurance marketplace that helps consumers discover Medicare and life insurance plans. He spends his workdays spearheading the creation of impactful solutions, and AI is at the heart of it all, shifting the company’s approach from only cost-optimization to growth and new experience creation.
“We’ve moved from asking, ‘How do we automate this process?’ to, ‘How can AI create entirely new experiences for Medicare consumers and licensed agents?’” Patel said.
With this focus in place, Patel’s team builds AI-powered solutions that help consumers and licensed insurance agents get the most out of the company’s platform. And so far, their hard work has paid off.
“Our AI products have delivered significant improvements across multiple dimensions,” he said.
AI is unlocking impact across every department at GoHealth, including sales. Senior Vice President of Operations Connor Sea sees a future in which AI doesn’t just transcribe calls but proactively anticipates a customer’s needs based on their profile, history and real-time market changes.
“By efficiently and effectively using AI, our agents will be able to spend virtually all their time as true healthcare advisors, making a complex choice simple for the consumer and delivering on our mission of ensuring peace of mind,” he said.
For GoHealth’s teams, AI is the tool needed to reach a particular goal, not the goal itself. That’s why Marketing Operations Manager Sneha Edupuganti always uncovers the “why” before implementing AI into a process, ensuring the technology is truly helping her team members work more effectively.
Leaning on this meticulous approach, her team has leveraged AI to create a positive customer journey that blends technological innovation with a human touch.
“We want our customers to have the most authentic process, whether it be with our AI agent or human agent,” Edupuganti said.
Below, Patel, Sea and Edupuganti share more about the work their teams are doing to carve out an AI-driven future for GoHealth.
How GoHealth Builds AI-Driven Solutions
Describe your role and responsibilities.
As a product lead for our AI/ML products, my team has three primary responsibilities. First, we bring advanced AI research into practical product strategy, bridging the gap between what’s possible and what’s valuable for our Medicare consumers and licensed agents. Second, we drive the product roadmap for our AI products suite, from selecting foundation large language models to building proprietary ML algorithms, and creating outcomes-driven experience in agent- and consumer-facing applications. Third, we ensure responsible development and deployment across our products portfolio. My role involves overseeing products and ML engineering while translating complex AI capabilities into tangible business outcomes-driven AI products development.
How is AI shaping the way your product and technology teams think about innovation at GoHealth?
AI has fundamentally shifted our approach from reactive problem-solving to proactive opportunity creation. Instead of building traditional rule-based systems, we now start with foundation models and explore what’s possible. Our teams think in terms of rapid prototyping with LLMs, testing concepts quickly and scaling what works. We’ve moved from asking, “How do we automate this process?” to, “How can AI create entirely new experiences for Medicare consumers and licensed agents?” This mindset change has accelerated our innovation cycles and opened up product possibilities we never considered before, like our Voice AI platform and near-real-time call quality and agent coaching insights.
Have you seen measurable impact like faster releases, improved accuracy or better outcomes for users?
Absolutely. Our AI products have delivered significant improvements across multiple dimensions. PlanFit’s matching engine has dramatically improved how well we connect consumers with plans that meet their needs, leading to better retention outcomes. The Voice AI platform has enhanced our quality assurance processes and reduced the time needed for agent coaching. Our LLM-based FAQ system has reduced agents’ learning and development time while improving onboarding. Beyond individual metrics, we’re seeing faster product development cycles because our AI-assisted infrastructure and programming lets us iterate quickly and test new features without starting from scratch each time.
How do engineers shape what AI investments happen next?
Our engineers are deeply involved in identifying AI opportunities because they see the technical constraints and possibilities firsthand. They propose investments based on what they observe in our data pipelines, user interactions and operational challenges. For example, the idea for automated call insights came from engineers who noticed patterns in our transcription data that humans were missing. They also help us evaluate new AI capabilities from AWS Bedrock and other platforms, determining what’s ready for production versus what needs more development. Their technical perspective ensures we’re investing in AI solutions that are both innovative and implementable.
How do your product and engineering teams collaborate as they build AI tools?
We use embedded AI/ML engineers within each product build, so there’s constant collaboration rather than hand-offs between teams. Product managers work directly with engineers during the research phase, exploring what’s possible before defining requirements. We hold weekly technical reviews where product and engineering jointly evaluate model performance, discuss context engineering improvements and align on production scalability. This close collaboration is essential for AI products because the technology evolves so rapidly. Having engineers involved in product decisions and enabling product managers to understand technical constraints leads to better outcomes and faster iteration cycles.
“Having engineers involved in product decisions and enabling product managers to understand technical constraints leads to better outcomes and faster iteration cycles.”
What advice would you give other tech leaders about integrating AI in a way that’s ethical and human-centered?
Focus on insights-driven product development that aligns AI capabilities with specific product use cases, business strategy and intuitive user experiences. Don’t just implement AI for the sake of having AI. In healthcare, especially Medicare, you need industry-specific tailored solutions rather than off-the-shelf horizontal AI products from vendors. Generic AI tools can’t handle the complexity of healthcare regulations, carrier compliance requirements, consumer privacy standards and security protocols that Medicare demands. Build your AI strategy around your business context and regulatory environment. Invest time in understanding how AI enhances your specific workflows and customer needs. The most successful implementations solve real problems within your industry’s constraints rather than trying to force general-purpose AI into specialized healthcare applications.
How AI Streamlines Workflows at GoHealth
Describe your role and responsibilities.
My role as SVP of operations is to be the connective tissue between our cutting-edge technology and our execution on the ground. I oversee everything from our contact center efficiency and quality assurance to the technology adoption strategy for our agents.
My core responsibilities are designing and optimizing the end-to-end customer and agent journey to maximize throughput and reduce cost per acquisition; implementing systems to ensure every interaction meets the highest regulatory standards, especially crucial in the heavily regulated Medicare space; and championing the deployment of our proprietary tools, like PlanFit and our new voice AI, designed to embed intelligence directly into the daily workflow of our agents and support staff.
What excites you most about how AI could continue to evolve sales at GoHealth?
What excites me most is the move from using AI to assist the agent to using AI to create a hyper-personalized and proactive consumer experience in true partnership with a live agent. In the future, I see AI not just transcribing calls but proactively anticipating a customer’s needs based on their profile, history and real-time market changes. An example might be an agent alert within our customer relationship management software that notifies us that a member’s doctor is no longer in-network.
By efficiently and effectively using AI, our agents will be able to spend virtually all their time as true healthcare advisors, making a complex choice simple for the consumer and delivering on our mission of ensuring peace of mind.
How have you seen the use of AI impacting your sales team and the customers they serve?
The impact is two-fold. For customers, the customer experience is faster, more accurate and more consistent. They are immediately routed to the right expert after the Tier 1 screening, and our agents are able to answer detailed, nuanced questions in real time using our AI tools.
For the sales team, we’ve seen a boost in the number of QA scores an agent has received and as a byproduct of that the number of QA coachings conducted. By automating routine portions of a call review, such as call look-ups, our agent managers have been able to focus on more complex coaching areas, like active listening and needs probing.
The Key Performance Indicators Used to Track AI Success on Sea’s Team
- “Accuracy of Results: Agents are reluctant to trust new tools, especially those focused on AI, given even the smallest hallucination or inaccuracy could be a source of a member complaint and/or negatively impact their compensation. By ensuring accuracy and limiting LLMs to strict source materials, such as Evidence of Coverage documents, it ensures agents build familiarity and trust with the new tools.”
- “QA Compliance Score: The AQA tool mentioned allows us to score and coach on additional quality-related behaviors that inform our QA score. The score reflects our adherence to regulatory/compliance standards as well as being one of our near-term indicators around policy retention.”
- “First Contact Resolution for Tier 1 Inquiries: For our Voice AI, we track how often it effectively identifies a consumer’s eligibility without having to escalate to a live agent. A high FCR demonstrates true operational leverage.”
What’s one key decision your team made around AI priorities this year?
A key decision we made this year was to prioritize the deployment of Voice AI for our Tier 1 screening and customer service support. Rather than use AI to build sales agents, we focused on using AI to solve a critical bottleneck: lead qualification and routine inquiry resolution.
By focusing on these initial use cases, it allowed our highly skilled, licensed agents to concentrate only on high-intent, qualified leads and complex service issues, which is a much more effective use of their time and leads to higher satisfaction rates from our beneficiaries.
How AI Is Changing Daily Life on Sea’s Team
- “Intelligent Call Routing and Screening: Our Voice AI now performs the initial intake, ensuring the customer is qualified and that we have a foundational understanding of their eligibility and intent for calling before they ever speak to an agent.”
- “Initiative Execution and Compliance: We utilize a service to transcribe each of our conversations and then use AI to score a position on our quality assurance scorecard. Our AI QA tool ensures compliance with our sales methodology and regulatory requirements.”
- “Real-time Assistance and Note-Taking: During a call, our agents are supported by tools like PlanGPT. PlanGPT is a LLM that serves as a go-to resource for agents to compare complex Evidence of Coverage plan documents and tease out meaningful insights, such as grocery benefits eligibility requirements.”
How AI is Unlocking a Better Customer Experience at GoHealth
Describe your role and responsibilities.
As a marketing operations manager, I help shape the first experience customers have when they call. This year, I have been working on the integration of an AI-powered call workflow into our contact center. My focus is balancing two priorities: creating a positive, human-centered customer journey and connecting callers who are intentional about speaking with our licensed agents.
The role blends strategy, experimentation and cross-team collaboration. We’re constantly exploring how AI can make our qualification process smarter and the customer experience more seamless and authentic.
How is AI changing the way your team approaches marketing at GoHealth?
AI has helped our team make decisions quicker but with more insights. We can review the aggregated insights and trends from conversations, run an experiment within days and roll out champion versions quickly. This is helping us refine scripts, improve our lead quality and optimize our marketing resources close to real time.
What advice would you give other marketing teams looking to use AI responsibly?
I believe it is necessary to know the “why” before actually integrating AI into a process, because you want to be sure the AI is enhancing the work of your teams and making it so they can reach their goals more efficiently. Think of AI as a tool to reach the goal and not as if it’s the goal itself.
“Think of AI as a tool to reach the goal and not as if it’s the goal itself.”
Can you share an example of how AI has helped you create a better experience — for employees, partners, or customers?
With AI, our experimentation abilities have become faster and more precise. For example, we tested and rolled out a message in the initial customer call that states the call goal and confirms intent before proceeding to the licensed agent. This respects both the customers’ and agents’ time and efforts, leading to an efficient process and smoother experience.
What’s an example of an insight or outcome that surprised you once AI entered the workflow.
Not a surprise per se but it’s a reminder of how new AI is to everyone. Even while working with excellent partners on new ideas, implementation can take time, and that’s just the reality right now. Despite all the advancement and rapid progress, there’s still a lot to learn from each team. That’s why collaboration and communication with all stakeholders and transparency to users is key.
Name one emerging AI trend you’re watching and why it matters for the product.
I’m most interested in the emotional intelligence aspect of AI. We want our customers to have the most authentic process, whether it be with our AI agent or human agent. We’ve been testing our voices, pace, responses and more, and I want to continue exploring upgrades in this realm so our customers feel heard, always.
Frequently Asked Questions
How are leaders at GoHealth shaping the company’s AI strategy?
GoHealth’s leaders drive AI innovation by shifting the company from process automation to creating new experiences for Medicare consumers and licensed agents. Leaders like Pritesh Patel, Connor Sea, and Sneha Edupuganti guide teams to use AI for proactive insights, faster decision-making, and human-centered interactions — always grounded in responsible deployment.
How does GoHealth ensure its AI solutions remain ethical and human-centered?
Leaders stress that AI is a tool to reach a goal, not the goal itself. Teams first identify the “why” behind each AI application to ensure it genuinely improves outcomes for consumers, respects regulatory complexity in Medicare, and maintains an authentic, human-like experience — whether delivered by an AI agent or a live agent.
What kinds of problems do GoHealth teams use AI to solve?
Product, operations, and marketing teams use AI to create new Medicare experiences, match consumers to the right plans, improve call quality and coaching, automate Tier 1 screening, generate real-time insights, and test customer-facing messages quickly. AI is used to make the entire customer journey smoother, more accurate, and more proactive.
What is it like to work with engineering and cross-functional teams on AI initiatives at GoHealth?
Teams collaborate constantly, using embedded AI/ML engineers in product builds and conducting weekly reviews to evaluate model performance and scalability. Engineers directly influence future AI investments by identifying patterns in data, proposing new use cases, and assessing emerging capabilities from platforms like AWS Bedrock.
What skills or qualities does GoHealth value in people working on AI-related teams?
GoHealth looks for people who are comfortable experimenting, rapidly iterating, and adapting to new technologies. The company values individuals who can blend technical understanding with strategic, regulatory, and human-centered thinking — especially those who are eager to learn, collaborate across functions, and help shape new Medicare customer experiences.
