How to Navigate the Landscape of AI and Generative AI

There is no opting out of AI-driven transformation. To stay competitive, companies will have to adopt AI, which industry analysts project could add $16 trillion to the world economy by 20301—or risk falling behind.

Despite the fact that 92% of organizations are expecting steady or increased organizational focus on AI2, the level of AI maturity in many organizations is still fairly low. High AI maturity is reflected in AI that is properly built, productionalized, consumed, maintained and trusted. It also has the potential to drive substantial business outcomes.

So, whether you are just starting with AI or have been on the AI journey for some time, how do you ensure you are doing AI right? What is the best path for maximizing the business value of AI and minimizing potential risks?

There are three critical success pillars  to follow when it comes to AI: AI Readiness, AI Enablement and AI as Product.

AI Readiness

First, AI Readiness, which encompasses talent and infrastructure. You need strong talent—people who understand the technical as well as the business aspects of AI—to help confirm your AI is properly built, used and maintained. Infrastructure readiness refers to both the data and model infrastructures that are used to build the models. Because AI models are only as good as the data they consume, trustworthy data and accessible and robust data infrastructure are necessary. Your model infrastructure, likewise, must be accessible, scalable and flexible, so you can experiment and deploy quickly without sacrificing performance or compliance.

AI Enablement

Next, AI Enablement. This is about clearing a path for AI. It means making strategic investments, securing leadership and business support, and having a clear AI vision and strategy. You also need best practices and governance frameworks to promote consistency, and most importantly, ethical and responsible use of AI, which we define as AI that is fair, unbiased and transparent. However, because no AI system can be guaranteed to be completely free from bias or error, continuous monitoring and oversight are essential. Without these enablers, even the most advanced models won’t deliver sustainable or trusted value. 

AI as Product 

Finally, AI as a Product. This is where you operationalize AI beyond prototypes. Define the who, what, where, when and how of the AI initiative. Build end-to-end capabilities, monitor performance and maintain models over time. Reuse components to accelerate development and reduce costs. Treating AI as a product helps drive continuous improvement and long-term impact when properly managed and validated.

When these pillars come together, you get AI that is properly built, operationalized, consumed, maintained, trusted and capable of delivering business value. And remember, you can build, buy or partner at each of the steps of the journey—but the key is doing it right.

Claritev’s AI Journey

Claritev—a technology, data and insights company with over 45 years of experience serving healthcare payors, providers, patients and employers—has a long history of leveraging the latest technology to improve the affordability, transparency and quality of healthcare. Our use of AI in our BenInsights platform, which enables employers and plan sponsors to optimize their health benefit plans, illustrates how organizations can progress from foundational readiness to advanced agentic capabilities.

Let’s walk through the AI journey we took with BenInsights, which followed the three critical pillars of AI success.

We started with AI Readiness. This meant investing in an expert AI team, robust data infrastructure and flexible model platform. Without these basics, introducing and then scaling AI was not possible. This cleared the path for everything that came next, including predictive modeling using traditional machine learning approaches such as risk models, forecasting and analytics. It’s where we and our clients began to see tangible business value from data-driven insights. For example, our clients can see whether unnecessary ER visits or high-cost injectables drove their cost increase in the previous month and identify strategies for member engagement that can drive the cost down.* 

Then came AI Enablement. This was where strategic investment and leadership support became critical. We established a clear AI vision and a well-defined strategy. We also kicked off an AI Center of Excellence to drive governance and AI best practices.

Once enabled, AI moved into a Product mindset. Here, we focused on end-to-end capabilities—AI model performance monitoring, maintenance and reusability. Treating AI as a product helps deliver business value, drives continuous improvement and enables scalability.

Finally, we were agile and responded to the new generative AI tools becoming available with Agentic AI Readiness. We built on our three pillars of success by adding the Agentic AI readiness capability. Agentic AI is anticipated to enhance customer experience and interoperability through autonomous, agent-driven workflows. We are researching and developing systems that can reason, plan and act independently within human-supervised environments, delivering potential next-level efficiency and personalization.

The AI journey is progressive, and each stage builds on the previous one. Organizations that invest early in readiness and enablement, followed by enabling AI embedded in their products will be best positioned to leverage AI and Agentic AI for competitive advantage.

We hope this gets you ready and excited about your own AI journey. This journey is not just about technology—it’s about building the right strategy, infrastructure and mindset. Bon voyage! 

Disclaimer: This article is provided for general informational purposes only and does not constitute a guarantee of results or legal, compliance, or technical advice.

*The examples provided are for illustrative purposes only and do not constitute a performance guarantee. Actual results may vary depending on client-specific variables.