Enterprise AI Growth in Healthcare: What Payers Should Focus on by 2025

14 min. read

An image where 3 professional from healthcare industry analyzing how enterprise AI is growing in their sector.

Key Takeaways

• 25% of payers have Enterprise AI strategies in place, despite the huge potential to save $150-300M in admin costs for every $10B in revenue

• Most companies can’t get past the early stages (Active/Operational) with just 30% of Enterprise AI test projects making it to full use

• Enterprise AI growth happens in five clear steps: Awareness → Test Projects → Strategic Adoption → Fine-tuning → Company-wide Change

• To succeed, companies need to tackle data issues ethical worries, choose the right vendors, and train staff before expanding Enterprise AI use

• Companies should link Enterprise AI spending to specific business aims and set up rules for Enterprise AI use from the start

Enterprise AI maturity in healthcare has moved from being a bright idea to becoming a top agenda item for executives. And the data highlights the challenge ahead. By 2050, the number of Americans aged 50 or older will rise to 221.13 million. This marks a sharp 61.11% increase compared to 2020 [9]. This aging population combines with a staffing shortage. Experts predict a workforce gap of up to 55,200 primary care doctors and 86,700 specialists by 2033 [9].

The response? Not as ready as you’d expect. In 2024 15% of providers and 25% of payers developed clear Enterprise AI strategies. That shows a gap between knowing and doing. Even though 75% of providers and payers boosted IT budgets, most lack a solid plan to make sure these funds lead to real improvements.

Healthcare is stuck in what some call “implementation purgatory.” Enterprise AI has loads of research backing its use, but other industries roll it out faster. The problem isn’t the tech itself, it’s putting it into action. Leaders in healthcare point out a big issue. Current Enterprise AI governance models focus too much on technical details and don’t help to put plans into practice.

That doubt makes sense. The obstacles include split data systems, tough regulations, and limited staff. However, organizations have shown what can happen when they go from testing ideas to using them on a larger scale. Look at UC Davis Health’s S.M.A.R.T. and S.A.F.E. clinical system. It checks if Enterprise AI tools used on patients and healthcare teams meet standards for safety, fairness, accuracy, and evidence [9].

This piece lays out how healthcare payers can turn Enterprise AI goals into real results by 2025. It breaks down five stages of growth, identifies ways to make great progress, and explains how tools like Torsion’s Discovery & Strategy services and the GenAI Weather Map help groups figure out where they stand and move ahead faster.

Grasping Enterprise AI Growth in Healthcare

Enterprise AI is changing the way healthcare runs at its core. 95% of healthcare leaders agree generative AI will transform their field [10]. However, understanding this and putting it into action are two very different things.

How Enterprise AI growth will matter to healthcare payers by 2025

The numbers tell the story. According to McKinsey, Enterprise AI solutions can cut administrative costs by $150 to $300 million and reduce medical costs by $380 to $970 million for every $10 billion in revenue. At the same time, they have the potential to boost revenues by $260 million to $1.24 billion. Companies with advanced Enterprise AI maturity outpace others by excelling in productivity staying ahead of competitors managing risks better, and moving faster with innovation.

This goes beyond using technology. It is about staying competitive and surviving in the long run.

The key difference: Adopting Enterprise AI versus mastering Enterprise AI

Most hospitals and healthcare organizations often mistake purchasing new tech for building the skills they need. 45% of Enterprise AI projects make it past the idea or testing stage [10]. Even worse, just 30% of completed pilot projects get implemented [10].

Why is this difference important?

Enterprise AI adoption focuses on trying out tools and testing prototypes.

Enterprise AI maturity sees AI as part of the bigger picture. It revolves around weaving Enterprise AI into daily operations, setting up rules to guide its use, and creating scalable skills and systems.

Buying is just a transaction. Transformation involves real change.

How healthcare moves through five stages of Enterprise AI maturity

Healthcare systems follow five clear steps as they improve their Enterprise AI capabilities [5].

Awareness: Exploring Enterprise AI with limited and scattered pilots Active: Testing out models within data science groups

Operational: Using Enterprise AI in production to deliver measurable outcomes Systemic: Expanding Enterprise AI into digital workflows

Transformational: Integrating Enterprise AI into the core strategy of the organization

Studies reveal that most healthcare groups are at stage 2 (Active) or stage 3 (Operational) [5]. The gap between where they are now and true transformation presents both obstacles and opportunities.

Curious about where your organization fits? Torsion’s Discovery & Strategy services let payers measure their progress through tools like the GenAI Weather Map to move from uncertainty toward a clear strategic plan.

The Five Stages of Enterprise AI Maturity to Evaluate Payers

A visual diagram showcasing AI maturity model in healthcare industry.

Healthcare payers do not achieve Enterprise AI expertise. Progress happens step by step, with each stage having unique traits and obstacles. Your current position shapes the choices you need to make next.

Stage 1: Starting with awareness and exploration

Organizations begin their Enterprise AI journey by exploring its possibilities. About 28% of enterprises focus on helping leaders understand Enterprise AI concepts, setting up basic rules, and trying out simple projects. Payers test AI-powered tools to help members research plans, understand insurance choices, and track health data.

Kaiser Permanente follows a foundation-first strategy guided by seven core principles to use Enterprise AI. They focus on privacy, equity, and building trust. One critical takeaway is that governance frameworks need to be in place before rolling out the technology.

Stage 2: Pilot projects and preparing data

Around 34% of organizations progress from small-scale trials to more structured innovation efforts. Contact center chatbots and tools for assisting members often serve as the starting point. A reality check kicks in for many: 47% of healthcare organizations identify poor data quality as a key obstacle.

Data silos and API roadblocks can become major challenges. Businesses need to unify disconnected systems to scale Enterprise AI. Torsion’s Proof of Concept services assess your data setup and point out gaps that need attention.

Stage 3: Strategic adoption and integration

This stage marks a turning point. 31% of companies create scalable Enterprise AI frameworks and encourage environments to test and learn [3]. Enterprise AI starts working across various departments instead of staying in isolated projects. Payers rely on Enterprise AI-powered analytics to find cost-saving opportunities and make better use of resources [7].

This step marks the line between testing ideas and putting them into action. To succeed, leaders need to stay committed, teams across departments must align, and clear metrics should show the return on investment. Check out our self-assessment tool to see where your organization stands and get specific suggestions.

Stage 4: Optimization and Scaling

This is where elite companies operate. 7% of enterprises reach this stage [3]. At this point, Enterprise AI is integrated into how decisions are made. Businesses achieve 20% or more in lower admin costs and 10% drops in medical expenses [9].

Payers fine-tune how they manage services, speed up authorizations, and ensure quicker delivery of care [9]. The advantage here becomes both clear and long-lasting. Torsion’s Enterprise Deployment & Scaling services helps organizations push forward during this crucial phase of growth.

Stage 5: Full Transformation Across the Organization

This stage marks the point where Enterprise AI becomes embedded in every aspect of the company’s DNA. Gartner predicts that by 2027, GenAI tools will cut administrative costs by 30% for health insurers handling over a million members [10]. , 75% of outdated applications will get replaced pushed out by growing Enterprise AI use and industry-specific cloud platforms [10].

Here, Enterprise AI touches all parts of the business driving combined value in operations. Torsion’s Optimization & Governance services support companies to sustain progress, expand their transformation, and stick to rules and ethical practices.

Building a Strong Enterprise AI Maturity Framework

A visual representation of an AI roadmap or framework.

Image Source: Gartner

To build an Enterprise AI maturity framework, you need more than just good intentions. You must have a strategic plan that fits your company’s specific operations and business goals.

Aligning Enterprise AI with payer business goals

For healthcare payers, alignment begins by setting clear measurable business goals. McKinsey’s research shows the potential: Enterprise AI solutions could have an impact of $150-300 million in administrative cost savings, $380-970 million in medical cost reductions, and revenue increases of $260 million to $1.24 billion for every $10 billion in revenue [11]. Success depends on linking Enterprise AI projects to specific objectives—to cut costs, to boost member satisfaction, or to enhance operational efficiency [12].

What gets measured gets managed. Companies need to set up KPIs to monitor AI’s real-time effect on revenue growth, customer retention, and cost savings [12]. The top-performing payers concentrate on quick operational results instead of vague possibilities.

Setting up an Enterprise AI governance maturity model

A solid governance structure serves as your cornerstone, not an afterthought. Many companies push Enterprise AI adoption through tests without proper safeguards—leading to big risks down the line [1]. Your governance framework should tackle ethics reviews human oversight procedures, and data security standards from the start [5].

Teams with diverse skills yield the best outcomes: IT clinical care, legal, privacy, ethics, and finance all working together [13]. The most crucial step? Putting in place clear decision-making power for AI rollout across different levels of the company [13].

Mapping out Enterprise AI maturity assessment

Torsion starts by assessing capabilities. Try our self-assessment framework to gauge where you stand now and pinpoint key next moves. Companies gain from step-by-step rollout [11]—kicking off with simpler areas like sign-ups and invoicing to show clear benefits before expanding.

The plan should emphasize quick deployment and fast returns zeroing in on applications that boost operations right away.

Mixing new ideas with rules and caution

New healthcare ideas and following rules aren’t opposing goals, they’re linked needs. Build rule-following into your idea process from the start [14]. This involves:

Creating Enterprise AI solutions with built-in regulatory frameworks
Setting up sandbox environments to test and validate
Using ethical frameworks that put patient outcomes and organizational trust first

Companies that see compliance as a design constraint instead of a deployment barrier often get to market faster and see better long-term results.

Want to check how ready your framework is? Use our in-depth GenAI Weather Map to measure where your organization stands now and find ways to make big improvements.

Big Challenges Payers Face in Enterprise AI Maturity

An image visually depicting the challenges organizations face when implementing AI in their existing systems.

The road to Enterprise AI maturity isn’t straight. It’s more like moving through a landscape where smart choices lead to a competitive edge while mistakes waste resources and slow progress.

Data fragmentation stops Enterprise AI from working well

Broken-up data remains the main roadblock for most payers. 96% of hospitals use certified EHRs yet 72% of providers can’t get full patient information because systems don’t talk to each other [15]. A 500-bed hospital loses over $4 million each year due to these silos [15].

Data stuck in different parts of an organization creates gaps that hurt Enterprise AI accuracy. When models can’t see complete patient histories, medicine lists, or test results, they make incomplete guesses that make people trust and use them less [16].

Torsion’s Discovery & Strategy services help find these data problems before they mess up your Enterprise AI plans.

Responsible Enterprise AI needs thoughtful planning

Technical hurdles are just one piece of the puzzle. Many Enterprise AI systems work like “black boxes” where decision-making steps aren’t clear [2]. Enterprise AI models often learn from datasets that don’t represent minority groups [2] making existing healthcare gaps even wider.

Here’s the truth: tackling bias isn’t about being fair, it’s about being effective. Enterprise AI models trained in isolated data environments might “do great in their specific settings but struggle when used more leading to doubt less use, and even possible harm” [16].

Partnerships with vendors shape long-term success

Picking an Enterprise AI vendor needs the same care as choosing a key business ally. Picking the wrong one leads to setup problems, hurts patient faith, and drains key assets [17]. Key points to check include:

Know-how in healthcare and rules specific to it
Easy fit with current systems
Clear Enterprise AI choice-making steps
Strong data handling and HIPAA rule following
Full setup help and teaching

Lack of skilled workers slows growth plans

Staff limits create big hurdles. For instance, 42% of healthcare chiefs say not having skilled workers stops Enterprise AI from growing well [8]. 75% of those asked say training current staff helps make it work [18].

The gap is glaring: 89% of healthcare leaders see the need for improved Enterprise AI skills, but 6% have put serious training programs into action [19].

Measuring returns guides strategic spending

McKinsey’s study shows what’s at stake: health insurers using available Enterprise AI tech could get 13-25% net savings on admin costs and 5-11% on medical costs, plus 3-12% more revenue [20]. Yet 36% of health systems don’t have clear ways to rank Enterprise AI projects [21], with many saying unclear returns are their main roadblock to using Enterprise AI.

Want to check how advanced your company is with Enterprise AI? Get our GenAI Weather Map and use our self-check tool to see where you stand now and figure out your next smart moves.

To wrap up

Enterprise AI for big companies has gone from a cool new tech to a must-have for health insurers. The facts are clear: groups that get good at Enterprise AI will save hundreds of millions, while those stuck in test mode might fall behind rivals who go all-in on using Enterprise AI for real.

The road ahead demands a strategic emphasis on three key areas: data infrastructure, governance structures, and workforce growth. Winning comes from seeing Enterprise AI rollout as a company-wide shift—not just new tech adoption. Companies must tackle data silos, set up ethical guidelines, and grow in-house skills that go beyond just technical know-how.

Your company needs a clear plan that lines up with specific business goals. Whether you’re aiming to boost office efficiency make members happier, or improve health outcomes, your Enterprise AI plan should link straight to measurable results from the start. Our GenAI Weather Map self-check helps companies gauge where they stand now and spot high-impact next moves to speed up progress.

The chance to act is getting smaller. Most healthcare payers are still in the early stages, but industry leaders are already seeing big returns from Enterprise AI systems in full use. Enterprise AI will change healthcare for sure—your organization just needs to decide if it will lead or follow.

Want to check your Enterprise AI progress and make a plan for real results? Contact Torsion to turn ideas into action. Healthcare is changing with Enterprise AI right now. What you do today will decide how well you compete.