Is AI just hype?

If you look at the numbers, it’s tempting to think so. Many companies are pouring money into AI… only to see an average ROI of barely 6%. Not exactly the revolution everyone promised.

But here’s the real story:

The problem isn’t AI. It’s how organizations adopt, deploy, and optimize it.

AI doesn’t fail; AI strategies do.

And the gap is huge. According to an IBM report, best-in-class companies are earning nearly 13% ROI on AI projects, more than double the average organization's ROI. These leaders aren’t just experimenting; they’re integrating AI into workflows, redesigning processes, and relentlessly optimizing for value.

So the question isn’t “Is AI worth it?”

It’s “Are you using it the way high-ROI organizations do?”

Let’s break down why AI ROI is falling short and what it takes to turn hype into measurable impact.

Why AI Isn’t Delivering the Returns You Expected?

You expected AI to transform care delivery: faster diagnoses, smoother patient experiences, streamlined workflows, and a more productive team.

However, in reality, many AI projects are exciting during presentations and pilots, yet barely move the needle once deployed.

Why does this disconnect happen?

It often comes down to a set of foundational gaps that quietly erode ROI and stall real progress:

• Unclear Objectives: Many AI projects start without a clearly defined problem, desired outcome, and measurable goal. When teams don’t know what success looks like, it’s impossible to deliver it.
• Weak or Missing Strategy: Without a roadmap that aligns people, processes, data, and technology, organizations end up with scattered pilots, duplicated efforts, and minimal enterprise-level impact. A robust strategy ensures AI is deployed where it moves real clinical or financial outcomes.
• No Baseline or Benchmark: Without pre-AI metrics like turnaround time, error rate, denial rate, or clean claim rate, you can’t prove improvement or calculate ROI.
• Deploy-and-Forget Mindset: AI models require continuous tuning, workflow redesign, retraining, and user feedback loops. Without optimization, models degrade, adoption drops, and value stalls.
• Low Buy-In and Limited Adoption: If your team doesn’t understand how AI helps their daily work, adoption remains shallow. Low trust means low usage, which may result in low ROI.
• Data Gaps and Fragmented Systems: Poor data quality leads to inaccurate predictions, higher error rates, and user frustration. Without strong data foundations, even the best AI tools struggle to deliver impact.

Strategies to Maximize the ROI of Your AI Initiatives

Most organizations nail Phase 1: Experimentation: the demos, the pilots, the “look what this model can do.”

Some even leap straight into Phase 3: Scale, investing in platforms, licenses, and enterprise-wide rollouts.

But the real ROI?

That happens in the middle, the part most people skip:

Phase 2: Optimization.

It’s not flashy or glamorous, but it’s where AI moves from interesting to impactful.

Here’s what true optimization actually looks like:

Assess/Reassess the Goals 

Get crystal clear about what you want AI to achieve. If AI is already integrated into your workflows, take a step back and reassess those goals, because broad objectives like “improve efficiency” or “reduce workload” won’t give you measurable results.

Be specific:

• What specific problem or gap do you want AI to solve?
• What outcome or improvement are you aiming for?
• What is the expected timeframe to achieve this improvement?

Evaluate and Validate 

Once your AI is live, you need to keep validating it. A live system isn’t automatically a reliable one.

Monitor key indicators such as:

• Accuracy, precision, and recall
• False positives and false negatives
• Consistency across different teams 

For example, if you’re using an AI tool that flags coding errors, don’t just trust the alert. Verify whether those alerts are accurate and clinically relevant. This is especially important in healthcare, as coding guidelines and regulations continue to change.

Assess Workflow Integration 

AI delivers real value only when it fits seamlessly into day-to-day workflows. 

Ensure your AI tools:

• Integrate smoothly with your existing systems
• Eliminate unnecessary steps instead of adding more
• Reduce switching between screens or applications
• Enhance, not disrupt, the flow of clinical or administrative work

Interoperability is key. When AI communicates effortlessly with your EHR, billing platform, or documentation tools, users experience less friction, adoption rises, and the impact becomes immediate and measurable.

Strengthen Data Pipelines 

AI is only as strong as the data feeding it. If your data is inconsistent, incomplete, or locked away in silos, even the most advanced models will struggle to perform well. Optimizing your data pipeline means fixing quality issues, standardizing formats across systems, integrating missing data sources, and ensuring your AI receives clean, timely information; ideally, in real time or close to it. It also involves automating data ingestion wherever possible to reduce manual errors. 

Establish KPIs 

Having clear metrics helps you evaluate AI performance and pinpoint workflow bottlenecks. But what truly drives impact is choosing the right KPIs. When your KPIs align directly with your organizational goals, your AI initiatives stop being experimental and start delivering measurable, meaningful results.

Instead of focusing solely on technical metrics such as accuracy or response speed, select a broader set of indicators that capture operational, financial, and clinical outcomes.

Common KPIs to track include:

• Patient wait times 
• Net Revenue
• Turnaround times for documentation, prior authorization, triage, or claim submission
• Error rates and rework volume, especially in coding and documentation
• FTE hours saved
• Scheduling efficiency and no-show reduction
• Claim acceptance or denial rates
• Patient engagement and satisfaction scores

Retrain or Fine-Tune Based on New Patterns 

Your AI performs best when it continues to learn from the world as it looks today. In healthcare, this is especially critical: new diseases emerge, new treatments are introduced, medical codes are updated, patient volume shifts with the seasons, and regulations evolve constantly.

If your AI isn’t retrained or fine-tuned to reflect these changes, its performance will gradually decline, even if it worked perfectly at launch. Regular updates ensure the system adapts to new patterns, remains clinically relevant, and continues to deliver accurate, reliable results.

‎AI optimization shouldn’t be reactive; it should be a routine part of your operations. 

A quarterly review cycle helps keep your system sharp. Analyze usage and adoption trends, track performance, monitor error patterns, gather user feedback, and refresh training data. This rhythm ensures your AI evolves with time and delivers maximum ROI.

How to Calculate AI ROI?

Measuring AI ROI in healthcare can feel tricky because the benefits span clinical, operational, financial, and experiential outcomes, many of which aren’t immediately quantifiable. 

Here's a structured approach to calculate AI ROI in your practice: 

1. Determine the Total Cost of AI

First, identify all the costs involved in implementing and maintaining your AI systems. This includes both the one-time investments required to get the system up and running and the ongoing expenses needed to keep it performing reliably. Here’s a clear breakdown:

One-time costs may include:

• Software and hardware acquisition
• Infrastructure upgrades (servers, storage, cloud capacity)
• Data preparation, cleaning, labeling, and integration
• Model development, configuration, fine-tuning, and validation
• Staff training and onboarding
• Vendor onboarding, compliance checks, and security reviews

Ongoing costs typically include:

• Software licenses or subscription fees
• API usage, compute charges (GPU hours), or cloud consumption
• Maintenance, support, and MLOps/DevOps resources
• Upgrades and performance audits

2. Quantify the Total Benefits

Next, measure the value your AI system delivers. Use the KPIs you defined earlier and categorize the benefits clearly.

Tangible benefits are the ones you can assign a direct dollar value to, such as:

• Operational efficiency: reduced turnaround time, fewer manual hours, increased throughput
• Clinical improvements: fewer errors, faster diagnoses, improved documentation accuracy
• Financial gains: increased revenue capture, decreased denials, lower operational costs
• Compliance improvement: fewer audit findings, reduced coding inconsistencies

Intangible benefits may not have immediate dollar values, but they have significant long-term impacts. They include:

• Improved patient satisfaction and experience
• Enhanced clinical reputation and trust
• Lower staff burnout and higher job satisfaction
• Better decision-making due to higher-quality data
• Stronger competitive positioning

3. Calculate the ROI

Once you have total costs and total benefits, plug them into a simple financial formula.

Let's take an example:

A small primary care practice with two providers adopts an AI documentation and coding assistant to reduce charting time and improve revenue capture.

Total Cost (Annual)

• One-time costs:
  • Setup + integration: $3,000
  • Staff training: $1,000
• Ongoing costs:
  • AI subscription: $4,800
  • Support + updates: $1,200

Total Costs = $10,000

Total Benefits (Annual)

• Time savings:
  • Each provider saves 15 min/day
  • 15 min × 240 days × 2 providers = 120 hours
  • Hourly value: $50
  • Time savings value = $6,000
• Coding accuracy improvements:
  • $100/month per provider
  • $100 × 2 × 12 = $2,400
• Reduced claim denials:
  • Annual savings: $2,800

Total Benefits = $11,200

4. Monitor, Optimize, and Recalculate

Calculating your return on investment isn’t a one-time task. As you optimize your workflows, improve adoption, and refine your AI system, performance and ROI naturally improve.

To sustain and strengthen ROI:

• Monitor performance monthly
• Optimize workflows quarterly
• Retrain or update models regularly
• Refine KPIs as priorities evolve
• Scale high-value use cases 

Recalculating ROI periodically helps you track the true trajectory of your investment and uncover new opportunities to drive even greater value.

Turning AI From Hype Into Real Business Impact

AI isn’t falling short; its management and optimization often are. When you align your goals, refine workflows, consistently monitor performance, and measure impact, AI becomes a dependable engine for efficiency, revenue growth, and improved patient outcomes.

At Glenwood Systems, we help practices unlock the true value of AI through our comprehensive suite of practice management tools: GlaceEMR, GlaceRCM, GlaceScribe, and more. With smarter EHR workflows, intelligent automation, seamless integrations, and data-driven optimization, your practice achieves higher productivity, reduced administrative burden, and improved revenue performance.

Want to build an ROI-focused AI strategy that truly works for your practice?

Let’s connect and make it happen.