Category: Uncategorized

Over the past decade, I’ve watched healthcare providers invest in electronic health records (EHRs), revenue cycle management (RCM), and a broad range of health IT solution areas spanning clinical, operational, and administrative functions. Yet, one issue continues to drain resources and morale like no other: prior authorization, also known as “prior auth.” Despite being intended to ensure appropriate care and control costs, in my analysis, prior auth costs the U.S. healthcare system at least $41.4 billion to $55.8 billion annually, at least, depending on how you model and factor in labor costs, delays, and the downstream clinical impact. What is even more bothersome is that prior auth isn’t just an operational inefficiency but a symptom of a deeper failure to prioritize system redesign over entrenched inefficiencies, temporary workarounds, and conflicting incentives.

Why Prior Auth Won’t Fix Itself

What I’ve come to believe, contrary to prevailing narratives, is that the prior auth crisis is not a failure of process or technology, but people and mindset. For years, U.S. healthcare leaders, particularly within provider organizations, have largely abdicated meaningful engagement with system redesign or resistance to imposing external forces. Instead, they’ve defaulted to compliance despite any dysfunction, relying on short-term patches, manual workarounds, and narrowly scoped initiatives to ensure payment. Most efforts have been reactive, designed to navigate and endure the complexity than challenge it.

Survey data from the American Medical Association (AMA) paints a stark picture. Physicians and their teams spend 13 hours a week handling an average of 39 prior authorizations per doctor, so burdensome that 40% of practices have staff solely dedicated to it. Nearly 9 in 10 physicians report it drives burnout and inflates healthcare utilization. Even more troubling, 94% say prior auth harms clinical outcomes, 93% say it causes care delays, 82% report it leads to treatment abandonment, and 29% cite serious adverse events as a direct result.[1]

Underneath any cultural resignation is the sense that such administrative complexity is “simply healthcare,” and the sheer magnitude of it is “how U.S. healthcare works.” This has shaped decades of efforts and investments that have further baked such dysfunction into the very DNA of the system. By not challenging and, in turn, reengineering prior auth from the ground up, it was standardized. The problem has not only hardened but has also been operationalized and institutionalized, resulting in such friction and colossal costs.

The problem with prior auth isn’t that it’s only expensive to do, but also that it’s resistant to change. Unlike RCM, which has evolved over the years toward better end-to-end, front-loaded models that begin well before the claim is filed or the patient is even seen, prior auth too often gets triggered late in the care episode, after key decisions have been made. It’s still mostly payer-facing and payer-driven in the continuum of care, with misaligned, frequently conflicting incentives and inconsistent criteria across the board, except in the case of “payviders” or integrated delivery networks, where the divide is less pronounced and may promote rather than block collaboration despite it needing to be more in the patient’s or member’s interest than the system for true value-based care delivery.

U.S. healthcare providers have repeatedly mistaken digitization for modernization. Converting paper into PDFs instead of structured data, automating outdated steps, or adding a portal to a broken process. These were not transformative moves. At best, they converted paper files into EHRs or manual billing into RCM, without questioning the process design behind them. Does it drive approval any faster? Does it reduce burden or improve care quality and experiences? Rarely. Even traditional automation tools, like RPA, while helpful for repetitive administrative tasks, were never built to handle unstructured data or the dynamic, exception-heavy nature of prior authorization workflows. These tools, in essence, served more as digital band-aids over deep systemic wounds, not solutions.

Market Signals Tell Us to Move On

According to IDC survey data, 52.5% of U.S. healthcare providers are now adopting composable IT architectures to drive electronic prior auth (ePA), moving toward modular, plug-and-play systems designed for agility and continuous evolution. Meanwhile, only 6.6% remain dependent on rigid, custom-built platforms. The message is clear: the market is shifting toward flexibility, interoperability, and intelligent orchestration.

I won’t go so far as to say the tide is turning, but signals are getting louder. Across the board, I’m seeing more healthcare leaders on both the technology buyer and supplier sides acknowledging that traditional automation has reached its limits. The complexity of an area like prior auth demands something more adaptive, scalable, and intelligent.

Enter agentic AI, not just as another layer of automation, but a new class of automation. Where agents shine is that they can bridge the gap between automation with intelligence, autonomy, and context awareness, working not just faster, but smarter. As opposed to traditional rule-based systems or narrowly trained models, agentic AI can adapt, interpret, and learn on the fly. This is a significant leap from simply executing pre-coded functions.

What sets agents further apart is their ability to perform zero-shot reasoning, as well as their capability to handle new inputs or scenarios that haven’t been trained on by leveraging generalized knowledge across domains. This adaptability reassures healthcare leaders that agentic AI can function even in the face of edge cases, real-time policy updates, and unstructured clinical complexity, making it particularly well-suited for prior auth, where variability is the norm, not the exception.

Rather than following static rulesets or requiring periodic retraining, agentic AI can:

Interpret unstructured data by leveraging NLP and LLMs to extract relevant information from free-text sources such as physician notes, discharge summaries, radiology reports, and lab results. This enables the system to understand the clinical rationale behind a treatment or diagnostic order, allowing for more accurate and context-aware authorization decisions without requiring structured, template-based documentation.
Adapt dynamically to evolving payer rules, rather than relying on static rule engines or periodic manual updates. Agentic AI can ingest real-time payer policy feeds, API-accessible rule libraries, or even scrape payer portals (when necessary) to automatically apply the most current criteria. This eliminates the lag between policy changes and system response, reducing unnecessary denials caused by outdated logic and helping ensure compliance is maintained proactively.
Execute complex, multi-system workflows autonomously by orchestrating interactions across tech stacks and layers, be it EHRs, eligibility verification systems, third-party prior auth platforms, and payer endpoints. It can initiate requests, validate documentation, follow up on pending statuses, and escalate exceptions without manual handoffs. This end-to-end orchestration eliminates redundant clicks, fragmented touchpoints, and disconnected workflows that slow the process.
Continuously learn and optimize performance with built-in feedback loops. Agentic AI can analyze the outcomes of approvals, denials, resubmissions, and appeals, and use that data to fine-tune logic over time. This continuous optimization instills confidence in healthcare leaders that the loops can enhance prior auth quality, increase first-pass rates, and minimize administrative rework, leading to improved financial and operational outcomes.

The silver lining is that this isn’t a vision but is already happening.

What This Means for U.S. Healthcare Providers

For CIOs:

A scalable, modular approach to intelligent automation that aligns with existing IT investments.
Rather than costly rip-and-replace initiatives, agentic solutions can integrate more seamlessly (e.g., into EHRs, practice management, and RCM systems) via APIs, FHIR interfaces, and event-driven architectures.
Agents can be further embedded within existing workflows or operate as orchestration layers on top of legacy infrastructure.
Composability, interoperability, and accelerated ROI that support modernization without disruption, delivering improved speed, flexibility, and clinical alignment.

For CMIOs/CNIOs:

Clinically intelligent automation that complements rather than complicates workflows.
Agents can interpret free-text notes, align with evidence-based care protocols, and apply payer-specific criteria without forcing changes in behavior, helping to reduce ‘death by a thousand clicks’ and ‘unlimited mouse miles’ while still supporting contextual decision-making, improving accuracy and timeliness of authorization workflows without burden.
Preservation of clinician experience while improving patient experiences and outcomes.

For RCM Leaders:

Immediate and measurable value by dynamically aligning clinical submissions with payer policies in real time, shortening authorization turnaround times through intelligent workflow automation, and improving clean claim rates by ensuring complete and compliant documentation at the point of capture.
Real-time visibility into authorization status, exception handling, and appeal triggers, empowering billing teams to work smarter, not harder, and to optimize reimbursement without unnecessary overhead.

Beyond these roles, the greater opportunity lies in agentic AI laying the groundwork for intelligent automation across the board, thereby elevating healthcare provider workflows and operations to be more adaptive, resilient, and scalable altogether.

A Final Thought: Don’t Automate Dysfunction

I’ll close with this: stop framing prior auth as solely a technical or workflow issue when it’s not. It’s more of a systemic and cultural issue and distinctly related to U.S. healthcare, reflecting how the experience has been deprioritized in favor of bureaucracy. If AI gets layered on top of that without redesigning the underlying processes, then it will just be scaling dysfunction and be largely counterproductive. This is not to say agentic AI is a silver bullet, no, but it offers a way forward, one that can help automate and distribute intelligence rather than dysfunction. The question isn’t whether we should adopt it, but how quickly, responsibly, and effectively we can do so. If prior auth remains a sinkhole for U.S. healthcare in five to ten years, it won’t be due to a lack of innovation or tools, but rather a lack of leadership, imagination, and willpower.

If you are a client or subscribe to our research, access the full report here: From Administrative Drain to Clinical Gain: The Case for Agentic AI in Prior Authorization for Healthcare Providers. To become a client or learn more about our research, please visit idc.com.

[1] Prior Authorization (PA) Physician Survey 2024 | AMA. Available at: https://www.ama-assn.org/system/files/prior-authorization-survey.pdf (Accessed: 17 July 2025).

The smart glasses market has been growing rapidly in the last couple of years, mostly led by the second generation of Meta Ray-Ban glasses that have come onto the market and taken consumers by storm. Interestingly, the first generation of Meta’s smart glasses, the Ray-Ban Stories released in 2021, fizzled not really capturing the consumers’ interest and underwhelming in terms of sales. In comparison, the second generation did more than 900k sales in just the fourth quarter of 2024, and holds over 65% global market share. We have also seen devices from major brands like Google with Google Glass and Bose with Bose Frames, which have failed to take hold and have both been discontinued. So why the current growth?

The Tech Behind the Specs

Well, part of the answer is clearly improving technology, this means the glasses can be relatively light whilst cramming in more features. Meta Ray-Bans second-generation Wayfarers weigh around 50 grams, which is only 5 grams heavier than the 45-gram non-smart Ray-Ban Wayfarers. At the same time, the glasses manage to pack in a 12-megapixel ultrawide camera, open-ear speakers on each arm of the glasses, five microphones, 32 GB of internal storage, the ability to connect via Bluetooth and Wi-Fi, and batteries capable of powering the glasses for up to 4 hours of use. Not too bad for an additional 5 grams. This impressive bundle of features rolled up in such a small and unobtrusive form factor means that consumers are now viewing  smart glasses as a legitimate technology product with significant real world use cases.

The last couple of years have also seen rapid advances in Artificial Intelligence, which, when integrated into smart glasses, gives them far more functionality than just a pair of bulky glasses that have a camera and speakers bolted to them. These new AI features let users access information and interact with the world in real time, for example using the built-in cameras first person viewpoint to seamlessly identify landmarks by asking “What’s that building in front of me?”. Or allowing the user to spontaneously ask questions, just as they would with a search engine on a smartphone, but without the hassle of getting the phone out of their pocket and typing out the question, then trolling through results. This elevates the glasses from a nice-to-have gimmick into a useful tool for everyday life. There is clearly still a long way to go though; the AI features are still relatively primitive, and it’s doubtful that most people will be eager to randomly start asking AI questions out loud in public, given people’s desire for privacy.

There are also interesting technological developments in the use of smart glasses as discreet hearing aids, with many people suffering from partial hearing loss being reticent to wear traditional hearing aids, due to the attached stigma and the implied acceptance of one’s age. This is a sizable and growing market with The World Health Organisation (WHO) estimating roughly 20% of the worlds population has some degree of hearing loss, this translates to over 1.5 Billion people. EssilorLuxottica has recently released it’s Nuance Audio smart glasses that have built-in microphones that pick up sounds the glasses are pointed at and then amplify them through built in speakers in the arms of the glasses. The idea being a partially deaf individual wearing the glasses in a somewhat noisy environment, like that found in a popular bar on a Friday night, can more easily hear a person they are trying to hold a conversation with. Transcribeglass has taken a slightly different approach to the same problem. Their smart glasses also use microphones to pick up conversations in the glasses field of view, but then the conversations are transcribed in writing onto the glass in front of the wearers eye. Allowing the hearing impaired individual to read conversations like subtitles in a film. Transcribe’s glasses can also be used to translate foreign languages in real time, giving them an even broader market appeal. Both companies approaches are interesting and highlight a huge opportunity in the market for a discreet solution to help individuals with hearing loss and foreign language translation, which could create a significant tail wing for smart glasses sales.

Going Hands Free

We have also seen additional use cases being added, like video streaming—especially with Meta making their glasses easily compatible with their social media platforms, allowing things like live-streaming Instagram Reels from the glasses. Smart glasses have the advantage that they can record first-person videos whilst allowing the person recording to remain in the moment. This was one of the key talking points from Apple when they launched their Vision Pro, but the bulky screen in front of people’s faces, coupled with the slightly off-putting projection of their eyes, means that “in the moment” is a relative term. Thinner, more normal-looking smart glasses from the likes of Meta allow the wearer to be as in the moment as any other glasses wearer. This will allow people to experience key events like birthday parties or watching New Year’s Eve celebrations and then still have the videos to last a lifetime, or more likely, post on their social media. There are obviously still problems with this technology, like restricted memory storage capacity and the quality of the videos recorded, but these have been rapidly improving in recent years and will likely continue to do so.

Meta Dominates, But Rivals Are Emerging

Right now, the smart glasses segment is a small one that is dominated by Meta, with the next biggest competitor being Chinese technology company Huawei, Meta owning 66% and Huawei 6% in 2024. But seeing the success of the smart glasses market, other competitors are circling. As mentioned previously, EssilorLuxottica, the owner of the brand Ray-Ban and the company with a virtual monopoly on the standard glasses market, has recently launched the Nuance Audio smart glasses. Amazon is also a significant player with its line of Echo Frame smart glasses doing hundreds of thousands of sales in recent years. More niche players like Transcribeglass, Xreal and Gentle Monster are also sprouting up to embrace the opportunity. Additionally, it has long been understood that much of the development of Apple’s Vision Pro and Samsung’s Project Moohan, both virtual reality goggles, has been aimed at laying the R&D groundwork for competing in the future mass-market virtual and augmented reality products. Apparently even Apple understands a first-generation augmented reality device priced at $3,500 is unlikely to have broad market appeal. The increasing focus on smart glasses is clearly demonstrated by Samsung’s recent show casing of its own prototype smart glasses, and the launching of Android XR, an operating system designed out of a collaboration between Google, Samsung and Qualcomm, to work on Smart glasses and Augmented/Virtual Reality goggles.

Can Smart Glasses replace the Mobile Phone?

In many ways, the form factor of a device that can project in front of the eyes is more useful and intuitive than a technological brick you keep in your pocket. This is because the world becomes your screen, you can project tabs and information in every part of your field of vision, should you choose, or simply wear them like a normal pair of clear glasses. This gets around the fact that your phone, though increasingly powerful, has a relatively limited screen size, which can make doing things like watching films and writing out emails annoying. Whereas with a larger field of view, you can watch films as if they were projected on the side of massive buildings or possibly even have 3D interactions within films and games, with the visuals being projected all around you. Both Apple and Meta have shown off hand-tracking technology with the Apple Vision Pro being able to do this through a series of cameras on the headset tracking your hand movements, and the Meta Orion concept glasses getting a similar effect by having the user wear a gesture-tracking wrist strap. This hand-tracking technology allows for the possibility of normal, intuitive interactions with technology through hand gestures. This could be as simple as projecting a virtual keyboard in your field of view and then tracking your hand movements to ascertain what key you are typing, or it could mean interactive gameplay. For example you could play a virtual game of tennis with the glasses tracking and interpreting your wild tennis swings, and seamlessly turning them into a coherent game of tennis against an AI or another player wearing smart glasses half a world away.

The holy grail for smart glasses will be an augmented reality screen that can seamlessly overlay projections onto the real world. Of course this can be done with larger devices like Apple’s Vision Pro and the Meta Quest, but to be able to do this in the slimmed-down format of a normal-ish looking pair of glasses that regular people would consider wearing on a daily basis would be a massive step forward. This, coupled with a significantly improved AI interface, larger battery life, and a slimmed-down sleeker design, could allow these glasses to go from a niche but interesting product type to a product with broad mass-market appeal. If the technology can be improved enough, there is a possibility that in a number of years smart glasses could start taking market share from mobile phones, as they should be able to replicate the majority of the phones features but in a more intuitive form factor.

Conclusion & Forecast

Smart glasses are a rapidly growing market as technological advances are beginning to make the form factor viable. Further advances in technology, greater consumer awareness, and new entrants into the market will likely continue this growth into the long run (IDC is currently forecasting 18.7 million units in 2029 compared to 2.7 million units in our most recent full year of data 2024). A little way off the 1.44 billion mobile phones sold worldwide in 2024, but if there is any product positioned to eventually replace the smartphone, both industry analysts and technology giants are betting it will be smart glasses.

Across the Asia-Pacific region, enterprises are exploring generative AI (GenAI) with urgency, but scaling remains elusive. IDC research shows that while organizations ran an average of 23 GenAI proof-of-concepts (POCs) between 2023 and 2024, only 3 reached production. Of those, just 62% met expectations. The real challenge? Turning experimentation into enterprise value.

 Why Agentic AI Matters for GenAI Use Cases

GenAI’s potential extends far beyond content creation. But to realize its full value, organizations must move past isolated tools and embrace Agentic AI, intelligent agents that operate with autonomy, context, and integration across systems.

Agentic AI is the missing link between promising GenAI pilots and impactful enterprise transformation. It enables scalable, reusable use cases that drive results in productivity, quality, cost-efficiency, and resilience.

What Makes Agentic AI Different?

Unlike static models, Agentic AI introduces enterprise-grade capabilities, including:

Context retention for continuity across interactions
Multi-step task execution for complex operations
Exception management to handle real-world unpredictability
Security compliance for enterprise environments

This marks a shift from isolated AI functions to end-to-end automation, turning GenAI from a productivity tool into a strategic business engine

Super Use Cases: Where Agentic AI Delivers Impact

Not all AI use cases are created equal. The most successful organizations are focusing on “super use cases,” scalable, process-centric applications that integrate AI into decision-making, workflows, and operations. These include:

Customer support orchestration
Fraud detection and resolution
IT and HR automation
Context-aware marketing and personalization

These use cases aren’t just feasible with Agentic AI; they thrive because of it.

Building with Reusable Design Patterns

To scale Agentic AI, enterprises must move beyond bespoke solutions. Reusable design patterns enable the rapid and flexible deployment of AI. Key patterns include:

Task planning: Break down goals into AI-executable steps
Tool orchestration: Connect agents with enterprise platforms
Self-reflection: Learn from past actions to improve accuracy
Collaboration: Enable multiple agents to work in sync

These patterns act as blueprints, fueling faster time to value across diverse use cases.

Transforming Work, Not Just Tools

While personal GenAI apps, like note-takers and summarizers, are helpful, they’re quickly becoming commoditized. The real edge lies in deeply integrated, business-specific applications. Agentic AI enables a rethinking of work itself:

Marketers will optimize for LLMs, not just search engines.
CX leaders will deploy agents to unify channels, systems, and data.
Ops teams will automate workflows end-to-end.

In 2025 and beyond, Agentic AI won’t just support the work—it will redefine how work gets done.

Accelerating Agentic AI Adoption

As businesses increasingly explore automation, from robotic systems to intelligent assistants and sophisticated agents, agentic AI is poised to reshape daily work across industries. However, many organizations are unprepared to manage the dual challenge of evolving work practices and adopting new technologies.

Leaders need support navigating cross-functional change, especially as new roles like Chief AI Officer (CAIO) emerge. Technical professionals must expand their skill sets to include agentic development platforms, while nontechnical staff will need to learn workflow automation and natural language prompting.

Successful adoption will depend on aligning change management strategies with regional work cultures and technology maturity levels. In 2025 and beyond, agentic AI will not just change tools; it will redefine how work gets done.

Some key considerations:

Technical teams need to master agentic development platforms.
Non-technical users must learn prompt-based automation.
Leaders should align transformation efforts across regions, each with its own pace and culture of adoption.

Success relies on cross-functional collaboration and a clear strategy to integrate agentic AI into daily operations.

Measuring What Matters: A Holistic View of AI’s Business Value

One of the biggest barriers to AI adoption is the difficulty in measuring return on investment. To address this, IDC’s AI Business Value Benefit Framework outlines nine key dimensions, ranging from revenue growth and customer experience to innovation, resilience, and sustainability, that help organizations evaluate both the direct and indirect impacts of AI. By taking a broader view of AI’s value beyond just cost savings, this framework enables businesses to align AI investments with long-term strategic goals and drive meaningful outcomes across operations.

For more on this topic, please refer to the IDC report From AI Return on Investment to Business Value.

Final Word: Your Next Move Starts Here

Agentic AI turns GenAI from an experiment into a strategic differentiator. By focusing on super use cases and embracing reusable patterns, enterprises can move confidently from POC to production and unlock the full promise of AI.

Your next move? Let’s make it count, with Agentic AI at the core.

Take the next step and realize the full business value of AI with our three practical webinars:  

Accelerating Automation with Agentic AI: From Hype to Value
Responsible and Secure AI: The Cornerstone to AI-Driven Growth  
Mastering Data Management to Power Enterprise AI 

To learn about the new strategic imperatives in store for CIOs in the era of Agentic AI, download this eBook.