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The Inflection Point

Why Traditional CME is No Longer Sufficient for 2025 and Beyond

The landscape of Continuing Medical Education (CME) is undergoing a seismic shift, driven by a convergence of systemic pressures that render traditional educational models increasingly inadequate. The core challenge is no longer simply about disseminating information; it is about ensuring that new knowledge is effectively translated into clinical practice to improve patient outcomes .

This section will establish the foundational argument that the current CME ecosystem is failing to meet the demands of modern healthcare, creating a critical and costly " knowledge-to-practice gap ." This gap is exacerbated by overwhelming pressures on clinicians and the stark economic realities facing health systems, creating an urgent and undeniable case for technological transformation.

Paradigm Shift to KT

For decades, Continuing Medical Education (CME) and Continuing Professional Development (CPD) have been the primary mechanisms for lifelong learning among physicians . However, a growing body of evidence suggests these models, while valuable, are fundamentally limited. CME and CPD are often characterized as being "teacher and learner driven," focusing on individual knowledge acquisition rather than systemic change, which makes them ill-equipped to address complex population health challenges or the intricate dynamics of the clinical environment.

In response to these limitations, the paradigm for 2025 and beyond is shifting decisively toward Knowledge Translation (KT) . KT is defined by the World Health Organization (WHO) as "the exchange, synthesis and ethically sound application of knowledge—within a complex system of interactions among researchers and users—to accelerate the capture of the benefits of research". This represents a more "holistic construct" that subsumes and builds upon CME, focusing directly on the ultimate goal: changing health outcomes through the application of evidence-based knowledge at the point of care.

The WHO's 2025 Global Research Agenda on KT underscores the urgency of this shift, identifying a persistent failure to effectively "share the lessons that we have already generated". This highlights a chronic gap between the vast repository of medical evidence and its practical application.

In this new landscape, KT practitioners are emerging as vital change agents, tasked with guiding organizations to " turn evidence into action " to improve patient outcomes. This evolution from passive learning to active implementation sets a new professional standard that advanced educational technologies, such as AI-driven video, are uniquely positioned to support and scale.

From CME/CPD...

Teacher-Driven

Individual Knowledge Acquisition

...to Knowledge Translation (KT)

Systemic Change

Accelerating Patient Outcomes

The Clinician Reality

The modern clinician operates at the epicenter of multiple converging crises, a reality that fundamentally reshapes their ability to engage with traditional, time-intensive educational formats. Any effective CME solution for 2025 must be designed with a deep understanding of these profound constraints.

Physician burnout remains a pervasive challenge. Exclusive data from the American Medical Association (AMA) reveals that in 2024, 43.2% of physicians still reported at least one symptom of burnout. While this marks an improvement from post-pandemic highs, it is a crisis fueled by systemic inefficiencies, mounting administrative burdens , and ever-increasing regulatory and technological requirements.

It is estimated that physicians spend a staggering 30-50% of their time on non-clinical tasks, time that is stolen from patient care and professional development. Compounding this is the relentless velocity of new medical information. The rapid evolution of healthcare technology is dramatically outpacing current educational paradigms, creating what has been described as a "critical gap between innovation and practitioner competencies ". This constant flood of new data, guidelines, and techniques contributes significantly to cognitive overload , making it nearly impossible for clinicians to stay current using conventional methods.

These pressures have led to a clear and dramatic shift in learning preferences . A landmark 2025 survey of over 1,500 healthcare professionals (HCPs) conducted by myCME found that an overwhelming 75% of respondents prefer CME activities that last one hour or less. This demand for "short form and bite sized content" is not merely a preference but a necessity dictated by the realities of clinical practice.

Yet, this constraint does not signal a lack of motivation. The same survey revealed that 95% of HCPs actively incorporate new information from CME into their daily routines, demonstrating a powerful, intrinsic drive to learn and improve patient care. This creates a critical paradox: clinicians possess a high motivation to learn but have an extremely low bandwidth for traditional educational engagement. The primary barrier is not a lack of will, but the prohibitive format and delivery of conventional CME. Therefore, the central challenge is to design educational solutions that are not just engaging, but are engineered for extreme time efficiency, fitting seamlessly into the fragmented and high-pressure workflow of the modern physician.

Physician Burnout

Learning Preference

95% of HCPs incorporate new info from CME.

The Economic Imperative

The failure to effectively translate knowledge into practice is not an abstract clinical or academic problem; it carries a staggering and quantifiable economic cost that reverberates through the entire healthcare ecosystem. This immense financial burden creates a powerful, board-level incentive for health systems and payers to invest in technologies that can demonstrably close the knowledge-to-practice gap.

The costs begin at the operational level. A 2025 report from Philips found that 83% of healthcare professionals lose clinical time due to incomplete or inaccessible data, with 45% losing more than 45 minutes per shift. Annually, this translates to more than four weeks of lost clinical time per professional—a direct and massive drain on productivity and a key driver of burnout. Similarly, inefficient processes like patient transfers can consume an average of 42 minutes of a nurse's time for a single event, further compounding the operational strain.

These micro-level inefficiencies scale up to create macro-level economic damage. A 2025 analysis of proposed Medicaid policy changes, which would delay the adoption of new guidelines and restrict access, projected devastating consequences by 2034: the annual loss of 302,000 jobs, a $135.3 billion reduction in GDP, and the creation of $7.6 billion in new medical debt.

Furthermore, knowledge transfer failures are a direct contributor to medical errors and malpractice claims. Poor communication, a primary symptom of an ineffective knowledge system, is a leading cause of preventable patient harm . One analysis attributed $1.7 billion in malpractice costs and nearly 2,000 preventable deaths to communication failures alone, while another found that 80% of serious medical errors were the result of miscommunication during patient handovers.

These costs are ultimately borne by health systems already grappling with declining margins and a projected medical cost trend of 8.5% for 2026. By framing the knowledge translation gap in these concrete financial terms, the argument for investing in a solution shifts from a clinical improvement initiative to a core business strategy. The cost of inaction is no longer theoretical; it is a measurable liability on the balance sheet of every health system.

Operational Efficiency

45+

minutes lost per shift

4+

weeks lost annually

Malpractice Costs

Crisis & Cost: The Quantifiable Impact

Clinician Well-being

Physician Burnout Rate

43.2%

of physicians experience symptoms

Learning Engagement

Preferred CME Duration

< 1 Hour

75% of HCPs

Operational Efficiency

Lost Clinical Time per Shift

> 45 minutes

for 45% of HCPs

Economic Impact

Malpractice Costs

$1.7 Billion

from miscommunication


The Generative AI Revolution

A New Engine for Medical Content Production

The challenges facing CME—the need for speed, relevance, and engagement in a cost-constrained environment—have, until now, been largely insurmountable.

However, the maturation of generative Artificial Intelligence, particularly in video, represents a technological leap that provides a direct and powerful solution. This section details how current AI video technologies can address the core content creation challenges in medical education, moving from abstract potential to the concrete capabilities that make scalable, high-quality, and dynamic video production feasible for the first time.

Market Context

The imperative for a new CME model is occurring within a highly receptive and rapidly growing market. The global medical education market was valued at approximately $40.03 billion in 2025 and is projected to reach $72.35 billion by 2034 , reflecting a compound annual growth rate (CAGR) of 6.8%.

Another analysis projects even more aggressive growth, from $191.5 billion in 2024 to $678.6 billion by 2033 , a CAGR of 14.9%.

This growth is fueled by two primary drivers: the persistent global shortage of skilled healthcare professionals and the accelerating demand for advanced digital learning solutions, including AI and virtual reality (VR). The generative AI in healthcare market, as a specific sub-sector, is poised for explosive growth, with projections showing an increase from $3.3 billion in 2025 to $39.8 billion by 2035 , a CAGR of 28%.

A New Production Paradigm

Generative AI video platforms are fundamentally rewriting the rules of content production, dismantling the time and cost barriers that have long plagued CME providers. Traditional video production is an inherently " costly and time-consuming " process, often requiring specialized crews, equipment, and lengthy post-production cycles.

AI platforms like HeyGen and others completely upend this model, enabling the creation of professional-grade educational videos in a matter of hours, not weeks, often without the need for a physical production team.

This streamlined process represents a profound shift in operational efficiency, with analyses suggesting a 75% reduction in production and distribution costs and a 10x faster deployment time compared to traditional live events or webinars.

High-Fidelity Visualization

For AI video to be viable in medical education, it must meet an exceptionally high standard of quality and accuracy. Recent advancements in text-to-video generation models have crossed this critical threshold.

A recent review in PMC highlighted the potential for models like OpenAI's Sora to create high-fidelity simulations of intricate neurosurgical procedures and to visualize complex physiological processes.

Platforms like NVIDIA's Isaac provides a framework for creating "high-fidelity digital twins" for surgical robotics training , while companies like Blackford and Viz.ai are using AI for the analysis and reconstruction of medical imaging.

Dynamic Content

Perhaps the most transformative capability of AI video is its inherent dynamism. Clinical practice is not static; it evolves continuously with the publication of new research and the release of updated clinical practice guidelines . The future of clinical guidance is envisioned as " dynamic, living guidelines that update automatically based on new evidence".

Traditional CME content, captured in a static video or PDF, becomes obsolete almost as soon as it is published. This creates a dangerous lag between best practice and actual practice.

AI video solves this problem directly. Platforms like HeyGen are explicitly designed to facilitate instant updates: "Effortlessly update guidelines, procedures, and research findings... Modify scripts, adjust visuals, and generate a revised version in minutes—no need for costly reshoots".

The key competitive advantage of AI video in CME is therefore not just cost or engagement, but content velocity . An organization leveraging this technology can keep its entire educational library aligned with the latest evidence.


AI in CME: Designing for Efficacy

Instructional Science in a New Era

The true potential of AI in medical education lies not just in its efficiency as a production tool, but in its power as a superior pedagogical instrument. By leveraging AI, it becomes possible to implement proven principles of learning science at a scale that was previously unimaginable.

Mitigating Cognitive Overload

A foundational concept in modern educational psychology is Cognitive Load Theory (CLT), which provides a scientific framework for designing instruction that aligns with the known limitations of human working memory.

CLT posits that for learning to be effective, instruction must carefully manage three types of cognitive overload :

Intrinsic Load

The inherent difficulty of the material. AI manages this by breaking down complex procedures into sequential segments.

Extraneous Load

The mental effort required to process instruction. AI reduces this with dual-channel processing.

Germane Load

The effort dedicated to creating knowledge schemas. AI optimizes this with interactive scenarios.

By operationalizing these principles at scale, AI transforms instructional design from an art into a science, creating educational experiences that are systematically engineered for maximum efficacy.

Hyper-Personalized Learning

A primary failing of traditional CME is its one-size-fits-all approach, which ignores the diverse knowledge levels, specialties, and learning needs of individual physicians.

AI provides a powerful solution to this challenge by enabling a shift from a static, one-to-many broadcast model to a dynamic, one-to-one adaptive learning experience .

Learner Profiles

Analyzing data on specialty, pre-assessments, and content interactions.

Personalized Pathways

Dynamically curating modules to target and remediate identified knowledge gaps.

This creates a continuous feedback loop: as a physician engages with the content, their performance on embedded quizzes or decision-making tasks triggers real-time adjustments to their personalized learning pathway , ensuring the experience is constantly refined to match their individual pace and level of understanding.

Studies have already demonstrated that AI-based adaptive learning platforms enhance knowledge and skill development in medical contexts, confirming the value of this personalized approach.

Microlearning & Just-in-Time

The data is unequivocal: clinicians are time-poor and demand educational content that is brief and highly relevant. AI video is the ideal delivery mechanism for microlearning—the strategy of providing short, focused educational bursts that can be consumed in just a few minutes.

This approach directly combats the " forgetting curve " by enabling spaced repetition , a proven technique for increasing long-term knowledge retention.

By delivering content in modules lasting two to three minutes, accessible on a smartphone, microlearning fits seamlessly into the fragmented daily schedule of a busy healthcare professional.

Learning at the Point of Need

More importantly, it enables a move toward " just-in-time learning ," where education is integrated directly into the clinical workflow. Instead of being a separate, scheduled event, learning becomes a readily available resource at the point of need.

A physician encountering a rare condition could instantly access a 3-minute AI video summarizing the latest diagnostic guidelines.

The Next Frontier: Advanced Applications in Simulation and Immersive Learning

While AI video offers immediate advantages in the production and delivery of didactic content, its most profound impact will be in transforming how complex clinical skills are taught and assessed. This section explores the most advanced and forward-looking applications of AI video, demonstrating its capacity to move beyond knowledge dissemination into the sophisticated realms of clinical reasoning, procedural mastery, and data interpretation.

AI Avatars for Clinical Reasoning

One of the most challenging aspects of medical education is teaching clinical reasoning —the complex process of synthesis, judgment, and decision-making. AI-driven avatars are creating a new frontier for this type of training by enabling highly realistic, interactive, and scalable virtual patient encounters . These are not simple chatbots; platforms like e-REAL Labs are developing sophisticated "digital humans" that can replicate complex interpersonal dynamics and provide real-time, personalized feedback based on the learner's conversational and behavioral cues.

Using generative AI, instructional designers can create intricate patient scenarios with branching logic, where a learner's diagnostic questions and treatment decisions lead to different clinical outcomes, mirroring the cause-and-effect nature of real-world practice.

Advanced technologies like OmniHuman-1.5 can generate AI-driven avatars from a single image and audio track that are capable of conveying subtle emotional subtext, allowing for the simulation of difficult conversations, such as delivering bad news or addressing vaccine hesitancy. This provides a safe and repeatable environment for learners to practice and refine not only their diagnostic skills but also their communication and empathy.

A critical challenge in this domain is the need to design culturally competent and representative avatars. Studies have shown that AI models can perpetuate demographic biases present in their training data, underscoring the absolute necessity of using diverse datasets and careful design to ensure these educational tools promote health equity rather than reinforcing existing disparities.

Interactive Avatars
Branching Logic
Empathy Training

AI-Powered Surgical Training

The convergence of AI video, advanced simulation, and extended reality (XR) technologies is creating a new paradigm for surgical training. This approach allows for personalized, data-driven coaching and objective skill assessment to occur safely outside of the high-stakes environment of the operating room.

The potential of this model was powerfully demonstrated in a 2025 study from Mount Sinai. Researchers found that surgical trainees could learn a complex step of a kidney cancer procedure with 99.9% accuracy using an AI-driven instructional model delivered via an XR headset, entirely without the presence of a human instructor.

This highlights a path toward standardized, scalable, and highly effective procedural training. AI is also being integrated directly into VR simulation systems to automate the process of skill assessment. These systems can analyze a trainee's movements, efficiency, and error rates, providing objective, real-time feedback that accelerates the learning curve.

The development of high-fidelity "digital twins" of surgical robots and patient-specific anatomy further enhances the realism and effectiveness of this training. The economic implications are substantial, with some estimates projecting that the efficiencies gained from AI in healthcare could generate up to $150 billion in annual savings for the U.S. healthcare system by 2026.

Procedural Accuracy

Visualizing Clinical Trial Outcomes

The effective translation of new clinical evidence into practice depends on clinicians being able to quickly understand and interpret the results of complex clinical trials. Traditional methods of data presentation, such as dense tables and static charts, are often inadequate for conveying the nuances of trial outcomes.

There is a clear trend in medical communications toward using advanced data visualization to turn "science into stories". AI video is a powerful tool for this purpose. It can be used to transform complex, multi-dimensional datasets into compelling and intuitive visual narratives.

For example, innovative visualization techniques like AstraZeneca's "Maraca plot," which shows a composite of patient outcomes over time, or the "Tendril plot," which visualizes the timing and distribution of adverse events, can be animated and paired with AI-generated expert narration.

This approach makes the key takeaways from a clinical trial more accessible, understandable, and memorable for busy clinicians, accelerating the adoption of evidence-based practices. This shift from static data to dynamic narrative represents a significant advance in how medical knowledge is communicated.

Clinical Trial Outcomes


The Trust Framework

Ensuring Clinical Validity and Regulatory Compliance

The adoption of any new technology in medicine, particularly one as transformative as AI, hinges on a single, non-negotiable factor: trust. For AI-generated CME to be accepted by clinicians, health systems, and accrediting bodies, it must be demonstrably accurate, ethically sound, and fully compliant with established standards.

This section details a comprehensive framework for achieving this, addressing the critical challenges of clinical validity, human oversight, and regulatory adherence.

Clinical Accuracy

The single greatest risk in using generative AI for medical content is the potential for " AI Hallucinations "—the generation of outputs that are plausible and confidently delivered but factually incorrect or nonsensical. In the high-stakes environment of medical education, where accuracy is paramount, such errors are unacceptable. This risk is not theoretical; a systematic review of text-to-video generation explicitly identified "content inaccuracies" and "data biases" as primary limitations that must be rigorously addressed before widespread adoption. These hallucinations often stem from insufficient or biased training data, highlighting the danger of using general-purpose AI models without a robust medical validation layer.

Therefore, a multi-stage, expert-led verification process is not an optional add-on but a core requirement for any trustworthy AI CME platform.

AI Hallucinations vs. Clinically Valid Content

Human-in-the-Loop Validation

The most effective and responsible method for ensuring the clinical accuracy of AI-generated content is a Human-in-the-Loop (HITL) validation workflow. This approach strategically combines the speed and scale of machine learning with the nuanced judgment and expertise of human professionals.

In this model, AI is used to generate the initial draft of the educational content, but this output is treated as a starting point that must be rigorously reviewed, corrected, and approved by a qualified human expert before it is ever presented to a learner. A best-practice workflow for this process should be structured and multi-dimensional. A March 2025 paper in medRxiv proposed a robust five-dimension framework for the evaluation of clinical AI systems by board-certified clinicians, or Key Opinion Leaders (KOLs). This framework can be adapted for the review of AI-generated CME content, with experts assessing each module on:

Query Comprehension

Does the AI accurately interpret the underlying clinical question or learning objective?

Response Helpfulness

Is the content clinically relevant and useful for the target audience?

Correctness

Is every factual statement accurate and supported by current evidence?

Completeness

Does the content address all clinically relevant aspects of the topic?

Potential Clinical Harm

Does any part of the response contain information that could lead to unsafe medical practices?

Adherence to ACCME Standards

For AI-generated education to be recognized as legitimate CME and to grant accredited credits, it must strictly adhere to the standards set forth by the Accreditation Council for Continuing Medical Education (ACCME) and other relevant bodies. The existing standards are fully applicable to AI-generated content.

Standard 1: Ensure Content is Valid

This is the most critical. It mandates that all clinical recommendations presented in accredited education must be based on "current science, evidence, and clinical reasoning" and must provide a "fair and balanced view of diagnostic and therapeutic options".

Standard 2: Prevent Commercial Bias

Requires a "clear, unbridgeable separation between accredited continuing education and marketing and sales". This is especially important for industry-sponsored CME, as the AI must be trained and prompted to avoid any promotional bias.

ACEhp Ethical Principles

The Alliance for Continuing Education in the Health Professions has issued a formal position. It outlines key principles including promoting transparency, prioritizing human review and validation to prevent misinformation, and actively working to eliminate bias in AI algorithms.


Strategic Blueprint for AI Video

Successfully integrating AI-powered video into the CME ecosystem requires more than just advanced technology; it demands a clear business case, a thoughtful implementation strategy, and a robust framework for measuring impact. This section provides a practical, actionable guide for health system leaders, medical societies, and life sciences companies.

A Multi-Layered ROI Framework

The return on investment (ROI) for AI video in CME is multifaceted and extends far beyond simple production cost savings. A comprehensive business case should be constructed around a four-layer framework that captures the full spectrum of value generated. This approach allows organizations to move beyond a simple cost-benefit analysis to a strategic assessment of how AI-driven education aligns with core institutional goals.

Value Layer
Key Metrics
Calculation Method / Data Source
Source(s)
Layer 1: Cost Reduction
Production Savings vs. Traditional; Reclaimed Clinical Hours
[Vendor Pricing] - ; x [Number of Users]
Layer 2: Efficiency Gains
Content Update Velocity; Reduced Admin Time
LMS Analytics; Internal Time Tracking
Layer 3: Educational Impact
Knowledge Lift (Pre/Post Scores); Competency Improvement
In-Video Assessments; Simulation Performance Data
Layer 4: Strategic Value
Physician Engagement Score; Reduction in burnout -Related Turnover
HR Surveys; Organizational Well-being Data

Direct Cost Reduction

75% reduction in content production

This is the most tangible layer, encompassing hard savings from a 75% reduction in content production and distribution costs compared to live events, as well as the elimination of travel and accommodation expenses for both faculty and learners.

Operational Efficiency

10x faster content deployment

This layer quantifies the value of improved speed and reduced administrative overhead. Key metrics include 10x faster content deployment, which allows education to keep pace with medical innovation, and a significant reduction in the administrative time required by CME professionals to manage programs.

Educational Impact

This layer measures the core purpose of CME—improving physician knowledge and skills. Using frameworks like Moore's Levels of Outcomes, organizations can track metrics from Level 3 (knowledge gain, measured via pre/post assessments) to Level 4 (competence, measured via simulation performance).

These improvements can be linked to downstream financial benefits, such as reduced readmission rates or improved adherence to quality metrics.

Strategic Value

This layer captures high-level organizational benefits that are critical to long-term success. This includes a projected 40% increase in physician engagement with educational content and a reduction in costly physician turnover that is often linked to burnout and a lack of professional development support.

From Pilot to Enterprise

The successful enterprise-wide adoption of AI-driven CME requires a deliberate, phased approach that begins with a focused pilot program and is supported by a robust change management strategy. Technology implementation in a hospital setting is recognized as being "as much about people and culture as it is about systems and tools".

A successful pilot program should be designed to demonstrate quick, tangible wins and secure executive buy-in. Best practices include defining clear, measurable (SMART) objectives, assembling a cross-functional team of stakeholders, and selecting a high-value, manageable use case—for example, creating a series of microlearning videos for a newly implemented clinical guideline.

Pilot Program

Demonstrate quick, tangible wins and secure executive buy-in with a focused, manageable use case.

Leadership Communication

Executives must clearly articulate the "why" and align AI education with strategic goals.

Stakeholder Engagement

Involve clinicians, department heads, and IT early through focus groups and feedback loops.

System Integration

Seamless integration with existing LMS and EHR systems for learning within the flow of work.

Measuring What Matters

The ultimate measure of CME effectiveness is its impact on patient care. Therefore, evaluation must extend beyond knowledge acquisition to assess tangible changes in clinical performance. The ACCME explicitly requires accredited providers to analyze changes in learners' competence, performance, or patient outcomes resulting from their educational program.

A powerful and practical method for measuring this is the " Commitment to Change " (CTC) model. A randomized controlled trial demonstrated that clinicians who were prompted to make specific commitments immediately following a lecture were significantly more likely to report making a corresponding change in their practice at both 7 days (91% vs. 32%) and 30 days (58% vs. 22%) compared to a control group.

91% vs 32%

at 7 days

58% vs 22%

at 30 days

The AdVids Voice

To establish a leadership position in this emerging market, the AdVids brand must be communicated consistently through every facet of the AI-generated CME experience. This requires a carefully calibrated brand voice that balances a forward-thinking, innovative tone with the unwavering authority and trustworthiness demanded by the medical community. The tone of voice used in scripts for AI avatars and narration should be clear, empathetic, and professional, avoiding overly technical jargon while maintaining scientific precision.

The visual identity—from the design of on-screen graphics to the appearance of the AI avatars themselves—must be clean, modern, and aligned with AdVids' brand guidelines to create a cohesive and recognizable user experience. Most importantly, the content strategy itself must reflect the brand's core message: positioning AI not merely as a production tool, but as a strategic partner in solving the deepest challenges in medical education.

A Commitment to Novelty

The unique value of this research and the resulting article will be derived from a meta-synthesis of disparate data streams. Rather than simply reporting on isolated trends, this analysis will actively connect findings from different domains—economics, learning science, technology, and clinical practice—to generate novel, high-level strategic insights that are not present in any single source.

Connect Disparate Data

The analysis will forge explicit links between seemingly unrelated data points. For example, it will connect the knowledge-to-practice gap to the urgent need to combat Physician burnout by offering education that respects clinicians' time, thereby building a business case for CME as a wellness initiative. It will correlate the principles of Cognitive Load Theory with the challenge of information overload , positioning AI-driven instructional design as a direct antidote to a primary driver of clinical stress.

Create New Frameworks

By combining the 4-step content production model, the 5-dimension clinical validation framework, and the 4-layer ROI model, this report will construct a unique, end-to-end "Strategic Framework for AI-Powered CME."

This novel intellectual property will provide a comprehensive and actionable roadmap for organizations, guiding them from initial concept through to clinical impact and value demonstration. This commitment to synthesis ensures the final article will offer original, high-value insights that advance the conversation and establish a clear thought leadership position.