 |
|
|
|||||
When most founders begin their journey, they focus on a good product and the right market. But what happens when your customers dont yet know they have a problem? What happens when theres no market, even when you know you have a solution people need? Its rare to find success stories of simultaneous company and market building because its not a challenge that every organization faces. But if youre innovating within your industry, its a problem you should expect and prepare for because it means having to operate in two realitiesthe internal reality where you know the challenges in your industry and how youre going to solve them, and the external reality where nobody else has recognized the problem that needs to be solved. In a highly regulated industry like healthcare, safety, and stability create an inertia that often works against innovation. Many products fail simply because they lack market demand and infrastructure. To succeed, you should look beyond the solution and craft a compelling narrative that tells the entire story of your product and why its needed. As a founder of Paragonix, I navigated these two worlds firsthand during the development of our organ preservation technology. For decades, people transported fragile human organs on ice in coolers you can find in a hardware store. No one was really asking Is there a better way to do this? until we did. Here are five things I learned about bridging the gap between internal conviction and market skepticism: 1. Name the market When youre defining a market that doesnt exist yet, one of the most overlooked steps is giving it a name. A name gives stakeholders a tangible anchor and helps sell the version of reality where the market is already real. Then the focus shifts to creating shared belief, and that only happens by getting out there and talking to people. Its a lot like painting a landscape. Your company may ultimately be just a small piece of the background, but the more detail you give your audience, the more theyll come to understand the whole picture and how you fit into it. Ironically, you may start to see competitors using the language youve created, but thats still a win. After all, if someone else uses the market terminology I created, its a huge validation of the landscape I painted. 2. Compile ample data Particularly in the healthcare industry, compelling data about your product is the proof of concept that unlocks belief. You need strong basic science or engineering validation to demonstrate how your product works, which helps your future customers realize that something theyre doing isnt working. Then, you need clinical science showing that your product is not only effective but safe and superior to the existing standard of care. In my experience, that massive data collection effort is what ultimately convinces the market that they need your product. Before we created the Paragonix SherpaPak Cardiac Transport System, our first portable donor organ preservation system, close to 100% of donor hearts arrived at their destination without any temperature control, monitoring, or reporting, potentially impacting patient outcomes by injuring donor hearts. From the data we collected, we knew there was a dire need for a solution and that our technology could provide the answer. 3. Amplify early adopters Healthcare is an industry where adoption risk is high, and validation relies heavily on peer trust, making it vital that you amplify success cases from early adopters. These initial risk-takers are more than customers; they are essential co-creators of the new market category and can help you actively cultivate conviction within the industry. Whether you choose to create a structured advisory council or not, check in often and give them ample opportunity to provide feedback. Doing so doesnt just secure their commitment to sharing positive outcomes with the public; it helps you transform implementation hurdles into strategic operations. 4. Consider the entire ecosystem As your market scales, its important to study the entire product journey and its surrounding ecosystem. You need to know the adjacent problems, complementary products, and be able to spot future technological needs that sit on the border of your current solution. This is the part that keeps you innovating in a smart, seamless direction, putting you one step ahead of the competition. As a founder, Ive seen firsthand the importance of talking to not just stakeholders but also end-usersthe clinicians, administrators, buyers, and even patients who arent decision-makers but can amplify your product and market vision. They can offer feedback on workflow integration, usability, and pain points that ensure youre delivering solutions people both love and leverage. 5. Listen to negative feedback When youre in the early stages of company development, a positive outlook is almost a requirement for overcoming the fear, anxiety, and worry that can threaten to hold you back. Thats one reason that it can be hard to accept feedback from people who dont like your product, dont grasp your vision, or who actively avoid collaborating with you. But as a company leader, you need to listen to what detractors say. When theyre right about something, it can be a tough pill to swallow, but acting can protect the health of your company as it grows. If theyre wrong, its still important to listen. Developing thick skin is a skill that no one can take away from you and will be useful throughout the entire journey. FINAL THOUGHTS Building a company is hard, but building an industry is harder. Markets dont emerge on their own, but leaders who are willing to question long-standing assumptions and replace them with evidence and structure can build them. When you succeed, the impact extends far beyond your organization because you did more than win the market; you raised the standard for an entire industry. Lisa Anderson is the president and cofounder of Paragonix Technologies, a Getinge company.
Category:
E-Commerce
Much of healthcare still operates like a series of snapshots. For most routine care, you go in once a year for a physical. Maybe you get a few labs drawn. If something looks off, you might get a follow-up or a prescription. But within the constraints of a short visit and limited longitudinal data, care often ends with broad guidance like eat better or check back next year. Meanwhile, your health is changing every day. Metabolic function, inflammation, aging, and chronic disease dont switch on overnight. They unfold gradually over time, shaped by lifestyle factors including sleep, nutrition, movement, stress, as well as genetics and environment. But unless you cross a diagnostic threshold or show up with symptoms, the system doesnt intervene. Too often, care is triggered only when something has already gone wrong. Thats because were still practicing episodic, event-driven care, not trend-based care. THE LIMITS OF EPISODIC DATA You cant deliver truly personalized proactive prevention with episodic data alone. A single cholesterol reading can be clinically meaningful, particularly at extremes. The same is true for a day of elevated blood sugar. But outside of acute thresholds, context and trajectory matter. To detect risk early and intervene meaningfully, we need a care model informed by continuous trends, not isolated events. This is where AI, and specifically agentic AI, can make a difference. WHAT AGENTIC AI REALLY MEANS When people hear agentic AI, they often assume it means handing over decisions entirely to machines. In reality, agentic AI refers to systems that can act autonomously within defined goals, constraints, and oversight. Think of autopilot in aviation. Autopilot manages routine complexity by continuously monitoring conditions, detecting turbulence, and making micro-adjustments. Pilots maintain oversight and control, but theyre no longer burdened with manually managing every variable. In healthcare, agentic AI functions the same way. It continuously observes multiple data streams, identifies subtle but meaningful changes, and delivers timely, relevant insights that enhance clinical judgment, not replace it. This is not theoretical. Health systems are already integrating AI into diagnostics, operations, and clinical workflows, embedding it into electronic health records, imaging systems, and decision-support tools to manage complexity and surface risk earlier. These deployments signal a shift from isolated AI applications toward infrastructure-level intelligence operating continuously alongside clinicians. FROM VOLUME TO MEANING We already have more health data than we know what to do with. The challenge isnt collection. Its synthesis. Agentic AI helps us move from data overload to actionable insight. By analyzing longitudinal signals, including biological, behavioral, and environmental data, it reveals patterns that allow us to act before risk escalates. This is especially powerful in managing chronic conditions, aging, and metabolic health, areas where prevention is possible, but only when signals are caught early. Research shows that combining longitudinal wearable data with clinical records improves our ability to predict future risk. What agentic systems add is the ability to translate those predictions into timely, predefined actions rather than leaving insights dormant until the next visit. PATIENTS ARE ALREADY LIVING IN A CONTINUOUS WORLD At the same time, people are increasingly turning to AI tools to fill the gap. Recent reporting from OpenAI shows that more than 40 million people use ChatGPT daily for health questions, with roughly 70% of those conversations occurring outside normal clinic hours. OpenAI also reported about 600,000 health-related queries per week from underserved rural communities. The behavior is clear: People want real-time answers that the healthcare system is often not structured to provide between visits. This creates a growing gap between how people live and how medicine is practiced. Agentic AI offers a way to close it by acting as the connective tissue between daily life and clinical care. It doesnt replace clinicians. It doesnt make healthcare autonomous. It makes it responsive. A NEW INFLECTION POINT Autopilot didnt revolutionize aviation by removing the pilot. It changed aviation by making the system manageable, extending human capability through continuous support. Healthcare is now at a similar inflection point. Data volumes will continue to rise. Clinical capacity will remain limited. And episodic care will grow more misaligned with how disease and aging actually develop. Agentic AI offers a path forward by enabling systems to take bounded, predefined actions in response to continuous monitoring, whether by surfacing emerging risk patterns to clinicians or by triggering patient-facing actions like scheduling follow-up visits when concerning trends persist. The result is care that occurs earlier, with better timing, rather than at the moment of acute decline. The technology for agentic AI already exists. Regulatory pathways are emerging as well, but adoption depends on whether incentives, workflows, and leadership priorities evolve to support continuous care. Like autopilot in aviation, agentic AI in healthcare will be introduced gradually, first in well-bounded, lower-risk workflows, then expanding as systems, incentives, and governance structures evolve to support continuous intelligence at scale. To unlock its full potential, healthcare needs reimbursement models that reward prevention, clinical architectures designed for longitudinal data, and governance frameworks that enable responsible deployment without freezing progress. Agentic AI doesnt require a reinvention of regulation, but it does require modernizing operations, governance, and accountability. The systems that move first will define the next era of healthcare. Noosheen Hashemi is founder and CEO of January AI.
Category:
E-Commerce
Ellie Frazier first started posting content three years ago, sharing day-in-the-life vlogs and content tips for fellow creators. As her following grew, she began noticing other creators posting videos with uncannily similar scripts to her own. The clips felt the same. The editing style, identical. In one example, Frazier stretched in front of a window; another creator stretched in front of a window. Frazier chopped vegetables; the other creator chopped an orange. On its own, that might not seem especially striking. But the voiceover script used by the other creator was also almost verbatim Fraziers words. Theres a very stark difference between taking inspiration from everybody and giving credit, versus stealing somebody’s voiceover script word for word multiple times in a row, says Frazier in a recent post. Taking credit in the comments for it being their own work. @elliewfrazier its just not cool she doesnt even follow me #contentcreators #contentcreatortips #socialmediatips original sound – ELLIE FRAZIER Plagiarismpresenting another person’s ideas, words, images, or work as your own without creditwhile often difficult to litigate, is a cardinal sin in most industries. And yet social media largely operates as a law unto itself. TikTok will remove content that violates or infringes someone else’s intellectual property rights, including copyright and trademark. However, many posts on the platform do not clearly meet the legal threshold for copyrightable intellectual property, meaning enforcement is often left to creators themselves. With swaths of content uploaded every day, copycat creators frequently weigh the risk of being discovered against the possibility of profiting from a viral concept with minimal effort. There is even content devoted to explaining exactly how to plagiarize others work. @josh.little_ Good artist copy, great artists steal. @@Josh original sound – Josh Little Determining who copied whom is also largely a futile exercise. On a platform that thrives on mimicry, true originality is rare. The lifecycle of a trend is familiar: One person creates an original video. If it goes viral, thousands copy it. Some tag the original creator. But as the trend snowballs, that credit is often lost to the algorithm. Once it has been replicated enough times to be labeled a trend, the concept is widely regarded as fair game. Frazier isnt the first to spotlight the growing issue of digital plagiarism. In a first-of-its-kind lawsuit brought in 2024, one TikTok creator attempted to sue another for copying her neutral, beige, and cream aesthetic and posting content with identical styling, tone, camera angle and/or text. More than a year later, the so-called Sad Beige Lawsuit was dismissed after the claimant chose not to move forward. Imitation may be described as the sincerest form of flattery, but online plagiarism ultimately benefits no one. The original creator loses credit for their idea. The copycat forfeits an opportunity to develop a distinct voice. And audiences are left scrolling through an endless stream of low-quality videos, each one nearly indistinguishable from the last.
Category:
E-Commerce
All news |
||||||||||||||||||
|
||||||||||||||||||