It’s been a banner year for healthcare, and not in a good way. As a healthcare provider, if your patients had trouble filling a prescription, if your organization struggled to submit claims to generate much-needed revenue, or if your organization had to ask a patient to reschedule a non-essential medical procedure, then you are likely a casualty of healthcare concentration risk. Concentration is a type of systemic (external) risk that occurs when extreme dependencies within an organization’s business, operating, or commercial model create a single point of failure. When a systemic risk event for healthcare occurs, it sets off a chain of failures and disruptions with negative implications for healthcare organizations (HCOs) and dire consequences for patients. You don’t need to understand concentration risk for your organization to be impacted by it, but it won’t get better until you do. In our new report, Concentration Risk In Healthcare, we outline the necessary steps that HCOs must take to identify and mitigate healthcare concentration risk in five key areas. Avoid These Five Sources Of Concentration Risk We’ve previously written about how HCOs must take proactive action against concentration risk and how oligopolies in the pharmacy benefit manager market, for example, accelerate the spread of medical deserts. To be resilient in response to disruptions caused by natural events, market conditions, or other systemic risks, HCOs must identify and mitigate concentration risk in five common areas or suffer the consequences of lost revenue, reputational damage, and, at worst, putting lives at risk: Labor. The existing labor supply and demand problem in healthcare will only intensify as the patient population grows and ages. The skills and knowledge gap, left behind by retiring clinicians and changes in training practices, further exacerbates this issue. Additionally, as reliance on technology increases, critical documentation skills are often missing during cybersecurity crises or routine downtimes. HCOs must prioritize flexible staffing solutions and knowledge transfer. Technology. Overreliance on a single technology vendor can leave HCOs vulnerable to data breaches and service disruptions, especially when electronic medical records and telehealth services are indispensable. HCOs must diversify their technology partnerships, ensure interoperability between systems, and establish robust cybersecurity measures. Artificial intelligence. Dependence on AI algorithms for critical decision-making processes, such as prior authorization, can lead to wrongful denial of care in favor of speed and cost-cutting. HCOs must balance AI innovation with proper precautions and guardrails. Data. Relying on limited or biased datasets for decision-making, research, and AI training can introduce biases into patient care, thereby perpetuating existing inequities. Don’t limit the effectiveness of emerging healthcare technologies. HCOs must aim to collect and utilize diverse datasets and implement rigorous data governance practices. Monopolies and oligopolies. When only a few big players dominate a market, customers can suffer if a disruption causes major shortages. Hurricane Helene’s damage of a single North Carolina plant that is responsible for 60% of the nation’s IV fluid production has hospitals nationwide experiencing a shortage, which is likely to be exacerbated by Hurricane Milton. This concentration of power in the hands of a few large entities can reduce competition, increase prices, and hinder innovation, leading to a complacent focus on incremental improvements rather than resilience. HCOs must identify single points of failure resulting from monopolies and oligopolies and develop mitigation strategies at the enterprise level. Don’t Wait For Disaster: Act Now To Mitigate Concentration Risk Read the full report to dive deeper into identification techniques and effective mitigation strategies. Forrester clients should schedule an inquiry or guidance session with Alla Valente and Arielle Trzcinski to discuss how you can protect your organization from the fallout of concentration risk. Tiffany Do contributed to this blog post. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In 2022, Deloitte expected that the global semiconductor industry would need to add a million skilled workers by 2030, or more than 100,000 annually. Two years later, that forecast still holds. But key industry trends continue to compound the talent challenge as the industry races toward $1 trillion in revenue by 2030, according to a new report by Deloitte, the accounting and consulting giant. The company said that advanced skills driven by demand for Generative AI (GenAI) mean that the talent needed for advancing technologies is often in high demand and can be difficult to attract and retain in a competitive talent market. The report’s timing is interesting, considering the U.S. is reportedly considering limiting sales of AMD and Nvidia AI chips aboard. Deloitte foresees a $1 trillion chip industry by 2030. The semiconductor industry is facing an aging workforce without a clear plan for succession, which may be further exacerbated by low industry appeal compared to the broader tech industry. I suppose this is because the chip industry isn’t as sexy as working for AI or social media companies. Global solutions needed for a global challenge Deloitte foresees a shortage of chip workers. Localization of manufacturing, as well as overall global demand trends, is contributing to a talent and skills shortage that spans the globe. Semiconductor companies are often left competing over the same insufficient pool of existing talent. And talent outcomes are tied to global chips laws. Both the U.S. and European chips legislation include specific objectives and grant application requirements regarding workforce development that companies should commit to in order to receive funding, remain in compliance, and achieve growth objectives. Geopolitical concerns and supply chain fragility continue to contribute to the onshoring of manufacturing (advanced node, trailing node, memory) and back-end ATP (assembly, test, and packaging) processes. A history of cycles The cyclical chips industry experienced its seventh downturn since 1990, with revenues declining 9% to $520 billion for 2023. As a result, development of some new fabrication capacity has been extended, which has also likely delayed some of the immediate, short-term need for talent. This downturn is expected to be temporary, with revenue set to grow by 16% in 2024 to an all-time high of $611 billion. With the industry back on track to reach the $1 trillion figure for 2030, talent will be needed to fuel that growth. But now there’s more time to optimize talent forecasts, mix, pipeline, skills and capabilities, and development plans. A richer understanding of the challenges driving the semiconductor talent shortages can enable semiconductor leaders to deploy targeted strategies to help address their looming talent needs. Advanced skills being driven by demand for GenAI Lots of countries are focusing on domestic chip industries. According to Deloitte’s 2023 Smart Manufacturing: Generative AI for Semiconductors Survey, 72% of industry leaders surveyed predict that GenAI’s impact on the semiconductor industry will be “high to transformative.” Respondents saw high potential for Generative AI’s use throughout their business, with heavier value realization expectations within core engineering, chip design and manufacturing, operations, and maintenance. Although GenAI may help alleviate some engineering talent shortages by addressing routine tasks and giving engineers more time to perform their core jobs better and faster, the GenAI skill set scarcity remains. The semiconductor workforce is expected to need to exponentially grow its GenAI skill sets due to their shortage in the market. And leaders in the field are often in high demand across most sectors ofthe economy. Semiconductor companies should consider offering more novel benefits beyond competitive compensation, such as having a seat at the table, to better attract AI talent and leadership. Having proficient GenAI talent is key in driving the industry’s ability to innovate and reap the benefits of this transformative technology. Looming talent cliff and low industry appeal An aging workforce, regulatory changes, newly required skill sets, and shifting employee expectations are changing the landscape of semiconductor talent. The lack of brand awareness and appeal in the semiconductor industry compared to better-known technology brands can make addressing these challenges more difficult for the industry. Semiconductor companies seem to recognize that attracting and retaining new and diverse talent is more important than ever, yet it continues to be a challenge for many organizations. Building diversity can be difficult; currently only one-third of the U.S. semiconductor industry employees identify as female and less than 6% as Black or African American. The U.S. semiconductor workforce is also older than other technology industries: As of July 2024, 55% of the U.S. semiconductor workforce is 45 or older, with less than 25% under the age of 35.11 In Europe, 20% of the industry is 55 or older, with Germany expecting about 30% of their workforce to retire over the next decade. Inconsistent knowledge management, and the lack of new talent to adopt institutional knowledge, presents an additional workforce barrier for many semiconductor companies. Relative to other sectors of the technology industry, semiconductor organizations can offer a sense of trust, stability, and projected market growth—attractive qualities to the most recent college entrants. While semiconductor companies may have struggled with brand recognition and a competitive employee value proposition, investing in recent high school graduates could help reinvigorate talent pipelines that may be more attracted to stability and flexibility over rapid advancement. A global shortage The need for semiconductor talent is a global issue. Countries are not producing enough skilled talent to meet their workforce needs. And companies can’t continue to tussle over the same finite talent pool while still expecting to successfully grow the industry, launch new (and expand existing) fabs, and keep up with rapid technological advances. In the United States, where the majority of annual graduates with a master’s degree in semiconductor-related engineering fields are foreign students, 80% of those graduates do not stay in the United States post-graduation. According to Deloitte China and Asia Pacific’s most recent APAC Semiconductor Industry Trends Survey,

Voters overall are divided in their predictions about how Vice President Kamala Harris or former President Donald Trump would perform as president – with negative expectations outweighing positive ones for both candidates. And while majorities of voters see both Trump and Harris as bringing change to Washington – though more say this about Trump than Harris – they are also split over whether that change would have positive or negative effects. Would Trump and Harris be above or below average presidents? Voters are more likely to say each of the presidential candidates would be poor or terrible presidents than to say they would be good or great at the job. More voters today say Trump would be a “good” or “great” president than say this about Harris (41% vs. 36%). But similar shares of voters say each would be a “poor” or “terrible” president (48% say this about Trump, 46% about Harris). Views of a potential second Trump presidency are more polarized than views of a potential Harris presidency: Voters are more likely to say Trump would be great than to say this about Harris (22% vs. 14%). But they’re also more likely to say Trump would be terrible (38%) than to say the same for Harris (32%). Voters are more likely to predict Harris would be an “average” president (18% say this about her, 11% about him). Supporters’ views of their candidate While most supporters of both candidates offer positive predictions about how their candidate would perform as president, Trump supporters are more likely to say a potential Trump presidency would be good or great than Harris’ supporters are to say this about her. 84% of Trump supporters say he would be a good or great president, including 46% who say he would be great. Just 13% say he’d be an average president. 73% of Harris supporters say that she would be a good (44%) or great (29%) president, while 24% say she’d be an average president. Very small shares of each candidate’s supporters (just 2% each) say their candidate would be a poor or terrible president. Supporters’ views of the opposing candidate About nine-in-ten among both Harris supporters (91%) and Trump supporters (89%) predict that the opposing candidate would be a poor or terrible president. Harris supporters are particularly likely to say Trump would be a terrible president (76% say this). By comparison, 67% of Trump supporters predict Harris would be terrible. Who would bring change – for good or bad – to Washington An overwhelming majority of registered voters say that Trump would change the way things work in Washington, but they are fairly divided over whether that change would be for the better or for the worse. While 41% say Trump would change things for the better, a somewhat larger share (48%) say he would change things for the worse. Relatively few (10%) say that he would not change things much either way. In contrast, three-in-ten voters say Harris would not change things much either way in Washington, while 41% say she would change things for the worse and 29% say she would change things for the better. Harris and Trump supporters have different opinions on whether their candidate would change the way things work in Washington: 40% of Harris supporters say that Harris would not change the way things work much in Washington, while 59% say she’d change things for the better. 86% of Trump supporters say Trump would change things for the better. Just 12% say he would not change things much. Overwhelming shares of both Harris (92%) and Trump (83%) supporters say the opposing candidate would change things in Washington for the worse. But Trump supporters are more likely to say Harris would not change things much (16%) than Harris supporters are to say this about Trump (6%). Harris presidency: Biden’s policies versus a new direction Nearly six-in-ten voters (58%) expect Harris to continue President Joe Biden’s policies, while about four-in-ten (41%) expect her to take the country in a different direction. Among the 58% who say Harris would continue Biden’s policies, far more say this would be a bad thing (41%) than say it would be a good thing (16%). Those who say she’ll take the country in a different direction are more likely to say this would be good (30%) than bad (10%). Harris supporters More than half of Harris supporters (58%) say she would take the country in a different direction – and they nearly unanimously view this course positively. About four-in-ten Harris supporters (41%) say that she would continue Biden’s policies and most of this group (33%) say doing so would be a good thing for the country. Trump supporters Conversely, an overwhelming majority of Trump supporters (76%) say Harris would continue Biden’s policies – and this group nearly unanimously sees that as bad for the country. Only about a quarter of Trump supporters (23%) say Harris would take the country in a different direction – and most of this group (19%) say that would be a bad thing. Have Harris and Trump clearly explained their views on issues? When it comes to several major issues, voters are fairly divided on whether the candidates have clearly explained their policies and plans, with two notable exceptions. 75% of all voters say Harris has clearly outlined her views on abortion, including 93% of her supporters and 59% of Trump backers. About six-in-ten voters (61%) also say Trump has been clear about his views on abortion. 70% of all voters say Trump has clearly explained his policies and plans for addressing illegal immigration. Nearly all of his supporters (94%) and about half of Harris’ supporters (48%) say Trump has been clear about his plans on this issue. At least half of each candidate’s supporters say their candidate has clearly outlined their policies and plans for each of the policy domains asked about in the survey. But no more than a quarter of each candidate’s supporters say the other candidate has been

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI governance company Credo AI launched a new platform that integrates with third-party AI Ops and business tools to gain better visibility around responsible AI policies.  Credo AI’s Integrations Hub, now generally available, lets enterprise clients connect platforms where they build generative AI applications like Amazon Sagemaker, MLFlow and Microsoft Dynamics 365 to a centralized governance platform. Platforms where these applications are often deployed, like to Asana, ServiceNow or Jira can also be added to Integrations Hub.  The idea is that enterprises working on AI applications can use Integrations Hub to connect to a central governance platform like Credo AI’s governance platform. Instead of needing to upload documentation proving safety and security standards, the Integrations Hub will collect metadata from the applications that contain those metrics.  Credo AI said Integrations Hub will directly connect with existing model stores, which are then automatically uploaded to the governance platform for compliance checks. The hub will also bring in datasets for governance purposes.  Navrina Singh, founder and CEO of Credo AI, told VentureBeat that the integrations hub was designed to make AI governance, whether following data disclosure rules or internal policies around AI usage, become part of the development process at the very beginning.  “All the organizations that we work with, primarily Global 2000 [companies], are adopting AI at a very fast pace and are bringing in new breeds of AI tools,” Singh said. “When we looked across all the enterprises, one of the key things we wanted to enable for them was to extract the maximum value of their AI bets and make governance really easy, so they stop making excuses that it’s difficult to do.” Credo AI’s Integrations Hub will include ready connections with Jira, ServiceNow, Amazon’s SageMaker and Bedrock, Salesforce, MLFlow, Asana, Databricks, Microsoft Dynamics 365 and Azure Machine Learning, Weights & Biases, Hugging Face and Collibra. Any additional integrations can be customized for an additional fee.  Governance at the onset Surveys have shown that responsible AI and AI governance, which normally looks at how applications meet any regulations, ethical considerations and privacy checkups, have become top of mind for many companies. However, these same surveys point out that there are few companies that assessed these risks.  As enterprises grapple with how to be more responsible around generative AI, providing ways for organizations to easily figure out risks and compliance issues has become a new niche for many companies. Credo AI is just one of the companies offering different avenues to make responsible AI easily accessible. IBM’s Watsonx suite of products includes a governance platform that lets users evaluate models for accuracy, bias and compliance. Collibra also released a suite of AI tools around governance that creates workflows to document and monitor AI programs.  Credo AI does check applications for potential brand risks like accuracy. Still, it positions its platforms more as a means to meet current laws around automated platforms and any potential new regulation that would come out.  There are still very few regulations around generative AI, though there have always been policies governing data privacy and data retention that some enterprises would have already been following thanks to machine learning or data rules. Singh said there are some geographies that do ask enterprises for reports around AI governance. She pointed to New York City Law 144, legislation prohibiting automated tools for employment decisions.  “There are certain technical evidence you have to collect, like a metric called demographic parity ratio. Credo AI takes this New York City law and codifies it to check your AI Ops system, and since it’s connected to your policies and to where you built your HR system, we can collect that metadata to meet the requirements of the law,” Singh said.  source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Meta founder and CEO Mark Zuckerberg, who built the company atop of its hit social network Facebook, finished this week strong, posting a video of himself doing a leg press exercise on a machine at the gym on his personal Instagram (a social network Facebook acquired in 2012). Except, in the video, the leg press machine transforms into a neon cyberpunk version, an Ancient Roman version, and a gold flaming version as well. As it turned out, Zuck was doing more than just exercising: he was using the video to announce Movie Gen, Meta’s new family of generative multimodal AI models that can make both video and audio from text prompts, and allow users to customize their own videos, adding special effects, props, costumes and changing select elements simply through text guidance, as Zuck did in his video. The models appear to be extremely powerful, allowing users to change only selected elements of a video clip rather than “re-roll” or regenerate the entire thing, similar to Pika’s spot editing on older models, yet with longer clip generation and sound built in. Meta’s tests, outlined in a technical paper on the model family released today, show that it outperforms the leading rivals in the space including Runway Gen 3, Luma Dream Machine, OpenAI Sora and Kling 1.5 on many audience ratings of different attributes such as consistency and “naturalness” of motion. Meta has positioned Movie Gen as a tool for both everyday users looking to enhance their digital storytelling as well as professional video creators and editors, even Hollywood filmmakers. Movie Gen represents Meta’s latest step forward in generative AI technology, combining video and audio capabilities within a single system. Specificially, Movie Gen consists of four models: 1. Movie Gen Video – a 30B parameter text-to-video generation model 2. Movie Gen Audio – a 13B parameter video-to-audio generation model 3. Personalized Movie Gen Video – a version of Movie Gen Video post-trained to generate personalized videos based on a person’s face 4. Movie Gen Edit – a model with a novel post-training procedure for precise video editing These models enable the creation of realistic, personalized HD videos of up to 16 seconds at 16 FPS, along with 48kHz audio, and provide video editing capabilities. Designed to handle tasks ranging from personalized video creation to sophisticated video editing and high-quality audio generation, Movie Gen leverages powerful AI models to enhance users’ creative options. Key features of the Movie Gen suite include: • Video Generation: With Movie Gen, users can produce high-definition (HD) videos by simply entering text prompts. These videos can be rendered at 1080p resolution, up to 16 seconds long, and are supported by a 30 billion-parameter transformer model. The AI’s ability to manage detailed prompts allows it to handle various aspects of video creation, including camera motion, object interactions, and environmental physics. • Personalized Videos: Movie Gen offers an exciting personalized video feature, where users can upload an image of themselves or others to be featured within AI-generated videos. The model can adapt to various prompts while maintaining the identity of the individual, making it useful for customized content creation. • Precise Video Editing: The Movie Gen suite also includes advanced video editing capabilities that allow users to modify specific elements within a video. This model can alter localized aspects, like objects or colors, as well as global changes, such as background swaps, all based on simple text instructions. • Audio Generation: In addition to video capabilities, Movie Gen also incorporates a 13 billion-parameter audio generation model. This feature enables the generation of sound effects, ambient music, and synchronized audio that aligns seamlessly with visual content. Users can create Foley sounds (sound effects amplifying yet solidifying real life noises like fabric ruffling and footsteps echoing), instrumental music, and other audio elements up to 45 seconds long. Meta posted an example video with Foley sounds below (turn sound up to hear it): Trained on billions of videos online Movie Gen is the latest advancement in Meta’s ongoing AI research efforts. To train the models, Meta says it relied upon “internet scale image, video, and audio data,” specifically, 100 million videos and 1 billion images from which it “learns about the visual world by ‘watching’ videos,” according to the technical paper. However, Meta did not specify if the data was licensed in the paper or public domain, or if it simply scraped it as many other AI model makers have — leading to criticism from artists and video creators such as YouTuber Marques Brownlee (MKBHD) — and, in the case of AI video model provider Runway, a class-action copyright infringement suit by creators (still moving through the courts). As such, one can expect Meta to face immediate criticism for its data sources. The legal and ethical questions about the training aside, Meta is clearly positioning the Movie Gen creation process as novel, using a combination of typical diffusion model training (used commonly in video and audio AI) alongside large language model (LLM) training and a new technique called “Flow Matching,” the latter of which relies on modeling changes in a dataset’s distribution over time. At each step, the model learns to predict the velocity at which samples should “move” toward the target distribution. Flow Matching differs from standard diffusion-based models in key ways: • Zero Terminal Signal-to-Noise Ratio (SNR): Unlike conventional diffusion models, which require specific noise schedules to maintain a zero terminal SNR, Flow Matching inherently ensures zero terminal SNR without additional adjustments. This provides robustness against the choice of noise schedules, contributing to more consistent and higher-quality video outputs  . • Efficiency in Training and Inference: Flow Matching is found to be more efficient both in terms of training and inference compared to diffusion models. It offers flexibility in terms of the type of noise schedules used and shows improved performance across a range of model sizes. This approach has

State of the India Smartphone Market India ships around 145-150 million new smartphones per year for the domestic market, ranking it second globally after China in annual shipping volume. There are approximately 650 million smartphone users in India or about 46% smartphone penetration in the country. There is no other market of this size with such huge untapped potential, making India a very attractive market for all smartphone ecosystem participants from brands to component makers. India’s smartphone market grew modestly in 2021, coming out of a challenging 2020 (due to pandemic-led shutdowns). This growth was driven by the need of a better device for remote learning/work and increasing media consumption on the go. However, in 2022 and 2023 the market faced challenges because of the rising average selling price (ASP) for devices (growing by a CAGR of 38% from 2020 till 2023), improving device quality, and continuing income stress especially in the mass consumer segment. This in turn has elongated the average smartphone replacement cycle in India from 24 months to almost 36 months currently, further restricting the growth of the new smartphone market. Why Are Consumers Choosing Used Smartphones? All the above mentioned factors are contributing to the increasing popularity of used smartphones in the past few years. As the quality of smartphone hardware improves, increasing device prices are keeping the new smartphone models out of reach of the mass segment. The aspiration to own a good device without paying much is making the used smartphones a very attractive choice for consumers wanting to upgrade or even with first-time smartphone users. Another important factor in the popularity of used smartphones is the rising preference for 5G smartphones. As of now only approximately a third of the 650 million Indian smartphone users have a 5G smartphone, the rest are still using 4G phones.  However, the price differential between 4G and 5G smartphones and the lack of wide availability of 5G models under INR 10K (US$125) is restricting their upgrade to a 5G device thus forcing many consumers to go for mid-priced used smartphones. According to the latest IDC research (IDC Used Device Tracker), India ranks third globally in used smartphone units’ annual volume after China and the USA, and is one of the fastest growing markets.  In 2024, IDC forecasts 20 million used smartphones will be traded in India with a YoY growth of 9.6%, outpacing new smartphone shipments of 154 million units in 2024, growing at 5.5% YoY. Apple and Xiaomi Are the Top Choices! The “premiumisation” of India’s smartphone market or more aptly the rising aspirations of the Indian consumer to upgrade to a mid-premium or a premium phone is also contributing to the popularity of used smartphone space. While Apple has seen healthy growth of new iPhone shipments in India in the past few years, it is also leading the used smartphone space, capturing a quarter of the market as per IDC Quarterly Used Device Tracker. Everyone in India wants to buy an iPhone because of its premium brand positioning and status signaling value, but not everyone can afford one. The used phone market comes to the rescue of many such aspirational consumers going for previous gen models like iPhone 11, 12 and 13 series. Xiaomi led India’s new smartphone market for 20 straight quarters from 3Q17-3Q22. As a result, it has a huge user base which is reflected in the used smartphone market as well. Xiaomi sits at the second position followed by Samsung. These top 3 brands combined make up around two-thirds of the used smartphone market in India. Who are the Market Players? IDC’s used smartphone research tracks both second hand and refurbished smartphones being traded via organized refurbished players in the market. It excludes the peer to peer sales. In India, several startups in this space like Cashify, Budlii, Instacash, Yaantra, etc. have tried to organize this hitherto largely unorganized market. With their efforts around marketing and omnichannel presence across both online and offline counters, these players have been able to build confidence and trust among consumers regarding the quality of the used smartphones on their platforms.  Cashify is one of the biggest platforms in this market with over 200 stores in 100 cities, many in Tier 2 & 3 towns. For Yaantra, the company is owned by Indian e-commerce giant Flipkart, with branding named as Flipkart Reset. It is mainly focused on its online portfolio. For offline space, the company has partnered with Airtel to be available in the telco stores in only two Indian cities for now (Delhi & Hyderabad). From Here, the Only Way Is Up! IDC forecasts the used smartphone market in India to grow at 8% CAGR in the next 5 years, reaching 26.5 million units per annum in 2028. It is evident that the used smartphone market in India is gradually taking shape with interesting channel play by key trading players making smartphones more affordable to a larger audience. This is also reassuring for the ever-discerning Indian consumer when they explore buying a used smartphone without worrying about the quality of the device and spending too much. From an overall market perspective, growth in used smartphone market in India can certainly be a factor in increasing smartphone adoption in India, creating a parallel revenue stream for channel players, help vendors in addressing e-waste concerns around discarded devices, and generate employment (skilled/unskilled). This market can certainly play a major role in achieving the goal of bringing a billion Indians in the smartphone fold in the next few years. source

US healthcare industry giant CVS Health is considering a strategic breakup of its retail and insurance units. A potential split would mark a significant shift in the company’s “one-stop shop” strategy that it has already invested billions in to realize. Its vision to date has been to create a seamless healthcare experience for consumers and employers by integrating its retail pharmacy, health services, and insurance segments. What’s Happened: Financial Woes Across A Complex Portfolio Cornered CVS CVS is under pressure from investors to improve its financial performance. As CVS CEO Karen Lynch explained in the Q3 2024 earnings call, the company has developed a multiyear plan to generate as much as $2 billion in savings by “ … continuing to rationalize our business portfolio and accelerating the use of artificial intelligence and automation across the enterprise as we consolidate and integrate.” A WARN filing prompted the company to share that this also includes reducing its workforce by nearly 2,900 employees. In recent months, challenges have mounted across the CVS portfolio — and also highlighted its strongest assets. Health insurance carrier Aetna is ailing as more members resume using medical services. CVS’s 2018 acquisition of Aetna aimed to create a healthcare powerhouse but has since encountered significant integration challenges while at the same time facing scrutiny over the vertical integration of the portfolio. In 2024, CVS to date has cut its earnings guidance three times due to escalating medical costs pressuring Aetna’s bottom line. One culprit: Post-pandemic, Medicare Advantage beneficiaries have resumed using medical services and visits to the doctor. Former Aetna President Brian Kane is now gone. But costs from Medicare Advantage plans will continue to skyrocket due to utilization and newly included benefits that have become table stakes for seniors. Pharmacy benefit manager (PBM) prosperity faces potential pitfalls. 2024 began with the loss of large, long-tenured clients, including employer Tyson Foods and narrowed business with health insurer Blue Shield of California. Midyear, the FTC called PBMs manipulative middlemen and highlighted their role in spreading medical deserts. In September, the FTC filed action against CVS Health’s PBM, Caremark Rx, with allegations of the PBM and its competitors engaging in anticompetitive and unfair rebating practices. These methods reportedly artificially inflated the list prices of insulin drugs, restricted patient access to lower-priced options, and shifted the burden of high insulin costs onto vulnerable patients. The suit builds on industry concerns regarding concentration risk in the PBM market. Retail stores provide a sturdy stronghold. CVS has over 9,000 physical locations in the US. Per Definitive Healthcare’s ClinicView, as of 2023, CVS also holds over 60% of the US retail clinic market. In Q3 2024, CVS’s retail clinics outperformed other business segments, benefiting from competitors’ retreats, such as Walmart’s exit due to lack of profitability and Walgreens’ shift to specialty pharmacy expansion. CVS’s digital experience enhancements and broader in-store services, especially for chronic conditions and mental health, have boosted sustained customer engagement and retention. Services like vaccinations continue to provide an (ongoing) one-time revenue boost and remind customers of the available convenient care options in their local store. What A Breakup Would Mean For Key Stakeholders While CVS is distracted pondering its next moves, competitors in the health insurance and pharmacy space should position themselves to take market share now. If a breakup plays out, we may see greater focus within each of the (erstwhile) CVS business units. Unlocking financial gains through technological advances, however, will take time and could lead to: Health insurers picking up new populations. If Aetna becomes independent, expect some of its members to shop around for new insurers. After all, Aetna’s synergy with CVS was one of its key selling points. Competitors should highlight established adoption of emerging technologies such as generative AI, proof of efficiencies that reduce administrative burden for providers, and care advocacy services that drive member trust and appropriate utilization of healthcare services. Retail pharmacies expanding services. If a breakup happens, expect retail competitors to try to poach CVS shoppers with expanded pharmacy services like home delivery and virtual consults. We expect retailers like Amazon and Walmart to make prescription transfers easy to execute and to market price transparency and better prescription drug pricing that benefits consumers. Degradation in the consumer experience. Consumers have benefited from the vertical integration of the PBM and retail pharmacy. Dismantling this connection would lead to disjointed experiences and push employers with frustrated employees into the open arms of competitors that have preserved their integration, such as UnitedHealthcare or Cigna. One CVS group could come out ahead in a breakup: CVS’s Caremark PBM. As all PBMs face regulatory scrutiny over concentration risk, a breakup could actually put CVS ahead of the curve. We will continue to watch as this potential strategic shift evolves. Forrester clients can schedule time with us — Arielle Trzcinski and Sucharita Kodali — to talk more about the future of health insurance and retail health. source