This National Park Isn’t as Crowded as Reports Suggest
This National Park Isn’t as Crowded as Reports Suggest
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https://www.fodors.com/world/north-america/usa/california/experiences/news/yosemite-officials-say-overcrowding-reports-dont-tell-the-full-story
Yosemite tourism officials say reports of record overcrowding overlook key trends, noting visitor growth has been spread across the year, and many hotels still have summer availability.
Yosemite National Park may not be as crowded this summer as recent reports suggest. While widely covered in national media, officials in California’s Mariposa County have provided additional context about visitation trends in the park and the impact on surrounding businesses.
“It’s important to look at the broader picture,” Jonathan Farrington told Fodor’s. Farrington is the executive director of the Yosemite Mariposa County Tourism Bureau, which promotes visitor industry businesses in one of the four California counties that border Yosemite National Park and have park entrances. Mariposa County has two park entrances, one of which, the Arch Rock Entrance, is among the most popular.
While the July 4th holiday was busy, as it typically is, Farrington noted that the overall increase in visitation occurred outside the peak summer months, and that park data doesn’t reflect a massive boom in visitors—up just 4.5% in June, and up a modest 10% versus last year.
“Much of the year-to-date increase has occurred outside Yosemite’s traditional peak summer season. A relatively dry winter, followed by an exceptionally warm and dry March, led to an early snowmelt and the unusually early opening of Tioga Road, Glacier Point Road, and the Mariposa Grove of Giant Sequoias. These earlier-than-usual openings gave visitors more opportunities to experience the park throughout late winter and spring. As a result, the increase in visitation has been spread over a longer period rather than being concentrated during the busiest summer months.”
In short, visitation is up in the park, but it’s not the massive summer surge that had been portrayed. Some area businesses have even seen declines in visitor traffic, largely related to big drops in the numbers of international arrivals.
Ron Halcrow, owner of the Yosemite Plaisance Bed & Breakfast, said that businesses that have historically supported international visitors to the park have seen a clear drop. “Last year,” he told Fodor’s, “our occupancy for the year was 72% below our average occupancy of the previous ten years [excluding 2020]. To date this year, our occupancy is 22% below what it was in 2025.” In a typical year, says Halcrow, up to three-quarters of his guests come from outside the United States.
Officials in Mariposa County also shared that lodging operators in the county have said significant room inventory is still available, with several operators offering discounts to attract new bookings during the mid-summer period, which is extraordinary given historical comparisons.
They also provided additional context on the end of the reservation system. The system had previously required visitors to get an advance reservation to enter the park, which had received mixed reviews from users on whether it had actually streamlined the flow of traffic into the park. Instead, visitors can check real-time information on waits to enter the park at the major entrances by visiting a dedicated National Park Service site.
Mariposa County officials shared that summer holidays are generally the busiest days for the park entrances, and that entrance lines and parking availability outside of holiday periods differ significantly from holiday peaks—which include Memorial Day, the July 4th holiday, and Labor Day.
Yosemite is one of the country’s most visited national parks. In 2025, it ranked fifth nationwide, welcoming some 4.2 million visitors—roughly in line with fourth-place Grand Canyon. Great Smoky Mountains, Zion, and Yellowstone rounded out the top five. Visitation in the park also tends to focus on big banner attractions such as Yosemite Valley, Glacier Point and Mariposa Grove, while 95% of the park remains protected wilderness.
Mariposa County officials note that two-thirds of the park’s visitation occurs between May and September, while the park is open year-round, offering peak waterfall viewing in the spring and autumn color during the fall.
Multiple Countries Have Issued Travel Alerts to the Middle East (Again)
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https://www.fodors.com/world/africa-and-middle-east/experiences/news/multiple-countries-have-issued-travel-alerts-to-the-middle-east-again
Airlines are cancelling flights after new strikes.
Multiple countries have issued warnings against travel to the Middle East, and airspace over the Gulf is heavily restricted. Airlines have extended flight suspensions to destinations throughout the region, and governments in the United States, Australia, the United Kingdom, New Zealand, the Netherlands, and Canada have warned travelers to exercise caution.
This week, the U.S. State Department advised Americans worldwide to exercise increased caution. It told travelers to expect and prepare for airspace closures, flight cancellations, and travel disruptions. “Americans outside the Middle East should reconsider travel to and through the region. Those who do travel to or from the region should monitor information about airport and airline operations.” It also warned that Iran and supportive groups might target Americans worldwide, including businesses and institutions.
Iran, Lebanon, Iraq, Syria, and Yemen are currently under a Level 4: Do Not Travel advisory from the U.S. State Department. Israel, Oman, Jordan, Qatar, and the UAE are under a Level 3: Reconsider Travel alert.
Canada has also updated its travel advisories this week for Bahrain, Jordan, Qatar, UAE, Oman, Israel, Palestine, and Saudi Arabia. The country is asking citizens to avoid all nonessential travel due to military action. The UAE advisory states, “Missiles, drones and other projectiles struck targets in the UAE. Military activity in the region could resume on short notice and cause travel disruptions, including flight cancellations.” Iran, on the other hand, is under a Level 4: Avoid All Travel warning.
On July 21, Australia issued advice for Australians traveling to or currently in the Middle East. The country asked travelers to avoid travel entirely to Iran, Iraq, Lebanon, Palestine, Syria, and Yemen, which are under a Level 4: Do Not Travel warning. Meanwhile, Bahrain, Israel, Kuwait, Oman, Jordan, Saudi Arabia, Qatar, and the UAE are under a Level 3: Reconsider Travel warning. Australia’s Department of Foreign Affairs posted in a bulletin, “Avoid non-essential travel to these locations. If you need to transit, stay as short a time as possible and avoid unnecessary activities. Airspace may close at short notice. Flights can change or stop suddenly. Borders can close.”
Neighboring New Zealand also issued a similar warning, asking travelers to avoid nonessential travel to Bahrain, Jordan, the UAE, Oman, Kuwait, and Saudi Arabia. Iran, Iraq, Palestine, Israel, Syria, and Lebanon are all on the Level 4: Do Not Travel list. “Military conflict could further escalate in the coming days. If the security situation deteriorates further, travel disruptions and airspace closures may occur.”
Earlier this month, the ceasefire between the U.S. and Iran fell through, and the two countries have launched new strikes against each other and allies. For 12 consecutive nights, the U.S. has struck Iran, and Iran has retaliated by attacking allies in the Gulf, including Jordan. Amid these tensions, oil prices have reached $100 a barrel, and 17 U.S. military personnel have died.
Worldwide Flight Cancellations
Earlier this month, the European Union Aviation Safety Agency warned airlines to avoid the airspace of the UAE, Bahrain, Qatar, Kuwait, and the Gulf of Oman. On July 23, it also added Jordan to the list after Iran struck U.S. allies.
Dutch airline KLM announced that it has suspended all flights to Dubai, Riyadh, and Dammam until September 6, 2026. The airline is also not flying in the airspace of Iran, Israel, Iraq, and others in the Gulf region. Air France has also extended its suspension of flights to Beirut, Riyadh, and Dubai, “due to the security situation in the region and restrictions that may affect certain airspaces.”
Other European airlines are also reducing Middle East operations. Greek airline Aegean will not fly to Dubai until August 31, while Latvian airline airBaltic has suspended Dubai flights until October 24. The Lufthansa Group (Lufthansa, SWISS, Brussels Airlines, Austrian Airlines, and more) has gradually increased services to Tel Aviv, but flights to Dubai, Abu Dhabi, Amman, Muscat, Tehran, Beirut, and Dammam remain suspended.
Additionally, travelers will also not be able to book some destinations such as Dubai on Air Canada, British Airways, Cathay Pacific, and Singapore Airlines.
How COOs maximize operational impact from gen AI and agentic AI
https://www.mckinsey.com/capabilities/operations/our-insights/how-coos-maximize-operational-impact-from-gen-ai-and-agentic-ai?
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By defining the right operating structure, data governance model, and change management approach, COOs can help their companies make the most of their AI investments.
Better, faster, easier, cheaper: That’s the promise of gen AI. For at least some companies, it’s becoming the reality as well, as leaders find new ways for gen AI—and the increasingly capable agents it enables—to automate, augment, and accelerate work across virtually every function. Early adopters are using gen AI to help strengthen supplier negotiations in procurement and improve quality control in equipment maintenance (see sidebar “Gen AI’s potential across operations”). One digital marketing platform is even using gen AI to manage “long tail” sales accounts that were previously too labor-intensive to serve, for an annual revenue gain of more than $30 million.
Yet, as encouraging as these results are, there’s still much to do. In a recent McKinsey survey of 118 US C-suite executives, only 19 percent said that gen AI increased their company’s revenue by more than 5 percent. It’s a similar picture elsewhere: In mid-2024, just 17 percent of organizations worldwide said that they derive more than 10 percent of EBIT from gen AI.
Not surprisingly, about half of senior executives in that survey describe their organization’s development and release of gen AI tools as too slow—despite the fact that three-quarters also say they have at least a draft of their gen AI strategy. Only 12 percent of these organizations have been able to find revenue-generating use cases for gen AI. And while the ultimate goal for these organizations is to achieve gen AI maturity, with gen AI fundamentally changing how work gets done, a mere 1 percent of executives say their organization has reached that point.
That’s where the COO plays a critical role, as illustrated by several recent success stories where gen AI and gen-AI-based agents have helped redefine how a company creates value. Specifically, the COO can help build enterprise capabilities for gen AI-based rewiring in three ways: First, they can define the company’s operating structure for gen AI, identifying the highest-potential domains for gen AI deployment and building the capabilities needed to scale the technology effectively across the enterprise.
Second, they can shape the organization’s data governance, addressing the complex challenges associated with extracting and structuring data from legacy operating systems and minimizing risks associated with inaccuracy. Third, and most important for sustaining gen AI’s advantages over time, they can oversee change management initiatives so that people learn, use, and improve the tools and processes gen AI enables.
Getting these three factors right takes work, not just in operations but also in collaboration with other leaders, such as the chief information officer (CIO). But it’s how companies’ investments in gen AI can pay off: by reshaping how work gets done every day.
Ensuring gen AI creates real business value
Getting gen AI wrong could be costly: not just in wasted investment but also in missed opportunities. Companies that move quickly are already securing major advantages, increasing the stakes.
Sensing gen AI’s possibilities, senior leaders of a European equipment maker with more than €10 billion in revenue wanted to avoid one of gen AI’s most common pitfalls: fragmentation in development. Too often, individual functions and business units design gen AI tools that optimize their own tasks but fail at the enterprise level—such as a production-scheduling tool that raises factory output higher than the logistics department can absorb.
The company’s COO recognized that, in facing the future of operations, he and his team needed much more than a list of potential gen AI use cases. They needed to rethink the entire operating model to see how this new automation could transform people’s work.
Rethinking operating structures for gen AI
To an even greater degree than seen in earlier waves of technology-based transformation, gen AI touches virtually every part of a business organization. This expanded scope for coordination makes the operating structure particularly important to get right, both to identify the highest-potential gen AI opportunities at the enterprise level and to see them through to fruition. From the beginning, therefore, the European equipment manufacturer brought together the COO, CIO, chief technology officer (CTO), and heads of manufacturing, procurement, supply chain, and quality control, along with business unit leads responsible for marketing and sales, to undertake a gen-AI-prompted reassessment of its operating assumptions.
Centralization. The equipment manufacturer’s leaders recognized that sustaining this sort of centralized approach would be essential, especially as the organization developed foundational capabilities in fields ranging from platform architecture to risk and ethics. At least initially, a center of excellence (COE) or “factory” model, with a steering committee providing executive leadership and an operating committee overseeing day-to-day work, would help keep stakeholders collaborating to generate lasting value (Exhibit 1).
Exhibit 1
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The next question is where the COE should sit within the larger organization. Under the most centralized approach, the COE directs gen AI strategy and reports directly to the CEO, operating in parallel to the business units (Exhibit 2). By enforcing enterprise-wide standards and minimizing the risks of duplication and resource waste, this option is often the most practical one at the very earliest stages of gen AI exploration. For the equipment manufacturer, following this model has so far yielded a prioritized road map of relevant use cases for €300 million in EBITDA improvement.
Exhibit 2
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As the company builds more confidence, it could evolve toward one of two middle alternatives in which the business units develop their own gen AI capabilities. In some instances, the COE takes the lead and the business unit executes, while in others, the business unit takes the lead with support from the COE. Only a few organizations have fully decentralized their gen AI function and left it to the business units to run.
Identifying domains. Developing a clear structure helps organizations find the right balance in designing gen-AI-based solutions that are large enough to achieve meaningful end-to-end impact yet small enough to be achievable within a reasonable time frame. Thinking in terms of domains can push gen AI past the “pilot purgatory” stage, in which organizations spend time and resources and incur opportunity costs on developing gen AI tools that have little effect beyond saving workers a few minutes a day.
Most important is to start by assessing the strategic fit for gen AI, with an expansive view of the art of the possible so that the solution can have a lasting effect. For example, a finance function might start by identifying a pain point—such as analysts being overloaded with simple requests from other managers that would take days to answer. An initial response might be to create a gen AI chatbot that would allow anyone in the company to directly query finance data on their own.
This addresses the initial problem by enabling faster query resolution and freeing up analysts for higher-value work. But a deeper examination would seek the root causes for the frequent queries, and whether a more sophisticated gen AI tool—perhaps an agent or a set of agents—could start to produce certain analyses automatically when certain scenarios occur.
This sort of thinking implies another major question: Can we keep gen AI from destroying value? Automating financial analyses for internal purposes, such as to find lessons relevant to new product launches, tends to be substantially less risky than automating analyses for compiling into quarterly securities reporting. And that leads to a final question: Is gen AI the right solution? For some reporting, simple and (comparatively) inexpensive analytic AI may be completely adequate.
Data governance
Centralization helps operations leaders deal with what 70 percent of gen AI high performers reported as a challenge: managing data (Exhibit 3). With gen AI, the accuracy, availability, and usability of operational data become even more important, yet old challenges persist. A global materials company provides a typical example, with teams in different functions each developing their own unique information about the same products. The R&D department’s data focused on safety issues; the application engineering team developed tailored customer solutions; commercialization owned the product descriptions; and customer support assembled a set of highly specific product details to answer user queries. With no single source of truth, conflicts naturally arose in the underlying data, which gen AI models struggled to parse.
Exhibit 3
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To resolve the issue, the company is now following a centralized data management system that harmonizes data from different sources, eliminating discrepancies and ensuring that all teams have access to the same accurate information. Crucially, the system emphasizes human oversight to maintain high data quality and reliability, especially for AI-generated answers. A robust governance structure further validates and regularly updates data.
Change management
As with earlier waves of digital innovation, gen-AI-based transformations are less about the technology itself and more about rethinking how humans work. If anything, gen AI’s potential to enhance creativity and innovation makes change management even more central, particularly as its impact depends on integrating human and gen AI capabilities.
It’s a tall order. A gen AI transformation must not only account for the complexities of an evolving technology landscape while yielding clear business results but also address risk concerns (see sidebar “Mitigating risk”), overcome skill gaps, and foster innovation and adaptability. And gen AI itself must keep improving, with AI agents subject to their own performance management systems.
Setting a bold aspiration for enterprise-wide impact. These obstacles are all too familiar to the typical COO, who is charged with leading the continuous-improvement efforts that sit at the core of next-generation operational excellence. They were the starting point for a tech industry COO who recognized gen AI’s potential to break long-standing operational logjams—and understood that success would depend on how well people embraced gen AI solutions.
The tech company’s work with gen AI started by tackling one of its thorniest cross-functional problems, where complex coordination led to frequent delays in generating highly tailored statements of work that outlined the details of the technology services each client would buy. Assembling a statement of work required the relationship manager to collect input from experts in internal functions ranging from finance and legal to data security, as well as from the delivery managers and solution architects leading the day-to-day work—and the client, too. Rework and errors were a fact of life, slowing response times to such a degree that relationship managers missed deadlines for important requests for proposals.
To build a tool that could generate statements of work for more than a dozen product lines, the company needed to scale quickly. The answer for this organization was to centralize. Leaders created a single working group comprising three main teams: one for engineering, one for business and data requirements, and one for change management.
The three teams collaborated extensively, particularly in reimagining workflows that would take full advantage of gen AI’s efficiencies. Previously, for example, creating a statement of work involved elaborate rounds of requirements gathering, feasibility analysis, and risk assessment—inevitably generating rework as later reviews identified issues that affected earlier decisions. By analyzing thousands of earlier statements, the new tool developed templates that highlight the most frequent potential problems up front. Specialist experts in legal, compliance, or related functions can instead focus their efforts on problems that don’t have a clear precedent.
Increasing employees’ confidence in a gen AI solution. The change management team’s involvement proved crucial not only in building the tool but also in ensuring uptake once it was deployed. Following the core principles of the influence model, leaders ensured that each product line had its own dedicated change champion, who served as an intermediary between users and the working group to develop and adapt statement-of-work templates that would meet user needs. The change champion would then help communicate with users and build their skills both in using the tool and in improving its capabilities.
The ultimate result is a templatized statement of work that replaces hundreds of document variations, each taking days to produce, with just five that now require only hours to build. This has eliminated thousands of hours of repetitive labor, freeing experienced employees to focus more on high-value work.
Strengthening COO–CIO collaboration
These examples illustrate how using AI to rethink a stream of value can yield much more improvement than simply automating a few tasks. It also requires a much closer integration between the COO and CIO, whose traditional incentives have often been in tension.
COOs charged with modernizing complex, legacy operations have often found off-the-shelf IT solutions to be a difficult fit at best. Yet the cost and complexity of bespoke technology can create substantial burdens for the IT function and the CIO. Some of the friction has dissipated as newer technologies, such as edge computing and standardized industrial communications protocols, have taken hold—along with modular IT architecture and more flexible development practices. But there’s more to be done.
AI’s short innovation cycles and high resource needs have raised the pressure for technology investments to yield their projected returns on schedule, if not sooner. When COOs and CIOs collaborate more effectively, troves of data can become usable insights for revamping operations and creating entirely new sources of value.
The technology company shows how this collaboration can produce results. The COO of the business took the lead in identifying the transformation opportunity and developing it so that it met operational requirements. The CIO’s involvement expanded the vision of what was possible, such as by finding new opportunities to adapt enterprise-wide gen AI investments for the specific data needs of creating statements of work. Along the way, the CIO’s team became more agile in working with the operations team so that the entire project could meet milestones.
COOs already know that dozens of narrow gen AI use cases are unlikely to add up to lasting operational improvement. Instead, gen AI’s potential comes from how it helps leaders rethink entire value chains. This is at the heart of the COO’s role and its future.
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