The booking funnel is fractured by generation, with younger travelers leaning more on technology and social media.

In July, travel technology company iSeatz commissioned that included responses from 1,000 Americans travelers over the age of 18. The survey, conducted by Talker Research and titled “The Modern Traveler 2025,” revealed that the booking path no longer follows traditional patterns.

“Today’s travelers are not just choosing destinations. They are navigating a digital journey from discovery to booking, and they expect it to feel effortless, intuitive and personalized at every step,” said Kenneth Purcell, founder and CEO of iSeatz. “These rising expectations do not come out of nowhere. Consumers have been conditioned by the digital ease of e-commerce, social media and streaming platforms.”

ISeatz found that travelers are discovering travel opportunities in a more fluid process, which also requires more steps. 

“Instead of following a straight path from idea to booking, most travelers now move back and forth between inspiration, research, comparison and planning across a variety of platforms,”  iSeatz said in its report.

This shift is tied to social media for younger generations, while older generations are still relying on friends and family for recommendations.

Generally speaking, 43% of respondents said they are inspired by loved ones. But younger generations are more often inspired by social media: 52% of Gen Z and 46% of millennials said it is their “primary source” for travel inspiration.

The report found that during the research phase, 45% of Gen Z members prefer using social media over traditional search engines. Overall, 43% of travelers still use traditional search engines like Google and Bing, but 27% go to social media first.

Nearly 40% of travelers also said social media influencers had a “significant impact” on how they book and where they travel, with that figure ticking up among younger generations. Sixty-two percent of Gen Z respondents said influencers impact their decisions.

Social media’s influence is further illustrated by Phocuswright research that found almost two thirds of travelers made a trip purchase or visitation based on content they viewed while trip planning.

Considering the survey results, iSeatz said some travel brands are missing the mark.

“There isn’t currently enough technical infrastructure to support discovery-to-booking experiences within social platforms,” iSeatz said. “That’s a missed opportunity: 53% of millennials and 52% of Gen Z say they’d book travel directly from social media if it were secure and seamless.”

That is a gap that some travel brands and social media platforms—including Expedia and Instagram, Booking.com and TikTok and TourRadar—are trying to solve.

But regardless of age or generation, the funnel is still fragmented, according to iSeatz. 

“Travelers often jump between social feeds, search engines, review sites and booking engines, which creates both friction and opportunity. Travel brands that can bridge these gaps will be better positioned to capture interest and convert it into action.”

Additional AI findings

The rise of artificial intelligence (AI) is having an impact on traveler behavior too, as other reports have also found.

Around one in five travelers reported regularly using AI, and that percentage ticks up among younger travelers, with 35% of Gen Z and 34% of millennials using AI regularly. 

And with AI tools maturing, travelers are anticipating more personalization, iSeatz found.

“Fifty-seven percent of travelers already expect brands to anticipate their preferences and needs based on past behavior,” iSeatz said in its report. “Millennials, in particular, are driving this shift. Seventy-four percent say personalization is a baseline expectation, not a bonus.”

And the majority of travelers are not strongly opposed to sharing their data to make that happen.

“The travel companies that succeed in this new landscape will be the ones that understand their customers deeply and design every touchpoint around what today’s travelers value most,” iSeatz said.

The era of agentic AI is here, or so we are told, bringing super-smart AI assistants capable of carrying out complex tasks on our behalf.

This represents the next generation of AI beyond current chatbots like ChatGPT and Claude, which simply answer questions or generate content.

Those building (and selling) the tech tell us we are on the verge of a fully-automated future where AIs cooperate and access external systems to carry out vast numbers of routine knowledge and decision-making tasks.

But just as emerging concerns around hallucinations, data privacy and copyright have put up barriers to generative AI that some organizations have found insurmountable, agents have their own set of obstacles.

So, here’s my rundown of the challenges that developers of AI agents, organizations wanting to leverage them, and society at large will have to overcome, if we’re going to deliver the promised agentic future.

Trust

The biggie. To achieve the critical mass of adoption needed to fuel mainstream adoption of AI agents, we have to be able to trust them. This is true on several levels; we have to trust them with the sensitive and personal data they need to make decisions on our behalf, and we have to trust that the technology works, our efforts aren’t hampered by specific AI flaws like hallucinations. And if we are trusting it to make serious decisions, such as buying decisions, we have to trust that it will make the right ones and not waste our money.

Agents are far from flawless, and it’s already been shown that it’s possible to trick them. Companies see the benefits but also understand the real risks of breaching customer trust, which can include severe reputational and business damage. Mitigating these risks requires careful planning and compliance, which creates barriers for many.

Lack Of Agentic Infrastructure

Another problem is that agentic AI relies on the ability of agents to interact and operate with third-party systems, and many third-party systems aren’t set up to work with this yet. Computer-using agents (such as OpenAI Operator and Manus AI) circumvent this by using computer vision to understand what’s on a screen. This means they can use many websites and apps just like we can, whether or not they’re programmed to work with them. However, they’re far from perfect, with current benchmarking showing that they’re generally less successful than humans at many tasks.

As agentic frameworks mature, the digital infrastructure of the world is likely to mature around them. Most people reading this will remember that it took a few years from the introduction of smartphones to mobile-friendly websites becoming the norm. However, at this early stage, this creates risk for operators of services like e-commerce or government portals that agents need to interact with. Who is responsible if an agent makes erroneous buying decisions or incorrectly files a legal document? Until issues like this are resolved, operators may shy away from letting agents interact with their systems.

Security Concerns

It doesn’t take much imagination to see that, in principle, AI agents could be a security nightmare. With their broad and trusted access to tools and platforms, as well as our data, they are powerful assistants and also high-value propositions for cybercriminals. If hijacked or exploited, criminals potentially have decision-making access to our lives. Combined with other high-tech attacks, such as deepfake phishing attempts, AI agents will create new and potentially highly problematic avenues of attack for hackers, fraudsters and extortionists. Agents must be deployed by individuals as well as businesses in a way that’s resilient to these types of threats, which not everyone is yet capable of doing.

Cultural And Societal Barriers

Finally, there are wider cultural concerns that go beyond technology. Some people are uncomfortable with the idea of letting AI make decisions for them, regardless of how routine or mundane those decisions may be. Others are nervous about the impact that AI will have on jobs, society or the planet. These are all totally valid and understandable concerns and can’t be dismissed as barriers to be overcome simply through top-down education and messaging.

Unfortunately, there’s no shortcut available here. Addressing this will involve demonstrating that agents can work in a reliable, trustworthy and ethical way. Pulling this off while also building a culture that manages change effectively and shares the benefits of agentic AI inclusively is the key here.

Agents Of Tomorrow

The vision of agentic AI is quite mind-boggling: Millions of intelligent systems around the world interacting to get things done, in ways that make us more efficient and capable.

As we’ve seen, however, the obstacles to this are just as likely to be human as they are technological. As well as solving fundamental issues like AI hallucination, and building infrastructure that enables agents in ways that are trustworthy and accountable, we have to prepare society for a fundamental shift in the way people work with machines.

Accomplishing this will pave the way for AI agents to hit the mainstream in a safe way that enhances our lives rather than exposes us to risks.

36Kr learned that “Maimai Technology Group” (hereinafter referred to as “Maimai Technology”) has completed a Pre – A round of financing exceeding 100 million yuan, with a post – investment valuation exceeding 1 billion yuan. The round was jointly led by institutions such as Qihong Yuyuan, Xinglian Capital, Spring Light Lane, and Honglian Qiyuan, with some existing shareholders participating in the follow – on investment. The funds from this round of financing will mainly be used for the R & D and innovation of core technologies such as AI agricultural large models and intelligent sensing devices.

According to Li Nan, the founder, chairman, and CEO of Maimai Technology Group, this financing is the largest – scale early – stage financing in the smart agriculture field recently.

Maimai Technology is an artificial intelligence company that takes data, algorithms, and scenarios as its core foundation and “technology + consumption” as its core business model. It transforms traditional agriculture through agricultural production technologies and information technologies such as the Internet of Things, artificial intelligence, cloud computing, and blockchain. Its headquarters is located in the core area of Zhongguancun, Beijing.

When talking about why he entered the agricultural sector, Li Nan admitted that it stemmed from two heart – wrenching contrasts. When he worked in a large Internet company before, he saw that cutting – edge technologies such as aerospace remote sensing and virtual reality were concentrated in the consumer entertainment field, while the agriculture that supported consumption lagged behind in technology. Later, during a rural survey, he found that over 95% of farmers in rural areas were over 55 years old, and “people under 55 neither liked nor knew how to farm.” Agriculture was facing a crisis of “no successors.”

Under such an impact, Li Nan entered the agricultural field and spent half a year assembling a so – called “luxury” founding team. Different from traditional agricultural enterprises or pure technology companies, its core team consists of three types of cross – border elites, forming a stable triangular structure of “understanding agriculture, strong in technology, and good at commercialization.”

Since 2018, Maimai Technology’s business model has been iterating according to industrial needs.

In the first two years, in Li Nan’s words, the company was more like an “agricultural technology solution integrator,” piecing together various technologies to solve specific problems of farmers, targeting the technical needs of customers. Later, in order to become a technology company that understands the industry best, Maimai Technology completed the innovation of a new model, providing customers with technical capabilities and helping them complete the connection between production and sales.

What really helped Maimai Technology build its moat was the investment in relevant research on artificial intelligence starting in 2021. After several years of exploration, Maimai Technology has successfully built a core foundation of “model + data + scenario” and a complete crop growth model system covering “description, diagnosis, prediction, and decision – making,” deeply empowering key links in agricultural planting management and establishing advantages in crop models, data computing power, and in – depth scenario development.

The Super Brain Smart Agriculture Big Data Platform is a digital infrastructure for smart agriculture built by Maimai Technology based on technologies such as the Internet of Things, AI, big data, satellite remote sensing, and blockchain, combined with agricultural professional knowledge and front – line business experience. The platform takes “models, scenarios, and data” as its core elements and realizes the collection, processing, analysis, and application of data across the entire agricultural industry chain through a three – layer architecture (information perception layer, data processing layer, and intelligent analysis layer), providing data empowerment for the entire process of agricultural production, circulation, and sales.

According to Li Nan, the “crop large model” developed by Maimai Technology is not a “simplified version” of a general large model, but a “coupled system of small models” focusing on vertical scenarios, which can accurately solve specific industrial problems. For example, a vertical model can only solve the problem of peach planting in Central China, while different models are needed for plum and cherry planting in East China.

As of now, Maimai Technology has completed the R & D of nearly a thousand vertical scenario models for 15 major categories and over 200 sub – categories of staple food crops such as wheat and rice and cash crops such as strawberries and blueberries. It has also deployed multi – point data collection in places such as Beijing, Jingmen in Hubei, Chongqing, and Hainan for model verification and optimization.

The value of technology ultimately needs to be verified by industrial results. A citrus – growing customer in Hubei previously faced three major pain points: a high proportion of blemished fruits, which prevented them from entering supermarkets; a low percentage of large fruits, resulting in low selling prices; and insufficient sugar content, making the fruits less competitive in taste. After analysis, the Maimai Technology team took “water” as the core regulatory factor and quantified two key dimensions through the model: one is the water environment (drought duration, air humidity, soil moisture), and the other is the crop’s water demand pattern (upper and lower limits of water demand at different growth stages).

After four years of practical application, the results are remarkable: the blemished fruit rate has dropped to less than 5%, the large – fruit rate has stabilized above 85%, and the sugar content has increased by 2.5 – 2.7 units, ultimately helping the customer successfully enter some high – end supermarket channels.

Taking a large blueberry model in Yunnan as an example, based on in – depth analysis of the blueberry crop mechanism, Maimai Technology scientifically demonstrated the natural environment in Mengzi and established a comprehensive blueberry model system, including environment simulation and optimization models, soil water and fertilizer simulation and optimization models, etc. Eventually, the application of the crop growth model helped increase the blueberry yield by up to 30%.

In the strawberry – planting large model, Maimai Technology combines its self – developed crop growth model with agricultural Internet of Things technology. Through the intelligent collection and analysis of strawberry – planting environment data, it can make accurate decisions on environmental parameters such as light, temperature, humidity, and carbon dioxide in the strawberry – planting environment. Practical data shows that the application of the strawberry growth model can shorten the strawberry growth cycle by 10% and increase the yield by 20%.

In terms of R & D, Maimai Technology has two core R & D institutions: the National R & D Center and the Agricultural Industry Research Institute. The National R & D Center has assembled 7 national – level expert teams and has a professional R & D team of 270 people, focusing on the R & D of hard technologies such as drones, automated equipment, and visual recognition technology, covering the entire chain of technological breakthroughs in agricultural artificial intelligence. The Agricultural Industry Research Institute focuses on research at agricultural bases, specifically studying crop growth mechanisms and product optimization, and is committed to breakthroughs in cutting – edge technologies such as large crop growth models.

In terms of technology accumulation, Maimai Technology holds over 120 patents and software copyrights related to smart agriculture and has served over 170 Fortune 500 – level customers in China. On the production side, it is deeply involved in the technology and production guidance of over 70 modern digital farms, with a total production capacity exceeding 10 billion yuan; it has established ecological cooperation with over 190 modern digital farms, with a total production capacity exceeding 18 billion yuan.

It is worth mentioning that the company has had positive audited net profits for six consecutive years, and its revenue growth rate has remained above 200% for many years. Currently, the company has initiated the planning for a Series A round of financing and has clearly set the strategic goal of officially striving for an IPO in 2027. In the future, it will continue to achieve exponential growth in revenue and profit driven by technology.