As global markets reel from President Donald Trump’s tariff policy changes, the United Kingdom’s central bank warned that the widespread use of AI for trading and investing could lead to a “herding” behavior that could raise the chance of sudden market drops, especially during times of stress because firms might sell off assets at once.

As more firms use AI for investing and trading, there’s a risk that many will end up making the same decisions at the same time, the paper said.

“Greater use of AI to inform trading and investment decisions could help increase market efficiency,” per the paper. “But it could also lead market participants inadvertently to take actions collectively in such a way that reduces stability.”

For example, the use of more advanced AI-based trading strategies could lead firms to “taking increasingly correlated positions and acting in a similar way during a stress, thereby amplifying shocks,” according to the paper.

Such market instability can affect the amount of capital available to businesses since they can’t raise as much when markets are down.

The report comes as global equity and bond markets have been on a roller coaster since the Trump administration announced a minimum of 10% tariffs on imports from all countries, with China, the European Union and a few other countries getting hit with higher rates.

The Dow Jones Industrial Average has fallen by 6.2% since Trump’s April 2 announcement, while the S&P 500 gave up 7.1% and the Nasdaq Composite fell by 6.9%. The benchmark 10-year Treasury yields rose from 4.053% to 4.509% over the same time frame as investors flocked to safety.

Federal Reserve Chair Jerome Powell said tariffs are “likely to raise inflation in coming quarters” and “it is also possible that the effects could be more persistent,” according to a transcript of his April 4 speech before the Society for Advancing Business Editing and Writing. Inflation is a key statistic influencing monetary policy such as the direction of the Fed funds rate.

Powell’s comments came five days before Trump decided to pause tariffs for 90 days for nearly 60 countries, except China.

Read also: Trump Boosts Tariffs on Low-Value Packages Again After China Retaliates

AI and Systemic Shocks

The use of AI in algorithmic trading could exacerbate these extremes because many companies rely on the same AI models or data, leading them to act similarly, according to the BoE paper.

Although AI might make markets more efficient by processing information faster than humans, it could also make them more fragile and less able to handle shocks, the paper said.

The central banker said the International Monetary Fund (IMF) identified herding and market concentration as the top risks that could come from wider adoption of generative AI in the capital markets.

The IMF’s 2024 report said the adoption of AI in trading and investing is “likely to increase significantly in the near future.” While AI may reduce some financial stability risks through improved risk management and market monitoring, at the same time “new risks may arise, including increased market speed and volatility under stress” and others.

On the positive side, AI could help financial services firms manage risk more effectively by making better use of the data they already have, the BoE paper said. With stronger risk management, firms are less likely to be caught off guard when prices suddenly drop.

That means they might not need to rush into selling off assets all at once, which is what happens during a fire sale. The resulting damage caused by market selloffs could be mitigated or even avoided.

The central banker also pointed to another potential mitigating factor. If investment managers use AI to tailor strategies specifically for each client, it could lead to more market stability since people won’t hold the same assets.

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Companies Use AI to Help Mitigate Tariff Impacts

The ability of AI to make businesses more efficient is coming in handy as President Donald Trump’s back-and-forth on tariffs is making the markets swoon.

A Zilliant survey found that 83% of U.S. C-suite leaders are using AI to adapt their pricing strategies to economic volatility.

AI can help by monitoring and understanding tariffs in real time; finding new suppliers or sources for raw materials; improving scenario planning; raising worker productivity; and reducing costs.

Bank of America Invests in New Initiatives Like AI

Bank of America is allocating $4 billion toward new initiatives including AI in 2025, or nearly a third of its overall tech budget.

The financial services giant is seeing the benefits of using AI and machine learning, a journey it began in 2018 after launching an AI-powered virtual assistant called Erica to help consumers with financial matters. That’s four years before ChatGPT became a household name.

Gains across its business include a 50% reduction in calls to IT support after employees began using Erica for Employees, an internal AI chatbot. Developers were able to raise their efficiency by 20%. Employees save tens of thousands of hours per year by using AI to prepare materials ahead of client meetings, while sales and trading teams are more quickly and efficiently finding and summarizing Bank of America research and market commentary.

AI Helps Businesses Streamline Payment Processes

AI is becoming the equivalent of a corporate “pacemaker” as the technology helps enterprises manage their financial operations by automating and regulating billions of dollars in disbursements.

The result is that AI is becoming a profit center that helps businesses streamline their payment processes, ensure disbursements flow on time and in the right amount, and better manage their capital.

More than 80% of chief financial officers at large companies are either already using AI or considering adopting it for a core financial function, according to a forthcoming PYMNTS Intelligence report, “Smart Spending: How AI Is Transforming Financial Decision Making.”

Salesforce Sees Massive Growth in Data Cloud Platform

Salesforce is experiencing explosive growth in its data cloud platform, driven by enterprise demand for generative and agentic AI, technologies that rely on clean, unified, real-time data to be effective.

In an interview with PYMNTS, Gabrielle Tao, senior vice president of product management at Salesforce, said most companies struggle to unlock the full value of their scattered and siloed data.

Meta’s Open-Source Llama 4 Bad for Rivals Like OpenAI

Meta released its open-source Llama 4 models this week: Llama 4 Scout and Maverick.

They are the first multimodal models from Meta, meaning they can ingest images, not only text. Scout has a 10 million token context window (the amount of space for prompts). The previous record holder was Google’s Gemini 2.5, with 1 million and going up to 2 million.

Llama 4 is a challenge to proprietary models from OpenAI and Google.

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The Google parent company’s stock is down 16% this year, is underperforming other AI stocks like Microsoft and Meta, and is the cheapest among the Magnificent Seven companies, Bloomberg reported Thursday (April 10).

Investors are concerned that Alphabet’s dominance in search means it has nowhere to go but down, that the company has fallen behind its competitors in AI, that it is making substantial investments in AI, and that younger consumers are using AI services rather than traditional search, according to the report.

Since January 2023, around the time OpenAI’s ChatGPT appeared, Alphabet’s share of the global market for search engines has slid from 92.9% to 89.6%, the report said.

At the same time, Alphabet has several strengths that suggest its stock is undervalued, the report said.

Its ad business is not as exposed to tariff risk as other businesses, its revenue is expected to grow about 16% this year, and its wide range of businesses includes not only search and AI but also things like media and autonomous vehicles, per the report.

It was reported in February that investors were questioning the amount Alphabet is spending on AI. The company is estimated to have spent $50 billion on the technology in 2024 and is expected to spend more in 2025.

A combination of elevated investment in AI and challenges to revenue growth — such as the launch of low-cost AI models by DeepSeek — led to scrutiny by investors.

In December, it was reported that Google CEO Sundar Pichai told the tech giant’s employees that “2025 will be critical.”

“I think it’s really important we internalize the urgency of this moment and need to move faster as a company,” Pichai said. “The stakes are high. These are disruptive moments. In 2025, we need to be relentlessly focused on unlocking the benefits of this technology and solve real user problems.”

The pressures facing Google include challenges to its search ads and cloud services businesses, increased competition and regulation.

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But as enterprises increasingly adopt artificial intelligence (AI) to help them manage their financial operations, it’s turning the rapidly evolving technology into the equivalent of a corporate pacemaker, automating and regulating the smooth flow of billions of dollars in disbursements.

In the process, AI is becoming a profit center that helps businesses streamline their payment processes, ensure disbursements flow on time — and in the right amount — and better manage their capital.

A forthcoming report titled “Smart Spending: How AI is Transforming Financial Decision Making,” a collaboration between PYMNTS Intelligence and Coupa, finds that more than eight in 10 CFOs at large companies are either already using AI or considering adopting it for a core financial function: accounts payable, or the process by which companies pay their suppliers, vendors and contractors.

The trend is particularly pronounced among the biggest enterprises, with 75% of companies earning more than $10 billion annually having already blended AI into their payment processes. Imagine payment processes flowing seamlessly, expenditures laid bare with new clarity and operational efficiency improving — just like an elite athlete with a pacemaker.

What exactly does AI do in the realm of accounts payable?

For one, it can assist in managing payment terms and optimizing a company’s working capital. Those improvements in turn can fuel enhanced visibility into a balance sheet and smarter spending decisions. More than two in three enterprise CFOs are willing to invest in AI solutions that provide real-time visibility into expenditures, a sign of the fundamental shift that AI is driving in financial management.

The report, which publishes on Friday (April 11), shows how CFOs increasingly recognize the power of AI not just to streamline one of their most important and routine functions but also to boost their margins.

Irregular Heartbeat

But the path to integrating AI into an existing accounts payable system is notoriously bumpy.

Nearly two-thirds of CFOs report difficulties in making the different systems work together seamlessly. Another hurdle is the lack of customization offered by some AI tools, making it hard for companies to tailor the technology to their specific needs.

For service-based businesses, high upfront implementation costs can also be a major concern. Additionally, companies with complex operations or those spanning multiple regions may find it challenging to implement AI due to varying local regulations. In some instances, particularly in the technology sector, there are concerns about whether AI-generated results are always consistent or replicable, which can affect trust in automated decision-making. Call it AI’s heart arrhythmia.

Despite these challenges, the demand for AI solutions in financial management is growing.

CFOs are increasingly willing to invest in AI tools that offer real-time visibility into expenditures, with 68% indicating a willingness to pay for this capability. There is also significant interest in AI that can support vendor negotiations, optimize budgets, detect fraud and provide predictive analytics. The report details how this willingness to invest in AI reflects a growing recognition of the technology’s potential to transform financial operations and create a new competitive edge.

Key Takeaways

•   AI is being widely adopted in accounts payable by large companies to automate processes and improve efficiency

•   Companies are experiencing tangible benefits from AI adoption including better visibility into spending, reduced payment errors and improved vendor relationships.

•   Integrating AI into existing financial systems can be challenging due to compatibility issues, lack of customization and implementation costs.

• There is a strong and growing demand for AI-powered financial tools that offer real-time spend tracking, budget optimization and predictive analytics, indicating a significant shift toward AI-driven financial management.

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During a press briefing Wednesday (April 9) convened by Google Cloud, Richard Clarke, chief data and analytics officer of Pittsburgh-based Highmark, and Sameer Sethi, chief AI officer of Hackensack Meridian, discussed their practical experiences with rolling out artificial intelligence (AI) in a highly regulated industry.

“We really see this as a transformational technology,” Clarke said.

Highmark, which operates both a payer and provider network, has rolled out GenAI tools to more than 14,000 of its 40,000 employees. Already, they’ve surpassed one million prompts, using Google Cloud’s Gemini and Vertex AI as foundational tools.

“It’s been really a cornerstone for us to get comfortable with that, because we’re obviously dealing with incredibly sensitive information and data every single day,” Clarke said.

The impact also goes far beyond efficiency. Clarke said Highmark is using AI for software development, in its contact centers, for prior authorization processes and most notably, in ambient listening tools, which are technologies that use voice recognition to transcribe and interpret conversations between providers and patients.

“That has been a true gift to bring joy back to practice for many of our clinicians,” Clarke said.

Sethi said Hackensack Meridian is taking what he calls a “hyperscale” approach to AI deployment.

The Edison, New Jersey-based health system has grouped its AI investments into six strategic focus areas:

• Personalized and equitable patient and employee experiences

For example, it ensures that employees can get the right information they need at the time they need it for their jobs. For patients, they will have access to and understand their medical records before meeting with the physician, among other use cases.

• Administrative and clinical efficiencies

Automating tasks like note-taking and manual data entry to speed up administrative tasks. For example, rather than nurses manually entering test results from paper documents, AI tools now scan and integrate them into electronic medical records.

• Burnout alleviation

Reducing the administrative burden on doctors and nurses will mitigate burnout.

• Disease prediction

Looking through the patient’s data not only for early disease detection but to predict potential illnesses from signals in their medical records.

• Precision treatment

Using AI to customize treatment to the individual patient level that is attuned to their needs.

• Research and innovation

Finding more ways to advance medical innovations, with help from the latest technologies.

Read more: Microsoft’s AI Healthcare Push Aims to Boost Efficiency, Enhance Patient Care

A Multi-Agent Future for Healthcare

Beyond current implementations, both leaders are eyeing the next frontier: AI agents capable of reasoning, planning and taking action across systems.

“It’s the ability to go from insight to action,” Sethi said.

Sethi described a future vision where a single agent could handle an entire patient journey. From setting a patient’s appointment, to arranging for a ride to and from the hospital, making sure a wheelchair is available as requested, and picking up the medication en route to bring the patient home.

Those are six or seven steps done by human workers today, Sethi said.

While still aspirational, some pieces are already in place. Sethi said Hackensack has built a “nurse agent” that allows nurses to query vast documentation and receive recommended next steps, replacing time-consuming keyword searches.

Clarke echoed the importance of the transition to AI.

“Most of our work has been internal to our workforce,” he said. But “I’m very excited about this next phase (of AI agents) directly interacting with our patients … where high quality guidance is there all the time, always on.”

Clarke also said that the challenges they thought would impede AI adoption a few years ago, such as hallucinations and costs, “are really just not turning out to be the barriers that maybe we thought they were.”

Techniques like RAG have been reducing hallucinations, and costs per token have been coming down as well, Clarke said. Pairing that with the benefits they get from deploying generative AI, Highmark sees value being created.

As for hurdles to AI agent adoption, Clarke said that AI needs access to data and systems, as well as an orchestration framework for everything to work. Another factor is the organization’s own risk tolerance — how comfortable are they in letting AI agents autonomously complete tasks?

At Highmark, Clarke said they place use cases in “shadow mode” before moving to full automation on tasks in which they have high confidence the AI will do them right, which are typically lower-risk activities.

At Hackensack Meridian, Sethi said the hurdle is deciding which part of a human task they should automate.

“We went through these journeys as a part of the robotics process, automation phase,” Sethi said. “Building the automation, or the agents to actually do this, is not the hardest part. Rather, it’s figuring out what can and should be automated. That’s what slows us down.”

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These autonomous digital entities, capable of browsing, buying, trading, negotiating and even governing, are at the tip of payments and commerce innovation.

Amazon, for example, is testing a feature using agentic AI to help customers buy items from other brands while remaining within Amazon’s platform; while Paid, a startup, emerged from stealth last month to scale its financial infrastructure that helps the builders of AI agents get paid.

Yet for blockchain-native ecosystems, the rise of AI agents represents both a critical opportunity and a pivotal challenge. The opportunity is clear: intelligent agents can supercharge decentralized applications (dApps), DAOs, DeFi protocols and other on-chain ecosystems by automating complex interactions and executing tasks at scale.

The challenge, particularly for businesses? It is ensuring these agents can be trusted, and that their actions are traceable to real human intent when needed.

The crypto space has long championed the values of decentralization, permissionlessness and privacy. But in doing so, it’s often sacrificed usability and safety. Bots have gamed airdrops. Anonymous fraudsters have drained DeFi protocols. Coordinated misinformation campaigns have exploited DAOs.

PYMNTS covered Sunday (April 6) how cryptocurrency have thefts surged by 303% in the first quarter of 2025 alone.

As AI agents begin to flood the same ecosystems, these problems could multiply — unless a better trust layer is built.

Read more: Blockchain Interoperability Hits the Right Note for Crypto Payments

Trust Is Missing Layer Between AI and Blockchain

With AI agents taking on more responsibility, digital identity becomes more than a trust layer. It becomes the interface. In Web2, user identity is fragmented across platforms, controlled by centralized entities and vulnerable to exploitation. In Web3, identity is portable, composable and verifiable. AI agents will need to navigate this new terrain with clarity and consistency, whether they are interacting with humans, smart contracts or each other.

Just as websites have domain names and SSL certificates to signal trust, AI agents will need identity protocols to prove their legitimacy and intent. This could be especially critical in high-stakes domains like finance and payments, where trust is paramount and failure can be costly.

“Every crypto protocol needs to consider how this new technology will fit into their operations,” Harrison Seletsky, director at SPACE ID, told PYMNTS in an interview. “Verifiable on-chain identities will simplify AI-to-human and AI-to-AI interactions, making them safer by giving AI agents a humanly recognizable name, thereby cleaning out bots and bad actors from mispresenting themselves.”

Imagine a world where every AI agent has a unique, human-readable identity: something like trader.bot.eth or governance.vote.ai. These identities would be more than just names; they would carry on-chain credentials, transaction histories and behavioral reputations that are cryptographically verified.

In a financial context, this could be transformative. Once an AI agent has a track record, a known creator and a wallet that’s been through know your customer (KYC) or holds certain credentials, the calculus around trust and identity could change.

See also: Stablecoin Sandwiches? Here’s What CFOs Need to Know About Crypto Jargon

Integrating AI Into Blockchain Ecosystems

The potential for AI and blockchain payments to merge into a seamless, efficient and secure ecosystem is becoming increasingly undeniable to its proponents. However, it will require more than just technological breakthroughs. Collaboration between the AI and blockchain communities, as well as regulators and traditional financial institutions, will be crucial.

Chainalysis co-founder and CEO Jonathan Levin told Karen Webster in an interview posted Monday (April 7) that the integration of AI tools offers the potential for enhancing crypto literacy, compliance and the fight against illicit financial activity, particularly as crypto becomes further integrated into mainstream financial landscapes.

After all, PayPal announced Friday (April 4) that PayPal and Venmo users can now buy, hold, sell and transfer the cryptocurrencies Chainlink (LINK) and Solana (SOL) directly in their accounts; while Ripple on Wednesday (April 2) integrated its U.S. dollar-denominated stablecoin, Ripple USD (RLUSD), into its cross-border payment solution, Ripple Payments.

The marketplace is responding with secure solutions that balance AI and blockchain. On Tuesday (April 8), the blockchain-focused AI cybersecurity startup Octane emerged from stealth and announced it secured $6.75 million in a seed funding round. Octane uses machine learning to continuously analyze blockchain codebases, identify vulnerabilities and remediate them before they can be exploited.

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The new A8-SupplyChain models are domain-specific and have the deep contextual understanding needed to autonomously translate complex technical documentation into structured, actionable sequences, the company said in a Friday (April 4) press release.

“We built A8-SupplyChain specifically to tackle the problems that general-purpose GenAI can’t: delivering accurate, transparent and fully traceable reasoning through complex technical documentation and real-world workflows,” Articul8 Founder and CEO Arun Subramaniyan said in the release. “This is not just another model — it’s a fully autonomous system built specifically for mission-critical environments.”

The A8-SupplyChain models support complex enterprise production environments and platforms, including customers and partners, according to the release.

They can use unstructured data, including PDFs, engineering diagrams, maintenance logs, quality systems and structured tables, the release said.

Because they are trained on high-fidelity technical documentation, the models deliver AI-driven recommendations without extensive manual customization, per the release.

“With A8-SupplyChain, we’re giving aerospace and defense leaders something new: a fully orchestrated system that doesn’t just generate answers — it understands, adapts and drives outcomes,” Subramaniyan said in the release. “This is the next leap forward in enterprise AI — intelligent systems that operate at scale, with context, precision and trust built in.”

Enterprises are turning to AI to automate not just repetitive tasks but also more complex processes like compliance monitoring, fraud detection and supply chain optimization, PYMNTS reported in January.

Articul8 was established in January 2024 by Intel and DigitalBridge Group, which joined forces to create it as an independent company that provides enterprise customers with a secure and vertically optimized GenAI software platform.

The company’s platform enables business to harness the power of AI while keeping their data secure; offers a turnkey GenAI software platform that delivers speed, security and cost-efficiency to large enterprise customers; and supports a range of hybrid infrastructure alternatives, allowing customers to choose cloud, on-premises or hybrid deployment options.

Today, in addition to the new A8-SupplyChain, Articul8 offers domain-specific models for various industries, including energy and semiconductor, according to the Friday press release.

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Nowhere is this clearer than when it comes to artificial intelligence.

“This is an unprecedented time in history,” WEX Chief Digital Officer Karen Stroup told PYMNTS during a discussion for the What’s Next in Payments Series “The Rise of Digital Labor: Exploring Agentic AI in Banking and FinTech.” “The technology is evolving every six months. In general, organizations change on five- or six-year cycles. And so, the technology is outpacing us.”

While AI has been evolving for decades, the latest development — agentic AI — looks poised to shape the future of payments and financial services in new ways.

“For me, the way that I think about [agentic AI] simply is: It’s going beyond automation and taking it to the next step,” Stroup said. “There are two things that are key in agentic AI solutions. The first is the ability to reason and decision. That means that it can take in new information, it can learn, it can adapt, and most importantly, it can make a decision. And the second is it can take action.”

These capabilities are what make agentic AI “game changing,” she said.

As agentic AI solutions continue to evolve, Stroup said she envisions even further breakthroughs in efficiency and innovation. The rapid technological advancement is reminiscent of the early days of the internet — an era marked by curiosity, experimentation and opportunity.

Experimentation Key to Enhancing Enterprise Workflows

For its part, WEX, a leader in financial technology solutions, has approached agentic AI through careful experimentation. Stroup described the company’s strategy as starting with strong data governance policies before diving into testing new technologies, and then iterating while experimenting.

“Before we started experimenting, before we really started getting any traction, we partnered with our legal team, our risk and compliance team, and external third parties and said, ‘Hey, what would make this safe from a compliance perspective?’ Because we’re the stewards of our customers’ data, we take that really, really seriously,” Stroup said.

WEX’s experimental approach includes exploring agentic AI in various domains, such as compliance and customer service. One example Stroup highlighted was the automation of payment processes for suppliers — a friction point previously managed by scripts.

“We said, ‘Well, this is a really manual process, but we know that there’s a good payoff,’” she recalled. “We’re using AI to go in there, one, to onboard new suppliers quickly, which is great for everyone involved. And two, to use AI to do the routing instead of an older RPA process.”

Still, implementing new technologies is not without cost, and businesses must weigh the benefits against the investment required. This is another area where experimentation can shine.

“I think about it in terms of cost-benefit,” she said. “If you’re going to experiment with agentic AI or any type of AI solutions, you want to focus on two things. One is the areas where you’re most likely to have success. And two, is there going to be a good return on that investment?”

Trust, too, remains a foundational concern when deploying agentic AI in financial services. WEX’s strategy involves starting small, testing in controlled environments, and continually verifying AI models against known benchmarks.

“Trust is earned through transparency, traceability, explainability and through real-world validation,” Stroup said.

“If you look at the assumptions, then you can trust the output,” she added. “And I think the same is true in building confidence in AI.”

Read also: WEX Sees Embedded Experiences Impacting Supply Chains

A New Era of Problem-Solving

Stroup said she is excited about the potential of agentic AI to transform how WEX and other companies approach problem-solving. The technology is not just about automating processes but about empowering teams to work smarter and more effectively.

“WEX’s long history in deeply understanding our customers’ problems and payments — and the expertise and everything around payments — enables us to experiment quickly,” she said. “We can say, ‘Hey, what if we X, Y and Z?’ And then you can create an experiment designed for that.”

For WEX, embracing an experimental mindset to AI innovation is paying off. The company’s innovative AI-driven solution for automating supplier payments has not only improved customer experience but also boosted revenue.

“What is incredibly exciting to me and to WEX right now is we have new opportunities to solve problems in new ways,” Stroup said. “And the best way we’re going to unlock that is by experimenting and learning through safe, secure, but frequent iterations to see what’s possible.”

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The feature that’s in beta is called Buy for Me. It is live in the Amazon Shopping app for a subset of the company’s customers in the United States and with a limited number of brand stores and products, Amazon said in a Thursday (April 3) press release.

“We’re always working to invent new ways to make shopping even more convenient, and we’ve created Buy for Me to help customers quickly and easily find and buy products from other brand stores if we don’t currently sell those items in our store,” Oliver Messenger, shopping director at Amazon, said in the release.

For the other brands from which the AI agent can buy, the feature offers “increased exposure and seamless conversion,” Messenger said.

Buy for Me is integrated into Amazon’s shopping experience so that when customers search for branded items, they may see, along with results from Amazon and its third-party sellers, additional products from other stores in search results labeled “Shop brand sites directly,” according to the release.

Customers can link to those sites directly or, if they see a link to Buy for Me, select that option, learn about the product, and then ask Amazon to use its agentic AI capabilities to buy it for them, the release said.

If they ask Buy for Me to make the purchase, customers will receive an order confirmation email from the brand store and will be able to track their orders in the Amazon Shopping app, per the release.

“Earning trust is a cornerstone for the success of AI agents, and we’ve designed this experience to operate transparently in its interactions with customers and brand stores,” the release said. “The customer is in control of the AI agent acting on their behalf, and brands have the choice if they want to participate and benefit from the increased brand visibility, customer engagement and sales.”

Amazon said in February that it was testing the “Shop brand sites directly” feature that enables users of the Amazon Shopping app to find select products from other sites and go there to purchase them.

With that feature, when customers click on the link to one of those products, they receive a notification that they are leaving Amazon, go to the brand’s website and can then make purchases directly from that brand.

PYMNTS reported in December that AI agents will transform how consumers shop online.

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Google has added new features for vacation planning to Search, Maps, Lens and Gemini, according to a Thursday (March 27) company blog post. The features including getting new ideas for trips with a personal AI assistant, tracking prices of hotels and automatically saving locations the user wants to visit.

“Travel is a key vertical for Google, as it drives high-intent searches across hotels, restaurants, flights, attractions and local experiences — categories with strong monetization,” wrote Justin Post, an analyst for Bank of America Global Research, in a research note sent to clients.

The new Google features “will expand the volume and diversity of search queries, allowing Google to deliver more relevant results and target more high-value commercial content,” Post said.

“We think the integrations also enhance Google’s ability to compete with emerging AI search platforms, such as ChatGPT, Perplexity and others, which have been improving travel planning capabilities,” the analyst said.

Google’s travel enhancements come as users increasingly turn to AI assistants to help them with vacation plans. According to a PYMNTS Intelligence report, “At Your Service: Generative AI Arrives in Travel and Hospitality,” 52% of respondents surveyed expect AI planning assistance.

AI startup Perplexity is taking it a step further by announcing partnerships with Tripadvisor and Selfbook. Travelers can now book hotels within Perplexity, wrote Perplexity CEO Aravind Srinivas in a LinkedIn post. Srinivas also said the company will soon announce details on discounts on hotel bookings offered within Perplexity’s AI chatbot.

The new capability is part of Perplexity’s new “answer modes” feature to enhance search for travel, shopping, places, images, videos and jobs, Srinivas said.

“Going beyond answering a wall of text” by adding images, videos and enabling financial transactions “is a necessary step for being a daily-usage product and taking on Google,” Srinivas said.

Travel companies themselves are using GenAI to help travelers with their vacation planning, further minimizing the need for Google searches. For example, Booking.com is using OpenAI’s AI models for its own solutions.

Read more: Seamless Journeys: AI’s Rising Role in Coordinating Consumer Travel

Google Fights Back With New Travel Features

Google is fighting back with enhancements to its travel features.

“The new AI features have the potential to drive incremental queries on Google’s platform by engaging users earlier in the decision-making process,” BofA analyst Post said. “Rather than transactional keywords like ’cheap flights to Paris,’ users can potentially now ask broader questions such as ’suggest scenic road trip routes in California.’”

Starting this week, Google is rolling out the following travel features:

• AI Overviews in search is adding more cities and countries.

Travelers can get trip ideas for more cities, not just main metropolitan areas like New York or Rome. Users can also get trip information for specific regions or entire countries.

For example, they can ask AI to “create an itinerary for Costa Rica with a focus on nature.” They will get photos and reviews from other users and see locations on a map. Users can export the information and share them through Gmail or Google Docs — or save them as a list on Google Maps for on-the-go access.

• Travelers can now track hotel prices.

Google Flights is a popular way to find and track flights. Now, Google is bringing the same functionality to hotel prices for specific dates and cities. When searching for hotels, a price-tracking toggle will show up for users to enable. Google will send an email if prices drop significantly.

• Google Maps automatically identifies places that interest users.

Users planning travel often take screenshots of places they’d like to visit, whether through social media, news articles or travel blogs. Google Maps can automatically identify these places and organizes them in a list. Places users saved will show up on Google Maps.

• Gemini now lets users create their own AI expert for free.

Google is now offering users free use of “Gems,” a tool for creating their personal AI expert. For example, they can create their custom trip planner AI assistant, then ask it to help them pick a new vacation destination, find things to do and even what to pack for the trip.

• Google Lens is adding more languages.

Through the Google app, Google Lens lets travelers identify things of interest around them, such as ornately carved wooden doors in Florence, Italy. It can also be used to translate signs, menus, flyers and other text in another language. Lens is planning to add capabilities in Hindi, Indonesian, Japanese, Korean, Portuguese and Spanish.

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