Filter in or out as many as 200 cities, as well as hotel and car rental class and meals of the day and watch as the per-diem calculator automatically adjusts per diems to your program. Drill down into cost breakdowns and export the results.
W Hotel City Center - September 23, 2019
Le Meridien Etoile - 4 October, 2019
London Marriott Hotel Regents Park - 7 October, 2019
Artificial Intelligence is the buzzword for 2017. AI has infiltrated industries as diverse as personal shopping, executive recruitment and medical diagnostics. Transformational fervor around AI has fizzled in the past, but AI-powered messaging platforms have energized innovation of late.
"The only way that we
can serve our travelers is by turning them into data," travel management
provocateur Eric Bailey told BTN. As Microsoft director
of travel, venue sourcing and payment, he has pretty much ditched travel policy for a traveler-centric approach, and machine
learning has emerged as critical to the effort.
Enter the chatbot travel assistant. Startups have thrown down the gauntlet, enabling "virtual travel assistants" as early as 2015; Pana's irreverent pitch to enterprises in October 2015 began: "Friends Don't Let Friends Use Concur." Several more have followed suit: HelloGbye,
Mezi, TripActions, 30SecondsToFly and others have introduced mobile travel assistants, touting booking capabilities, with or without integrated corporate policies; offering proactive alerts and disruption support; and even expanding recommendation capabilities to local restaurants and entertainment, which is atypical for
corporate travel agency services.
So far, the startups have targeted individual corporate travelers and smaller enterprises that have lightly managed programs, minimal preferred supplier agreements and straightforward travel itineraries. But it gets sticky for a bot to digest data and make judgments for negotiated, global
managed travel programs that contend with policy parameters, frequent trip disruptions and traveler-initiated changes.
That hasn't stopped established managed travel players from Concur to a number of travel management companies from pursuing similar AI strategies. After it acquired Hipmunk last October, Concur was particularly excited about the Hello Hipmunk AI tool, Concur EVP of platform and data
services Tim MacDonald told BTN. He said Concur planned to act as an incubator for Hipmunk to work on bots to benefit the Concur enterprise product suite. On the TMC front, Carlson Wagonlit Travel's Carla avatar has been in beta for several years, while Australia-based FCM Travel Solutions introduced its
message-based mobile travel assistant Sam, which stands for SmartAssist Mobile, last July in the U.S. FCM plans to roll Sam out to Europe and Asia this year. American Express Global Business Travel has not announced a chatbot, but Oliver Quayle, VP of product marketing and innovation for Amex GBT and KDS now that
the two have merged, cited GBT's database as central to KDS's post-merger innovation map, which looks like it will favor AI and machine learning and may build on the predictive and personalized approach of KDS's Neo booking tool.
ThoughtSpot may be a small company, with 100 travelers, but it's got big ambitions and international office locations. After launch, it chose a mega agency and a well-known expense provider. ThoughtSpot enjoyed the service and the agency rates, but as an agile startup, it was looking for a level of technology innovation it wasn't getting from the big agency.
What's the Goal?
"When I talk internally, I always tell the story of the great travel agent during my time as a road warrior at IBM when I traveled 150 nights a year," said Travelport senior director of product innovation Nathan Bobbin. "She knew everything that I wanted in my constantly
changing schedule. She'd say, 'Hey, I know you're supposed to go home, but instead, you are going to Tel Aviv. So here's the trip I booked for you. You're at the Marriott because I know you love Marriott; I didn't take the early flight because there was no aisle seat. There's an extra connection, but I knew
you'd be OK with that …' And she was always right because she knew all that stuff about me. That's the experience powering the dream of AI, machine learning, personalization and mobile, where all those capabilities come together so that everyone can have one of those amazing travel agents in their
Microsoft global director of travel, venue sourcing and payment Eric Bailey painted a comparison that makes today's standard corporate online booking tool experience seem absurd. "You can almost go through the insane conversation you would have with an OBT: 'Please give me an hour with a
plus- or minus-three-hour range as to when you want to leave. In return, I'll give you the lowest price without any reference to what your preferences are, and I don't care if it's a dollar cheaper or $1,000 cheaper.'"
Personalization and predictive results are just two features chatbot travel assistants aim to provide. Amid 24/7, globalized business and travel disruptions, imagine round-the-clock alerts and predictive rebooking support. Also within reach are expanded services that can recommend local
restaurants and make reservations or that can identify entertainment and fitness options that appeal to the individual traveler. Indeed, some apps like Mezi and Pana claim they already can handle some of these details.
How Do We Get There?
The short answer is data—and lots of it. Co-founder and CEO Swapnil Shinde described Mezi's behind-the-scenes structure: "When a user sends a message to Mezi, several different chatbots begin collaborating with one another. If AI detects that the user's intent is to look and book flights,
a flight chatbot will start talking to the customer. If the intent is hotel, a hotel bot will talk to the customer. So then we have a bot for dining and one for payments; we even have a bot for reminders and marketing."
Everyone who talks about travel
bots mentions Lola. Co-founded by Paul English, former co-founder of Kayak, it has focused on consumer travel, though its website
refers to "premier partnerships" that give executives of "select
companies" access to its services. Early press focused on the team's effort to evolve
travel agency tech, with an expectation that the company would pursue that
market, where artificial intelligence could transform the current green-screen
environments and increasingly fragmented content.
All these bots are powered by data feeds. Mezi uses global distribution system content, Expedia content, Priceline content. TripActions, for another example, has agreements with Sabre, Booking.com and Priceline. But travel content is just one data set. Chatbots also look at flight schedules,
delays and cancellations. Data on weather or traffic conditions may power alerts, and historical conversations and bookings that the traveler has made within the tool can power recommendations. Mezi uses only historical data, while other tools begin with a profile and add to it through usage.
Other apps have forged data partnerships to bolster personalization. HelloGbye accesses American Express card data for hotel transactions to deliver personalized hotel results to users even if they don't have a long history or a complete traveler profile stored with the HelloGbye tool. Juiced
up with Amex card data, the bot will digest the individual's historic bookings but also mine the transactions of other travelers to return "Amazon-like" recommendations. Making sense of these large volumes of data and turning them into relevant recommendations for individuals requires machine learning.
Managed travel technologies have, for a long time, relied on rules engines to drive automation. TMCs use "if this, then that" scripts in mid-office systems to run quality-control and quality-assurance routines on trip reservations. Configuring an online tool to bias preferred
partners in search results is another example of a rules engine's work. All of that is familiar territory for agents and travel buyers.
Re-shopping tools like Yapta and Tripbam are based on automated mid-office routines that TMCs have performed for more than a decade, but with a twist. They define a cohort of comparable hotels or flights instead of shopping the same hotel or flight. That change—knowing the right cohort to
re-shop—introduced one of the first "machine learning" innovations for managed travel.
"As those kinds of innovations expand outward from basic rules engines to more and more complex rules, you get to a point where you can't manually configure all of the rules because there are just too many scenarios or you don't know what the if/then [action] should be," said
Evan Konwiser, digital traveler VP for Amex GBT. "You get to a point where you have to create more mechanized solutions for a rules-engine effect. That's where machine learning comes in, where you allow the algorithms to effectively set the rules themselves based on the data that is available. The enhancement
of machine learning is that those algorithms self-evolve, meaning they can take feedback from a user from any number of [data] sources and they can improve the algorithms over time without somebody going in and manually configuring changes." In reality, though, these algorithms are tweaked manually all
the time as R&D pursues increased accuracy and better insights.
Algorithm-based machine learning has to happen in the background of chatbot apps—or of any travel booking tool—to increase personalization for the user. If built from historic data, that could include everything the traveler booked, plus details on what they did not choose. Or a
bot could start with a rich data profile with all of the user's declared personal information, travel preferences and loyalty alliances and then layer on subsequent usage data.
Machine learning also can help the tools themselves perform better as trips get more complex. When trip complexity makes it impossible for Mezi's bots to collaborate effectively, the request transfers to a human travel agent. "The travel expert will step in, our chatbot will learn from that
interaction, and next time [the bots] will be smarter in handling all Mezi users. The platform is designed in a way that it becomes smarter with every conversation," said Shinde.
Natural Language Processing
Chatbots' success relies on broad access to data, elegant user interfaces and personalized results, but natural language processing has been a game changer in making chatbots usable. It also has pushed innovation hard toward perfecting messaging platforms and delivery.
Online demos for HelloGbye and Pana, for example, show moderately complex trip requests going through the chatbots. Though chatbots work with both written and spoken communication, these demos showed spoken queries: Someone describes the trip aloud—complete with idioms—including
class-of-service and style preferences like "modern" or "four-star," and the tools return personalized results nearly instantly. HelloGbye also demonstrated a "conversation" in which the user was booking two travelers. After the bot returned search results, the booker realized he'd
forgotten to say that only one traveler would be flying in business class and that the travelers would be staying in different hotels. He corrected himself, and the bot processed the revision and both bookings without a problem.
That's the kind of advancement BCD Travel director of emerging technologies Miriam Moscovici said is critical for message-based apps to succeed. "Realizing the ability to have a cumulative conversation has been a dramatic change for natural language [processing]. Eight or nine years
ago, you could ask a chatbot what the flight is to Los Angeles on a given day, and it would respond with some options and some prices, but that was it," Moscovici said. "Now, you can respond again to the chatbot and say, 'What about a day earlier?' Entire lengths of conversations can be harmonized into
one understanding of what the question is and be able to execute commands."
It's not always perfect, contrary to the flawless natural language processing depicted in online product demos. Some online reviewers of these message-based apps said the chatbots misunderstood phrases, but such slipups didn't dramatically deflate app ratings, which were positive overall. There were plenty of business travelers in the reviewer mix, but it was unclear how heavily managed they were. One has to assume, given the small list of corporate contracts these startups can claim, that most were lightly managed or unmanaged. Getting enterprise clients is a tough hurdle to clear.
Also, great content and a fantastic natural language interface may not be a match for serious travel disruptions. This is an area where chatbots need to make judgment calls, and they may not be so good at that yet. Judgment also comes into play with policy exceptions or waivers that travelers access through their agencies; these might be needed even for minor disruptions or for productivity reasons if the trip is important enough. Travelport's Nathan Bobbin, who sees many startups come through the doors of the Travelport Labs technology incubator, hasn't seen an AI-powered travel bot that's up to the task for such scenarios.
Confounding the Bots
Historically, travel technology has struggled with managed travel policies, which set up clunky rules systems. But that's not the problem for chatbots. Travel policy rules are machine learning's dream. They are clear, straightforward and can overlay all the travel content that the bot can pull in. The problem for bots is the opposite: They don't know when to make exceptions.
"It's easy for a company to be draconian with a travel policy, but it's not easy to keep employees if you are. They're gonna split," said Bobbin. "The point of managing travel is enabling your travelers to make the right decisions for the business inside the context of the policy, and that's hard: creating a policy that's flexible but effective." Computers are not good at that, he said. "For 100 percent of managed travelers, AI travel bots can maybe handle 20 to 30 percent of the use cases. That's the most frustrating part because that doesn't mean we can solve 100 percent of the needs for 20 percent of the market. If that were the case, we'd go out and win that 20 percent."
Nearly every [startup] pivots very early on to being agent powered with a little bit of AI rather than AI powered with an agent escalation."
That's where Amex GBT's Evan Konwiser said agencies actually have a leg up in the chatbot race. "There have been a lot of startups that have played in this game, but nearly every one of them that I've seen pivots very early on to being agent powered with a little bit of AI rather than AI powered with an agent escalation," Konwiser said. "To me, that's a very important data point and a very important lesson for our industry. The TMCs already have the agents."
According to Konwiser, chatbot agents do not provide the right traveler experience to be first out of the gate in the messaging channel. Rather, he said, it's smarter to start with humans working through the messaging channel and then to layer in AI. "For [GBT], it's the long game of creating that new channel, of creating the right AI self-service systems on top of that channel so that we can create the right efficiency game. But we're doing it from a traveler experience standpoint more than we're doing it from a pure efficiency standpoint. The answer is not to throw down some cheap two-function bot to our travelers."
Who Will Get There First?
If there's one deja vu element in the story of managed travel chatbots, it's the tension between technology companies' and TMCs' approaches to the solution. The technology companies don't necessarily grasp the service side, while the TMCs are typically not as adept at the technology piece, instead trying to introduce solutions through the scope of their agents.
Johnny Thorsen—founder of the first managed travel SMS messaging app, conTgo, which Concur bought in 2013—unsurprisingly believes the tech companies may have the edge. "There are so many variables and scenarios that it's not like when you have done X percent, then the rest are done automatically," said Thorsen, who is now SAP Mobile Services senior director of value solutions. "That's really critical. That's why it will be interesting to see, two years from now, who the leading AI solution is. In my mind, they need to know more about the technology than travel. The AI engine they're building [has] to be capable of analyzing each conversation and detecting a possible pattern. Once there is a pattern, that's when they can start influencing what comes out. That's not about travel knowledge; it's about content knowledge and understanding the conversation."
Bobbin pointed out the obvious: "We're in a very nascent market with AI and managed travel." He added that "somebody will bring the technology to bear to a point where you can serve 20 percent of the market 80 percent of the time, and that's when this will take off. But I don't think we're there yet." He guessed AI innovation would take five years to catch up with aspirations.
Somebody will bring the technology to bear to a point where you can serve 20 percent of the market 80 percent of the time, and that's when this will take off."
Plenty of startups think they are already there. HelloGbye has gone all in on AI. A spokesperson told The Company Dime last month that the platform offers a "rich conversational platform that is fully digital. No humans," and while the company acknowledged potential service gaps, it questioned whether any other model could scale. Mezi, Pana and TripActions have taken a more moderate approach, engaging agents pretty extensively, at least at first.
Mezi's Shinde has been building on his agent base for two years. "You can imagine that on Day One, maybe 90 percent of our requests were solved by humans and 10 percent were solved by AI," he said. "Today, AI has become so powerful that around 75 percent of the requirements [we get] for a flight request or a hotel request are solved by our AI-powered chatbot; no human [agent] is involved."
While Mezi's journey is nearly identical to what Konwiser described as the best path forward, there's another factor not involved for Mezi at this point: enterprise clients. That said, more than 30 percent of Mezi users pay with corporate cards at least part of the time, Shinde said, adding that Mezi would announce enterprise clients "in the next several months." Per usual for chatbot apps, Mezi is targeting companies with travel spend under $10 million.
Don't Discount the Midmarket
That market, however, is huge. And it's one for which the larger agencies have had a devil of a time providing solutions that can scale down without tying up resources. Many smaller clients, as a result, have been left in the unsatisfying role as the smallest fish in the large pond.
Online travel agencies like Egencia and TMCs like AmTrav that have been the refuge for small- and midmarket companies looking for lighter-touch programs, will need to keep an eye on their smaller clients if they plan to keep them. Indeed, TripActions co-founder and CEO Ariel Cohen told BTN his primary market is companies that spend $500,000 to $5 million annually on travel. But the ones that save the most money and thrive using TripActions are not starting travel management from scratch, he said. "Rather, they are replacing their current agency and technology relationships."
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