Tools & Platforms
‘AI receptionist’ IVR takes flight with customer service
AI agents that can answer business phones and route — and even resolve — customer service calls more efficiently than traditional technologies are here.
Small businesses, which have much smaller and less complex IT footprints than large enterprises, are leading the way in ripping out interactive voice response (IVR) answering systems and replacing them with AI agents.
Until now, businesses have had to choose between hiring more administrative employees they can’t afford to answer phones, or send customers into an “IVR death spiral” to get to sales or service, said Alan Brundage, chief operating officer of Jay-Hill Repairs, a Fairfield, N.J.-based company that services commercial kitchen gear throughout the state.
“AI does that a little bit better — it routes [calls] very well,” said Brundage, whose 50-employee company recently ditched its IVR for an AI agent.
Contact center-as-a-service stalwarts such as RingCentral, AWS, Five9, Genesys, Nice, Twilio and Sprinklr offer AI agents to answer incoming calls. Others, such as CallMiner, also provide their versions. The technology could eventually replace the current IVR and its rigid menu-driven topography, which doesn’t always pay off the time customers invest in navigating it.
Small businesses can be more nimble than large enterprises in taking advantage of AI agents. They also don’t have big contact centers with IT stacks that reverberate with unintended consequences when one application — such as IVR — is ripped and replaced.
That’s what RingCentral has seen, said Jake Fry, a solutions engineer at the company. More than 3,000 customers, including early adopters prior to its June 30 release, have launched RingCentral’s AI Receptionist agent.
While some enterprises are on their way to adopting phone-answering AI agents, small businesses that have high volumes of customer calls — such as car dealers, salons, doctors’ and dentists’ offices, and insurance providers — have jumped right in, according to RingCentral.
“There’s not a company out there that doesn’t have an AI initiative — half of them don’t know what the heck that means, but they know they need to throw AI at something,” Fry said.
Keeping commercial kitchens cooking
At Jay-Hill, restaurants and other commercial kitchens across the Garden State call at their worst time — when machinery such as ovens, fryers, refrigerators and freezers break down, often during peak hours of breakfast, lunch and dinner.
The company is replacing its IVR with RingCentral’s AI Receptionist. So far, after integrating content from the company’s website regarding which brands of equipment services it services and pricing data, Brundage said that Jay-Hill has greatly reduced the less-urgent calls from prospective customers who are just looking for general information and can focus on customers who have more urgent repair calls that need a truck dispatch.
Not only has the AI agent reduced the number of calls that technicians and office staff must field in a day, but it has also proven to be a better routing tool for the technicians, Brundage said.
The IVR might have sent a call to the wrong technician or a billing question to the wrong person, which wastes time for both the customer and the company, he said. AI Receptionist is more accurate than IVR, and it integrates with Jay-Hill’s ERP and CRM systems, which can also help save time when a technician is lining up a truck to visit a customer.
Jay-Hill was an early adopter of AI Receptionist, and the company experimented with it earlier this year by turning it on during peak mealtime hours. Those initial tests reduced live call volume by 5%, Brundage said.
He said that at first, he worried about how the customers — especially those in a situation where crucial equipment was not working at their restaurants — would react negatively to something that announces itself as AI, so they paid close attention to playbacks of calls.
“We wanted to see how many times people were just going, ‘Oh, this is AI. Give me a representative! Representative!’ and screaming ‘Representative!’ into the phone,” Brundage said. “We had that — not as much as I thought we would — but we did get a little bit of that.”
Once Jay-Hill saw that the customers accepted the AI agent and measured success with better call routing and resolving some of the calls staff had to do before, it’s moving forward with the next phase: AI-assisted dispatching of repair technicians. After collecting and confirming what needs to be fixed and the location of the customer, AI Receptionist sets in motion the right truck, equipped with the right parts, and texts the customer details.
He predicted that customers will likely appreciate the automated texts, especially in their typically loud environments.
“If you’ve ever been in a commercial kitchen, it’s as loud or as chaotic as the TV shows make it out to be,” Brundage said. “I joke that if you watch Hell’s Kitchen or The Bear, those are mild in comparison.”
Private security on call
Music City Protection is another small business of about 40 employees that’s adopting AI Receptionist. The Nashville firm offers numerous security services, including executive protection, construction details, armed transport and security for corporate buildings and events like weddings.
Brittanee’ Hughes, director of business administrations, said only two people answer the phones at Music City Protection’s offices. Many of the calls are, like at Jay-Hill, people looking for information about the company — services, pricing or security training on earning a Tennessee license for handgun carry, armed security and unarmed security.
Music City Protection decided to be an early adopter of AI Receptionist instead of hiring new administrative staff. The phone rang a lot before the company turned on the AI agent, but after training it, running extensive tests, updating website content and fine-tuning business processes, call volume is down 60%, Hughes said.
Calls routed to Hughes’ phone now involve direct business, like people ready to sign up for services, prospective employees applying for jobs or trainees who want to take the company’s courses. While AI can handle many pricing and scheduling questions, the company has set up an exception for when a prospective customer asks for a large quote and routes that straight to Hughes’ phone.
AI Receptionist has automated many processes around the company’s trainings, such as answering students’ questions about enrollment or next steps after they complete a course. Hughes advises companies that use agents such as AI Receptionist to regularly check call logs and monitor what drives hangups to make sure they’re not missing out on business leads. That should be done on an ongoing basis, but especially during the early days of using the technology.
“During that trial period, I had a lot of friends, a lot of employees and even myself and our chief of security operations doing a lot of calling, asking questions or intentionally trying to trip [the agent] up to see if this is a response that we’re satisfied with,” Hughes said. “You do get those one-off cases, but you don’t want to miss an opportunity because of something that you didn’t get a chance to fully vet.”
Mass adoption coming?
When set up well, AI agents that answer the phone can improve on standard contact center metrics such as customer satisfaction, time to answer and hold time compared with IVR. The systems can also perform more comprehensive call analytics because they transcribe every conversation, as opposed to manually handpicked calls with technology that predates AI. These agents can also offer information on call volume and type, which can inform contact center workforce management.
Other benefits to the technology both Brundage and Hughes point out — as well as the industry association Conversational Design Institute — include 24/7 availability, cost efficiency, more accurate call routing and deeper data collection.
RingCentral’s Fry said that in the case of AI Receptionist, early adopters have used call analytics to identify calls that the agent cannot solve. Then, based on that information, they make decisions such as continuing to allow these calls to go to a human or voicemail, updating a knowledge base or other website content, or retraining the AI to better address or route customer questions.
“Analytics is a great ROI tool for a customer to validate that it’s worth it,” Fry said. “They are getting insight into what calls are resolved, versus unresolved.”
In AI Receptionist’s early days, marketers have also crossed over into customer service to use the agent’s conversational insights feature, Fry added. Seeing what the customers are talking about — and asking for help with — can inform new products or marketing campaigns.
Don Fluckinger is a senior news writer for Informa TechTarget. He covers customer experience (CX), digital experience management and end-user computing. Got a tip? Email him.
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Artificial Intelligence
getty
If AI is going to matter at all in our economy, it has to matter for small businesses first.
As a search fund entrepreneur, I’ve met and worked with more than 300 CEOs and founders in cities across the U.S. from New York to Las Vegas, Sunnyvale to Maryland. In every conversation, the same concern surfaces: the AI models in our smartphones are more advanced than the technology stacks running our businesses. While the devices in our pockets update monthly, most business systems remain unchanged for years. And nowhere is this more evident than in small businesses.
This matters because small businesses employ more than 61 million people, nearly half the private workforce. Yet just a fraction of 1% are building the kinds of technology ventures that attract institutional capital.
So this means that the overwhelming majority of small businesses are self-funded and family-run. They’re bootstrapped by owners who pour their own savings into businesses that anchor the communities they care about. These are the businesses still running payroll on technology built a decade ago.
Family Businesses Meet AI Startups
Trusted relationships build businesses.
getty
The other day, I joined a lunch meeting with 80 small business owners who lead multi-generational family firms. This gathering was a masterclass in human networks. However, not once was technology mentioned in the entire meeting.
Every three weeks, all 80 members gather at a private club on Park Avenue for a three-course meal. The purpose was to share business priorities and make referrals and introductions for each member. There were green pens and notepads on the tables embossed with the motto: “Trusted relationships build businesses.” Over lunch, if each of the 80 members received just five new customer introductions, that’s over 400 new channels opened before dessert.
Four hours later, I was downtown at the city’s newest restaurant for a startup forum. Here, every conversation was about AI and technology, from AI-powered roll-ups and cybersecurity to founders turning New York’s vacant warehouses into sushi pop-ups.
Startup Forum in NYC Restaurant
New York, NY
Two Worlds In One City
Across every city I’ve traveled to, these two business communities live side by side but rarely meet. One is led by families built through trusted introductions and intellectual property developed over decades. The other is driven by startups fueled by the race to deploy the newest technology at scale.
What happens when these two worlds connect? Imagine today’s most advanced technology powering small family-owned businesses.
For the past 40 years, one model of entrepreneurship has created more than $10 billion in value by doing exactly this: investing in established small businesses and building them with new technology and leadership. The Search fund model, first launched at Stanford in 1984, was designed to bring innovation into established firms. One of the earliest search funds invested in a 50-person roadside assistance company and built it into Asurion, now a global tech-care enterprise with 23,000 employees and 300 million customers. Another transformed a compliance services firm into RIA-in-a-Box, a leading SaaS platform used by over 2,600 firms nationwide.
What once took years and significant capital investment can now be done in months. Today, enterprise-grade tools once reserved for Fortune 500 corporations are within reach of nearly every business. The playbooks that small businesses have relied on for decades to build multi-generational, value-based businesses can, when paired with AI, scale impact in weeks instead of years.
Across core functions, generative AI is cutting work times by more than 60%. If you can sketch an idea on a napkin, it can be built in hours, not weeks. Supply chains, compliance, document processing and technical workflows are already showing double-digit productivity improvements. In some cases, technical tasks have been reduced by as much as 70%.
Search funds are one proven path to bringing technology into legacy businesses. Others are emerging as well, including AI consulting firms, AI studios and AI-powered roll-up strategies, each with their own strategies to rebuild established firms with the most advanced technology available today.
The tools are here, the cost has never been lower, and the door is wide open—for now.
If AI is going to matter at all, it has to matter for small businesses first. Once it’s put to work, it will power growth across Main Street and fuel an economy that directly supports half the workforce.
Deep Tech creates technologies that form the backbone of national defense and industrial sovereignty
Generative AI may be the fastest-adopted technology in history, but in healthcare it is still largely seen through the narrow lens of chatbots. That perception, argued experts at WHX Tech-EHS Summit in Dubai during a panel discussion, risks blinding policymakers and providers to its deeper potential, and to the barriers that stand in its way.
“It’s like saying the internet is only e-mail,” said Christian Hein, former Novartis vice-president for digital transformation. “Chatbots are just the front-end. The real power lies in what sits beneath, which is an information engine capable of synthesising scientific literature, drafting clinical trial protocols, automating reimbursement coding and extracting unstructured data from medical records.”
The discussion, moderated by health AI consultant Sigrid Berge van Rooijen, opened with a question: Is generative AI destined to remain a glorified customer-service tool?
For Tatyana Kanzaveli, founder of Open Health Network, the danger lies in merely bolting new technologies onto outdated systems. “We cannot just deploy GenAI to augment old business processes,” she said. “Imagine agentive AI predicting when MRI equipment is about to fail, ordering the part, scheduling the engineer and coordinating the fix automatically. Or a digital twin monitoring your health data, arranging prescriptions, transport and care without you lifting a finger. That is the world we should be building.”
Bharat Gera, who has spent 25 years working on digital health transformation, echoed the need for caution but also saw promise in simple tools such as summarisation. “Doctors spend huge amounts of time reading patient histories. Summarisation is a powerful use case, here and now,” he said. But he warned against overloading clinicians with alarms and unvalidated signals: “Healthcare is fundamentally human. If we forget that, technology will make things worse.”
Regulation, risk and responsibility
If technology is racing ahead, regulation is struggling to keep pace. Amil Khanzada, CEO of Virufy, highlighted how laws differ dramatically across jurisdictions. “In Dubai, anonymised medical data cannot be sent overseas. In Pakistan, there isn’t even a privacy law yet. Patients have the right to delete their data, but what happens once that data has already trained a model? Do you retrain it from scratch?”
Consent forms, he added, are another minefield. “You can try to use generative AI to summarise them, but you still need human validation. And patients often sign without reading. The legal and ethical risks are enormous.”
Kanzaveli pointed to the dangers of misplaced trust. “Generative AI is persuasive. You trust it. But in healthcare, a wrong answer can mean a missed diagnosis, or worse. We spent longer building the risk-management framework for a virtual psychologist than we did building the engine itself. That is our responsibility.”
The human factor
Perhaps the most sobering intervention came from Anne Forsyth, Vice-Chair of Digital Health Canada and IT lead at Toronto’s Women’s College Hospital. She recounted how a cancer diagnosis was delayed for a year because test results were stuck in a hospital IT interface. “What do you tell the patient? Tech will never be perfect. We must always plan for failure,” she said. “If you are building GenAI tools for hospitals, think about what happens when they fail, and what supports clinicians will have.”
Hein agreed. “Technology is easy. Change is the hard part. The real work is persuading people that you are there to augment, not replace them. Without that, AI will never scale.”
A future too important to ignore
Despite their differing emphases, the panellists agreed that generative AI is already reshaping healthcare and that ignoring it is not an option. “There is no industry that can remain competitive without deploying these technologies,” Kanzaveli said. “The only question is how responsibly we do it.”
As Berge van Rooijen concluded, the challenge is not whether generative AI is more than a chatbot. It clearly is. The question is how to harness its promise without repeating the mistakes of past digital health revolutions, and without losing sight of the people at the heart of the system.
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