For Robert Gerstmann, the business communications space has been continuously expanding for a long time. However, this year, it looks like it will accelerate.

As chief evangelist and co-founder of Sinch, Gerstmann has worked since the company’s founding in 2008 to provide a platform for businesses to engage with customers through mobile messaging, voice and email. Facilitating more than 900 billion interactions every year, Sinch specialises in SMS, MMS, RCS and, more recently, AI-led customer engagement tools.

“AI makes it possible to handle high levels of personalisation and scale,” Gerstmann said. “We believe in the huge growth of these new messaging channels powered by AI.

“In our case, AI is also enabling a renaissance in voice.”

Prioritising openness amid the agentic AI boom

In February 2026, Sinch publicly announced agentic conversations, a new set of capabilities designed to operationalise AI agents across global communication channels. The company said this would subsequently enable enterprises to deploy intelligent agents across messaging, voice and email at scale.

Gerstmann said Sinch adopted this ‘openness’ strategy to maximise its addressable market.

“We allow businesses to bring their own agent, but we’re also offering a ‘bot builder’ within the Sinch platform for them to create a bot and have it integrated with all the communication services we have in the back end,” he added. “Some businesses will want to build on their own technology stack and use us more as a distribution network.”

As generative AI (Gen AI) and conversational channels like voice, RCS (rich communication services) and messaging apps become central to customer engagement, enterprises are shifting toward agent-driven models. To scale, Sinch said AI agents must do more than converse – they will need secure integration with enterprise systems to execute actions across channels.

For Gerstmann, AI is re-enabling voice.

“It’s really about voice bots,” he told Capacity. “Customer support is largely a voice and web chat use case today, but customer experience isn’t always fantastic, as businesses can make it hard for customers to contact them because they have limited capacity and it’s a significant cost driver.

“By deploying voice bots that are linked to internal systems – so that the bots are intelligent about what services the customer has had, the history of past problems and maybe additional purchases – all voice bots can actually provide a better experience than a human agent today.”

However, Gerstmann added that it is important for companies deploying such technology to be transparent about it so as not to mislead customers into thinking they are speaking to a person.

“If a voice bot can actually manage your problem, it’s going to be a better and more accessible experience,” he said.

Confronting lagging RCS adoption in Europe and the US

While WhatsApp and RCS are supposed to bring business communication into channels consumers already live in, European and US adoption by enterprises still lags behind Asia. Gerstmann suggested the reason is the length of time it takes to implement the technology.

“Enabling RCS involves significant rollout time – it wasn’t until two years ago that Apple added support for it,” he said. “The period during which you’ve had ubiquity has been quite short, and even today, the only markets where we have ubiquitous RCS today are the US, Germany, France, the UK, Spain and Brazil.”

As the RCS rollout continues, Gerstmann is eager to point out that without coverage, there is no enterprise demand.

“It’s about enterprise enablement,” he added. “Shifting from SMS to RCS or WhatsApp in step one is easy, the business doesn’t need to change much, we can even do that behind the scenes for them. However, if you want to leverage the rich elements and interactivity, that’s a completely new way of interacting with your customers, and it requires a lot of enablement.”

The enablement is as follows: explaining the use cases and possibilities, the technology change within the enterprises themselves, which falls into two categories – the direct customers or those who leverages platforms like Salesforce or Adobe.

“In those cases, you need both the businesses and platforms to adopt and support the technology,” Gerstmann said. “That’s what is taking time – it’s not lack of demand, it’s an implementation timeline question.”

Lovable partnership and turbocharging Sinch’s AI strategy

It was also recently announced that Sinch expanded its partnership with Lovable, the Sweden-based AI software creation platform, to power communications for AI-native applications. The partnership will see Sinch integrating its communications infrastructure into the Lovable Cloud to bring simple and scalable communication tools to its global community of builders.

“We believe a lot of future communications will be driven through AI platforms, so partnering with Lovable is a natural move for us,” Gerstmann explained. “We believe a lot of enterprises will want to create tailored software with these tools to integrate communications.”

He added that being integrated with Lovable makes it easier for those businesses to start consuming email, messaging and so on.

“It makes things easier for them and for us, that’s a very strong future channel,” he said.

Looking ahead, Gerstmann said Sinch is building what it calls intelligent orchestration – where the company gets data signals from the trillions of transactions that pass through its platform. It is then able to aggregate that and provide actionable insights back to businesses so they can optimise their communications.

“It’s ultimately [about] reaching consumers on the right channel, at the right time, with the right message,” he said. “We’re providing data so that brands can be context-aware, both across channels and across their various bots. A conversation that might start in a contact centre might then move to a commerce use case. We help the brand orchestrate across all of that.”

He added: “We’re essentially creating the connective tissue. We’re not the AI brain or the network provider, but we aggregate all the signals and serve them up to businesses who will then leverage large language models (LLMs) themselves.

“We surface the data so they can make the right decisions. I think that’s going to be a big thing.”

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