As healthcare organizations look to the future, Generative AI is becoming a crucial tool for driving long-term growth and improving patient care. With its ability to enhance operational efficiency, support innovation and optimize customer service, GenAI is gaining traction across the sector.
Realizing its full potential, though, requires substantial investment and a strategic approach. Healthcare firms are taking steps to integrate Generative AI into their operations, recognizing the need for both short-term gains and long-term commitment.
A PYMNTS Intelligence Report, Healthcare Firms Going Long on GenAI Investment, highlights key trends in how healthcare providers are leveraging Generative AI and the challenges they face in effectively scaling these technologies.
Healthcare firms are investing in Generative AI with an emphasis on long-term growth and innovation. According to the report, 90% of surveyed healthcare executives reported positive returns on GenAI investments, an impressive figure given the high costs and extended timelines associated with AI implementation.
This trend signals a change: major healthcare players view GenAI as a critical tool not only for staying competitive, but also for driving substantial advancements in patient care and operational efficiency. With an average full-scale adoption timeline of 7.4 years, the sector is aligning with broader cross-industry standards, suggesting healthcare is no longer on the sidelines of the AI race.
One of the report’s key findings makes a direct correlation between GenAI spending and return on investment. In the past 12 months, healthcare firms invested an average of $2.7 million in GenAI, but firms reporting the highest ROI significantly outspent others, investing around $6.4 million on average.
These firms see GenAI as a high-stakes investment where deeper financial commitment equates to better outcomes, from enhanced diagnostics to streamlined patient interactions. This insight underscores the need for healthcare firms to scale GenAI strategically, recognizing that meaningful returns may require higher upfront investments, particularly in areas with high potential for patient impact and operational efficiency.
Healthcare firms are leveraging GenAI for targeted applications that deliver immediate benefits in innovation and customer service. Consider 60% of healthcare executives report deploying GenAI in product and service innovation, enhancing research and development capabilities and supporting the development of new healthcare solutions. Meanwhile, GenAI-powered customer service tools, such as automated response systems and enhanced chatbot capabilities, are improving patient engagement and accessibility.
Interestingly, many firms are currently holding back on implementing GenAI in sensitive areas like fraud detection and cybersecurity. While GenAI’s potential in these areas is apparent, healthcare firms are cautious, prioritizing safer, less regulated applications in the near term to mitigate risks and maintain compliance.
As GenAI adoption accelerates in the healthcare sector, firms are seeing substantial returns on their investments by focusing on innovation and customer service. But the cautious approach to GenAI in areas like fraud prevention and cybersecurity reflects a prudent strategy, balancing innovation with operational security. For healthcare firms, the challenge will be to keep scaling their AI capabilities strategically, making GenAI a valuable long-term asset that supports both technological and patient-centered objectives.
Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
