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5 Trends These AI Experts Think Could Change Payments and Commerce

AI, artificial intelligence, connected economy

For businesses at the forefront of their industries, artificial intelligence (AI) has become unavoidable. 

An unavoidable certainty, that is. 

That’s why, for the past five months, PYMNTS has been sitting down with different AI experts, innovators and entrepreneurs each week to get their thoughts on the technology’s impact across the pillars of the Connected Economy — as well as what they see on the horizon as the innovation further permeates, and successively transforms, both payments and commerce. 

What we’ve learned from over a dozen conversations for the PYMNTS “AI Effect” series is that there are five common threads these experts, all drawn from various fields, see around AI’s uniquely powerful applications within the enterprise.

The first is that generative AI (GenAI) capabilities have fundamentally changed the way that individuals and businesses are able to interact with computers and information. 

AI and Digital Interfaces: A New Information Era 

The rise of the computer and digital technology has changed the way we live and conduct business. And that change was driven by a shift in behavior around how consumers and businesses created, stored and accessed information — forcing humans, for the most part, to behave like computers in order to productively engage with them. 

But GenAI’s capabilities the the potential to change that. 

“Computers can now behave like humans. They can articulate, they can write and can communicate just like a human can,” Beerud Sheth, CEO at conversational AI platform Gupshup, told PYMNTS. “No one ever thought a bulldozer could behave like a human, or fire, or any of the prior inventions throughout history. AI has animated society in a way that no other technology has before.”

“Large language models in general are extremely good at interacting with humans, gathering data and making knowledge and data accessible,” Pecan CEO and Co-founder Zohar Bronfman told PYMNTS during a conversation for the series the “AI Effect.” “They are the best technology humanity has ever made that helps make knowledge accessible.”

“Generative AI is so powerful because it is bringing AI to everyone … Before Ford cars, no one had a car, and then suddenly everyone had a car — and we got freeways, mechanics, it prompted so much further innovation,” Akli Adjaoute, founder and general partner at venture capital fund Exponion, told PYMNTS.

Second, the experts universally agreed that AI systems provide businesses with an unparalleled way to capture new efficiencies and streamline existing workflows. 

AI and Task Completion: A Shot in the Arm to Tedious Workflows

While the current explosion of interest in AI may seem new, earlier generations of predictive algorithms and machine learning systems have been silently performing tedious, high-value tasks for years.

And while today’s AI can also automate repetitive tasks and leverage data to make better decisions, streamlining workflows and reducing costs, the full spectrum of GenAI applications and the sheer speed of their task completion blows the capabilities of previous systems out of the water. 

“We always overestimate the first three years of a technology, and severely underestimate the 10-year time horizon,” Jake Joraanstad, CEO at Bushel, told PYMNTS.

“The ChatGPT light bulb went off in everybody’s head, and it brought artificial intelligence and state-of-the-art deep learning into the public discourse,” Andy Hock, senior vice president of product and strategy at Cerebras, told PYMNTS.

“And from an enterprise standpoint, a light bulb went off in the heads of many Fortune 1000 CIOs and CTOs, too,” Hock added. “These generative models do things like simulate time series data. They can classify the languages and documents for applications, say, in finance and legal. They can also be used in broad domains to do things like help researchers develop new pharmaceutical therapies or better understand electronic health records and predict health outcomes from particular treatments.”

“If you go into a field where the data is real, particularly in the payments industry, whether it’s credit risk, whether it’s delinquency, whether it’s AML [anti-money laundering], whether it’s fraud prevention, anything that touches payments … AI can bring a lot of benefit,” Exponion’s Adjaoute said to PYMNTS. 

Put simply, as James Clough, chief technology officer and co-founder of Robin AI, told PYMNTS, “lawyers who use AI are going to replace lawyers who don’t, rather than AI replacing all lawyers.”

But while AI is easy, it isn’t that easy. The third thing the experts stressed to PYMNTS was that not every company is a perfect 10 on the “ready for AI adoption” scale, and that talent and resource gaps surrounding the technology’s deployment will need to be addressed. 

Getting Ready for AI Adoption: Taking the First Step 

There are many, many businesses out there. And some companies may have mature data practices and sophisticated engineering teams, enabling them to integrate AI outputs into existing business processes with minimal friction. But the vast majority of companies do not — and to effectively and responsibly leverage AI systems to their organization’s benefit, they will need to address this gap before it starts to yawn. 

“Many big enterprises have extraordinary data assets, but data that’s ready to be used to train one of these models — things like whether it’s clean, de-duplicated, and do they know how to tokenize it and get it ready to be fed into one of these AI models — that’s a different matter,” Cerebras’ Hock told PYMNTS, noting that the percentage of people around the world who know how to build an AI system is small.

As Adrian Aoun, CEO at Forward, told PYMNTS, “things need to be built for a world of AI in order for that AI to work and scale.”

“I’ve been in the artificial intelligence and machine learning (ML) space for more than 20 years now,” Yoav Amiel, chief information officer at freight brokerage platform and third-party logistics company RXO, told PYMNTS. “When we build technology, we’re not building it just for its own sake, we build technology to help the business, but as we “are giving more and more decision-making power to technology … we need to make sure that if the machines are for some reason unable to make these decisions, we are not left without the ability to function.”

These concerns feed into the next big theme the experts flagged: the need to construct compliance and governance programs around enterprise AI systems, while at the same time ensuring their security. 

Taking the Next Step: Ensuring Data Security and Creating a Governance Program

Even taking AI out of the picture, many organizations can struggle with issues such as quality control, governance, compliance and cyber security when integrating sophisticated software solutions. 

AI compounds those needs. 

“Traditional ML was typically the realm of PhDs or well-trained data scientists, but everyone can start using generative AI just by signing up,” Kojin Oshiba, co-founder of end-to-end AI security platform Robust Intelligence, told PYMNTS, explaining that this situation inherently creates risk. 

“There’s a difference that we see between cybersecurity and AI security,” Oshiba added. “CISOs know the different components of cybersecurity, like database security, network security, email security, etc., and for each, they are able to have a solution. But with AI, the components of AI security and what needs to be done for each isn’t widely known. The landscape of risks and solutions needed is unclear.”

Integrating AI is only half the battle. The other side of the puzzle involves ensuring that the AI system is being applied to a real business problem — and that its results are usable and viable. 

“A model is only as good as the problem it solves,” Pecan’s Bronfman said to PYMNTS. “And to tie the model to the business problem requires an understanding of not only the accuracy, which is very technical, but also the efficacy, how well the AI model is solving the problem, and how it should be integrated into the business process, which is a more complex question.”

But, when these hurdles have been cleared, experts agreed that things start to get very, very exciting — because what they all see happening in the future is the emergence of AI systems that have a life of their own and require minimal human intervention. 

The Rise of Agentic AI Systems 

Heather Wilson, CEO of CLARA Analytics, told PYMNTS that she sees agentic AI applications as the next great innovation in the space. These agentic AI systems would provide decision support and handle routine tasks, allowing human employees to focus on more complex aspects of their work.

This is a future vision shared by many, with Pecan’s Bronfman predicting that the future of AI lies in automating decision-making processes and optimizing business operations by taking unsupervised actions. 

Robin AI’s Clough also predicted a shift from chat-based interfaces to more agentic AI models that move beyond providing answers to performing tasks.  

“It won’t be something you ask and get an answer back, but a system you can ask to do things for you,” he said. “…Instead of just drafting that email, it might draft the email and get the attachment and put it in your outbox and then click send as well. I think that shift from chats to agents is one of the most exciting things we’ll see in the next year.”

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