Generative artificial intelligence (AI) offers a great opportunity for firms to build better businesses.
All they need to do is successfully integrate the technology across their workflows.
Easier said than done? Maybe. After all, digital transformations are rarely as simple as flipping a switch.
Fortunately, PYMNTS Intelligence spent much of 2023 investigating AI’s enterprise-grade applications, as well as talking with experts from various industries about how to best capture them.
One thing was clear: while 2023 may be the year that generative AI burst onto the scene, 2024 will be a year when enterprise businesses leverage the technology to gain new efficiencies.
Read also: 12 Payments Experts Share How AI Changed Everything in 2023
The integration of AI is not too dissimilar from the on-boarding process required by any other enterprise-level software adoption or modernization process.
Namely, the first step is performing a self-assessment to establish a use case.
AI tools, like businesses themselves, are goal-oriented, meaning they need to be pointed toward a real business problem, not just kept on the shelf as a shiny object.
“No matter the ways and means in which AI is being harnessed, it’s incumbent on firms to mull how they can enhance value rather than just chase a trend,” Shaunt Sarkissian, founder and CEO of AI-ID, told PYMNTS in May.
PYMNTS Intelligence in the July 2023 report, “Understanding the Future of Generative Al,” a collaboration with AI-ID, found that large language models (LLMs) — the neural networks behind OpenAI’s ChatGPT and Google’s LaMDA — could impact 40% of all working hours, meaning that it shouldn’t be too hard for firms to find a target to point AI solutions at.
Next, firms need to decide whether they want to build an AI solution in-house, buy a solution, or partner with a provider.
Given the exorbitant computing costs and technical expertise needed to build an AI model, that option is likely off the table for all but the most deep-pocketed organizations.
“As exciting as AI technology is, it’s still new for most, and expertise is hard to come by,” Taylor Lowe, CEO and co-founder of LLM developer platform Metal, told PYMNTS in July.
It is also crucial for firms to identify how new AI software will impact existing business processes. These firms must also implement strategies to minimize disruption and downtime during the integration.
AI remains far from a plug-and-play solution, particularly for larger enterprise organizations with meaningful concerns around data security and output integrity.
See also: Demystifying AI’s Capabilities for Use in Payments
Next, firms need to identify their own computing limitations and the resources needed to effectively deploy AI.
Data migration and security are two crucial areas that need to be addressed around integrating AI, particularly the process of migrating data from existing systems to new AI software. Security measures will need to be put in place to protect sensitive data during and after the integration process.
That’s because, as Erik Duhaime, co-founder and CEO of data annotation provider Centaur Labs, told PYMNTS, “the algorithm is only as good as the data that it’s trained on.”
Data governance programs will need to be either created or modified to address the realities and nuances of enterprise AI.
“[W]e’ve got to be careful how we use this technology in a compliant manner,” i2c CEO and Chairman Amir Wain said to PYMNTS in June, cautioning against rushing full speed and embracing AI. “We cannot be at the bleeding edge of technology dealing with people’s money and funds. … We need to put a compliant framework around the tool.”
Once an organization has considered how they can employ generative AI to ensure it is a value-add, they will be able to adapt and redesign jobs around that.
The experts that PYMNTS has spoken with consistently emphasized that AI should be viewed as a way to augment and enhance, not replace, the work done by humans.
What that means is that it will be crucial to identify the learning curve for users to adapt to the new AI software.
Read more: Enterprise AI’s Biggest Benefits Take Firms Down a Two-Way Street
“You don’t want to boil the ocean and try to solve for everything at once,” Corcentric CEO Matt Clark told PYMNTS. “Firms need to look at [transforming their existing processes] as a kind of crawl-walk-run mentality to get to where they need to go.”
Training and support resources will need to be provided to help users transition to the new AI system. As PYMNTS wrote earlier, those who see AI as a replacement for human labor rather than as a labor-saving device could risk making themselves vulnerable to savvier competition.
“AI is going to be an imperative for every company, and what you do with AI is what will differentiate your products,” Heather Bellini, president and chief financial officer at InvestCloud, told PYMNTS. “Functionally, it might get rid of a lot of the manual work people don’t want to do anyway and extract them up to a level where they can do more things that have a direct impact on the business.”
Echoing that sentiment, Karandeep Anand, chief product officer at Brex, told PYMNTS in August that, “If you can even save some eight- or 10-people’s worth of work at the end of the quarter and finish and close the books within 24 to 48 hours [using AI], that is priceless.”
AI doesn’t replace labor outright. Rather, it transforms how work is organized and activates efficiencies by bringing multimodal processes together and streamlining that output.
That leads to the final step when integrating a new AI solution: ensuring the new software is capable of scaling to accommodate future growth and increased usage; as well as determining whether the software aligns with an organization’s long-term goals and evolving needs.
After all, firms that choose the best use case of enterprise AI for their business operations today will be the enterprises that win tomorrow.
We’re always on the lookout for opportunities to partner with innovators and disruptors.
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