It’s 23-Year-Olds vs AI in the Latest Business Battle Royale

Have you looked outside recently?

There’s a market-wide artificial intelligence (AI) gold rush going on.

That’s because AI-focused capabilities are already producing change-the-game efficiencies within industry-specific intelligent business applications.

But are today’s AI models smarter and more capable than fresh-faced consultants and newly minted MBAs?

It depends on what needs to get done.

The many 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.

Still, AI has the potential to fundamentally transform workflows across the marketplace by optimizing nearly every process under the sun (and then some).

And as business leaders — and junior staff — look to stay in front of the generative AI wave, they will need to pay equal attention to avoid being run over by it.

Read More: Enterprise AI’s Biggest Benefits Take Firms Down a Two-Way Street

Getting Lost in the Weeds Instead of Boiling the Ocean

While buzzy AI startups grab headlines with goals as lofty as they are undefined, enterprise use of generative AI needs to be hyper-focused on a business use case and centered around enhancements to existing business programs.

The potential within a business setting is a lot less “solving for the very nature of human intelligence and consciousness” or “knowing the universe,” a la Elon Musk’s xAI, and a lot more surfacing information in real-time and removing repetitive manual processes by fine-tuning large language models (LLMs) with both company domain and industry-specific data.

LLMs have the potential to free up huge swaths of human work hours, but their success from an ROI (return on investment) perspective depends on how those hours are repurposed.

There will always be the need to verify AI-driven outputs by keeping a human in the loop.

According to the Organization for Economic Co-operation and Development (OECD), 27% of jobs in the organization’s 38 member countries rely on skills that could be easily automated in the coming AI revolution.

And as PYMNTS reported, around 40% of executives said there is an urgent necessity to adopt generative artificial intelligence, and the generative AI market is expected to grow to $1.3 trillion by 2032, compared to $40 billion in 2022.

Already, AI has an impact streamlining procurement for eGrocery firms, personalizing travel itineraries based on insights from travelers’ reviews and opinions, and helping shoppers ensure the products they buy actually fit them, among many other emergent use cases across industries, including consumer products, life sciences, financial services, manufacturing, and telecommunications.

Read More: 10 Insiders on Generative AI’s Impact Across the Enterprise

Data and Details Determine Downstream Utility

PYMNTS research in the July 2023 “Generative AI Tracker” a collaboration with AI-ID, finds that 70% of business leaders believe generative AI will have the greatest impact on marketing organizations, business operations and logistics.

But no matter the technology’s potential, firms need to determine the right-now use cases to enhance value rather than just chase a trend.

“You don’t want to boil the ocean and try to solve for everything at once,” newly appointed 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.”

All effective AI strategies must be aligned with pre-existing industry-specific business priorities and their impact positioned to augment a range of company operations by accelerating staff and departmental performance through accretive process efficiencies.

Still, as easy as it is to use, AI remains, at least to date, far from a plug-and-play solution.

Enterprise technologies like AI need to be pointed toward a definitive business goal with a clearly auditable process and highly interoperable across operational workflows.

And, as PYMNTS wrote, the cost of doing AI business is something for firms to consider.

Maybe integrating AI into accounting and compliance operations to intelligently automate formerly laborious tasks is worth the energy and computing costs. In contrast, something like using AI avatars to greet clients might not be (or, depending on the firm, maybe it is the reverse).

Once organizations have a better idea of how to employ the technology efficiently, they can adapt and redesign jobs around that — keeping their MBA talent and recent college graduates happy and productive.