CFOs Treat Gen AI Like an Intern

The era of enterprise generative AI is underway, and it’s kicking off not with a bang but with a blueprint.

Rather than leaping into full autonomy, the latest PYMNTS Intelligence in the June 2025 Data Book “Gen AI’s Evolving Role in the Enterprise Reset” finds that businesses are embracing Gen AI like they would an intern: with training, supervision and clear expectations.

While headlines scream about artificial general intelligence and autonomous AI agents reshaping the future of work, the reality inside Fortune 500 enterprises is more restrained and pragmatic.

After all, there’s a big difference between an AI agent that can schedule a haircut and one that can rebalance a supply chain. The complexity, the risk, and the number of systems involved when it comes to letting Gen AI loose across the enterprise are not trivial challenges.

The message from COOs, CIOs and transformation leads is consistent: the impact of Gen AI technology is promising, but it’s not yet plug-and-play. According to the PYMNTS Intelligence report, surveyed sentiment indicates that the future of enterprise Gen AI lies not in moonshots, but in mastering narrow use cases with human-in-the-loop oversight, laying the groundwork for broader autonomy later on.

While tech providers are investing in agentic AI systems that can take multi-step actions and navigate digital environments autonomously, most enterprise buyers remain hesitant. The gap between what’s technically possible in a lab and what’s feasible in a real-world business environment is significant.

There is, however, optimism about where things are heading. The intern metaphor implies growth. Interns don’t stay interns forever. With enough experience, support and feedback, they mature into capable professionals. And so too, leaders believe, will Gen AI.

The promise is not just productivity, but adaptability. A well-trained AI system could one day act as a personalized operations aide, navigating organizational knowledge, suggesting process improvements and even managing routine decisions under human oversight.

For now, however, rather than allowing AI to operate across multiple systems with goal-directed autonomy, companies are embedding Gen AI within siloed functions. For example, legal teams may use Gen AI to draft routine documents, but retain human review. Marketing departments can generate initial content drafts, which professionals then edit. In software development, AI coding assistants may be used to accelerate work, but code is still reviewed and tested by engineers.

The consistent theme is human-in-the-loop. Across functions, enterprises are choosing workflows that keep humans in control of the final output. This isn’t just a reflection of current model limitations — it’s a deliberate risk management strategy. Enterprises must ensure compliance with regulations, protect sensitive data, and maintain accountability for business outcomes.

Read the report: Gen AI’s Evolving Role in the Enterprise Reset

These use cases reflect a co-pilot paradigm: AI as an accelerator, not an autonomous agent. Enterprises are not opposed to automation — they’re simply unwilling to sacrifice quality, control or accountability in the name of speed.

Current Gen AI systems can still produce errors, hallucinate or misunderstand context. For mission-critical functions, this level of uncertainty is unacceptable. Agentic AI and Gen AI alike may require new layers of monitoring, auditing and control that many organizations are still developing.

Until these challenges are addressed, enterprises are prioritizing practical, narrow applications of Gen AI, where output is useful but bounded, and where risks can be mitigated.

And rather than seeking to leap from manual processes to autonomous AI in one step, many enterprises are considering following a phased maturity model. This approach mirrors the evolution of previous enterprise technologies. Cloud migration, robotic process automation (RPA) and data analytics all followed similar paths — starting with narrow use cases and maturing into enterprise-wide capabilities only after governance, trust and operational processes were firmly established.

In the near term, Gen AI will continue to be deployed as a force multiplier for knowledge workers, a co-pilot in content-heavy tasks and an enhancer of operational efficiency. The vision of agentic AI remains aspirational — but not inevitable. Enterprises, for now, are making clear that automation must be earned and not assumed.

This approach may lack the drama of AI taking over entire workflows autonomously, but it reflects the operational realities and responsibilities of enterprise leaders. By treating Gen AI like an intern — coachable, productive but closely supervised — companies are laying the foundation for a future in which AI is not just a tool, but a trusted collaborator.

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