When pharmaceutical giant Moderna merged its tech and HR departments into a single unit in mid-May, the next stage of the AI revolution began. The move was a telling signal that AI is no longer just the domain of engineering teams or data scientists. It’s a full-enterprise concern.
For all the excitement around large language models and productivity gains, many companies still treat AI as a technical add-on — something for developers to worry about, or a lab project tucked inside IT. But Moderna’s restructure reflects a growing shift in mindset: that AI’s most transformative business impact may come from the ‘non-technical’ functions – HR, finance, marketing, communications – where repetitive workflows are plentiful and digital literacy is high.
Companies that understand this are already getting to work. Recognizing AI’s potential across the enterprise, of course, is just the first step. Unlocking that potential requires more than simply signing up for a ChatGPT license or giving teams free rein to ‘experiment.’ The companies that are most effectively scaling AI are doing it by taking intentional steps to activate it across their entire workforce.
Here are four key principles for talent leaders to keep in mind:
1. Start with the end in mind.
AI can do a lot. But unless you know what you’re trying to achieve, those capabilities quickly become distractions.
Enterprise teams need to start by identifying clear, high-leverage use cases. What pain points slow down performance? Where does work pile up unnecessarily? Which processes are routine and ripe for automation or acceleration?
This kind of discovery process isn’t limited to technical leaders. It works best when department heads — across HR, finance, marketing, legal, and others — are asked directly what success looks like, and where time is getting lost.
When those outcomes are clear, conversations about which tools to use and how to measure ROI become much more grounded. This goal-first approach avoids the all-too-common pitfall of forcing a flashy tool onto a problem it’s not designed to solve.
2. Pick the right tool for the job.
While ChatGPT has become shorthand for generative AI, it’s only one option among many. And it’s not always the best one.
Different departments need different solutions. For instance, a recruiting team trying to streamline initial outreach might benefit from generative content tools that can customize emails at scale. A customer service function exploring ticket triage could evaluate automation platforms or domain-specific LLMs. A finance team may need fine-tuned assistance in interpreting large data sets and generating summaries.
This phase also includes an essential step that’s often overlooked: defining governance. Employees need clarity around when AI is appropriate to use, how outputs should be validated, and what data should never be entered into a tool. These guardrails protect the business and encourage employees to experiment with more confidence and less fear of missteps.
3. Build skills that match strategy.
Training is where many AI adoption efforts stall. Either it’s too generic — a one-time overview of AI’s capabilities — or it’s too fragmented, with individual teams left to figure things out themselves.
A more effective approach begins with foundational training for all employees. This establishes a shared understanding of what generative AI is, how it works, and its potential — and limits — within a professional context.
From there, organizations can layer on targeted learning for specific teams. A legal department might focus on using AI for document review, with emphasis on accuracy and compliance. A marketing team might train on content ideation and media channel optimization. A product team may explore how to use AI to synthesize user feedback at scale.
The key is to connect learning directly to work. When people immediately see how a skill will help them perform better, adoption increases — and resistance drops.
4. Incentivize what works.
New capabilities only stick when there’s visible impact. That’s why it’s important to establish simple ways to track results and share what’s working.
Leaders can ask teams to quantify improvements — time saved, workflows streamlined, quality gains — and regularly spotlight standout use cases. The point isn’t to make your employees report exhaustively on their progress with AI; it’s to give teams reasons to stay engaged and help the broader organization see AI as a valuable part of everyday work, not a side project.
Some companies have even introduced internal showcases or ‘AI demo days,’ where departments share what they’ve tried and what they’ve learned. These practices normalize experimentation and help good ideas spread faster.
Moderna’s merger of tech and HR is just the beginning of a broader trend: organizations moving from a siloed, exploratory phase of AI adoption to a strategic, enterprise-wide model. In this model, everyone — from the recruiter to the revenue analyst — has a role to play in driving transformation.
To get there, companies must take AI out of the lab and into the hands of everyday employees. That means starting with business outcomes, choosing the right tools, building tailored capabilities, and celebrating what works.
AI will only transform the workforce if we treat it as not just a technical revolution, but an organizational one. The companies that empower every team to lead with it will be the ones that define the next chapter of innovation.
