Artificial intelligence is no longer a future-state consideration — it is reshaping how work gets done right now, across every industry and every function. For organizations that want to stay competitive, the question is no longer whether to prepare their people for AI, but how to do it well.
The challenge is real: AI tools are evolving faster than most training programs can keep up with, and a one-size-fits-all approach rarely works across a diverse workforce. The companies getting it right are thinking beyond simple tool adoption. They are building cultures of AI fluency — ones grounded in responsible use, practical application, and continuous learning at every level of the organization.
We asked leaders from some of today’s most forward-thinking companies to share how they are approaching AI workforce readiness — from enterprise-wide literacy programs to role-specific upskilling, and from governance frameworks to hands-on experimentation. Their answers reveal a common thread: the most effective AI strategies put people first.
Here is what they had to say.
What’s your company’s overall strategy for preparing your workforce to work effectively with AI, and what prompted you to prioritize this now?
At LexisNexis Legal & Professional, we’re building an AI-fluent culture where colleagues across the business can apply AI with confidence, judgment, and clear purpose in their day-to-day work.
AI fluency is becoming part of how work gets done here and is not limited to technical roles. It shows up in how people across functions solve problems, improve workflows, and contribute in new ways. That means colleagues are not just learning about AI but using it in real work from the start.
This reflects the nature of our business. We combine trusted content, human expertise, and advanced AI to support high-stakes decisions across law, business, and government. That makes responsible use essential, not optional. It’s not only about capability, but about knowing when and how to apply AI with care.
As AI continues to reshape how work gets done, we’re investing in our people so they can strengthen trust in outcomes, increase their impact, and help advance the rule of law through the work they do every day.
How are you tailoring AI training for different roles and skill levels across your organization, from technical teams to non-technical employees?
Our colleagues work across a wide range of roles, so our approach to AI enablement is intentionally tailored and practical.
Different roles engage with AI in different ways, so the focus shifts across the business. Technical teams build deeper capabilities such as model usage, integration, and responsible development, while non-technical teams focus on applying AI in real workflows to simplify tasks and improve how work gets done day to day. For leaders, the priority is setting direction, identifying meaningful opportunities, and ensuring responsible use at scale.
Learning doesn’t sit in a single program. It happens through hands-on experience, expert office hours, and peer communities, with AI champions across the business helping colleagues adopt and apply AI in practical ways. This creates a culture where people learn from each other, apply new skills quickly, and continuously improve how they work.
The result is an environment where colleagues are trusted to make decisions about how AI fits into their work, test new approaches, and turn ideas into practical improvements. That’s how new capabilities move from learning into real, everyday impact across the business.
Check out the LexisNexis Legal & Professional careers page here!
What’s your company’s overall strategy for preparing your workforce to work effectively with AI, and what prompted you to prioritize this now?
At GE Vernova, our approach to AI is centered on helping employees build the confidence, skills, and practical experience needed to use AI in ways that improve how we work, innovate, and deliver for customers. We see AI readiness as a company-wide priority because the opportunity is broad: to unlock productivity, improve quality, and help our people focus more time on higher-value work. As a company working to electrify the world while decarbonizing it, we believe AI can help teams move faster and work smarter across the business.
That strategy combines learning, access, and responsible governance. We are building foundational AI literacy through programs like AI 100, which gives employees a practical understanding of how AI works, what it can and can’t do, and how to use it responsibly in everyday work. We are also making AI accessible through Amp, GE Vernova’s generative AI application, which helps employees streamline routine tasks and extract insights more efficiently. At the same time, we are pairing adoption with strong governance and clear guidance on approved tools and responsible use. We prioritized this now because AI is rapidly reshaping how work gets done, and we want our employees to be ready not just to keep pace, but to lead.
How are you tailoring AI training for different roles and skill levels across your organization, from technical teams to non-technical employees?
At GE Vernova, we are taking a layered approach to AI learning so employees can build skills in ways that are relevant to their role, experience level, and career path. We start with a broad foundation that helps create shared AI literacy across the organization, and then provide more tailored learning for employees based on how they engage with the technology in their work.
For enterprise-wide learning, AI 100 gives employees a practical baseline in artificial intelligence, including how it works, where it can add value, and how to use it confidently and responsibly in day-to-day work. From there, employees can continue through the Generative AI Channel on GE Vernova University, which offers persona-based learning paths for Employees & Managers, Leadership, and Technical Developers. Training includes both GenAI and Responsible AI tracks, along with AI Fundamentals content covering areas such as generative AI, computer vision, and machine learning.
We also create opportunities for employees to learn through application and shared innovation. Tools like Amp help make AI practical and relevant across both technical and non-technical roles, and forums like Engineering & Technology Week give teams a chance to explore how AI, automation, data, and cybersecurity are driving real business impact. This year, we also introduced our first-ever Amp-athon, which gave employees a hands-on opportunity to experiment with AI, showcase ideas, and accelerate adoption by learning directly from one another across teams.
Check out GE Vernova’s careers page here!
What’s your company’s overall strategy for preparing your workforce to work effectively with AI, and what prompted you to prioritize this now?
At Vertafore, we’re treating AI as a company-wide priority, but we’re also being intentional about how we roll it out. The goal isn’t to replace people — it’s to help them do their jobs better. So we’re focused on building AI into both our products and our day-to-day work in a way that still relies on human judgment.
In practice, that means a few things: creating communities where people can learn from each other, hiring and upskilling talent, and making sure AI is part of our product roadmap. At the same time, we’ve put structure around it with an AI Council and a Risk & Responsibility program to make sure we’re using it thoughtfully.
We also made a deliberate decision to focus on people first. Before giving broad access to tools, we paused normal work for an AI Immersion Week. We made employee confidence our main measure of success and required everyone to go through Risk & Responsibility training so they understood not just what AI can do, but when it should be used. That approach paid off — we saw a clear increase in both confidence and willingness to use AI afterward.
As for why now, the pace of AI is hard to ignore, and our customers are expecting it. For us, the opportunity is to build AI into the workflows and products people already rely on — in a way that actually helps them make better decisions, not just faster ones. At a basic level, our approach is simple: give people the right tools, teach them how to use them well, and track what’s actually working.
How are you tailoring AI training for different roles and skill levels across your organization, from technical teams to non-technical employees?
Same starting line: Everyone does a quick AI Risk & Responsibility course, so folks know the guardrails before they try anything. (100% completion before AI Immersion Week.)
Give people the right tool: We match tools to roles: ChatGPT Enterprise for knowledge workers, code-assist (Copilot/Claude Code) for developers, and Microsoft Copilot for frontline. Licenses and access are tailored to drive real value.
Teach developers differently: Engineers get GitHub-led Copilot trainings, office hours, Code-Assist Champions, and weekly show-and-tell sessions.
Hands-on for everyone: AI Immersion Week = speaker series + team working sessions, so teams apply AI to their actual workflows. Everything was recorded on the Gen AI page on our intranet.
Real prompts + quick wins: We teach simple prompting (Context-Task-Format) and share tips in newsletters, so non-tech folks get repeatable wins fast.
Keep it rolling: After the week we run speaker series, office hours, champions programs, approved-tools lists and measure adoption, so training actually sticks.
Results: AI Immersion Week moved the needle — managers’ “very confident” ratings rose about +13%, the share of managers expecting to use AI nearly every day rose ~27%, and managers’ confidence in helping teams increased ~18% after the week. The program’s practical, company-wide approach was even recognized externally: Vertafore earned the AI Excellence Award from the Business Intelligence Group for our company-wide AI Immersion Week and our commitment to shaping the future of insurance.
Check out Vertafore’s careers page here!
What’s your company’s overall strategy for preparing your workforce to work effectively with AI, and what prompted you to prioritize this now?
At Certinia, our strategy isn’t about replacing human talent with automation; it’s about augmenting human potential. We’ve adopted a “People-First, AI-Enabled” approach that treats AI as a collaborative partner rather than a replacement tool. This strategy is built on the belief that the most successful organizations in the coming decade won’t be the ones with the best algorithms, but the ones whose people are best equipped to direct them.
We prioritized this shift now because the “wait and see” era of AI is officially over. We recognized that to remain competitive and maintain our culture of innovation, we needed to democratize AI literacy immediately. To turn this strategy into a daily reality, we have deployed a comprehensive suite of enterprise AI tools company-wide. By providing secure, high-level access to these technologies, we’ve removed the “barrier to entry” for our staff. This ensures that experimentation happens within a safe, supported ecosystem rather than in silos.
By integrating AI into the fabric of our daily operations today, we are future-proofing our workforce and ensuring that every team member—regardless of their starting technical proficiency—feels empowered and secure in an evolving landscape. Our goal is to move beyond mere efficiency and use AI to unlock deeper creative work that only our people can provide.
How are you tailoring AI training for different roles and skill levels across your organization, from technical teams to non-technical employees?
We believe that AI fluency should be a universal language at Certinia, but we recognize that the “dialect” varies by role. To bridge this gap, we’ve deployed a multi-tiered training architecture:
– The AI Accelerator Series: This is our foundational layer for all employees. It focuses on AI literacy, ethical use, and prompt engineering, ensuring everyone has a knowledge of the tools available company-wide, and a safe space to experiment and learn from each other.
– Advanced AI Upskilling for Product & Engineering: For our technical crews, training goes deeper into agentic workflows. For our product feature crews, we’ve integrated agentic AI tools directly into our development lifecycle. By learning to use these tools to automate repetitive workflows—such as project setup, pull requests, code generation and test automation—we’ve achieved massive gains in throughput velocity. This allows our engineers to move past rote tasks and focus on the complex architectural challenges that accelerate the value we deliver to our customers.
– Level Up Leadership: We realized that leading in an AI era requires a new managerial playbook. Creating human connections and leading with empathy are more important than ever. Employees need coaches and advocates that can help them build their skills, navigate change and chart their career path in a changing environment.
By providing these specialized pathways, we ensure that a non-technical marketer feels just as capable of leveraging AI as a senior developer, creating a truly unified, AI-literate workplace. This ecosystem is anchored by leaders committed to our People-First, AI-Enabled culture—a culture designed to ensure our employees don’t just navigate this technological shift, but truly thrive within it.
Check out the Certinia careers page here!
What’s your company’s overall strategy for preparing your workforce to work effectively with AI, and what prompted you to prioritize this now?
We’re being very deliberate about this. Our approach centers on three pillars: governance, adoption, and measurement. We’re establishing light AI governance and acceptable use policies aligned to our security standards, so teams can move fast within clear guardrails. We’re scaling tools like GitHub Copilot CLI and Claude Cowork across engineering and our business teams, and building an AI Power Users community where practitioners share what’s working in real time. And we’re measuring what matters — not just who’s using AI, but whether it’s improving delivery speed and developer experience.
We’re prioritizing this now because AI is advancing faster than most organizations can absorb, and we’d rather shape our culture and habits proactively than play catch-up. The early wins we’re seeing, like engineers completing multiple stories in the time they used to work on one, give us confidence this approach scales.
How are you tailoring AI training for different roles and skill levels across your organization, from technical teams to non-technical employees?
There’s no one-size-fits-all answer here, and we’re embracing that. The needs of someone building AI-powered solutions are fundamentally different from someone using AI to work smarter in their day-to-day role.
For the broader organization, foundational prompt engineering serves as a universal starting point, and we branch from there based on role. We’re also partnering cross-functionally to discover AI opportunities across business units — looking at where it can genuinely reduce friction in areas across our portfolio.
For our engineering teams, we’re making sure they have access to incredible tools so they can get as much experience as possible using them, and we’re embedding directly with the teams to learn how they use them. That creates a feedback loop where real practitioners surface what’s working and what isn’t.
The north star is the same for everyone: use AI where it’s actually useful, trustworthy, and tied to real business outcomes. We pair every adoption metric with a quality counter-metric to make sure we’re measuring impact, not just activity.