Often treated as a support function, learning and development (L&D) is now being called to the forefront. That means training must go beyond onboarding and upskilling employees to contribute to broader business metrics like productivity, customer satisfaction and revenue growth.

This is where artificial intelligence (AI) has proven invaluable for learning leaders. By automating tasks, enhancing personalization and delivering actionable insights, AI is not only streamlining training processes but also enabling L&D teams to become true strategic partners in achieving business outcomes.

The Business of Learning: A New Era

The “business of learning” goes beyond content delivery or learner engagement — it’s about embedding learning as a core organizational function that drives measurable results.

JD Dillon, chief learning architect at Axonify, frames it this way: “Solving problems is the language of business, and AI enables L&D to focus on solving the problems stakeholders prioritize. It helps us do it at scale in order to deliver greater value to the business and the people we support.”

How AI Helps L&D Achieve Alignment

1. Linking Learning to Business Metrics

A common challenge for training professionals is showing direct links between training programs and key business outcomes. AI changes that. By quickly analyzing vast datasets, it connects training initiatives to metrics that matter, like productivity, quality and retention.

Training manager Melissa Brown, CPTM, illustrates the potential of AI in a call center setting, where AI could be used to track metrics like first-call resolution rates or customer satisfaction scores, tying improvements directly to training interventions. “Imagine telling executives, ‘This training improved customer satisfaction by 15% this quarter.’ That’s the kind of thing that makes executives sit up, take notice, and potentially even reconsider budget allocations,” she explains.

AI can also forecast training impact through predictive analytics. For example, modeling how a new onboarding program might affect six-month retention or ramp-up times, AI allows for proactive adjustments to training strategies.

When executives demand ROI on every initiative, AI’s ability to link learning with critical business metrics gives learning leaders an advantage.

2. Improving Organizational Agility

L&D must quickly adapt to organizational needs, ensuring training aligns with shifting priorities and delivers measurable business impact. AI enables this agility by speeding up the development of customized, relevant training solutions.

Training Industry’s learning product manager, Alyssa Kaszycki, highlights how AI transforms the process of rolling out a new technology: “AI can help create a customized onboarding plan, manage the rollout communication, write push notifications for learning in the flow, and create an assessment to judge proficiency—and we can do that in a week instead of a month.”

AI empowers L&D teams to quickly:

• Generate tailored content: AI can quickly create job-specific onboarding plans or microlearning modules, accelerating employees’ speed to proficiency, ensuring they can contribute to business goals faster.
• Automate support materials: AI quickly produces job aids, templates and FAQs. This frees up L&D professionals to focus on high-impact initiatives while ensuring employees have the tools they need to succeed.
• Deliver just-in-time learning: AI-powered digital assistants provide employees with answers and resources on demand, minimizing disruptions and maintaining productivity.

3. Measuring What Matters

Traditional training metrics, like completion rates and learner satisfaction, rarely resonate with executives. AI empowers L&D teams to track more meaningful metrics, such as efficiency, effectiveness and ties to business outcomes.

For example, AI-powered pre-assessments can determine exactly which training modules employees need, skipping unnecessary content and tailoring learning paths to individuals. Dillon describes the impact this precision can have, saying, “If we can personalize the experience and say, ‘You don’t need to take all the training, just certain pieces,’ we’re accelerating speed to capability and contribution. There’s also a story to be told about the time saved from a cost savings perspective.”

AI helps L&D measure these priorities by:

• Tracking how training impacts business outcomes, such as sales growth or error reduction.
• Monitoring employee progress in real time to adjust training paths dynamically.
• Identifying gaps and strengths, adapting training to maximize impact while minimizing time spent off the job.
• Aggregating data from various sources to present a holistic view of learning’s ROI.

“Not only can AI help measure the impact of learning and development programs,” Dillon says, “but it also provides opportunities to improve L&D efficiency, reduce costs and create solutions across the organization.”

4. Communicating Value to Secure Buy-In

One of the most challenging aspects of L&D’s role is securing buy-in from stakeholders. AI not only supports measurement but also helps L&D communicate insights in a way that resonates.

For example, AI tools can visualize data to create compelling dashboards or craft concise, impactful narratives around training performance.

“AI can even suggest how to frame your success stories,” says Kaszycki. “Instead of saying, ‘88% of employees completed training,’ AI might suggest, ‘Nearly 9 out of 10 employees gained skills critical to driving business growth.’”

This ability to translate data into stories elevates L&D’s influence, fostering stronger partnerships with senior leaders.

Overcoming Challenges to Adoption

While AI’s potential is vast, its adoption isn’t without hurdles.

Here are some common adoption challenges and strategies to address them:

1. Managing Expectations
AI is not a silver bullet. L&D leaders must educate stakeholders on what AI can and cannot do, focusing on its practical applications for their organization. By setting realistic expectations with stakeholders, L&D leaders can ensure AI-powered tools are seen as assets — not overhyped solutions that disappoint.

“Find a high-impact project that addresses a real pain point,” suggests Brown. “When people see concrete results, buy-in for larger AI initiatives comes much easier.”

For example, an L&D team might use AI to automate the creation of job aids or onboarding guides for a rapidly growing team. By showing how this effort reduces development time while maintaining quality, they demonstrate tangible benefits that resonate with leadership.

2. Addressing Data Quality Issues
AI is only as good as the data it processes. If fragmented data is fed into an AI tool, the resulting recommendations will likely be inaccurate or incomplete, frustrating users and reducing confidence in the tool. Organizations must invest in cleaning and integrating learning data to ensure reliable outputs.

Brown advises, “If we were to try to implement an AI-powered learning recommendation system with messy, scattered data, we’d be setting ourselves up for failure. Invest in data cleaning and integration upfront. It’s not glamorous, but it pays off in the long run.”

3. Upskilling the L&D Team
According to Training Industry research, over half of training managers are not confident in their own ability to use AI. To take full advantage of the efficiencies and insights AI can contribute to an organization, training professionals must first become proficient in using AI tools themselves.

Low-stakes opportunities for experimentation — like generating AI-driven learning scenarios or testing AI-powered analytics tools — can build confidence and competence.

“Integrating AI into L&D is an ongoing journey,“ Brown says. “Start small, learn as you go, and keep your focus on better serving your users and the organization. Do that, and you’ll find people are much more open to embracing the change.”

AI for Better Business

While there’s been plenty of talk about AI in L&D, it will remain just that until training managers take action and seize this unprecedented opportunity to drive measurable business outcomes. By focusing on solving real organizational problems, enhancing agility and improving measurement practices, L&D can solidify its role as a trusted business partner.

The challenge lies in aligning AI’s capabilities with strategic priorities, ensuring data integrity and effectively communicating its value. With these steps, organizations can harness AI to not only improve learning outcomes but also drive meaningful business impact.

As Dillon puts it, “If we stay engaged in the broader strategic conversation around how work is shifting, we’ll find our way to an AI-enabled future where L&D solves meaningful business problems.”