Emerging Technologies, 2025

AI-powered tools abound, and to keep up, workers must learn with, about, and for artificial intelligence. As companies adopt the technology in many forms, talent development functions must add workforce AI literacy and AI upskilling to their learning portfolio. TD professionals also have some learning to do, as many of their own tasks can benefit from the use of AI. Learning must be continuous, whether it's about selecting the right technology, using it competently, understanding the ethical considerations, or maximizing its effectiveness.

According to Pew Research, 52 percent of US workers are worried about the impact of AI. Deskless workers see it differently. They've lived through every wave of automation. Now they're experiencing how AI helps them work smarter and perform at their best.

Only 4 percent of frontline workers see AI as a threat, according to The Deskless Report. While others focus on the risks, frontline teams are seeing the results.

AI is rapidly closing gaps that have long limited frontline performance. Personalization makes every login count, especially on the front line, where minutes matter. At Foot Locker, 93 percent of store associates in North America complete personalized training on their own phones, reflecting the value it brings to their jobs.

With AI-assisted translation, people can focus on building knowledge instead of decoding information. AI is also blurring the lines between learning technology and work technology. In one hardware chain, employees use AI-powered sales tools to find product details and upsell tips right in the aisle. Development is now part of everyday work, with no separate systems, no disruption, and no barriers between learning and doing.

For more on how AI is transforming frontline work, visit:

axonify.com/frontline-ai.

The world faces a monumental challenge: Sixty percent of the global workforce must upskill within the next five years. Yet, organizational readiness is low, especially with the rapid evolution driven by AI. This technological shift demands more immediate and widespread learning than ever because AI is reshaping work.

As many routine tasks and knowledge have become commoditized, the real indispensable abilities are now sound judgment, adaptability, and creativity. To thrive in this new landscape, we need a learning paradigm that can match the relentless pace and reality of the modern AI era. And we need it fast.

This future-proof model features three key elements:

· Adaptive learning, which precisely targets missing competencies by bypassing known material

· Real-time skill intelligence, which enables organizations to proactively close gaps before they cause disruption

· The strategic use of AI to make employees inherently smarter and better at their jobs, which is more important than simply increasing speed

The ultimate success of any AI learning platform won't rest on the algorithm alone. Its impact will be defined by the underlying infrastructure, specific skill data, and aligned business objectives. These contextual layers are what will guarantee meaningful learning outcomes in this pivotal moment.

degreed.com/experience/artificial-intelligence

Imagine you're down by four with only 1:12 left in a college championship—no timeouts, one play left. As an L&D leader, that's how staff augmentation often feels: high-pressure, do-or-die. The goal is to be savvy, not sad.

Savvy moves:

Use staff augmentation for quick capacity relief. A seasoned instructional designer or project manager can be like a wide-open receiver.

Ensure it aligns with strategic goals. Every addition should drive your business forward.

Define success clearly. Establish governance and expectations.

Choose vendors as teammates—not speed-runners—who co-design solutions based on needs.

Sad pitfalls:

Avoid using augmentation to mask strategy gaps. Adding someone else doesn't fix a broken playbook.

Don't drop people into projects without clear key performance indicators.

Ensure internal oversight. Augmented staff still need context and coaching.

Treat vendors as partners, not wildcards.

Ask the right questions:

· Do we need additional capacity or to fix a broken process?

· Is this aligned with our current learning and business goals?

· Are we prepared to guide external team members?

· Will this solve the actual problem or just apply a temporary patch?

Staff augmentation works when it's done with strategic intent.

elblearning.com

Are you reaching your external partners with your training? As L&D departments add training external audiences to their list of responsibilities, there are a few factors to consider when it comes to designing and implementing training programs.

Can they access your training? Can the systems communicate? Is there a source of truth for reporting and versions? How do you know whether your training is effective?

As your reach grows, so do the technical complexities of creating, sharing, and maintaining training with multiple LMSs. Successful partner and customer education programs need a strategy for reaching external audiences and measuring success.

Three options for reaching external audiences:

· Sharing by publishing in multiple standards and handing the course to partners, then republishing and resending updates. No control over the course.

· Delivering via a portal or LMS. Partners come to your content by accessing another tool.

· Distributing with a content distribution solution, LCMS, or dispatch tool. Partners access training in their system of choice via a proxy file, but you maintain content control.

To decide which is the best fit, consider training goals, how often updates are needed, and the effort your partners will need to put in to access the latest content.

rusticisoftware.com

Despite rapid advances in generative AI, most organizations remain unprepared to integrate it effectively. Studies reveal that while 89 percent of leaders view AI as strategically important, only a small fraction have equipped their employees with the skills to use it confidently.

Access to AI alone isn't moving the needle and tool launches are failing. This "readiness gap" comes down to skills, and the key to closing it is AI literacy. More than technical knowledge, AI literacy combines awareness, ethical understanding, practical problem solving, and the ability to collaborate effectively with AI. When employees are trained to use AI as a thinking partner rather than a replacement, productivity, creativity, and confidence all rise.

The shift parallels earlier workplace revolutions, but today's pace is faster, and the stakes are higher. Organizations that invest in AI literacy are building a workforce that can adapt continuously, think critically, and lead transformation from within. In an era defined by constant change, that may be the most valuable skill of all.

For more on AI literacy and workforce development, visit:

umu.com/ailiteracy

The workplace is undergoing transformation as AI reshapes how employees learn and communicate. Employee disengagement costs median-size S&P 500 companies about $228 million annually, McKinsey reports. With only 31 percent of employees feeling engaged at work, organizations are embracing video-first learning strategies powered by AI to address this challenge.

This shift reflects more than preference—it's driven by compelling data. Research shows 95 percent of content is retained when shared via video compared to text-based formats, yet 50 percent of information is forgotten within 24 hours without reinforcement. AI-powered tools address this by enabling bite-size, interactive content with smart chaptering that enables employees to navigate directly to relevant sections. Advanced AI functionality even enables workers to ask questions and receive answers extracted directly from video content, dramatically reducing time spent searching for information.

AI is democratizing content creation at scale. Tools that automate video editing, generate multilingual captions, and analyze engagement patterns help teams create personalized training materials efficiently. Organizations implementing these strategies report that 48 percent of employees consider video the most engaging communication form, with 57 percent higher retention rates than traditional methods. This convergence represents a fundamental rethinking of workplace knowledge acquisition—increasingly adaptive, inclusive, and data-driven.

vimeo.com

Among hiring leaders, nearly one in three now require AI proficiency as a baseline for candidate consideration, signaling a seismic shift in workforce expectations. This trend poses a dual challenge for organizations: narrowing the candidate pool while risking exclusion of existing staff who lack AI fluency.

To bridge this gap, companies are deploying skills-assessment tools to identify AI knowledge deficits and align development efforts with business priorities. Rather than generic training, organizations are focusing on targeted competencies—such as workflow automation, data interpretation, or model fine-tuning—that deliver strategic value. Leadership plays a key role by contextualizing AI use cases and embedding them into everyday workflows.

A rising best practice is AI mentorship: embedding intelligent assistants or adaptive learning platforms that offer role-specific guidance, adaptive pathways, and real-time feedback. By complementing human coaching, such systems can scale support for many employees simultaneously.

Case in point: Organizations with mature AI strategies report greater confidence in upskilling and talent mobility programs. As AI becomes a baseline expectation, L&D teams must pivot from optional skill offerings to foundational workforce development—lest talent stagnates and strategic agility erodes.

workera.ai