When the pandemic normalised remote work, L&D teams scrambled to move training online. Most converted in-person programs to video calls and hoped for the best.

The results were mixed. Some content transferred well. Other content—particularly hands-on skill development—suffered in translation.

Now, as organisations invest in AI training for distributed workforces, we’re encountering these challenges again with higher stakes. AI skills require practice and experimentation, not just information transfer.

Here’s what I’ve learned about making AI training work for hybrid and remote teams.

The Core Challenges

Engagement Decay

Video calls are tiring. Attention fades faster than in person. The passive consumption mode that works for Netflix doesn’t work for capability development.

For AI training specifically, this matters because:

• Skill development requires active engagement
• Complex concepts need cognitive attention
• Practice requires motivation to push through frustration

If participants are half-present, they won’t develop capability.

Practice Supervision

AI skills develop through experimentation with feedback. In person, an instructor can look over shoulders, catch mistakes early, and provide guidance.

Remotely, participants work in isolation. Mistakes compound. Frustration builds without support. The instructor can’t see what’s actually happening.

Technical Barriers

Remote AI training requires reliable technology:

• Stable internet connections
• Working access to AI tools
• Ability to share screens and demonstrate
• Compatible browsers and devices

When technology fails, learning stops. And technical troubleshooting eats into precious training time.

Peer Learning Loss

Much learning happens between formal sessions—conversations over coffee, quick questions to the person sitting nearby, informal observation of how colleagues work.

Remote work reduces these interactions. People learn in isolation, developing idiosyncratic approaches without peer calibration.

Time Zone Complexity

Global teams span time zones. Live sessions inevitably inconvenience someone. And asynchronous alternatives sacrifice the interaction that makes training effective.

Design Principles for Remote AI Training

Flip the Classroom

Don’t waste synchronous time on content that could be consumed asynchronously. Use video calls for:

• Discussion and Q&A
• Collaborative practice
• Troubleshooting challenges
• Peer sharing

Pre-work delivers foundational concepts. Synchronous time applies them.

This approach respects participants’ time, reduces video fatigue, and maximises the value of scarce live interaction.

Build in Structured Practice

Design explicit practice activities with clear success criteria. Rather than “experiment with AI tools,” specify:

• Specific tasks to attempt
• Time allocation for each task
• What outputs to produce
• How to document what worked and what didn’t

Structure compensates for the absence of in-person supervision.

Create Accountability Mechanisms

Without the social pressure of in-person attendance, completion rates drop. Build in accountability:

• Small group cohorts that progress together
• Regular check-ins with managers or coaches
• Visible progress tracking
• Peer accountability partnerships

People follow through when they’re accountable to others.

Enable Asynchronous Peer Learning

Create channels for ongoing peer interaction:

• Slack or Teams channels for questions and tips
• Discussion forums for sharing experiences
• Peer review of work outputs
• Spotlight sharing of effective approaches

These channels can exceed in-person peer learning if actively cultivated.

Plan for Technical Failure

Assume technology will fail and plan accordingly:

• Provide clear technical requirements in advance
• Test access before sessions, not during
• Have backup communication channels
• Record sessions for those who encounter issues
• Build buffer time for troubleshooting

Technical problems are normal. Comprehensive planning minimises their impact.

Session Design for Remote AI Training

Optimal Session Length

Virtual sessions should be shorter than in-person equivalents. My guidelines:

• Maximum 90 minutes for synchronous sessions
• Include breaks every 30-40 minutes
• Mix presentation with interaction
• End 5 minutes early to prevent scheduling collisions

Longer sessions are possible but require more breaks, more interaction, and exceptional facilitator skill.

Engagement Techniques

Maintain engagement through:

• Frequent polls and check-ins
• Breakout rooms for small group work
• Chat channel for ongoing commentary
• Hands-on activities interspersed throughout
• Cold calling (with warning) to maintain attention
• Visual variety in presentation materials

Engagement requires deliberate design, not charismatic delivery alone.

Practice Activities

Structure hands-on practice for remote settings:

• Clear written instructions (don’t rely on verbal only)
• Defined time boxes
• Independent work followed by pair or small group sharing
• Screen sharing for troubleshooting
• Documented outputs for review

Breakout rooms work well for practice—small groups reduce pressure while maintaining peer support.

Technical Setup for AI Training

AI-specific technical considerations:

• Verify everyone has tool access before starting
• Provide backup prompts if someone loses connection
• Share resources via persistent channels, not just screen share
• Have participants share screens when practicing (with consent)
• Record demonstrations for later reference

The Hybrid Complexity

Hybrid training—some participants in-person, some remote—is often the worst of both worlds. Remote participants become second-class citizens while in-person participants get distracted by technology.

If you must do hybrid:

• Invest in quality AV equipment so remote participants can see and hear
• Have a dedicated facilitator for remote participants
• Ensure activities work for both modalities
• Rotate between in-person and remote-first approaches

Better options:

• Everyone in person for key sessions
• Everyone remote for key sessions
• Asynchronous content with separate in-person and remote synchronous components

Pure modalities usually work better than blended ones.

Cohort-Based Approaches

For AI training, cohort-based programs often outperform self-paced content:

• Social pressure drives completion
• Peer learning supplements formal content
• Shared experience builds community
• Accountability structures are natural

Design cohorts with:

• Manageable size (8-15 participants)
• Regular synchronous touchpoints
• Ongoing asynchronous channels
• Clear cohort identity and expectations

Many organisations have found success working with AI consultants Brisbane and other major cities who combine structured AI skill development with cohort-based learning approaches.

Supporting Managers

Remote training success depends heavily on managers:

• Protecting time for learning activities
• Following up on application
• Creating environments for experimentation
• Recognising progress

Equip managers with:

• Visibility into training content and objectives
• Specific suggestions for reinforcement
• Questions to ask in check-ins
• Ways to model AI use themselves

Manager involvement predicts training transfer more than any design factor.

Measuring Remote Training Effectiveness

Remote delivery enables measurement that’s harder in person:

• Completion and engagement analytics
• Practice activity outputs
• Discussion forum participation
• Pre and post assessments

Use this data to:

• Identify struggling participants early
• Spot content that isn’t landing
• Recognise engagement patterns
• Demonstrate value to stakeholders

The Ongoing Challenge

Remote AI training isn’t a temporary adaptation—it’s the permanent reality for many organisations. Building capability in this context requires:

• Accepting that remote learning differs from in-person
• Designing specifically for the medium
• Investing in platforms and technology
• Developing facilitator skills for virtual delivery
• Creating peer learning infrastructure
• Supporting managers to reinforce learning

This is harder than in-person training in some ways. It’s more accessible and scalable in others.

The organisations that master remote AI capability development will have significant advantages. Their entire workforce can develop skills regardless of location, on schedules that work for them, with peers from across the organisation.

That potential is worth pursuing, even when the execution is challenging.