The Learning Experience Platform market has evolved significantly since 2020. Early fragmentation has given way to consolidation. AI capabilities have transformed what’s possible. And organisations have learned—sometimes painfully—what actually matters in platform selection.

If you’re evaluating LXPs in 2025, here’s what to consider.

LXP Market Context

Understanding the current landscape helps frame evaluation:

Market consolidation: The number of viable LXP vendors has decreased through acquisition and market exit. This means fewer choices but more mature options.

AI integration: Every platform now claims AI capabilities. The question isn’t whether AI is included but how effectively it’s implemented.

Integration focus: Platforms increasingly compete on how well they integrate with existing systems—HRIS, collaboration tools, content providers.

Experience emphasis: User experience differentiation matters more than feature lists. Learners have consumer-app expectations.

Outcome pressure: Vendors are being pushed to demonstrate learning outcomes, not just engagement metrics.

Evaluation Framework

I recommend evaluating LXPs across six dimensions:

1. Content Aggregation and Curation

Modern LXPs should:

• Aggregate content from multiple sources (internal, licensed, open)
• Provide intelligent curation that surfaces relevant content
• Support various formats (video, articles, courses, podcasts, documents)
• Enable social curation by learners and experts
• Maintain content freshness through automated and manual processes

Key questions:

• How does the platform handle content from different sources?
• What AI curation capabilities exist?
• How is content quality maintained over time?
• Can subject matter experts contribute to curation?

2. Personalisation

Effective personalisation requires:

• Skills-based recommendations tied to individual capability gaps
• Role and career path alignment
• Learning style and preference adaptation
• Timing optimisation for when learners are receptive
• Continuous improvement based on engagement data

Key questions:

• What data drives personalisation algorithms?
• How transparent are recommendations to learners?
• Can personalisation be adjusted at organisational level?
• How quickly does personalisation improve with usage?

3. AI Capabilities

2025 LXP AI should include:

• Intelligent content recommendations
• AI-powered search that understands intent
• Automated content tagging and organisation
• Learning path generation based on goals
• AI coaching and support features
• Natural language interaction

Key questions:

• What AI capabilities are actually deployed, not just announced?
• How does AI improve over time with organisational usage?
• What controls exist over AI recommendations?
• How does AI handle content that may be outdated or inaccurate?

4. Integration Ecosystem

Critical integrations include:

• HRIS for employee data and skills profiles
• Collaboration tools (Teams, Slack) for learning in workflow
• Content providers (LinkedIn Learning, Coursera, etc.)
• Calendar systems for scheduling and time allocation
• Analytics platforms for consolidated reporting
• Single sign-on for seamless access

Key questions:

• What pre-built integrations exist?
• How robust is the API for custom integrations?
• How is data synchronised across systems?
• What’s the integration implementation experience like?

5. Analytics and Measurement

Platforms should provide:

• Individual learning analytics for learners and managers
• Organisational capability dashboards
• Content effectiveness metrics
• Skills gap analysis
• Predictive insights about learning needs
• Outcome connection where possible

Key questions:

• What analytics are available out of the box?
• Can analytics be customised for organisational needs?
• How does the platform connect learning to business outcomes?
• What data export capabilities exist for external analysis?

6. User Experience

Experience factors include:

• Mobile-first design for learning anywhere
• Intuitive navigation that doesn’t require training
• Fast performance with minimal load times
• Accessibility compliance for all learners
• Attractive design that encourages engagement
• Social features that enable community learning

Key questions:

• What does actual learner feedback look like?
• How does the mobile experience compare to desktop?
• What accessibility standards are met?
• How frequently is UX updated based on feedback?

Evaluation Process

Phase 1: Requirements Definition

Before talking to vendors:

• Define must-have versus nice-to-have requirements
• Identify key use cases the platform must support
• Clarify integration requirements with IT
• Establish budget parameters
• Determine decision criteria and weights

Phase 2: Market Scan

• Identify platforms that might meet requirements
• Review analyst reports and market comparisons
• Gather feedback from peers at similar organisations
• Create initial shortlist based on fit

Phase 3: Vendor Engagement

For shortlisted vendors:

• Request demonstrations focused on your use cases
• Provide specific scenarios for vendors to address
• Ask for reference customers in similar contexts
• Request detailed integration and implementation information
• Get pricing clarity including all components

Phase 4: Deep Evaluation

For finalists:

• Pilot with actual users if possible
• Technical review with IT stakeholders
• Contract and commercial review
• Implementation planning conversations
• Executive stakeholder alignment

Phase 5: Decision and Negotiation

• Final decision based on weighted criteria
• Commercial negotiation
• Implementation timeline agreement
• Success metrics definition

Common Evaluation Mistakes

Avoid these frequent errors:

Feature focus over fit. The platform with the most features isn’t necessarily the best fit. Focus on features you’ll actually use.

Demo dazzle. Vendor demos are designed to impress. Ask to see the parts they don’t show voluntarily.

Ignoring implementation reality. Platforms are only as good as their implementation. Understand what successful deployment requires.

Underestimating integration effort. Integrations that look simple in demos often prove complex in practice.

Overlooking ongoing costs. Initial pricing may not reflect true total cost of ownership including administration, content, and integration maintenance.

Neglecting change management. The best platform fails if people don’t use it. Factor adoption effort into evaluation.

The AI Question

Every vendor claims AI capabilities. How to evaluate them:

Ask specific questions:

• What AI models power your recommendations?
• How is AI trained on organisational data?
• What controls exist over AI behaviour?
• How do you handle AI errors or inappropriate recommendations?

Request evidence:

• Case studies with measured AI impact
• Before/after metrics from AI implementation
• Third-party validation of AI claims

Test directly:

• If possible, pilot AI features with real content and users
• Evaluate recommendation quality firsthand
• Assess how AI handles edge cases

AI capabilities vary enormously despite similar marketing claims.

Total Cost of Ownership

LXP costs include:

Direct costs:

• Platform licensing (usually per user per year)
• Implementation fees
• Integration development
• Content licensing

Indirect costs:

• Administration and maintenance
• Training for administrators
• Change management for adoption
• Opportunity cost during implementation

Ongoing costs:

• Annual licensing increases
• Additional content purchases
• Integration maintenance
• Platform administration

Get clarity on all cost components before making decisions.

Recommendation

The LXP that’s “best” depends on your specific context:

• Organisational size and complexity
• Existing technology ecosystem
• Content strategy and sources
• User population characteristics
• Budget constraints
• Implementation capacity

There’s no universal answer. The right platform is the one that fits your specific situation and that you can successfully implement and adopt.

Focus evaluation on fit and implementation reality, not just features and demos. The best platform poorly implemented will underperform a good platform well implemented.

Take the time to evaluate properly. Platform changes are disruptive and expensive. Getting it right the first time matters.