AI optimization, including AI-powered authoring, pathway creation, and conversational searches, is being developed simultaneously in both LMS and LXP products. Both types of vendors are co-developing these capabilities and marketing them together.
The problem with modern consumers has never been one of demand and supply. It has always been about an over-influx of categories that are adorned by multiple sub-categories, and then a plethora of options amongst them too.
OPTION PARALYSIS. That is the problem of modern-day consumerism.
Option Paralysis ( /ˈɒpʃənˌpærəˈlɪsɪs/ ) noun
Option paralysis is the psychological state where an individual feels overwhelmed by an excessive number of choices, leading to difficulty or inability to make a decision.
And the academic world is also no stranger to it. It’s been over a decade since the emergence of Learning Experience Platforms (LXPs), and the question still persists. What more? How many more? And how to manage more?
Table of Contents:
1. LMS ≤ LXP : Turning tables
2. Rise of expectations
3. Remodeling to Learning Intelligence Platform
4. Executive Importance of Learning Intelligence
5. Embracing Learning Intelligence the way it should be
5.1 Data Foundations and Quality:
5.2 Integration and Connectivity:
5.3 Technical and Governance Challenges:
5.4 User Adoption and Experience:
5.5 Overcoming Misconceptions:
5.6 Limitations of Aggregation
Conclusion
1. LMS ≤ LXP: Turning tables
Up until the first 5 rounds (viz., years) in the ring, LXPs brought in a revolutionary integrating system that was the only source for all learning data in one place. After that, LMSs started to cooperate with other third-party systems to tackle the competition. What happened to Yahoo could easily be the fate of LXPs since the service is not a distinct one.
At present, the learning management systems have adopted AI-based features such as content recommendations, conversational search, and the creation of automated learning pathways, etc. The collaboration tools become part of learning workflows and include plug-ins for corporate communication platforms like Microsoft Teams that enable in-flow learning. Centralized communication and social learning components, initially the strengths of LXP, are now part of LMS offerings.
However, AI optimization, including AI-powered authoring, pathway creation, and conversational searches, is being developed simultaneously in both LMS and LXP products. Both types of vendors are co-developing these capabilities and marketing them together. Neither of them has the right of exclusivity. LXPs allow users to bring learning from the wider web back to the platform; thus, content creation and curation are made easier. This practice is expected to revive and grow.
Centralized communications make sense as LXPs aggregate content and user activity, positioning them as natural channels for internal communication related to training and learning.
2. Rise of expectations
LXPs created a market that solved a major problem. The comfort of having one consolidated place to access all the required systems was too immense to settle for even two or three systems separately. On top of that, LMS kept up exceedingly well with LXPs, raising the ambitions of all consolidated system consumers. Organizations now push for all the systems to be in one place. The growing expectation for platforms to integrate internal HR data, third-party content, and external labor market intelligence places pressure on vendors to deliver comprehensive, federated solutions.
Such a wide-ranging and diverse collection of requirements creates uncertainty in the market, makes it harder to assess vendors, and prompts buyers to clearly state their strategic priorities. Vendors are often pushing similar features, thus making buyers confused as to what exactly differentiates an LMS from an LXP.
3. Remodeling to Learning Intelligence Platform
Traditionally required multiple systems are creating ambiguity and unrealistic expectations in vendor and client conversations. Federated LMS consolidates multiple LMSs into a single front-end, reducing administrative complexity for low to medium-complexity organizations.
LIP rationalizes multiple learning content platforms and skill sources into a single intelligent view, combining internal organizational and external market skill data for personalized, skills-based learning recommendations. When dealing with large volumes of content libraries and adjacent learning stores, the enterprise-grade intelligent aggregation, instead of the single rationalized view, optimizes the learning journey.
4. Executive Importance of Learning Intelligence
The concept of learning intelligence is not limited to the word ‘aggregation’; it is also featured by the logic used to establish the relevance. This is because the proper learning that relates to business-critical outcomes requires a close alignment with the company’s skill landscape.
On the other hand, insights into the skills held internally, skill frameworks, and ontologies are often found in isolated silos in various systems and departments. Learning intelligence makes it possible for the places where skills are located to be cross-referenced with global workforce insights (skills inference) and at the same time with essential and nuanced organizational skills data (local skills), to that extent. Hence, medium to high complexity organizations that wish to use skills (and future skills gaps) as the justification engine for aggregated learning content will require a Learning Intelligence Platform.
5. Embracing Learning Intelligence the way it should be
The implementation of LIP requires organizations to deliberate over and resolve various considerations and challenges that are crucial for the achievement of the platform.
5.1 Data Foundations and Quality:
The core of accurate and maintained data is a must-have. This core consists of clean user data, version control, competency configurations, completion records, and content quality. The platform’s and the learning’s relevance are considerably impaired by the absence of solid data hygiene; thus, on the adoption and ROI side, there are negative effects.
5.2 Integration and Connectivity:
The platforms should have seamless and uninterrupted integration with the HR systems, content libraries, talent marketplaces, and other learning sources that already exist. The constant upkeep of integration points is vital in order to prevent disruptions. There might be cases when some content in external platforms like SharePoint may need to be migrated to enable complete tracking of consumption.
5.3 Technical and Governance Challenges:
Poorly configured systems, the absence of a single process, and the disjointed governance of the multiple platforms are problems that cannot be resolved by the learning platforms themselves. The very process of making the learning experience smooth is by having good governance and process harmonization.
5.4 User Adoption and Experience:
User engagement, even with the greatest technology, is still dependent upon intuitive interfaces, relevant content, and support from the organization. Learning platforms are unable to counteract such problems as low motivation or unclear learning priorities, which are the root causes underpinning them.
5.5 Overcoming Misconceptions:
Any LMS or Learning Intelligence deployment still needs to undergo a series of careful planning, continuous data servicing, and quality assurance to support the achievement of organizational goals.
5.6 Limitations of Aggregation
The Learning Intelligence Platform (LIPs) pools content and data together, but cannot deal with the poor quality of content or the lack of coherence in governance. Their worth is determined by the quality of source systems and data input.
Conclusion
Tech consumers and users need to realize and acknowledge that the new tech is only as good as the existing tech on which it is built. The LIPs, which are LIPs, and LMS (Learning Management System), which are Learning Management Systems, both work on component parts that need to be configured correctly and serviced regularly.
One cannot just deploy the system and wait for it to adapt, as platforms are dependent on connectivity with existing systems. LIP demands the continuous upkeep of the technical infrastructure to work at its best. Moreover, LIPs are limited to content and data aggregation only; they cannot fix experience issues caused by poor underlying configuration or content quality, and they cannot impose unified processes and governance across the host systems.
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