Prediction #10: Total Learning Architecture maturity will advance significantly
Prediction 10 of 10: The Total Learning Architecture (TLA) has been in development for over a decade, providing a standards-based, software-validated interoperable data architecture for enterprise learning ecosystems. But until recently, the TLA has been a vision, largely implemented in research laboratories rather than in real-world production pipelines. In 2026, the TLA ecosystem will hit several meaningful milestones. It will be better documented, better supported, and more implementable than ever before.
Prediction #9: Data privacy solutions, such as Self-Sovereign Identity, move into the Early Adopter phase
Prediction 9 of 10: The regulatory pressure for learning data privacy is growing. At the same time, organizations want richer learning analytics. These ambitions increasingly conflict with privacy constraints that limit data collection, retention, and cross-system sharing. Emerging approaches offer a path forward. Privacy-respecting data architectures are moving from Innovators to Early Adopters; some organizations will muddle through with compliance theater, but early movers will turn their stance on privacy into competitive advantage.
Prediction #8: Verifiable Credentials and Digital Wallets will expand in pilots, with policy mandates as potential accelerants
Prediction 8 of 10: The infrastructure for Verifiable Credentials is maturing rapidly. W3C published Verifiable Credentials 2.0 as a full Recommendation in May 2025. Open Badges 3.0 and the Comprehensive Learner Record 2.0 are finalized and VC-aligned. The T3 Innovation Network's Learning and Employment Record work continues advancing, with open-source tools via LinkedCreds and a new LER Resume Standard. The EU Digital Identity Wallet has a 2026 operational target. In 2026, we expect significant pilot expansion.
Prediction #7: “Skills-based” ambitions will continue, but expect market fragmentation while semantic data challenges get resolved
Prediction 7 of 10: “Skills-based” ambitions will continue, but expect market fragmentation while semantic data challenges get resolved. “Skills-based organization” has become the hottest phrase in HR and L&D strategy. Reports claim that 85% of companies used skills-based hiring in 2025, and at least one firm has coined the phrase “skills as system architecture.” But genuine uptake still faces practical barriers.
Prediction #6: AI automation will disrupt traditional learning pathways and expose content obsolescence
AI automation will remove the “bottom rung” of many career ladders and rapidly change large portions of other jobs and their associated training. Expect broken learning pathways and accelerated obsolescence of workforce training content.
Prediction #5: Learning Engineering will gain institutional momentum, especially in military and academic sectors
Learning Engineering will gain (even more) institutional momentum in 2026, moving from an emerging discipline to an operational capability, especially in military and academic sectors where evidence-based, data-informed learning systems are becoming mission-critical.
Prediction #4: The “Learning Tech Stack” will enter the strategic conversation
In 2026, learning tech stack thinking will enter the mainstream, shifting learning platforms away from closed system silos and toward composable ecosystems of content, analytics, and adaptive services connected through open APIs.
Prediction #3: Automated metadata generation will emerge as a strategic capability—and potentially a standalone business
As interoperable learning platforms scale, metadata becomes a bottleneck. In 2026, AI-generated metadata will emerge as core learning infrastructure, accelerating discoverability and personalization while increasing demand for validation, standards alignment, and trustworthy “Content Metadata as a Service.”
Prediction #2: Data quality tooling for xAPI will become a priority as learning technologies increasingly share data
As more tools and organizations share their xAPI data around, the question of data quality becomes (even more) urgent. A malformed statement, a non-conformant xAPI Profile implementation, or low-fidelity activity data no longer “just” create local problems; now flawed data can propagate through the network, degrading analytics and undermining trust across the digital landscape.
Prediction #1: “Learning slop” will create a signal-to-noise problem, catalyzing demand for data provenance standards
2026 Top Ten Predictions: “Learning slop” will create a signal-to-noise problem, catalyzing demand for data provenance standards.