Business area digitalisation analysis

Digitalisation of digital learning platforms

How to scale users, content and revenue whilst improving the learning outcome

Digital maturity

Typical digital maturity

Shows the level of technological and process digitalisation at which companies in the sector or business area typically operate today.

A typical market situation is assessed, not the most advanced companies.

The assessment consists of five equally weighted dimensions:

Core system usage
Whether ERP, CRM, WMS, MES, customer portals or other operationally important systems are widespread in companies.
Process digitalisation
How many core processes run in systems and how many are still managed manually.
Systems integration
Whether core systems exchange data between themselves or whether employees transfer information manually.
Data quality and readiness
Whether core data is structured, up-to-date, consistent and suitable for automation and analytics.
Advanced data use
Whether real-time analytics, forecasting, automated alerts, optimisation models or AI are used.

The final score is the average of the five dimensions.

1–5 scale

  • 1 very low maturity
  • 2 low maturity
  • 3 medium maturity
  • 4 high maturity
  • 5 very high maturity

A low maturity score does not necessarily indicate low potential. On the contrary, low maturity and a high level of manual work may indicate significant untapped digitalisation value.

high
Skaitmenizacijos potencialas

Digitalisation potential

Shows how much significant business value a typical sector or business area company can create by systematically digitalising core processes.

The rating is calculated on a 100-point scale across five dimensions:

Process frequency and scale 20 %
An assessment of how frequently the digitalised processes recur and what proportion of operations they represent.
Manual work intensity 20 %
An assessment of the extent to which processes depend on email, telephone, Excel, paper documents and repeated data entry.
Impact on revenue and costs 25 %
An assessment of the potential effect on sales, margin, customer retention, administrative costs, errors, downtime or inventory.
Growth and scale potential 20 %
An assessment of whether digitalisation would enable operational capacity to be increased without expanding headcount and costs at the same rate.
Impact on decisions and risk 15 %
An assessment of the potential effect on data reliability, decision-making speed, customer experience, and the reduction of errors and operational risk.

The final score is calculated according to the assessments and weights of all dimensions.

100-point scale

  • 0–20 very low potential
  • 21–40 low potential
  • 41–60 moderate potential
  • 61–80 high potential
  • 81–100 very high potential

A high score does not mean the solution will be easy to implement. It indicates the size of the potential value, not the implementation complexity.

89/100
Biggest challenge
Content lacks sufficiently structured metadata
Biggest opportunity
Data-driven and adaptive learning platform

The platform wins not by quantity of content, but by its ability to help users achieve measurable results at scale.

Digital learning platform operating model

Operations connect content creators, user acquisition, payments, learning experience, support and B2B clients.

Platform economies of scale

An additional user can have a low marginal cost.

Large volume of behavioural data

Every action can improve the product.

Content rights risk

Authorship and versions need to be managed.

Subscription model

Revenue is driven by engagement and retention.

Market and technology context

Platforms are being transformed by generative AI, adaptive learning, competency data and B2B academies.

  • Generative AISearch and content are created faster, but control is required.
  • Competency-based learningIt is important to see the capability acquired.
  • Subscription retentionGrowth is driven by active usage.
  • AI literacyUsers must understand AI limitations.

Typical operating process

01

Content creation

Audience, competency and content are defined.

02

Acquisition

User reaches the platform.

03

Registration

An account is created and payment is made.

04

Learning path

The user learns and receives assistance.

05

Completion

The result is recorded.

06

Retention

Further content is recommended.

Digital maturity model for the business area

0

Separate content, users and learning outcomes

Content is published digitally, but goals, competencies, tasks, progress, support and commercial data are not linked.

1

Separate administration and content tools

Registration, documents, content, assessment and communication are managed using different tools.

2

Digital standard process

Core registration, content delivery and some communication occur digitally, but exceptions remain manual.

3

Integrated core learning pathways

In key products, user goals, content, tasks, progress and support are linked, but content semantics and AI management are not yet consistent across the platform.

4

Data-driven and personalised platform Typical current situation

Content, competency, behaviour, outcome, subscription and B2B usage data are consistently used for product decisions, and AI scenarios are controlled.

5

Adaptive and reliably automated ecosystem Siektina

Content, support and administration are personalised, whilst AI and competency data are managed transparently.

Key finding

The problem is usually not a lack of content, but the absence of a connection between content, competence and outcome.

AI can improve support, but without sources and controls it increases risk.

First priority – one learning pathway with a clear outcome.

Related digitalisation topics

Adaptive learning platformAI learning assistantLearning data platformB2B competency analytics
Next step

Let us assess why users are not completing the learning pathway

The content data, personalisation, AI assistance and subscription model can be analysed.