Are we ready for AI? Let´s answer this important question!
There is an increasing hype around AI technologies among our Global MVNO industry, but very scarce real cases where AI has proven a visible business value. Through multiple interactions with diverse players in our MVNO domain, we have observed that in many circumstances, organizations simply do not yet have the required maturity to adopt and embrace AI technologies and properly leverage them to build business advantage. Instead, their initiatives to analyze and test AI tools turn into frustrating endless rounds of unsuccessful trials, consuming valuable resources and failing to really take off.
We have seen that these organizations frequently lack the structure, proper data strategy, knowledge on AI technologies, people skills and culture, to even start, and less succeed, in the implementation of successful AI use cases. This challenge is common to multiple other industry verticals. According to a recent MIT study (“The GenAI Divide”, MIT, 2025), which concluded that 95% of AI pilots fail to deliver ROI, despite growing investments and pilot projects, organizations are often unprepared to capture the full potential of AI.
For our MVNO community, this means that without sufficient organizational readiness, AI initiatives risk being fragmented or failing to create tangible business outcomes, and thus, AI Readiness Assessment becomes a prerequisite to ensure the successful piloting and scaling of AI technologies.
Building on Previous Key Insights
Our work on AI adoption among MVNOs has highlighted the transformative potential of AI when approached strategically. In “Intelligent Innovation: How AI is Transforming the MVNO Landscape”, MVNO Index, June 2025, we emphasized how AI can improve customer engagement, churn prediction, and network efficiency, but stressed that success strongly depends on organizational alignment and data maturity. Similarly, our article “How MVNEs & MVNAs Can Lead the Data Revolution”, MVNO Index, July 2025, discussed the centrality of data strategy, integration, and governance as prerequisites for meaningful analytics and AI-driven insights. Finally, in our last delivery, “Unlocking True Power of AI”, MVNO Index, August 2025, we explored how AI initiatives often fail because of a lack of change management, cultural readiness, and cross-functional collaboration.
The AI Readiness Maturity Score builds upon these key insights, providing a structured framework to assess organizational preparedness across critical dimensions, helping MVNOs identify strengths and potential gaps before investing and scaling AI efforts.
The Six Dimensions of AI Readiness
For MVNOs to succeed in AI adoption, they must evaluate their maturity across six key dimensions, thus assessing their AI Readiness Maturity Score:
1. Leadership & Governance
Strong executive sponsorship from MVNO C-level together with clear governance are a fundamental pillar to ensure that AI initiatives will be aligned with MVNO’s business strategy. Leadership teams must remain committed, set clear strategic prioritization, and stick to strict governance to ensure AI adoption success.
2. Data Foundations
As previously highlighted, high-quality, integrated, and accessible data is the foundation for AI. Without it, even the best models will fail. The role of the Chief Data Officer (CDO) is critical to ensure effective data leadership and governance, robust data management strategy, and flexible infrastructure to enable the MVNO to obtain actionable insights from its AI applications.
3. Technology & Infrastructure
Effective deployment and maintenance of AI tools require a scalable and flexible technological infrastructure. Cloud capabilities, APIs, automation, and monitoring frameworks are all critical elements to ensure that AI initiatives are not limited by infrastructural constraints.
4. People & Skills
Skilled technical and business experts are prerequisites for successful AI initiatives. MVNOs need trained data scientists and analysts capable of translating insights into business actions. Continuous learning, cross-functional collaboration, and skills development are vital to sustain AI programs over time.
5. Business Alignment & Use Cases
MVNOs must address AI tools towards clearly defined business challenges such as churn reduction, personalization of services and pricing optimization. Use cases must be selected and prioritized according to their measurable business impact, thus ensuring that investments in AI will deliver tangible ROI.
6. Culture & Change Management
Cultural readiness can’t be overlooked as it is also a critical success factor for AI adoption. MVNOs must foster innovation, experimentation, and data-driven decision-making, aligning their workflows and processes with an effective change management.
Maturity Levels
The AI Readiness Maturity Score defines five maturity levels, helping MVNOs benchmark their progress:
- Nascent (0–20): Insufficient maturity for AI adoption. Foundational elements are missing, and prior significant preparation work is required.
- Emerging (21–40): Minimum AI maturity criteria is met, although significant gaps still remain. AI pilots will have a high risk of failure.
- Developing (41–60): Foundations are in place, which allows for AI pilot projects and initial experimentation, with adequate guidance.
- Advancing (61–80): There is strong AI readiness across multiple dimensions. AI initiatives can effectively be scaled with measurable ROI.
- Leading (81–100): AI is fully embedded in the organizational strategy and operations, driving measurable business outcomes.
These five maturity levels provide a benchmarking scale for MVNOs, allowing them to understand their current position, anticipate challenges, and plan the interventions that will strengthen their readiness before scaling AI projects.
Key Take-aways
We have established MVNO’s need for assessing their AI Readiness Maturity Score and described both its dimensions and evaluation levels. Let’s now summarize the Key Take-aways of this assessment:
1. Strategic Alignment
Benchmarking its readiness will help MVNOs to ensure that AI initiatives will be aligned with corporate strategy and priorities, and that measurable KPIs are set.
2. Gap Identification
By examining these six dimensions, MVNOs can identify the areas requiring further preparation, whether in data quality, skill development, or cultural change, prior to onboarding on AI pilots.
3. Informed Decision-Making
Understanding their AI readiness helps MVNOs to avoid costly missteps and scale AI only when the organization is ready to extract its real value.
4. Cross-functional Engagement
Use of a structured framework facilitates teamwork across MVNO’s departments, ensuring that AI adoption is collaborative rather than siloed.
Conclusion
AI represents a transformative opportunity for MVNOs, offering the potential to improve customer experience, optimize operations, and drive revenue growth. Yet, the path from pilot projects to tangible business impact is challenging, as evidenced by industry studies highlighting widely spread AI underperformance.
The AI Readiness Maturity Score provides a conceptual framework for MVNOs to assess their readiness across the six dimensions (leadership, data, technology, skills, business alignment, and culture). By applying this framework, MVNOs can identify strengths, uncover gaps, and plan strategic interventions that will increase the likelihood of successful AI adoption.
As AI adoption continues to accelerate in the MVNO sector, organizations that invest in preparing the right foundations, rather than simply pursuing technological solutions, will be best positioned to realize the full value of AI.
References
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