RESEARCH REPORT
How telcos can address tech and data debt at scale
Communications Service Providers (CSPs) must modernize their digital cores to scale gen AI.
5-MINUTE READ
August 22, 2024
In brief
- AI opportunity, readiness gap: Most CSPs see AI as transformative, but 80% aren’t prepared to be data-first.
- Debt threatens agility: Tech and data debt rise as CSPs seek efficiency, agility and differentiation.
- Leaders act now: Accenture research shows challenges and how leading CSPs tackle debt to reinvent digital cores.
A renaissance for telcos in a new tech order
A new opportunity beckons for the industry. The AI-driven tech wave puts communications providers at the center of a reinvigorated value chain that is poised to become the digital backbone of the social and economic fabric, that could scale geographically. In this integration of communications and technology the redistribution of value can be exploited by telcos.
From a cycle of value erosion to a renaissance of connectivity telcos can stand in the center of the AI revolution. With this unparalleled opportunity to change their business outlook, telcos will have to be AI-inside if that want to sell AI-outside. By reinventing processes, networks and technology with automation and using AI to open new growth markets, the telecoms sector has the potential to enter a new era of higher growth and stronger margins. AI inside is the starting point.
From data debt to enterprise intelligence
For years, Communications Service Providers (CSPs, or ‘telcos’) have invested in technology transformation—modernizing stacks, migrating to the cloud and adopting AI. With generative AI (gen AI) accelerating this change, the potential for reinventing communications companies is within reach. Yet, for many CSPs that promise and those returns remain elusive.
Data debt: the unrealized value trapped in accumulated data assets
Our research shows that 72% of CSP technology executives consider data quality and consistency issues as the top barriers to managing their organization's data estate and scaling gen AI applications. Meanwhile, 66% are concerned by the growing volume and diversity of data. Over 70% point to data security and privacy concerns, while 63% note spiraling data maintenance costs.
However, a few CSPs are proving that it is possible to break free. These leaders —about 20% identified in our study— have already demonstrated effective approaches to overcoming data debt. Rather than merely managing risk, they’re proactively redesigning their data estates to enable agile decision-making and scalable AI.
Addressing data debt is not just an operational necessity—it is a strategic enabler for CSPs to fully harness the value of their data assets and accelerate gen AI-driven transformation. Our research and experience shows that telcos on their way to realizing this goal are led by three priorities: treating data as a product, building AI-native architecture and fully capitalizing on gen AI capabilities.
How telcos can modernize data to unlock Gen AI value and enterprise agility
01
Treat data as a unifying asset
Most telcos still treat their data as a technical byproduct they must keep. But it is owned by scattered functions with little alignment, so not exploiting its full value.
It is now time telcos consolidated this data estate, companywide. This will accelerate decision-making, align functions around a unified data view and foster widespread use of data products. Reducing redundancy and rework in a data-driven culture will quickly improve time to value. The 20% of telcos we identified as leaders in data maturity are already reaping these rewards.
02
Build an AI-native data architecture
79% of CSPs recognize the need for modern IT systems to streamline processes and drive flexibility. But 60% are still locked into legacy data tools in design and practice.
Hybrid data architectures that combine the strengths of data fabric and data mesh are the way past the disjointed data model. When designed well, the mesh model decentralizes ownership without recreating silos. The fabric model provides the connective tissue that integrates disparate data sources, ensuring relevance and accountability across the organization. The combined architecture enables CSPs to reimagine how data management processes operate across the entire data lifecycle. Most importantly, with this architecture, CSPs can realize the full benefits of data for AI, reliably bringing structured and unstructured data together in context.
03
Use gen AI to kickstart data-first enterprise
Gen AI needs high-quality data to be effective. But today nearly half of the executives we surveyed (49%) see data technology complexity as a major barrier to getting started. The good news is that the solution lies in the technology itself.
66
of executives rank rising data maintenance costs among their top three data infrastructure and management challenges
While high-quality data is essential to ensure gen AI efficacy, the technology is also a powerful tool for generating good quality, clean data. Large Language Models (LLMs) can significantly improve the time and effectiveness of producing clean, high-quality data. Gen AI offers CSPs an unprecedented opportunity to reinvent the entire life cycle of data, which reduces cost, enhances agility and improves decision-making. CSPs can use gen AI to generate synthetic data that mirrors their actual operations, customer behaviors and network activities to fill gaps in datasets for testing, model training and simulations. Multi-agentic systems can be deployed to constantly monitor data quality, detect anomalies and trigger real-time remedial actions.
While the window for transformation requires urgency, it also holds a promise. CSPs that act decisively—aligning their strategies, architectures and operations around data and AI—will not only resolve today's data challenges but secure their positions as tomorrow's industry leaders.
Transforming core capabilities to address Technology Debt
CSPs’ approach to IT transformation over the past decade has been through adaptation as the business embraced new models, architectures, and acquisitions. This approach multiplied the complexity of the IT components, compounding tech debt. As a result, initiatives have not always yielded high-value returns and business outcomes.
Technical debt is the cost to keep IT systems modern and business-ready
Our research shows that 84% of CSP executives say that their company will miss future growth opportunities if it fails to accomplish ongoing IT transformation. The rise of generative AI is pushing CSPs to address tech debt as a strategic priority. Accelerating efficiencies with next generation technologies will provide a valuable reallocation of resources to drive transformation.
Though most CSPs are aware of the impact of rising tech debt, there is a gap between the vision for the future and the current state. For instance, 93% of CSPs cite Cloud First infrastructure as a significant capability, but only 26% follow advanced practices enabling scalability and agility.
Tech debt impacts almost every facet of CSP operations. Failure to monetize assets, competition struggles, operation and maintenance costs, and slow time to market top the list of our global survey. All this indicates that there is a need for a comprehensive review of CSPs’ IT structure through a tech debt lens. Though most CSPs are aware of the impact of rising tech debt, there is a gap between the vision for the future and the current state. For instance, 93% of CSPs cite Cloud First infrastructure as a significant capability, but only 26% follow advanced practices enabling scalability and agility.
How to achieve success
CSPs’ IT & Business groups must take these three steps — together — to transform their core.
Technology transformation led by a clear strategy and a value-led mindset can maximize business impact and returns. Align technology vision with core strategic plays and assess where core technologies can provide unique value.
7%
of CSP executives are satisfied with the return on their IT modernization investments over the past three years
Migrate from legacy systems to the new architecture in a phased manner, prioritizing migration of critical systems first. This minimizes disruption to ongoing operations while also building in KPIs.
4x
difference in IT opex costs between more and less tech advanced CSPs (2021-2023)
A company-wide enterprise-debt program is required to simplify the product catalog, redesign customers’ and partners’ engagement journeys and streamline all operational processes. This requires a long look at both tech and talent capabilities.
32%
faster time to market for new products and services compared to CSPs with lower technological sophistication
Over the past three years, companies in the top quartile of technological advancement have been more cost-efficient than their low-maturity peers. They also set their sights higher for agility in the coming years.
0.8%
tech-advanced cos report lower IT costs
2.3x
less TCO rise for tech-advanced vs others
32%
faster Advanced CSPs foresee speed to market boost
Reinventing the core: From cost center to growth and AI innovation engine
By addressing data debt and tech debt head-on CSPs can transform all elements of their internal operations from a cost center into a powerful engine for growth and innovation. Anchored in cloud, data, AI, seamless interoperability, and APIs, the digital core blueprint empowers CSPs to build resilient systems with AI inside that adapt swiftly to market shifts. With generative AI accelerating automation, CSPs can unlock new efficiencies and capture future growth opportunities selling AI outside, positioning themselves as leaders in a rapidly changing landscape.
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