By Mark Newman, Chief Analyst, TM Forum
As the excitement around GenAI gives way to more considered analysis of its business value and how to put use cases into production, it is clear that many communications service providers (CSPs) lack a gauge by which to assess their organisation’s readiness to benefit from the technology. And that risks slowing down the industry’s ability to benefit from it.
Despite intense interest in GenAI, most CSPs remain slow at moving beyond proofs of concept to put the technology into large-scale production.
The reasons for caution are multiple, but one of the brakes on adoption is CSPs’ understanding of the extent to which their current organisational maturity allows them to benefit from GenAI deployment: Before they can scale GenAI, CSPs need to identify not only potential business opportunities, but also where they may require new processes, organisational alignments, technologies or skills.
Telecom operators committed to building their own GenAI use cases are likely to have started the process of making their organisations AI-ready. But even CSPs that opt to source GenAI use cases and applications from their existing IT vendors and systems integrators will face limitations on what they can buy without first laying the right foundations. Just as importantly, they also risk creating new technical debt and vendor lock-in.
GenAI leadership
One of the first steps for a CSP is to build an organization which has a powerful central AI focus and set of responsibilities, headed up by a CXO whose role is exclusively or predominantly focussed on data and AI. But CSP organizations also need devolved capabilities and accountabilities in those parts of the business where GenAI has greatest potential.
The data picture
There are several areas of organisational maturity for an AI CxO to consider. But given AI’s dependence on access to high-quality data, data maturity is clearly a critical factor when deciding how to make strategic and operational use of both GenAI and predictive AI. It also shapes how well CSPs can comply with privacy and security requirements, as well as national and regional customer data regulations.
Telcos were already working on how to make data available for AI and machine learning prior to GenAI entering the public stage at the end of 2022. Nonetheless, GenAI raises fresh questions for CSPs, including its role in leveraging unstructured data, as we explore in our recent report “Building an AI strategy: telcos put the foundations in place“.
Sourcing tools and skills
TM Forum has co-developed GAMIT – GenAI Maturity Index Tracker – with AWS. GAMIT is an interactive, online tool to help CSPs understand where they are on their GenAI journeys
Another major obstacle to widespread GenAI adoption is access to relevant skills. Areas where telcos struggle to find expertise include understanding how to choose, use and improve different large language models (LLMs) for specific use cases. There is also a dearth of GenAI-specific operations capabilities – sometimes called MLOps.
These and other challenges apply to everyone across the industry and as the develop their strategies, CSPs want to know how they compare to their peers worldwide. That applies both to the front-runners with deep pockets, and smaller companies that are wondering if and how predictive AI or GenAI can boost operational and financial performance and deliver on their strategies.
For this reason, TM Forum has co-developed GAMIT – GenAI Maturity Index Tracker – with AWS. GAMIT is an interactive, online tool to help CSPs understand where they are on their GenAI journeys relative to other CSPs, and the areas they need to prioritize to take use cases into production.
Built using data from global AI decision makers in CSPs, GAMIT allows individual CSPs to see how their GenAI maturity compares to benchmark their AI maturity against regional peers and global leaders while providing access to tools, documentation, and expert advice. Ultimately, GAMIT will help accelerate the roll-out of GenAI use cases at scale by providing CSPs with actionable insights and resources that help to identify key areas for improvement, prioritize AI use cases with the highest ROI, and overcome deployment challenges.
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