The AI technologies of today—including not just large language models (LLMs) but also deep learning, reinforcement learning, and natural-language processing (NLP) tools—will equip telcos with powerful new automation and analytics capabilities.
AI-powered automation is already driving significant margin growth by reducing costs. But to truly drive transformation telcos must ensure AI models are driven by accurate, high-quality, trusted data, and determine how to manage and govern massive volume at scale. And not just in ad hoc instances in pockets of the organization, but as a part of the infrastructure of the business as a whole. This is the essence of “Trusted AI Everywhere.”
“Trusted AI Everywhere” explained
Trusted AI poses significant challenges. One notable example is the tendency of LLMs to produce hallucinations—i.e., outputs that read smoothly and seem plausible, but are unfounded or nonsensical. Embedded biases, whether explicit or hidden, can also perpetuate harmful outcomes. Other challenges include the lack of transparency and explainability in AI systems and the need for continuous monitoring and inherent observability to maintain their effectiveness. Addressing these and other challenges is a precondition for trusted AI.
To do so, open source AI is particularly primed to spearhead the AI revolution. Not just because it’s rapidly closing the feature-and-function gap with commercial/proprietary solutions, but because it’s inherently transparent and adaptable. Open source’s benefits, proven in the realm of enterprise software, resonate even more in the context of AI. The ability to customize and scrutinize source code helps ensure trust and security, and the benefits of open source’s collaborative governance model help mitigate commercial AI’s “black box” problem.
To that end, “Trusted AI Everywhere” marries the ethos of trusted AI with the insight that AI’s maximum impact comes when it’s seamlessly integrated across a telco’s entire enterprise. This isn’t about isolated pockets of trustworthy AI, like chatbots in the contact center; it’s about ensuring pervasive trustworthiness, reliability, and observability. These concepts of observability and explainability are crucial not only because they make it easier to diagnose and resolve issues, but also because they contribute to our understanding of the behavior of AI solutions—whether they’re applied to network optimization, customer service, data analytics, or other use cases.
“Trusted AI Everywhere” encompasses three primary aspects
First, it involves the use of Ggenerative AI and LLMs, along with other AI technologies, to empathetically interact with users—customers, employees, and partners—improving the quality of interactions. By leveraging AI-powered sentiment analysis and affective computing, telcos can transform the interaction experience, promoting increased engagement, improving the efficacy of marketing and operations, and enabling enhanced decision-making.
Moreover, this first aspect of “Trusted AI Everywhere” extends beyond customer service or marketing. By promoting a question-and-answer driven interactive experience—and by synthesizing, contextualizing, and surfacing insights derived from an enormous amount of information—AI solutions can transform human decision-making, leading to better, more responsive decisions and actions.
Second, trusted data forms the bedrock of trusted AI, as AI models are only as good as the quality of their underlying data platform. Eliminating inconsistencies, errors, and redundancies is pivotal, as is understanding the lineage of data. Finally, the data sets used to drive AI models must be diverse, complete, unbiased, and representative of the problem space for which the model was designed. In a sense, these are classic data management problems; given the scale and complexity of AI development, however, they’re considerably more difficult to address. In addition, AI development poses significant challenges to data governance, especially with respect to explainability, regulatory compliance, security, and privacy.
The third and final component is omnipresence—the “Everywhere” component of “Trusted AI Everywhere.” The potential of AI is best realized when it is embedded across a telco’s business processes, not only as a means to improve or optimize these processes but in order to ensure observability into them. Customer service is one obvious application for embedded AI, which can provide personalized, efficient, and round-the-clock customer engagement. AI can also play a foundational role in helping telcos optimize their networks and operations, with observability enabling telcos to more quickly and reliably detect and pinpoint network performance problems, developing automated AI solutions that support both proactive health monitoring and the autonomous rectification of issues. Autonomous networks continue to be a goal for the most advanced telco operations. The same is true of supply chain management and logistics, human resources, finance, product development, and other essential business processes. Business partner interactions—distributors, resellers, roaming partners, and content providers—can similarly be driven by automated systems.
Another dimension of “Everywhere” is that telcos must deploy AI from the network edge to their core businesses. In addition to embedding AI to support back office (billing and payment processing, network operations, etc.) and front office (customer service, sales and marketing, etc.) functions, this might take the form of using AI to automate predictive maintenance for edge devices, like RAN base stations and towers or WAN endpoints. It could involve optimizing the way the fleet is deployed or developing an ability to dynamically schedule which routes they take. It might entail leveraging AI to improve the availability, performance, and security of core network infrastructure, e.g., supporting dynamic traffic prediction and load balancing across network technologies, adaptive network configuration, fault prediction and avoidance, and power consumption.
Conclusion
“Trusted AI Everywhere” inaugurates a paradigm shift in the telco space, focusing on integrating AI seamlessly across all telco operations. Key pillars of this change are:
- A unified data infrastructure ensures access to quality data, irrespective of its location, be it on-premises or various cloud services. This equips telcos to capitalize on AI’s insights.
- A preference for transparent, open-source AI over proprietary systems. Open-source solutions offer trust and explainability, essential for decision-making and reducing AI adoption risks.
- Pervasive AI integration up, down, and across a telco’s business operations.
Learn more about how Cloudera helps Telcos deliver Trusted AI Everywhere.
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