How a solid generative AI strategy can improve telecom network operations

Generative AI (gen AI) has transformed industries with applications such as document-based Q&A with reasoning, customer service chatbots and summarization tasks. These use cases have demonstrated the impressive capabilities of large language models (LLMs) in understanding and generating human-like responses, particularly in fields requiring nuanced language understanding and inferencing.

However, in the realm of telecom network operations, the data is different. The observability data comes from proprietary sources and encompasses a wide variety of formats, including alarms, performance metrics, probes and ticketing systems capturing incidents, defects and changes. This data, whether structured or unstructured, is deeply embedded in a domain-specific language. This includes terms and concepts from technologies like 5G, IP-MPLS and other network protocols.

A notable challenge arises from the fact that standard foundational LLMs are not typically trained on this highly specialized and technical data. This needs a careful strategy for integrating gen AI into the telecom operations domain, where operational efficiencies and accuracy are paramount.

Successfully using gen AI for network operations requires tailoring the models to this niche context while addressing unique challenges around data specificity and system integration.

How generative AI addresses network operations challenges

The complexity and diversity of network data, along with rapidly changing technologies, presents several challenges for network operations. Gen AI offers efficient solutions where traditional methods are costly or impractical.

• Time-consuming processes: Switching between multiple systems (such as alarms, performance or traces) delays problem resolution. Generative AI centralizes data into one interface providing natural language experience, speeding up issue resolution by reducing system toggling.
• Data fragmentation: Scattered data across platforms prevents a cohesive view of issues. Generative AI consolidates data from various sources based on the training. It can correlate and present data in a unified view, enhancing issue comprehension.
• Complex interfaces: Engineers spend extra time adapting to various system interfaces (such as UIs, scripts and reports). Generative AI provides a natural language interface, simplifying navigation across complex systems.
• Human error: Manual data consolidation leads to misdiagnoses due to data fragmentation challenges. AI-driven data analysis reduces errors, helping ensure accurate diagnosis and resolution.
• Inconsistent data formats: Varying data formats make analysis difficult. Gen AI model training can provide standardized data output, improving correlation and troubleshooting.

Challenges in applying generative AI in network operations

While gen AI offers transformative potential in network operations, several challenges must be addressed to help ensure effective implementation:

• Relevance and contextual precision: General-purpose language models perform well in nontechnical contexts, but in network-specific use cases, models need to be fine-tuned with domain-specific terminology to deliver relevant and precise results.
• AI guardrails and hallucinations: In network operations, outputs must be grounded in technical accuracy, not just linguistic sense. Strong AI guardrails are essential to prevent incorrect or misleading results.
• Chain-of-thought (CoT) loops: Network use cases often involve multistep reasoning across multiple data sources. Without proper control, AI agents can enter endless loops, leading to inefficiencies due to incomplete or misunderstood data.
• Explainability and transparency: In critical network operations, engineers must understand how AI-derived decisions are made. AI systems must provide clear and transparent reasoning to build trust and help ensure effective troubleshooting, avoiding “black box” situations.
• Continuous model enhancements: Constant feedback from technical experts is crucial for model improvement. This feedback loop should be integrated into model training to keep pace with the evolving network environment.

Implementing a workable strategy to maximize business benefits

Key design principles can help ensure the successful implementation of gen AI in network operations. These include:  

• Multilayer agent architecture: A supervisor/worker model offers modularity, making it easier to integrate legacy network interfaces while supporting scalability.
• Intelligent data retrieval: Using Reflective Retrieval-Augmented Generation (RAG) with hallucination safeguards helps ensure reliable, relevant data processing.
• Directed chain of thought: This pattern helps guide AI reasoning to deliver predictable outcomes and avoid deadlocks in decision-making.
• Transactional-level traceability: Every AI decision should be auditable, ensuring accountability and transparency at a granular level.
• Standardized tooling: Seamless integration with various enterprise data sources is crucial for broad network compatibility.
• Exit prompt tuning: Continuous model improvement is enabled through prompt tuning, ensuring that it adapts and evolves based on operational feedback.

Implementing a gen AI strategy in network operations can lead to significant performance improvements, including:

• Faster mean time to repair (MTTR): Achieve a 30-40% reduction in MTTR, resulting in enhanced network uptime.
• Reduced average handle time (AHT): Decrease the time network operations center (NOC) technicians expenditure addressing field technician queries by 30-40%.
• Lower escalation rates: Reduce the percentage of tickets escalated to L3/L4 by 20-30%.

Beyond these KPIs, gen AI can enhance the overall quality and efficiency of network operations, benefiting both staff and processes.

IBM Consulting, as part of its telecommunications solution offerings, provides reference implementation of the above strategy, helping our clients in applying gen AI-based solutions successfully in their network operations.

Source: ibm.com

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New IBM study: How business leaders can harness the power of gen AI to drive sustainable IT transformation

As organizations strive to balance productivity, innovation and environmental responsibility, the need for sustainable IT practices is even more pressing. A new global study from the IBM Institute for Business Value reveals that emerging technologies, particularly generative AI, can play a pivotal role in advancing sustainable IT initiatives. However, successful transformation of IT systems demands a strategic and enterprise-wide approach to sustainability.

The power of generative AI in sustainable IT

Generative AI is creating new opportunities to transform IT operations and make them more sustainable. Teams can use this technology to quickly translate code into more energy-efficient languages, develop more sustainable algorithms and software and analyze code performance to optimize energy consumption. 27% of organizations surveyed are already applying generative AI in their sustainable IT initiatives, and 63% of respondents plan to follow suit by the end of 2024. By 2027, 89% are expecting to be using generative AI in their efforts to reduce the environmental impact of IT.

Despite the growing interest in using generative AI for sustainability initiatives, leaders must first consider its broader implications, particularly energy consumption.

64% say they are using generative AI and large language models, yet only one-third of those report having made significant progress in addressing its environmental impact. To bridge this gap, executives must take a thoughtful and intentional approach to generative AI, asking questions like, “What do we need to achieve?” and “What is the smallest model that we can use to get there?”

A holistic approach to sustainability

To have a lasting impact, sustainability must be woven into the very fabric of an organization, breaking free from traditional silos and incorporating it into every aspect of operations. Leading organizations are already embracing this approach, integrating sustainable practices across their entire operations, from data centers to supply chains, to networks and products. This enables operational efficiency by optimizing resource allocation and utilization, maximizing output and minimizing waste.

The results are telling: 98% of surveyed organizations that take a holistic, enterprise-wide approach to sustainable IT report seeing benefits in operational efficiency—compared to 50% that do not. The leading organizations also attribute greater reductions in energy usage and costs to their efforts. Moreover, they report impressive environmental benefits, with two times greater reduction in their IT carbon footprint.

Hybrid cloud and automation: key enablers of sustainable IT

Many organizations are turning to hybrid cloud and automation technologies to help reduce their environmental footprint and improve business performance. By providing visibility into data, workloads and applications across multiple clouds and systems, a hybrid cloud platform enables leaders to make data-driven decisions. This allows them to determine where to run their workloads, thereby reducing energy consumption and minimizing their environmental impact.

In fact, one quarter (25%) of surveyed organizations are already using hybrid cloud solutions to boost their sustainability and energy efficiency. Nearly half (46%) of those report a substantial positive impact on their overall IT sustainability. Automation is also playing a key role in this shift. With 83% of leading organizations harnessing its power to dynamically adjust IT environments based on demand.

Sustainable IT strategies for a better tomorrow

The future of innovation is inextricably linked to a deep commitment to sustainability. As business leaders harness the power of technology to drive impact, responsible decision-making is crucial, particularly in the face of emerging technologies such as generative AI. To better navigate this intersection of IT and sustainability, here are a few actions to consider: 

1. Actively manage the energy consumption associated with AI: Optimize the value of generative AI while minimizing its environmental footprint by actively managing energy consumption from development to deployment. For example, choose AI models that are designed for speed and energy efficiency to process information more effectively while reducing the computational power required.

2. Identify your environmental impact drivers: Understand how different elements of your IT estate influence environmental impacts and how this can change as you scale new IT efforts.

3. Embrace sustainable-by-design principles: Embed sustainability assessments into the design and planning stages of every IT project, by using a hybrid cloud platform to centralize control and gain better visibility across your entire IT estate.

Source: ibm.com

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Using AI to conserve the endangered African forest elephant

In the Congo Basin, the second-largest rainforest in the world, the African forest elephant population has been in drastic decline for decades. This decline is the result of habitat loss caused by deforestation and climate change, along with rampant poaching.

We can observe the beneficial environmental effects of these species starting to disappear. As a keystone species in the habitat, the dwindling presence of the elephants has major implications you might not imagine. African forest elephants have been shown to increase carbon storage in their habitats. They are “ecosystem engineers” according to the World Wide Fund for Nature, clearing out lesser vegetation and making room for stronger, more resilient, flora to thrive.

While we know these changes will occur as the elephant population shrinks, actually seeing it happen presents challenges. The World Wide Fund for Nature-Germany aims to track and identify individual elephants to count them. With help from IBM, the WWF will be able to use a system of camera traps connected to software that enables automatic tracking as opposed to manual tracking.

Augmenting our vision with tech

That is where computer vision can serve as a fresh set of eyes. IBM announced earlier this year that it would team with WWF to pair camera traps with IBM Maximo® Visual Inspection (MVI) to help monitor and track individual elephants as they pass by the camera traps.

“MVI’s AI-powered visual inspection and modeling capabilities allow for head- and tusk-related image recognition of individual elephants similar to the way we identify humans via fingerprints,” explained Kendra DeKeyrel, Vice President ESG and Asset Management Product Leader at IBM. 

These capabilities allow for not only counting or spotting the individual elephants, but also tracking some of their behaviors to better understand their movement patterns and impact in the ecosystem. MVI particularly offers help in automating the process of identifying these elephants instead of having staff manually look at the images. Additionally, the AI’s advanced visual recognition capabilities can pull the identity of an elephant from an image that is blurry or incomplete.

“Counting African forest elephants is both difficult and costly,” Dr. Thomas Breuer, WWF’s African Forest Elephant Coordinator, said. “The logistics are complex and the resulting population numbers are not precise. Being able to identify individual elephants from camera trap images with the help of AI has the potential to be a game-changer.”

Strengthening our connection to the natural world

As more about the movement and migration of the African forest elephant is gleaned, more additional information can be pulled from our increased understanding of how the species is behaving and interacting with its environment. “IBM is exploring how to leverage IBM Environmental Intelligence above ground biomass estimates to better predict elephants’ future locations and migration patterns, as well as their impact on a specific forest,” DeKeyrel said.

That includes determining how much the African forest elephants can help with mitigating climate change. It’s understood that the presence of elephants helps to increase the carbon storage capacity of the forest. “African forest elephants play a crucial role in influencing the shape of the forest structure, including helping increase the diversity, density, and abundance of plant and tree species,” Oday Abbosh, IBM Global Sustainability Services Leader, explained. It’s estimated that one forest elephant can increase the net carbon capture capacity of the forest by almost 250 acres, the equivalent of removing a full year’s worth of emissions from 2,047 cars from the atmosphere.

Having a more accurate image of the elephant population allows for performance-based conservation payments, such as wildlife credits. In the future, this could help enable organizations to better assess the financial value of nature’s contributions to people (NCP) provided by African forest elephants, such as carbon sequestration services.

We know the animal kingdom is constantly shaping the planet, and being affected by our own activity even when we can’t see it. Due to continuing breakthroughs in technology, we’re increasingly getting a clearer picture of the world of wildlife that was previously difficult to capture. When we can see it, we can react to it, helping to protect species that need help and strengthening our connection to the natural world.

“Our collaboration with WWF marks a significant step forward in this effort,” Abbosh said, “By combining our expertise in technology and sustainability with WWF’s conservation expertise, we aim to leverage the power of technology to create a more sustainable future.”

Source: ibm.com

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