Artificial intelligence for IT or network operations, or AIOps, is an approach to managing complex IT operations that optimizes service availability and delivery, predicting and preventing problems before they occur. AIOps runs on multi-layered technology platforms that harness machine learning (ML), predictive analytics, and AI to automate, enhance, and improve business operations. At BMC, AIOps is integrated across our BMC Helix Operations Management solution. As a communication service provider (CSP), how can BMC AIOps benefit your daily operations? It comes down to:

1. Improved efficiency by automating event correlation, network monitoring, and performance analysis to eliminate manual toil.
2. Proactive incident resolution by analyzing vast amounts of real-time data across devices, applications, and network infrastructure to identify, predict, and detect root causes before these incidents can impact service delivery.
3. Faster incident response by diagnosing network and service anomalies. By integrating BMC HelixGPT generative AI, CSP IT and network operations teams can receive situation summaries, best action recommendations, and more.
4. Optimized resource utilization by providing insights into network traffic patterns, application performance, and infrastructure capacity management.

If you are a CSP, all of this sounds great. The difference is that BMC continues to advance observability, monitoring, and automated remediation. In fact, BMC has received two industry recognitions for its AIOps solution. First, BMC was honored with the Outstanding Catalyst Showcase Award at DTW 2023 for its pivotal role in revolutionizing CSP service assurance through AIOps. Second, BMC Helix was named a leader in The Forrester Wave™: Process-Centric AI for IT Operations (AIOps), Q2 2023.

Here are just a few capabilities BMC AIOps deliver to help CSPs realize the benefits I mentioned.

1. Service blueprints: BMC AIOps is the only solution on the market to offer CSPs and enterprises out-of-the-box service blueprints, which make creating and maintaining dynamic service models easier. With support for microservices, Kubernetes, cloud, and application performance monitoring (APM), these dynamic service models are automatically updated, ensuring accurate service models are used in today’s ever-changing IT environments.

2. Situation explainability powered by causal AI: This capability sets a new standard for incident resolution by correlating incidents with similar current and past occurrences to generate the best action to resolve the incident swiftly. User-driven feedback for situations provides greater contextual understanding and allows additional situation information to be added by a user for faster root cause isolation. This empowers CSPs and enterprises to recover from service outages and other potential risks more quickly—without manually scanning multiple incident logs. The BMC HelixGPT capability streamlines the process with concise, plain-language summaries of how issues were resolved. Probable root cause reports can be created and “remembered” for future use.

3. AIOps situation fingerprinting: Powered by advanced causal AI, situation fingerprinting automatically identifies whether a similar situation has previously occurred, eliminating the need to (re)diagnose it. This helps CSP network operations teams ease future identification to help speed mean time to repair (MTTR), reduce noise and staff toil, and improve service performance.

4. Improved deep container auto-detection: With the BMC Helix platform’s advanced discovery capabilities, all users can benefit from automated detection and an in-depth understanding of their containerized environments. This capability enhances knowledge-sharing among CSP network operations, site reliability engineers (SREs), services owners, and field teams responsible for modern, dynamic containerized environments. By enabling deeper container visibility, users will speed up MTTR while significantly reducing traditionally manual efforts. For CSPs, event and incident data and service prediction user interfaces (UIs) can analyze and predict service outages and remediate an issue before it escalates.

As we get closer to Mobile World Congress 2024, beginning February 26, BMC will share more exciting news for CSPs.

I welcome the opportunity to meet you in Barcelona in a few weeks.

Over the past few months, I have had the privilege of meeting with Telefonica, Vodafone, TalkTalk, and over 30 other leading communication service providers (CSPs) as they continue to converge network and IT operations management (ITOM). A critical topic of those conversations is how CSPs can leverage generative artificial intelligence (AI) to improve customer service and operational performance.

BMC is the first global vendor to embed generative AI across our entire service and operations management platform. You can read more about this ground-breaking announcement here.

Aside from that, we have been at the forefront of generative AI with BMC HelixGPT, a generative AI capability that can be tuned to the specific needs of CSPs. An immediate benefit for CSPs lies in empowering conversational use cases for network management and operations, driving actionable insights in natural language with a modular and adaptive generative pre-trained transformer (GPT) engine design. Utilizing large language models (LLMs), BMC HelixGPT captures and then learns from all data and insights to become an expert across all of your designated CSP data and systems for more accurate results and outcomes that are specific to your CSP.

We are collaborating with many CSPs on their generative AI journeys, advising how can help them achieve zero-touch, zero-trouble IT and network operations. Short-term use cases span network optimization, predictive network maintenance, customer experience transformation, and more. Here are few:

• Network optimization to identify performance patterns and anomalies and remediate incidents faster with clear situation summaries. You can see this in action through BMC Helix-GPT Powered Situation Summaries with Explainability.
• Predictive network maintenance that analyzes historical data and real-time information to determine potential points of network failure and then offers insights to resolve the issue. BMC HelixGPT-Powered Resolution Insights can save network engineers valuable time and resources.
• Customer experience transformation that improves contextual virtual agent experiences to improve responses with more natural and human-like conversations. You can see how through our BMC HelixGPT-Powered Conversational Search and Engagement

BMC’s leadership in generative AI is just one of the many ways we deliver positive business outcomes for CSPs. As we get closer to Mobile World Congress 2024, which will start on February 26, I will share more of the exciting CSP solution advancements from BMC.

Please stay tuned.

Several months ago, I wrote a blog outlining BMC’s application of Generative AI (GenAI) technology through BMC HelixGPT. Since then, GenAI has demonstrated its potential for creating diverse content (text, images, audio, video), computer code, configuration, meaningful conversations, and even entire novels already developed – likely authored with just a bit of prompt engineering.

Our mission at BMC is to provide actionable insights to operations teams. Any assistive AI technology targeting service and operations teams must gain trust, and the bar to clear while assisting operations is really high. These teams are overworked and under stress most of the time. Their attention span is limited, so actions must be focused. They work in an environment where availability and performance reign supreme. ‘Actionability’ is the key KPI. Correct & plausible don’t make the cut in our efficacy benchmarks, especially in the operations management environment.

Understanding composite AI

Composite AI integrates multiple AI models to create a more comprehensive and robust set of capabilities that complement each other. The advantage of Composite AI is that it leverages the strengths of various AI components, each specialized in different domains, to create a more versatile approach with more accurate, actionable outcomes.

Think of Composite AI as an analogy to the human brain, where researchers observe similar specialization and work breakdown (cite: https://www.nature.com/articles/nature18914). While the cortex is uniform under a microscope, various imaging techniques suggest different parts of the brain specialize to handle different tasks. These regions of the brain come together to gather and process information, maintain context, make decisions, recommend actions, recall knowledge, and then communicate these recommended ‘next step’ actions to various motor subsystems. Each lobe is assigned to perform specific tasks within the human brain. The Frontal Lobe is responsible for thought, memory, and behavior. The Parietal Lobe regulates language and touch. The Temporal Lobe manages hearing, learning, and emotions. The Occipital Lobe performs visual processing. A human brain can recommend the next best actions only when all of the lobes and functions within the brain come together.

Composite AI within the context of enterprise ServiceOps, similar to the functions of the human brain, integrates and automates different types of intelligence to determine the best possible actions. However, Composite AI completes these functions on a massive enterprise scale across billions of data points in real-time by utilizing purpose-built processing pipelines for telemetry data to distill raw observations into facts that build up the context of a problem as it transpires.

With the help of Composite AI, we get to cast the monitoring products of the past as our eyes and ears and ticketing systems rich with domain and environment specific knowledge as our recallable memory.

BMC Helix composite AI approach for improved actionability

The BMC Helix Composite AI approach consists of two main parts: sensory reasoning and knowledge-based action planning. The diagram below maps these two main parts in greater detail.

What you see on the far right-hand side of the diagram is data and a lot of it! BMC Helix captures data about all observable activities constantly flowing within your organization. Observable reality manifests itself on streams of topology, events, metrics, logs, incidents, change activities, defects, and even knowledge articles someone scribbled in a forgotten SharePoint folder somewhere. These traditionally siloed data lakes are often populated with information created automatically, user-generated information, and information through third-party integrations. Helix integrates all of that data into a comprehensive model of your organization that is indexed by service topologies, as the structure and architecture of the service tends to help reasoning about all sorts of diagnostic and remedial automation functions down the line.

Sensory reasoning synthesizes and processes all of the incoming data to figureout what’s going on in reality. Metric and event data from infrastructure, applications, networks (IP, Transport, Radio Access), and end users gets interpreted to detect anomalies. Here, various BMC Helix AI models are applied to detect anomalies such as unexpected traffic/load, resource utilization/saturation as patterns. BMC Helix then applies its proprietary AI algorithms to perform sensory reasoning to further process these anomalies into qualified situational explanations that capture what went wrong, what the root cause is what the impact seems to be. These BMC Helix AI algorithms include:

• Predictive AI applies AI techniques to predict future events or outcomes based on historical data and patterns. Components of predictive AI span machine learning (ML), training data, pre-trained models, regression, and time-series analysis. BMC Helix use case examples of predictive AI include proactive problem management, process change risk, and saturation forecasting.
• Causal AI integrates Knowledge Graph and Transformer-based AI techniques to understand and model relationships across observability data variables. It also determines the cause-and-effect relationships between events that unfold during a problem. Components of causal AI include reasoning about causal relations or patterns using topological data and a Knowledge Graph-based causality analysis, counterfactual ‘what if’ scenario analysis, graph modeling, and variability analysis assessing how causal relationships change depending on how the variables influence one another. BMC Helix use case examples of causal AI includes root cause isolation, incident correlation, and situation explainability.
• BMC Helix for AIOps leverages AI and ML to enhance enterprise operations by automating and optimizing tasks. BMC Helix for AIOps use cases include intelligent automation (such as for event management), root cause analysis, automated orchestration of routine tasks or workflows, automated integration with Enterprise Service Management, and third-party applications.

Through our Composite AI approach, the BMC Helix platform performs sensory reasoning across the entire IT stack: applications, containers, infrastructure, network, and even (if you have it) mainframe.

Now let’s dive into the second area of the BMC Helix Composite AI Approach, operations-informed, Knowledge-Based Actions. Here, all of the distilled observability insights about Situations from the sensory and reasoning AI algorithms are used to build context for the generative AI –specifically BMC HelixGPT. BMC HelixGPT then produces, in human-style language, the situation explanations with recommended ‘next best’ actions.

The entire BMC Helix platform, across our Composite AI approach, is based on topology aware custom low rank adaptors that allow us fine-tune models for very specific tasks and based on your determined enterprise domains. We also use retrieval augmented generation to result in more contextual, detailed responses about realtime data sources such as transaction traces, live metric data, etc. These capabilities vastly improve the accuracy of AI insights, leading to improved actionability, which is the main KPI we track as discussed in the beginning.

Applying the BMC Helix Composite AI to Operations Management

BMC Helix was built from the ground up to be a platform to process Observability and ITSM data at the telco scale. BMC Helix performs sensory reasoning based on observable reality – it provides the eyes and ears for the brain as it constantly processes vast amounts of monitoring data and formulates diagnostical reasoning as anomalies arise. BMC Helix harnesses all information flows specific to your enterprise data lakes, processing across time series and event streams. We employ Transformers and Knowledge Graph-based framework to achieve this data capture. In a future blog post, I will share a deep dive behind BMC Helix reasoning techniques involved.

We harvest and integrate monitoring data from existing tools into a unifying, comprehensive model that represents the structure and performance of targeted applications and IT services (modelled as a property-graph). BMC Helix does this dynamically without requiring any maintenance. As the architecture of the service changes with time, our AI discovers new boundaries/components, thanks to our BMC Helix for AIOps Service Blueprints.

We employ a pipeline of AI&ML modules to convert near-real-time monitoring data into aggregations about emerging and impending anomalies likely to degrade service KPIs. We collect all the available ticket data to generalize resolutions people discuss in chat streams or work logs.

To gain credibility with operational teams, we have built explainability at the foundation of Helix. Any insight we derive from monitoring and/or ticket data can be mapped back to raw data sources or sometimes more advanced reasoning and feedback components that allow the experts to review how AI reasons. Explainability also serves as a conduit to harvest domain expertise from humans. Expert feedback is our source for learning new heuristics and domain-specific knowledge, which we then generalize so that they can be applied to future problems using GenerativeAI.

HelixGPT learns domain and environment-specific knowledge about resolutions from existing ticket/issue databases. It acts like the part of our brain that learns and generalizes new concepts. We collect all the available ticket data to generalize resolutions people discuss in chat streams or work logs. We have a propriety GPT-based neural network architecture that knows to pay ’attention’ to actionable bits of these resolutions, so we can offer the operators remedial next best action even before the problem manifests at scale.

This necessitates the underlying GPT model to pay attention to vast graphs that describe the environment and the architecture of the target service, so we introduced graph-aware adapters that readily work on graph embeddings, as such vast data can’t really be expressed in natural language in context. HelixGPT learns domain and environment specific knowledge about resolutions from existing ticket/issue databases. It acts like the part of our brain that learns and generalizes new concepts. These graph-aware adapters (an industry first, patent pending) sway the network’s generation towards relational facts that matter in the environment (such as service dependencies, support-team memberships, et cetera), making us less prone to hallucination while keeping our generated insights highly actionable and specific to our users’ environment.

Together, BMC’s Composite AI approach with BMC Helix for ServiceOps, offers enterprises an integrated AI stack that sees/hears and learns/reasons about complex IT system issues – that’s how operations teams can solve problems through clear actionability.

While ChatGPT and GPT-3.5 ushered in a wave of innovations last year, the team behind BMC Helix had already been hard at work for the past few years exploring ways to adapt generative artificial intelligence (AI) technology to enhance enterprise service management applications, improve natural language conversations, emulate human language in chatbots, contextualize knowledge search, and make enterprise service management recommendations for case resolution. The team built several large language model (LLM) prototypes designed to be interoperable with the entire BMC Helix for ServiceOps platform. Our approach to generative AI was purposeful, focusing on the needs of our enterprise customers, and then delivering new use cases that would leverage the technology to resolve problems faster and with greater accuracy.

The result is BMC HelixGPT, a pre-trained generative AI LLM service that integrates into BMC Helix applications, learning from your enterprise’s knowledge (including user profiles and permission models) to deliver a tunable, prompt-driven conversational user experience. As 2023 nears the end, we have not only built a scalable generative AI foundation with BMC HelixGPT, but we have also released five new HelixGPT-Powered capabilities:

1. BMC HelixGPT-Powered Helix Virtual Agent
2. BMC HelixGPT-Powered conversations in BMC Helix Digital Workplace
3. BMC HelixGPT-Powered live chat summarization
4. BMC Helix GPT-Powered resolution insights
5. BMC HelixGPT GenAI LLMOps stack, a proprietary generative AI app builder tool (for advanced users)

This blog will be the first in a series that describes our journey in building BMC HelixGPT from end to end and shares key best practices for building a generative AI application with a powerful foundational LLM platform to power it all. If you are building generative AI apps or models, this blog series is for you. We will divide our journey into three parts:

Part 1 (this blog) will focus on unraveling the needs and expectations of a generative AI solution.

Part 2 will outline the components of the BMC HelixGPT platform reference architecture such as those from LangChain, which provide a framework to interact with LLMs, external data sources, prompts, and user interfaces.

Part 3 will show how BMC Helix for ServiceOps leverages BMC HelixGPT to power new enterprise and operations management use cases with leverages BMC HelixGPT’s LLMOps capabilities to power enterprise generative AI.

Getting started with generative AI

ChatGPT demonstrated to the world the possibilities of generative AI. What impressed people the most was its ability to quickly provide answers on a vast array of topics in clear, understandable language. Enterprises soon demanded a more tailored approach to the technology, with answers that would be more specific to their internal knowledge and data versus the “world knowledge” that early models were being trained on. To articulate the strategic generative AI direction for BMC Helix, we adopted a systematic three-step process that is universally applicable for enterprises considering generative AI product use cases:

1. Prioritize use cases based on business priorities.
2. Build proofs of concept and get early customer feedback.
3. Understand customer expectations.

Prioritize your enterprise generative AI use cases

One of the initial steps an enterprise must take is to prioritize use cases that align with business goals and priorities based on data availability, customer impact, team skills, and business considerations. In the enterprise service management space, we started with three key use cases that were most impactful for our customers:

• Virtual agent and knowledge search
• Resolution insights
• Summarization

Build proof of concepts and get user feedback early

Once use cases are identified, enterprises need to build proofs of concept to validate the concepts of generative AI. We built customer proofs of concept based on customer data for each of our top three use cases to get feedback through a design partnership program with our customers. While one team was using retrieval augmented generation (RAG)-based approaches and showcasing this to our customers with real customer queries, another team built fine-tuned models for multiple use cases. Early prototypes in resolution insights, generative search, and chatbots were highly impressive and provided us with the learning opportunity to understand and appreciate the unbelievable power and limitations of LLMs.

Understand enterprise needs and expectations for generative AI

After talking to multiple customers, their expectations of a generative AI solution became clear.

Specificity, accuracy, and trust

Enterprises want answers specific to their data, not generic answers yielded from broad world knowledge. Take, for example, a question regarding resolving a VPN issue, such as “how to fix a VPN connection issue?” Generative AI should generate an answer based on the enterprise VPN articles inside that enterprise and not a generic, plausible-looking answer generated by broad models. They want factual and truthful answers without any hallucinations that are commonplace. Enterprises also want the ability to verify answers with citations or references to build trust in how generative AI sources its answers.

Data security, privacy, and access control

Enterprises have variable user access controls about who can access different levels of data, so answers from generative AI solutions also need to adhere to those same access control policies. For example, a manager and an employee should get different HR answers to the same question because a manager has access to a larger set of documents. A few of the enterprises were also concerned about preserving the privacy of their data and ensuring that it would not be used to train a public model.

Real-time data ingest

Since enterprise data is constantly changing in real time, answers must be also based on the most up-to-date, available knowledge inside the company.

Avoid vendor lock-in of generative AI modeling

Finally, we heard that many enterprises wanted the flexibility to choose their own commercial models like Azure, OpenAI, or open source.

Our early prototypes and high-level requirements collectively shaped the foundational thinking behind what enterprise customers expect from a generative AI solution. In the next blog, I will explain how we addressed customer expectations in our BMC HelixGPT generative AI reference architecture. Stay tuned. In the meantime, you can learn more about BMC Helix GPT here.

Innovations like virtualization, converged network services, and the telco cloud offer exciting possibilities for communication service providers (CSPs) and their customers—provided they can solve the accompanying operational challenges. The evolution to software-defined everything can let operators activate services in minutes, not days; scale resources more flexibly to improve service while optimizing cost; and push workloads to the network edge to support new use cases in a more mobile and connected world—the list goes on.

But to realize this vision for the future of their industry, CSPs will need to modernize and transform service assurance in tandem with their environment. Traditional silo-based practices and technologies simply won’t be able to meet expectations for greater agility, prediction, and automation. As IT and network technologies converge and hybrid and public clouds reshape the infrastructure, CSPs will need to take a new approach to service assurance—one that uses a common, artificial intelligence for IT operations (AIOps)-powered platform. This will improve reliability, accelerate mean time to repair (MTTR), improve agility, and enable the shift to zero touch and zero trouble operations across the converged environment.

Virtualization leaves traditional service assurance behind

One of the main reasons network operators are adopting virtualization is to enable greater speed and agility. Customers are annoyed when a new service takes three days to be activated, and developers and operations teams want to be able to spin up new services that span multiple technology domains as quickly as possible. CSPs also want the flexibility to move workloads from the data center to the edge to support low-latency use cases like autonomous vehicles and virtual reality. In a software-defined world, operators have the freedom to reinvent their business at digital speed.

But service assurance is already proving to be a critical brake on this transformation. Designed for the massive, static, hardware-based, and slow-moving networks of the past, traditional approaches can’t keep pace with the dynamic and converged nature of modern environments. Siloed, duplicative, and overlapping assurance technologies for IT and network infrastructure make it more difficult to monitor services, fix faults, and manage resources for functions with dependencies in both domains, such as virtualized network functions delivered over hybrid cloud.

In the old days, when an issue affected the network, the network operations team could usually infer the cause by looking at a relatively small set of logs and monitors. In a converged environment, those investigations can span both network and IT technologies as well as a Google, Azure, or Amazon Web Services (AWS) Cloud, making root cause analysis a much more challenging prospect. Meanwhile, the use of shared cloud resources introduces new types of issues that traditional network monitoring tools can’t easily pick up, like a “noisy neighbor” virtual machine (VM) or a container starving other functions of resources. Correlating issues across silos and determining root causes becomes an exercise in frustration, while manual, disconnected processes increase MTTR and cost.

The threat to service quality is exacerbated by the reactive nature of traditional service assurance solutions. Aside from routine preventative maintenance, most operational behavior has consisted of waiting for something to break before acting—an approach that makes it impossible to maintain the reliability and availability customers now expect. When you can’t stop problems from affecting service, and it takes you longer to resolve them, customers end up with poor voice quality, jittery video, or stalled downloads that are more frequent and last longer. That’s a critical business problem for CSPs in hotly competitive markets where switching incentives are common and customer loyalty is fleeting.

To keep their converged infrastructure healthy and their services running at their best—and keep their customers, CSPs need to unify and automate service assurance—and ultimately drive to zero touch, zero trouble.

Building AIOps into service assurance

Slow, siloed, and largely manual processes make it far too difficult for operations teams to manage their environments and solve problems, much less work proactively to prevent problems and plan for future needs. What they need now is a way to achieve unified observability across both hybrid cloud and network infrastructures, quickly correlate this data, interpret its meaning, and act quickly to assure service quality.

With a unified, cloud-native AIOps platform, IT operations (ITOps), and network operations (NetOps) teams can leverage built-in intelligence to automatically identify the underlying conditions contributing to a disruption. Noise suppression helps teams work more efficiently by removing distractions and false alarms. As generative AI technologies like ChatGPT reshape the way people interact with systems, AIOps can translate complex root causes into natural language summaries and next-step suggestions. By correlating data across multiple network and technology domains, these technologies can understand the actual customer impact and provide timely and accurate notification—a key element of a satisfying customer experience.

Shifting from reactive to proactive, AIOps can help teams predict future issues and see packet loss earlier to improve network reliability. To enable automated remediation and self-healing, the platform can prompt a network orchestrator to take steps such as restarting a given device or changing a parameter on the configuration setting to resolve an issue before it impacts service level agreements (SLAs). A self-learning AIOps platform helps ITOps and NetOps teams improve agility by automating the configuration of monitoring and management rules for cloud-native and dynamic infrastructure services and applications. By analyzing trends, forecasting scenarios, and simulating demand, CSPs can plan accurately for the capacity needed to support new products effectively at a high level of quality.

Completing the vision for the modern CSP

While AIOps can help CSPs evolve toward a zero touch, zero trouble model, there will always be situations where human intervention is needed. In the previous blogs in this series, we talked about the requirements for a unified network service management platform to streamline that resolution flow, as well as the unified discovery needed to provide complete data and visibility across converged infrastructure. Together, these three capabilities form the foundation for a new era of autonomous networks delivered through dynamic, multi-domain, hybrid cloud environments.

To learn more, read the first two blogs in this series, Modern CSPs Need Unified Visibility Across Hybrid Cloud and Demanding Markets Drive CSPs to Transform Network Service Management.

Then visit https://www.bmc.com/blogs/bmc-helix-receives-catalyst-showcase-award/ to find out about how BMC recently won a TM Forum catalyst award.

As consumers demand new and better services, communication service providers (CSPs) are under pressure to deliver more services without charging more. In response, network operators are moving to adopt software-defined, hybrid cloud infrastructures and autonomous networks as a way to increase agility at scale while still controlling cost. But while the vision is a sound one, its practical reality is pushing traditional network inventory and asset management approaches to the breaking point. To deliver the transformation their business demands, CSPs need to break down silos between network and cloud domains, remove operational friction, and use data effectively to make better decisions, faster.

Lines blur between network and cloud

Traditionally, telecommunications networks and IT infrastructure represented two different worlds. Even within the operational network, telcos often ended up with dedicated silos for fixed-line service, mobile, carrier, transport, and so on—each with its own domain-specific network inventory and topology tools. The IT applications and services delivered on top of the network required their own dedicated management technologies, as well. This fragmentation was far from ideal in terms of efficiency, but as long as operators relied on relatively static, monolithic environments, the challenges it posed remained manageable.

In today’s fast-moving markets, however, CSPs are turning to virtualization and cloud as a way to increase agility, resiliency, and redundancy while reducing cost. To escape the constraints of physical network infrastructures, they’re delivering virtualized network services (VNS) and converged network services (CNS) over Kubernetes and virtual machine (VM)-based hybrid cloud infrastructures. Hardware devices—from routers and switches to packet gateways, radio access network (RAN) controllers, and mobile network cores—are being replaced with software. Open-source cloud-native network functions (CNFs) and commodity telco clouds offer faster ways to add services and respond to changes in demand while managing resources more efficiently.

As a result of this shift to software, the traditional separation between network and IT is now blurring. For network operations teams, which brings endless new challenges. If a problem arises with a virtualized network function being delivered over VMWare or OpenStack on hybrid cloud, for example, the network team’s remediation efforts can be hampered by a lack of visibility and insight into the dynamic elements of this converged infrastructure. Without access to complete data across both IT and network domains, it’s difficult or impossible to understand the dependencies between the two—especially when services depend on assets in a Google, Azure, or Amazon Web Services (AWS) cloud. Service assurance becomes slow, costly, and complex, putting customer experience at risk.

And reactive problem-solving is only part of the problem. Given customer expectations for flawless service at all times, CSPs need to proactively prevent issues in the first place, and to adapt quickly to demands that change by the minute. However, without the tools, data, and understanding they need to manage increasingly dynamic and elastic virtualized services, network operations teams struggle to be agile or effectively use automation. The challenge is compounded when operators acquire smaller companies, inheriting their technology environments, and do not have holistic insight, which further complicates an already arduous integration process. The rapid rise of cloud-native development also makes it too easy for business units to build and deploy their own applications without the knowledge of or visibility into network operations, incurring the security and compliance risks of shadow IT.

New rules around telecommunications cybersecurity, such as the U.K.’s Telecommunications (Security) Act (TSA) 2021, the Security of Critical Infrastructure Act 2018 in Australia, and similar U.S. legislation, are making these data silos and blind spots a matter of regulatory risk, as well. With assets spread across proliferating repositories located on-premises and in the cloud, CSPs face an urgent need for unified visibility to ensure compliance and protect their business from threats.

Converged infrastructure calls for converged discovery

As CSPs adopt network virtualization and a hybrid cloud infrastructure that includes telco clouds, they need to ensure full visibility, understanding, and data integration across both domains. In other words, they need a cloud-native, converged platform to discover the dynamic elements that make up their network, the underlying hybrid cloud infrastructures, and the dependencies among them. The idea isn’t to replace traditional network inventory tools—which will remain essential—but rather to augment them with new discovery capabilities tailored to the more agile, dynamic nature of environments transformed through cloud-native development and software-defined networking.

With this foundation of unified visibility and understanding, CSPs can evolve toward the unified service management of its IT and network technologies that is demanded by the dynamic and elastic nature of modern telco clouds. Both network service management platforms and AIOps platforms can be provided with complete data to enrich, automate, and contextualize workflows, predict faults more accurately, and remediate problems more quickly and efficiently. Dynamic application topologies, enriched with data ingested from application performance monitoring (APM) and other systems of management, can help network teams better predict the customer service impact of problems and changes and construct new service topologies more easily.

For customers, the impact of this unified approach can be dramatic. Network operations teams are better able to predict and prevent problems affecting service wherever they might occur, and when problems do arise, they can be diagnosed and repaired faster. For both network and IT teams, shared visibility and understanding removes friction and enables better collaboration. And for the business, converged asset discovery across hybrid cloud infrastructures helps improve security and regulatory compliance.

Toward zero-touch, zero-trouble networks

While converged discovery is an essential capability for the modern telco cloud, it’s only one element of a larger vision. The next step is for CSPs to put this single trusted source of asset data to work to enable both AIOps-powered service assurance and network service management. In the next two blogs in this series, we’ll explore each of these transformations and the benefits they enable for CSPs and their customers.

To learn more, read the first two blogs in this series, Zero Touch, Zero Trouble Starts with AIOps-Enabled Service Assurance and Demanding Markets Drive CSPs to Transform Network Service Management.

To learn more about our BMC Helix solutions for AIOps, visit https://www.bmc.com/it-solutions/observability-aiops.html

With many global communications markets saturated, one of the most important ways operators can grow their business is by convincing customers to switch from a competitor. That means delivering a better customer experience—or, conversely, avoiding the kind of poor experience that forces subscribers to seek an alternative. This intensified focus on quality is revealing critical liabilities in traditional communication service provider (CSP) operations defined by siloed data, tools, and practices. Transformation has become essential to deliver greater customer satisfaction, more efficiently, as part of the wider journey towards a zero touch, zero trouble future.

As they seek to modernize the way they run their networks and their business, operators are looking to the example of hyperscalers like Amazon Web Services (AWS), Google Cloud, Meta, and Microsoft Azure—companies that work at a similar scale to CSPs, but with far greater levels of speed, agility, and profitability. Telco clouds can help them reengineer their infrastructure for today’s requirements, but they’re only part of the answer. To remove the operational friction that can undermine service assurance, degrade efficiency, and slow innovation, CSPs must also break down silos and unify workflows across their increasingly converged IT and network domains.

Legacy silos undermine quality and slow innovation

The rapid evolution of communications markets has dramatically reshaped customer expectations. Just a few years ago, subscribers were willing to accept multiple provider touchpoints for different services. Instead of getting unified notifications or having a single point of contact for problems with products, services, and accounts, they understood that any given issue might take a few tries through different channels to resolve. This reflected the reality on the operator’s side, in which an IT environment delivered processes like billing and customer service management, and a separate network environment focused on actual service delivery.

Today, these separate domains generally remain the norm—at least in operational terms. IT and network operations (ITOps and NetOps) teams have their own separate workflow systems. The technologies used across these domains are often duplicative—two sets of tools to pay for and support and two sets of data—customized to meet specific, often legacy objectives. Integrations between the two domains are complex and brittle, if they exist at all.

Designed for a slower-moving era with more modest customer expectations, this fragmented environment makes network service management frustratingly inefficient for both customers and staff. If a customer has an issue with a network-enabled service or application, the request comes in through the IT system, waits to be passed over to a network team for resolution, and then waits to be pushed back to the IT system to update the customer.

But while the siloed nature of network and IT technologies has been slow to change, customer expectations have shifted more quickly. While customer and user interactions with CSPs are slow, unreliable, opaque, and fragmented, other technology companies offer far more seamless and satisfying experiences. This disparity makes it impossible for operators to project a fully modern brand image for the digital era.

Within CSPs, fragmentation slows innovation and business agility. Siloed IT and network data and processes are poorly suited to the new generation of converged delivery infrastructure, where virtualized networking runs on a commodity platform of hypervisors and containers. Cumbersome change management processes make service assurance a brake on the deployment of new services.

While some elements of IT and network will remain in separate domains—fixing a software error is an entirely different prospect than repairing failed hardware at a remote network site—there are vast areas of operations where a unified approach is both practical and increasingly essential. To meet the demands of today’s customers and the competitive imperatives of modern communications, CSPs need to drive convergence to the furthest extent possible.

Network service management for an autonomous future

As part of a wider journey toward a zero touch, zero trouble future, many operators are already looking to break down operational and process silos with a unified workflow platform across increasingly converged IT and network domains. After all, when you’re delivering virtualized network services (VNS) and converged network services (CNS) over containerized and virtual machine (VM)-based hybrid cloud infrastructures, many or most issues will involve technologies, data, and personnel across both domains.

Bridging the two through common data, tools, visibility, and services can enable ITOps and NetOps teams to collaborate more efficiently and deliver greater customer satisfaction. Specific capabilities can be tailored to meet the needs of various IT and network technical consumers, but teams will no longer need to waste time with manual information-sharing workarounds like email.

Traditionally, networks have been managed by technologies, with highly specialized teams dedicated to individual areas such as transport, access, mobility, orchestration, and so on. As networking becomes increasingly software-defined, this model is beginning to be replaced with teams organized around products, each spanning multiple constituent technologies. These small, agile teams follow more of a DevOps model, working together to solve issues and create solutions in minutes, not weeks.

Here, too, replacing separate IT and network management workflows with a unified platform saves cost while supporting new ways of working more efficiently, with everyone sharing common data and gaining visibility into the converged environment. New digital and network products can be brought to market quicker, and personnel can spend less time supporting the platform to devote their attention to transformation.

As CSPs move toward more autonomous networks, converged data and workflows enable the replacement of many types of manual toil by both task automation and higher-level artificial intelligence (AI)-powered functions. Operators can use AI innovation to address key challenges around change and incident management, common cause detection, and proactive service management, using data to prevent many problems from arising in the first place, and solving them more quickly and effectively when they do occur. Eliminating repetitive fixes while increasing productivity, operators can reduce cost-to-serve, accelerate mean time to resolution (MTTR), and improve customer satisfaction—with the kinds of experiences that retain existing customers while attracting new ones.

Building unified discovery and AIOps into network service management

The increasing convergence of IT and network technologies demands a greater understanding of the dependencies between the virtualized network and the underlying hybrid cloud platform on which it runs. That makes unified observability a crucial requirement to ensure optimal service for the new breed of network-enabled applications. The vision for converged discovery across converged infrastructure is explored in our earlier blog, “Modern CSPs Need Unified Visibility Across Hybrid Cloud.”

Meanwhile, both zero touch and zero trouble networking require AIOps to reduce noise, improve prediction, find the root causes of service disruptions, and remediate problems automatically. In our next blog, we’ll discuss the essential role of AIOps in the new generation of CSP network and technology operations.

To learn more, read the first two blogs in this series, Modern CSPs Need Unified Visibility Across Hybrid Cloud and Zero Touch, Zero Trouble Starts with AIOps-Enabled Service Assurance

Then visit https://www.bmc.com/blogs/bmc-helix-receives-catalyst-showcase-award/ to find out about how BMC recently won a TM Forum catalyst award

While physical workspaces are being redesigned to support hybrid workstyles, the safety and well-being of the employee remains a prerogative. In the US, the Occupational Safety and Health Administration, or OSHA, can levy fines from $15,000 to $150,000 per instance of safety violation. Equipment that isn’t performing optimally can also impact your organization’s carbon footprint. Globally, building operations account for 30 percent of energy consumption and 26 percent of energy emissions. Improving those numbers is integral to meeting ongoing sustainability initiatives.

Delivering a well-functioning facility that’s consistent with employee expectations becomes even more challenging in a hybrid environment. Faulty lights or broken chairs can go unreported for longer than necessary as employees spend less time in the office and become complacent about reporting the issue or lack visibility into the process for doing so.

The goal should be to minimize reactive maintenance; in fact, if you apply the Pareto principle to maintenance, 80 percent of it should be preventive. This requires a proactive approach to equipment maintenance that includes regular inspections, servicing, and repairs to detect and prevent any breakdowns before they occur.

A service-oriented approach to reactive and preventive maintenance

How does BMC Helix help you deliver a safe workplace? In the case of workplace maintenance, BMC Helix Digital Workplace empowers workers to log workplace issues into a multi-departmental portal in the same way they would raise an HR or IT request. BMC Helix also includes out-of-the-box case templates and workflows to address a wide range of workplace issues and expedite processes for issue resolution.

With regard to preventive maintenance, BMC Helix allows for the automatic scheduling of maintenance for assets or groups of assets on a weekly, monthly, or annual basis. Once assigned, a technician can then view their caseload by building, site, and floor. All assets are containerized within the BMC Helix CMBD alongside IT assets, removing the need for siloed and disjointed facilities maintenance systems. Each maintenance case includes checklists and related knowledge that can be accessed by technicians while on the go, so that inspections can be completed as efficiently as possible.

However and whenever we return to the office, we still want a safe and functioning workplace that puts the employee experience first. With a service-centric approach to workplace management, BMC Helix helps organizations focus on the employee experiences and align their workplace strategies to the changing world of work.

Learn more at bmc.com/digitalworkplace.

In today’s rapidly evolving business landscape, automation plays a vital role in streamlining processes and driving operational efficiency. However, as automation strategies become more complex, organizations often struggle to cope with the expanding workload types, volumes, and locations, leading to a lack of visibility and control. To address these challenges and unlock the full potential of automation, a standardized automation broker framework emerges as a crucial solution. In this blog post, we will explore the concept of an automation broker and how it empowers organizations to achieve a seamless integration, governance, and orchestration of automation workflows.

The need for a unified automation framework

With the increasing adoption of automation, organizations are faced with a sprawling automation landscape, encompassing various tools and systems. This expansion brings forth challenges in automation governance, making it difficult to maintain control and visibility. Balancing the democratization of automation with effective governance becomes crucial for organizations to harness automation’s benefits.

Understanding the automation broker

At the core of solving automation governance challenges lies the concept of an automation broker, which is a software component or platform that acts as a centralized hub, connecting different automation tools, systems, and services. Its primary role is to orchestrate and manage automation workflows, providing a unified interface for executing tasks and ensuring seamless integration.

Core capabilities

An automation broker offers several key capabilities to facilitate effective automation management:

1. Workflow automation: Automation brokers enable the creation and management of automated workflows that span multiple applications, both on-premises and in the cloud. These tools provide graphical user interfaces (GUIs) for designing workflows, visualizing interdependencies, and integrating disparate tasks and data sources.
2. Event-driven automation: Event-driven automation involves sensing inputs, validating them against configured rules and policies, and taking appropriate actions in response. Automation brokers facilitate the implementation of such sense-and-response workflows, using logic based on “if this, then that” principles.
3. Self-service automation: Self-service automation enables users to request and fulfill automation tasks independently. Examples include access requests and guest Wi-Fi, which are typically integrated into IT service management (ITSM) forms. Automation brokers ensure these self-service requests are seamlessly handled and processed.
4. Scheduling, monitoring, visibility, and alerting: Automation brokers offer essential components for maintaining visibility and meeting service level agreements (SLAs). They provide monitoring capabilities for tasks, schedules, and alerts, helping organizations proactively identify and address issues and optimize automation processes.
5. Resource fulfillment and infrastructure as code: Automation brokers facilitate resource provisioning, allowing organizations to provision compute, network, and storage resources across cloud and on-premises environments. They streamline the process by integrating with multiple adapters or plugins, making resource provisioning efficient and platform-agnostic.

Advantages of an automation broker framework

Implementing an automation broker framework brings several advantages to organizations:

1. Automation tool agnosticism: An automation broker eliminates the need for teams to use specific automation tools. As long as an automation tool provides a standard set of APIs, it can seamlessly integrate with the automation broker, enabling flexibility and choice.
2. Centralized audit trail and reporting: Automation brokers provide a centralized audit trail for all automation activities, allowing organizations to track and monitor the execution of workflows. This centralization also facilitates the creation of value dashboards that showcase the volume of automations, success rates, and return on investment (ROI) to IT leadership.
3. Enhanced governance and risk mitigation: By acting as a centralized hub, an automation broker enables better governance and risk mitigation. It helps identify and mitigate potential issues, preventing buggy codes or scripts from causing catastrophic outages that can impact revenue and reputation.
4. Streamlining collaboration and productivity: With an automation broker, organizations can foster collaboration across teams by providing a unified platform for automation. This also eliminates silos and enables teams to share automation assets, best practices, and knowledge, leading to increased productivity and efficiency.

Based on the above points below is a high level illustration of how one can develop an Automation broker Framework. The product and tools mentioned here are purely to explain the concept. Some of these blocks may change based on specific need and requirements for your respective organization but the core concept of unifying automation using the broker will remain the same.

Figure 1: Automation Broker Framework

Developing an effective automation broker framework

Implementing an effective automation broker framework involves considering several key aspects:

1. Integration with existing automation tools: Ensure seamless integration with a wide range of automation tools, allowing organizations to leverage their existing investments and tools of choice.
2. Scalable architecture and extensibility: Design the automation broker with scalability and extensibility in mind. This ensures it can accommodate future growth, handle increasing automation demands, and integrate with emerging technologies.
3. Security and access control measures: Implement robust security measures to protect sensitive data and ensure compliance. Define proper access controls, permissions, and authentication mechanisms to safeguard automation workflows and resources.
4. Monitoring and analytics capabilities: Integrate comprehensive monitoring and analytics capabilities into the automation broker, enabling organizations to gain insights, identify bottlenecks, and optimize automation workflows for maximum efficiency.
5. Integration with external systems: Enable seamless integration with external systems such as ITSM platforms, ticketing systems, and notification services. This integration enhances automation capabilities and ensures smooth communication and data exchange between the automation broker and these systems.

Conclusion

As organizations strive to become digital-first businesses in an always-on world, a standardized automation broker framework becomes indispensable. By leveraging the power of an automation broker, organizations can establish a unified automation ecosystem, streamline governance, and achieve seamless integration of diverse automation tools and systems. With the ability to orchestrate, manage, and monitor automation workflows, an automation broker empowers organizations to navigate the complexities of automation, unlock its full potential, and thrive in the ever-evolving digital landscape.

In recent years, the term “shift left” has become a buzzword in the software development industry. It refers to the practice of moving quality assurance and testing activities earlier in the software development lifecycle. The goal of shift left is to catch defects and vulnerabilities earlier in the process before they become more costly and difficult to fix.

However, as technology advances and the pace of software development accelerates, some experts are calling for a new approach: shifting orbit.

What does shifting orbit mean? Essentially, it means taking a broader view of the software development process and rethinking the way we approach quality and security. Rather than simply moving testing earlier in the process, we need to focus on creating a more holistic approach to software development that considers the entire lifecycle of a software application.

Firstly, a shift-orbit approach would require a fundamental change in the way we think about software development. Rather than treating quality as an isolated concern, it should be integrated into every stage of the development process. This means thinking about not just testing and validation, but also user experience, design, architecture, and coding practices that prioritize quality. One key aspect of shifting orbit is a renewed focus on collaboration and communication between different teams involved in software development. breaking down silos and working more closely together. This includes developers and testers as well as security professionals, product managers, and others who have a stake in the success of a software application.

Secondly, a shift-orbit approach would place a greater emphasis on continuous improvement. This means implementing a feedback loop that allows teams to learn from their mistakes and make improvements in real time. This feedback loop should include not just automated testing and validation tools, but also feedback from end users and other stakeholders to gather insights into the software’s usability and user experience. This feedback can inform ongoing improvements and help ensure that the software meets the needs and expectations of its users.

Thirdly, a shift-orbit approach would require a greater investment in technology for testing, validation, and monitoring and analysis of the performance of software applications in production, as well as the infrastructure for continuous integration and deployment. As software development becomes increasingly complex, it’s no longer possible for humans to catch every possible issue or vulnerability. Instead, we need to leverage the power of technology to identify and address potential problems before they become serious. For example, machine learning algorithms can be trained to identify patterns and anomalies in code, helping to flag potential issues before they become critical. Similarly, automation tools can be used to automatically test and validate code, freeing up developers and testers to focus on more complex tasks.

Finally, a shift-orbit approach would require a cultural shift to prioritizing quality over speed and agility. This means creating a culture of continuous learning and improvement where teams are encouraged to take risks and learn from their failures, and collaboration and communication are valued over individual achievements.

Overall, shifting orbit represents a more modern, holistic approach to software development that treats quality and security like critical components of the process rather than afterthoughts. By embracing this approach, organizations can build software that is not just functional, but also delivers an exceptional user experience and is secure, scalable, resilient, and ready to meet the demands of the future. For organizations that are serious about delivering high-quality software in a rapidly changing landscape, it’s an approach worth considering.