Recent breakthroughs in artificial intelligence (AI) have led to significant improvements in the capabilities of AI systems. This includes preeminent AI capabilities such as generative AI (Gen AI), anchored on large language models (LLMs) such as GPT-4, which can generate human-like conversational text, understand complex queries, and assist with a wide range of tasks.

AI and Gen AI have diverse applications across industries from healthcare and finance to entertainment and education. They can automate repetitive tasks, provide insights from large datasets, enhance customer service, and even create art and music. Organizations across industries see AI as a path to gain a competitive edge in the global market.

Since AI can optimize operations, reduce costs, and create new products and services, it’s fast becoming a major driver of economic growth and innovation. It comes as no surprise that venture capital firms are pouring money into AI startups, while governments and businesses are investing heavily in AI research and development at the same time.

Analysts expect AI to contribute significantly to global GDP, with PwC¹ estimating that AI could add up to $15.7 trillion to the global economy by 2030. In its 2023 report, “The economic potential of generative AI: The next productivity frontier,” McKinsey Digital estimated the economic potential of generative AI alone to be between $2.6 trillion to $4.4 trillion annually. These estimates expect an increase in investment in and adoption of AI technologies across various industries, driven by advancements in AI capabilities and the expanding range of AI applications.

As with every new technology, AI has its challenges. These include ethical and privacy concerns, job displacement, and developing appropriate regulations and governance frameworks to ensure that AI is safe, transparent, and responsible to use. This is important because AI applications face diverse regulations across industries—data privacy in healthcare, credit scoring in finance, and passenger and pedestrian safety with autonomous vehicles—to name a few.

Not everyone agrees with the hype, though. A recent report by Goldman Sachs, aptly titled “GenAI: Too much spend, too little benefit,” was significantly more circumspect about the potential of AI, with some of the contributors seeing only limited economic upside from AI over the next decade and arguing that the technology isn’t designed to solve the complex problems that would justify its costs.

Skeptics point to the high cost to deploy and run AI applications and chip and power shortages that would constrain AI growth, not to mention low rates of AI adoption by companies, fear over AI-driven cybercrime, potential harmful impact to the environment, and the often-expressed risk considerations spanning ethics, bias, and data inaccuracy. Further, some pundits are finding eerily similar trends between the AI hype and the dot-com bubble in the early 2000s.

Understandably, we will continue to see considerable volatility in AI technology in the next few years as AI companies seek to offer solutions that balance power and cost to improve business adoption. This contextual overlay is relevant to the question of whether AI is a digital evolution or cultural revolution or both (or perhaps neither). We will address this next.

AI—A digital evolution or cultural revolution?

Given that we are all so early in our AI journey, the concerns mentioned above, and how rapidly we are trying to move forward—AI has the potential to become a cultural revolution, not just a digital evolution.

When is a tech capability a digital evolution?
A new tech capability typically involves innovative approaches or advancements in technology. For any tech capability to be considered a digital evolution, it needs to have a significant impact on businesses. Specifically, the tech needs to be transformational, accessible, offer economic gains, and connect with existing technology; it must be all these things while still evolving.

When is a tech capability a cultural revolution?
But technology also becomes a cultural revolution when it profoundly changes societal norms, behaviors, and interactions, in addition to transforming business practices and technological landscapes. This is classified by affecting daily lives and human behavior. AI allows for economic and social equity and impacts education, healthcare, and regulation.

To illustrate, mobile technology is both a digital evolution and a cultural revolution. AI is on a solid trajectory to also become a foundational element of digital evolution. Its contributions to specific industries such as transportation, finance, and healthcare are impressive. Further, generative AI is showing promise as an enabler of pervasive automation, and in certain industries, it shows the potential to have a transformational impact.

However, AI is not yet as mature or widespread due to its ongoing development, complex integration requirements, regulatory and ethical challenges, and the specialized nature of many current AI applications. Additionally, the current high cost of implementing AI is formidable, which limits its adoption. As AI continues to evolve and address these hurdles, it will likely reach a similar level of ubiquity and transformative impact as mobile technology.

AI is also undoubtedly shaping culture in significant ways, particularly through its integration into various digital platforms and services, significantly improving customer service via chatbots, not to mention its tremendous potential in learning and education, health and wellness, and other socially and culturally impactful areas. However, it has not yet achieved the same level of cultural revolution as mobile technology due to its less visible presence, slower adoption pace, accessibility issues, and complex ethical challenges.

As AI continues to become more integrated into everyday life, its cultural impact is likely to grow. Time will tell, and in two to three years, we will more than likely have a considerably different picture of AI’s readiness as a foundational cornerstone of digital evolution and a cultural revolution.

At BMC, we continue to push the boundaries to leverage and learn from AI—and generative AI—through research, our customers, and our development. All drive business value, faster than humanly possible.

References:

1. PWC: PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution (2017)

Artificial intelligence (AI) is a paradigm shift for every business and industry, bringing out insights and driving action. But AI is only as powerful as the data and the infrastructure it runs on. To thrive with AI, organizations need trusted technology partners and a strong digital foundation that helps them evolve as the world evolves around them.

To make the most out of new and existing technologies, particularly AI, BMC is here to help enterprise leaders unify and act on data across an increasingly complex digital landscape—from cloud to mainframe to edge. With that, I want to lay out our approach, our current offerings, and our promise for the future, leveraging the power of AI.

Our approach

BMC has been delivering secure and trusted solutions for decades, with a breadth of products that already incorporate AI and machine learning (ML) capabilities, including causal AI, predictive AI, and generative AI (Gen AI). We enable customers to work more efficiently so they can innovate, and we build safe products they can rely on. We help organizations create their own unique solutions with our customizable portfolio, empowering them to solve their business challenges as they integrate AI and Gen AI into their evolving technology stack.

• Innovation: We automate business service and data workflows, which allows organizations to quickly produce AI/ML solutions and modernize business models across the entire multi-cloud enterprise. In addition, the BMC Helix and BMC AMI solutions use AI, including Gen AI, to help transform IT from reactive to proactive, predict and resolve issues, and provide an excellent employee experience.
• Safety and reliability: BMC has focused on data privacy and security across all our offerings for decades, and we are applying that same clear focus on governance to our AI models, associated training data, and applications. We also provide essential audit logs and controls to satisfy compliance mandates and bias avoidance—regulatory or otherwise.
• Personalized portfolio: Our flexible approach means customers have the option to bring their own AI, allowing them to meet security, privacy, and risk requirements, and the opportunity to have a ChatGPT-like interface with our BMC Helix and BMC AMI portfolios. We will continue to add Gen AI partners for this functionality, along with embedding our own domain-specific AI models into the BMC portfolio.

Driving business outcomes with the BMC portfolio

We see our customers using AI, particularly Gen AI, to succeed across the BMC portfolio. Our customers benefit by leveraging our products with AI functionality to increase productivity, improve service level agreements (SLAs), provide terrific employee and customer experiences, and speed up time to market for new products and services.

BMC offers AI and Gen AI capabilities in BMC Helix Operations Management and Service Management—the leading AIOps solution; Control-M—our market-leading service orchestration and automation platform (SOAP) for orchestrating application and data pipelines that deliver critical business outcomes; and the BMC AMI zAdviser service for measuring and improving your mainframe DevOps practices.

With the BMC Helix platform, customers use AI for event correlation, anomaly detection, root cause analysis, and intelligent remediation. We designed AIOps solutions to allow IT teams to focus on strategic initiatives, revenue protection, and continual optimization in a multi-cloud and mainframe environment, instead of being reactionary to issues that arise.

The BMC HelixGPT solution is at the center of our strategy for service management. In addition to what we already have in place, we are incorporating some exciting new areas of focus.

To start, we will expand to offer a fleet of AI assistants that give customers productive conversational experiences with BMC HelixGPT. For example, customers will be able to easily audit their knowledge base through natural language to remove duplicate entries, and enrich articles with publicly available information or peer insights.

Additionally, the BMC HelixGPT platform will help data analysts rapidly build and modify reports by allowing them to converse with their data to access the answers they need immediately. It will address one of the most intimidating parts of designing apps for business—creating and modifying workflows—by helping IT application development teams do so hands-free.

The internal AI/ML capabilities of the Control-M workflow automation solution deliver extensive predictive forecast analytics and SLA management to ensure production application and data workflows run on time, every time. Deepti Soni, Sr. IT Manager of Data Quality for Data Operations and MLOps at BMC customer, Domino’s Pizza, highlights how important the Control-M solution is to getting value from the organization’s data analytics initiatives, saying, “Control-M is mission-critical to Domino’s data-driven culture. It’s going to play a key role in helping us continue to grow and deliver cutting-edge innovation.” We are currently focusing our AI innovation for Control-M on helping our customers outside traditional IT operations (ITOps), such as developers and data engineers, build and deploy complex data pipelines into production with an intuitive, natural language, chat-like interface.

DevOps productivity and application performance for the mainframe today rely on the BMC AMI zAdviser Enterprise collaborative service, which leverages AI/ML and Gen AI to address critical needs. By offering Gen AI-driven recommendations for development process improvements, BMC AMI zAdviser Enterprise helps IT and DevOps managers understand key performance metrics, identify trends, and detect deviations from the norm, as well as alert stakeholders to any potential issues.

To further drive mainframe application transformation and facilitate critical knowledge transfer, BMC announced a beta program to test Gen AI-driven solutions like BMC AMI Code Insights with Code Explain. This new Code Explain feature creates easy-to-follow explanations of complex code in monolithic mainframe applications.

For our broader portfolio of BMC AMI solutions, we just announced a statement of direction for the BMC AMI platform, highlighting our continued leadership in AI and Gen AI. Starting this October and continuing throughout the next year, we will deliver innovative Gen AI services to assist with mainframe transformation, offering our customers the flexibility to choose between locally hosted or private cloud deployment. This will include a dedicated navigator for mainframe operations, ensuring unparalleled support and efficiency.

In the coming months, we will announce more innovation, including at BMC Connect in Las Vegas this October, across the entirety of our portfolio demonstrating how we will deploy AI to continue strengthening our customers’ capabilities to run their organizations.

Our commitment to customers

At BMC, we are building on decades of innovation to empower organizations across the globe with the technology to excel. We help enterprises maximize value from their data, transform operations from reactive to proactive, and deliver amazing customer and employee experiences, all with the power of AI. Organizations that thrive in the face of fundamental shifts like AI view technology as a strategic investment in their short- and long-term success. The AI paradigm shift is a call to action, a call to inspire, and a call to serve an ever-changing future ahead of us. And BMC is here to be your partner along the journey.

On Friday, July 19, 2024, I was on a plane from Frankfurt to San Francisco, and I was one of the lucky few whose flight was not cancelled or delayed. On that day, what was supposed to be a minor software update had brought IT systems at major airlines, banks, and organizations worldwide to a standstill. It is a stark reminder that the impact of changes, if not properly assessed, can have a huge impact on our daily lives.

IT organizations around the globe must strike (pun intended) a balance between maintaining the speed of software delivery required to support the modern pace of business and effectively managing the risk that these changes can cause IT service disruptions. One of our customers was able to identify the risk of the CrowdStrike update and remediate it quickly with the BMC Helix platform.

How they did it

The customer, a large company in the food and beverage industry in the US, has been using BMC Helix products for a while now, including both BMC Helix IT Operations Management and BMC Helix Service Management, allowing it to effectively practice our ServiceOps approach (more on it below).

As the CrowdStrike patch issue started developing and affecting its systems, this company’s command center was swamped with calls from its stores, warehouses, and manufacturing facilities. It was so intense that its IT team couldn’t get “off queue” from taking calls to resolving calls. The CrowdStrike issue was unique because the affected hosts needed to be patched manually. The company’s BMC Helix Digital Workplace management team quickly spun up its BMC Helix Digital Workplace service so its stores could self-report issues, allowing the staff to focus on remediating servers and desktops. Within one day, hundreds of requests had been submitted.

BMC Helix helped this organization’s IT team remediate the effects of the CrowdStrike patch and avoid the prolonged downtime that impacted—and is still impacting—so many people and businesses across the world. This was possible because the company’s BMC Helix environment was not affected by the CrowdStrike issue.

Our ServiceOps approach helps IT teams manage risk

At BMC, we believe bringing together ITSM and ITOM while also embedding artificial intelligence and machine learning (AI/ML), is the answer to the increasing demand on IT teams to operate with no downtime and deliver high-quality services. ServiceOps is about eliminating information, technology, and organizational silos so cross-departmental teams can work collectively to deliver highly resilient services.

At the foundation of our ServiceOps approach is BMC Helix, our open platform with which IT teams can improve overall IT service quality by sharing data, predicting issues, automating workflows, and preventing outages. In our approach to ServiceOps, AI helps IT teams surface signals and eliminate noise so they can more quickly use data to correlate IT incidents and identify service problems. Combining ServiceOps with observability and AIOps helps IT teams increase the reliability of their services.

Improve IT resiliency with BMC Helix Discovery

In situations like the one facing the customer above, and other IT organizations around the world on Friday, it is more important than ever to understand in real time whether your IT assets are up to date and to proactively identify vulnerabilities.

This is where BMC Helix Discovery, a key component of our ServiceOps approach, can help customers improve IT resiliency. BMC Helix Discovery plays an essential role in proactively resolving vulnerabilities by providing comprehensive visibility and automated discovery of IT assets across an enterprise.

It continuously scans the IT systems to identify and catalog hardware, software, and their dependencies, maintaining a dynamic and up-to-date inventory of assets. By dynamically maintaining an accurate and current configuration management database (CMDB), BMC Helix Discovery allows IT teams to quickly detect vulnerabilities, assess their impact, and prioritize remediation efforts. The solution also neutralizes vulnerabilities faster by helping teams understand their business impact. In situations like the CrowdStrike update, BMC Helix Discovery customers would benefit from understanding which parts of the business service would have been affected by the update.

Also, BMC Helix Discovery’s integration with security information and event management (SIEM) and vulnerability management tools enables automatic correlation of asset data with known vulnerabilities, facilitating efficient resolution and remediation of issues before they can be exploited. This approach not only enhances security but also minimizes downtime and operational disruptions, ensuring a more resilient IT infrastructure.

Interested in making your IT environment more resilient? Learn more about BMC Helix Discovery, BMC Helix Digital Workplace, and other BMC products at bmc.com/helix, or contact our sales team.

The mainframe has a long history of female trailblazers, with a legacy that continues through to today. Several women played a crucial role in the development of COBOL. Rear Admiral Grace Hopper’s championing of a computer language based on English words led to the creation of the FLOW-MATIC language, leading to her service as a technical consultant to the committee that defined COBOL in 1959. The sub-committee which created most of the new language’s specifications included both Gertrude Tierney of IBM and Jean E. Sammet of Sylvania.

At BMC, we’re proud of our history of continuous innovation. Women’s History Month presents a great opportunity to reflect on the women who have carried on the legacy of Grace Hopper, Gertrude Tierney, Jean Sammet, and others by receiving patents for the innovations they contributed to BMC’s solutions.

Being recognized for unique and groundbreaking contributions is quite an honor and a source of pride for the rest of one’s career. Senior Product Manager Irene Ford received patents in 2007 and 2010 for solutions that enable customers to mask sensitive data without writing custom programs. Reflecting on the experience, Irene says, “As I look back on this today, I am proud to have been able to work so closely with our amazing development team not only on these patents but also on the work we do every day to make our tools better for our customers.”

Fifteen women have been granted patents for their mainframe innovations at BMC:

Linda S. Ball (2001, 2006 & 2010)
Carla C. Birk (1997 & 2001)
Donna M. Di Carlo (2012)
Catherine Drummond (2022, 2023 & 2024)
Linda C. Elliott (2000)
Irene Ford (2007 & 2010)
Carol Harper (1988, 1989 & 1992)
Roxanne Kallman (2020, 2022 & 2023)
Karen Nelson-Katt (1998)
Lisa S. Keeler (2000)
Annette B. McCall (1995)
Pradnya Shah (2002)
Melody Vos (2005, 2006, 2007, 2012, 2015)
Lori Walbeck (2020 & 2021)
Wenjie Zhu (2022, 2023 & 2024)

I’d also like to take this opportunity to recognize our colleagues from other business units who have received patents:

Tamar Admon (2013)
Kalpa Ashhar (2016 & 2018)
Maribeth Carpenter (2023)
Jiani Chen (2015, 2017, 2019 & 2020)
Gwendolyn Curlee (2023)
Kanika Dhyani (2015, 2016, 2020 & 2023)
Priyanka Jain (2020)
Nitsan Daniel Lavie (2019, 2020 & 2024)
Donna S. Lowe-Cleveland (2005)
Pallavi Phadke (2018 & 2019)
Soumee Phatak (2020)
Carol Rathrock (1998)
Komal K. Shah (2014)
Annie Shum (2008)
Jeyashree Sivasubramanian (2016, 2018, 2019, 2020, 2021 & 2023)
Cynthia L. Sturgeon (2006, 2014, 2017 & 2018)
Priya Saurabh Talwalkar (2023)
Elaine Tang (2022)

Patent filings for Michal Barak, Komal Padmawar, Jennifer Glenski, and Priya Talwalkar are currently pending.

At BMC, we’re proud of our history of innovation and of the role that our female employees have played in making that innovation possible. We look forward to continuing to create solutions that serve our customers and move the industry forward with contributions from both current employees and future generations.

This is the second blog in my series following “Requirements for Building an Enterprise Generative AI Strategy,” where I highlighted the significant challenges and expectations of enterprise customers for generative AI, with detailed requirements for building a strategy. My recommendations centered on being grounded in enterprise knowledge, integrating references for trust and verifiability, ensuring answers are based on user access control, and creating model flexibility.

In this blog, I introduce a reference architecture designed specifically for generative AI applications, demonstrate how this architecture effectively addresses generative AI enterprise challenges around trust, data privacy, security, and large language model (LLM) agility, and provide a brief overview on LLM operations (or LLMOps). As a refresher, BMC HelixGPT is our approach to generative AI integrated across the BMC Helix for ServiceOps platform.

Reference architecture for generative AI applications

An application architecture describes the behavior of applications used in a business, focused on how they interact with each other and users. It is focused on the data consumed and produced by applications, rather than their internal structure. The industry has long recognized three prominent AI design patterns to build generative AI applications:

• Prompt engineering
• Retrieval augmented generation (RAG)
• Fine tuning pipelines

Instead of debating which approach is better, BMC HelixGPT seamlessly integrates all three.

The diagram below shows that our BMC HelixGPT application reference architecture for generative AI. The architecture consists of several layers: API plug-ins, prompt library, vector data source ingestion, access processing control, model-training pipeline, and assessment layer to assess hallucination/telemetry/evaluations, a “bring your own model” embedding layer, and an LLM orchestration layer. BMC HelixGPT extensively uses LangChain as the engine to orchestrate and trigger LLM chains.

Reference Architecture for GenAI

The BMC HelixGPT proprietary generative AI technology, combined with LangChain open source models, provide “bring your own model” flexibility for our customers. There are also retrieval plug-ins, access control plug-ins, and API plug-ins that integrate into enterprise systems. Like the holistic design explained in this June 2023 blog by Andressen Horowitz, we have three main flows:

1. Data ingestion and training flow: Data is read from multiple data stores, preprocessed, chunked, and trained through an embedding model (RAG) and training pipeline (fine-tuning). VectorDB stores the chunked document embeddings that allow for better semantic, similarity-based data retrievals.
2. Prompt augmentation using data retrieval: Once a user query arrives at the API layer, the prompt is selected, followed by data retrievals through VectorDB or API plug-ins to get the right contextual data before the prompt is passed to the LLM layer.
3. LLM inference: This is where there is a choice to use general purpose foundation models from OpenAI, Azure GPT models, or the self-hosted foundation model in BMC HelixGPT. Fine-tuned models are used when tuned for a specific task or use case. The response is evaluated for accuracy and other metrics, including hallucinations.

Now, let us look at how this reference architecture addresses the challenges of generative AI for enterprises and facilitates the rapid development of generative AI applications.

Overcoming common enterprise challenges with generative AI deployments

Enterprise versus world knowledge: accuracy and trust

Enterprises seek answers across diverse internal and external enterprise data sources such as articles, web pages, how-to guides, and more. Further, data can be contained in both unstructured and structured databases. BMC HelixGPT ingests, chunks, and embeds these sources through LangChain data loaders using embedding transformer-based models. LangChain provides a rich set of document loaders that it leverages. When a user question is received, we augment the prompts with document retrievals from VectorDB or APIs and use the LLM’s in-context learning to generate a response. This method anchors the LLM’s response to the retrieved documents or data, reducing the risk of hallucinations. BMC HelixGPT also provides the retrieved documents as citations, allowing users to verify the responses. To realize this advanced capability, our strategy integrates various LangChain capabilities, such as retrieval QA chains with sources and conversation history chains.

Access control, security, and data privacy

During the retrieval of document flow, BMC HelixGPT validates that the user has access permissions to read the documents and removes those documents from the prompt context that the user doesn’t have access to. This ensures that LLM-generated answers are always from only those documents that a user has read access to. Hence, the same question will generate two different answers aligned to the user’s role and permission model.

Model flexibility and security

The BMC HelixGPT reference architecture is based on a model abstraction layer that LangChain provides. This capability enables seamless integration of foundational general-purpose models, whether hosted or behind APIs such as OpenAI and Azure or open-source models running in customers’ centers. There are over 50 connectors to different model providers in LangChain, making it easy to add new providers or models modularly. Customers who prioritize data security have the option to host and run a foundational model in the datacenter. This model architecture caters to diverse enterprise customers and prevents vendor lock-in, including implementations that provide the strongest privacy and security guarantees.

An Introduction to LLMOps

Machine learning for IT operations (MLOps) for LLM is called LLMOps. LLMOps is a new set of tools and best practices to manage the lifecycle of LLM-powered applications, including data management, model management, and LLM training, monitoring and governance LLMOps is the driving force to build generative AI applications for BMC HelixGPT.

BMC HelixGPT is a platform that provides models and services that allow applications to harness the power of generative AI. It also provides LLMOps foundational services such as prompt engineering and RAG to power a spectrum of use cases ranging from summarization to content generation and conversations.

LLMOps is distinct from MLOps because it introduces three new paradigms for training LLMs:

• Prompt engineering
• Retrieval augmented generation
• Fine-tuning pipelines

My third and final installment in this blog series will dive deeper into BMC HelixGPT’s LLMOps capabilities.

At BMC, we always consider innovation to be the core of our strategies and our customers are the center of our business. In fact, from its inception over 40 years ago, our organization was built on the belief that embracing innovation is not an option—it’s an imperative. Over the past few years, we have driven a significant transformation at BMC—in every aspect: products, business model, and operational model, all the while embracing modern DevOps practices. Starting with our Autonomous Digital Enterprise (ADE) vision for the evolving role that technology plays in every enterprise, we have invested heavily in innovation. We have shifted our products to a cloud-native offering and to software as a service (SaaS).

We have invested in the development of our BMC Helix platform and embedded artificial intelligence (AI), machine learning (ML), and predictive capabilities into our entire product line to enable use cases that were not possible before. We are building a future where AIOps, Service Ops, DevOps, DataOps, and AutonomousOps are interconnected to help global businesses become ADEs. Through this Connected Digital Ops framework, we operationalize innovation, connecting and amplifying hybrid IT with the most innovative portfolio of infrastructure software, partners, and expertise.

Our mission is all about enabling technology to equip customers with everything they need to move faster, adapt quicker, innovate better, and make the most of both existing and future technologies. Each milestone we’ve collectively crossed is a testament to the dedication, resilience, and innovative spirit that pulses through the veins of everyone at the company.

Accelerating BMC India’s growth

Today marks the third year of my journey with BMC, and I am honored to share that it’s been an incredible ride, one studded by numerous learnings, shared victories, collaborative efforts, and a relentless pursuit of excellence! It’s a narrative of strategic moves, growth, adaptability, and the unwavering commitment of a team that stands for a vision shared by all 2,500 of us in BMC India.

Our pursuit of growth has been backed by the growth of India as a country. India is now an epicenter of technology innovation; it has consistently been an attraction for the global capability centers (GCCs) for businesses across the world spanning all industry domains, which also makes it a hub for technological advancements. India possesses the world’s second-largest pool of digitally skilled talent. Bolstered by a vibrant start-up ecosystem, strong service provider community, and cutting-edge academic research, Indian tech talent is delivering unmatched value. Geopolitically and socioeconomically, we are a very stable nation, thereby making us a sweet spot to invest in talent and business.

For us, BMC India is a microcosm of BMC. The solutions we sell are global in nature, meaning that our products do not vary from region to region. With almost every Fortune 500 company having a base in India, we get the strategic advantage of being in proximity to the end-user base of our products. With all the functions sitting under one roof, from research and development (R&D) to customer support to professional services, and other corporate functions, we have a great opportunity to innovate and deliver solutions for our customers. We have witnessed this in the numerous channels we have been able to open for our developers, designers, and architects to connect with the different customer personas, helping our customers get the most value out of our products.

With all these internal and external factors working to our advantage, we have been able to implement a unified strategy that not only brought cohesion to diverse teams but also ensured that the goals set in India resonated with the broader global strategy. To implement this strategy, we introduced a standardized set of goals that would provide a consistent measure of success across all initiatives run by different business units. This systematic approach allowed us to evaluate and refine our executables, ensuring that our efforts were not only impactful but also in line with our overarching global goals.

Utilizing a standardized framework that aligns with the global vision of the company to run a global capability center in India has been a key driver of our customer- and people-centric innovation. Witnessing BMC India’s evolution into a strategic center of excellence has been incredibly rewarding. Our insights and innovations have not only contributed to business growth but have also played a pivotal role in shaping key decisions at a global level. The influence of our India centers is now felt across the entire organization. By constantly embracing change and at times staying ahead of it, we have positioned ourselves as leaders in Connected Digital Ops and set the stage for continued success.

As we celebrate these milestones, it goes without saying that our journey is ongoing. The future holds boundless possibilities, and with our collective dedication and innovative spirit, I am confident that we will continue to shape the future of the technology industry at large and pay it forward to make our society a better place for the generations to come.

Generative AI is one of the most exciting emerging technologies today, potentially revolutionizing many industries. However, as with any new technology, there is always the risk of a “Trough of Disillusionment,” when interest in a technology wanes as its implementations fail to deliver on its promises and unrealistic expectations lead to disappointment.

BMC HelixGPT is a generative AI platform designed to help businesses avoid that risk and achieve real success with the technology. BMC HelixGPT is a domain-specific generative AI model designed to help enterprises manage their IT infrastructure.

Here are just a few examples of how BMC HelixGPT uses its knowledge of IT systems and processes to help businesses achieve impactful results by integrating generative AI:

• Automate IT tasks, such as provisioning new servers and configuring applications, to free up IT staff to focus on more strategic initiatives.
• Identify and resolve IT problems more quickly and efficiently by analyzing data from multiple sources to identify the root cause of problems and recommend solutions.
• Provide insights into IT operations that can help businesses to improve their efficiency and performance by identifying bottlenecks and suggesting ways to improve data flow and applications.

Generative AI is embedded throughout the BMC Helix for ServiceOps platform, and is already yielding benefits for BMC customers.

As generative AI continues to mature with increase adoption, its long-term success will depend on avoiding the “Trough of Disillusionment,” which could happen within the next few years as more businesses begin experimenting with the technology and its limitations become more evident.

However, businesses that use a platform like BMC Helix for ServiceOps and generative AI capabilities such as BMC HelixGPT can avoid that by leveraging the technology to solve real-world problems and using it responsibly and ethically. Here are some ways BMC HelixGPT mitigates common risks when integrating generative AI.

1. Tuning for IT, HR, enterprise service management (ESM), and operations use cases and all contextual data (whether offline, historic or in real time) ensures that BMC HelixGPT focuses on answers related to an enterprise based on its own enterprise data lakes, not outside information.
2. The embedded security layer is domain- and tenant-specific.
3. Enterprises have the power to not only restrict the scope of queries, such as to business-related questions only, but also have the flexibility to bring their own models from other GPT or generative AI vendors such as Azure, OpenAI, and Google, keeping their investments in generative AI safe and synchronized with advancements in GPT and large language model (LLM) technology.

Yes, these are early days for understanding the full power of generative AI. However, with the right business partner and trusted, proven solutions like those offered by BMC Helix, enterprises can capitalize on the promise of generative AI responsibly.

To learn more about BMC Helix for Service Ops, visit bmc.com/serviceops. To learn more about BMC Helix GPT, visit bmc.com/helixgpt.

It has been an exciting year for BMC! First, BMC was recognized as a leader in the prestigious Forrester Wave^™: Process-Centric AI for IT Operations (AIOps), Q2 2023 report. Today, I am proud to announce our latest achievement. BMC has been named a leader in the Forrester Wave^™: Enterprise Service Management, Q4 2023 report.

The recognition by Forrester as a leader in enterprise service management AND AIOps in a single year is an incredible accomplishment for BMC. More importantly, we feel it is a validation of BMC’s commitment to advancing desired business outcomes for our customers.

The BMC Helix Enterprise Service Management portfolio has grown significantly over the past 18 months. BMC Helix has extended its service management capabilities beyond IT to include purpose-built, out-of-the-box solutions for Human Resources Service Management, Customer Service Management, Workplace Service Management, and Portfolio Management. Additionally, enterprises across the globe are utilizing the same no code/low code capabilities of the BMC Helix ESM platform to build custom applications spanning IT and non-IT use cases. Examples span Finance, Procurement, Global Shared Services, Trade Compliance, and even Employee Parking Management.

We plan to offer even more non-IT service management solutions in the coming months. And, as part of our mission to prioritize the needs of modern enterprises and advance their success journey, we will continue to invest in artificial intelligence and machine learning (AI/ML), generative AI, AIOps, digital employee experiences, and ServiceOps. According to the ESM Wave report, “BMC is a comprehensive service operations solution for the enterprise.”

BMC Helix Enterprise Service Management received the highest possible score in 16 criteria, including vision, roadmap, and partner ecosystem. Other strengths denoted by Forrester include:

• Acquisitions: Investments in “ComAround Knowledge (KCS verified) have helped it [BMC] not only level up but, in many cases, surpass the competition.”
• Roadmap: Focused on “observability intelligence, AI-driven analysis, zero-touch resolutions, and proactive capabilities.”
• Ease of Use: BMC Helix “Innovation Studio’s drag-and-drop capabilities underscore the product’s user-friendly approach, allowing even those without IT expertise to create and manage workflows with ease.”
• Platform Extensibility: BMC Helix Enterprise Service Management “can also be configured to connect with development tools with a bidirectional flow of information and, with portfolio management, can support product-centric organizations.”
• Customers: “Reference customers are pleased with the flexibility of the platform and the ability to make changes to meet their needs with high speed of delivery and without highly trained engineers.”

I could share more details about each attribute’s differentiated value and the customer benefits that BMC Helix Enterprise Service Management offers. Instead, seeing the report is believing. BMC Helix has now emerged as only one of three leaders in enterprise service management. As Forrester Research notes, “BMC’s strategy over the past few years has continued to close the gap between the Leaders in this market.”

Please read the complete Forrester Wave^™: Enterprise Service Management, Q4 2023 report with our compliments.

Learn more about how BMC Helix Enterprise Service Management simplifies complexity, integrates intelligence, and fuels productivity across the modern enterprise by visiting the BMC Helix Enterprise Service Management website.

Today is BMC Connect Chicago, a time where our customers and partners come together to learn how to unleash the transformative potential of BMC Helix artificial intelligence and machine learning (AI/ML) and predictive analytics solutions across service and operations management (ITSM/ITOM). BMC HelixGPT, our generative AI technology, delivers concrete capabilities that demonstrably improve user experiences and productivity across ITSM/ITOM workflows.

Today, I am proud to announce our latest innovations in generative AI applications with new “BMC HelixGPT-Powered” use cases.

• Available Now: BMC HelixGPT-Powered Situation Summaries in operations management with AIOps, generate plain-language summaries and recommended next actions, making it faster for IT operators to understand and resolve issues rather than spend additional time decoding a string of output error codes.
• Available November 2023: BMC HelixGPT-Powered Resolution Insights in enterprise service management (ESM) and ITSM, where case and incident clustering is integrated with similar past resolutions of equivalent tickets to determine the recommended next actions for simplified, accurate case resolution.
• Available for early customer access: BMC HelixGPT-Powered Conversational Engagement and Search empowers end users to communicate more naturally with today’s virtual agents and chatbots. In this use case, BMC HelixGPT understands the query being posed more accurately and then places that query within a more extensive understanding and context to deliver more accurate responses in human-style language.

If you are unfamiliar with BMC HelixGPT, it integrates large language models (LLMs), harmonizing traditionally siloed data sources to deliver actionable insights for operational anomalies and autonomous resolution for mundane issues. BMC HelixGPT offers near real-time processing of operational data from all observable layers to provide an unadulterated view of reality for ITSM/ITOM teams. BMC HelixGPT is embedded across the entire BMC Helix service and operations management platform.

What does BMC HelixGPT deliver for our customers? BMC HelixGPT goes beyond traditional AI to provide clear, actionable insights and recommendations to resolve problems. Additionally, it becomes an “expert in your systems,” learning how to optimize processes and prevent points of failure.

Each use case will enable our customers to streamline business processes, solve problems faster, reduce the likelihood of human error, automate root cause analysis, and resolve incidents in real time. Perhaps the best part? Your enterprise can use these new BMC HelixGPT capabilities as part of the BMC Helix Fall 2023 releases without additional charges.

Learn more about the efficiencies and problem-solving capabilities of BMC HelixGPT here.

The Gartner^® Hype Cycle™ for ITSM, 2023* details the IT practices and transformational technologies that are hitting the “Peak of Inflated Expectations (e.g., IT/OT/ET alignment); those in the “Trough of Disillusionment (e.g., SOAR); and those that have reached the “Plateau of Productivity” (e.g., cloud management platforms).

What remains in the “Innovation Trigger” category once again? Service operations, or ServiceOps. Take a look:

What is ServiceOps?

In the report, Gartner defines ServiceOps as “The convergence of the infrastructure and application monitoring environments with the ITSM incident management practice to create a more effective and optimized mechanism for diagnosing and resolving incidents. The combination of the two environments with the context of AI can lead to a significant reduction in both the number and impact of incidents.”

Why is it important?

According to the report, “the ability to quickly diagnose and resolve incidents is critical for organizations as they develop and implement digital services. Intelligent automation can remove monitoring and service desk silos, creating the concept of service operations. This ensures that incident detection through monitoring can be better integrated with ITSM platforms, leading to incidents that can be more readily identified and resolved creating higher availability and more resilient services.”

What is the business impact?

In the report, Gartner explains the impact this way: “The convergence of ITSM and monitoring environments results in a more productive and seamless incident management practice. The contextual analysis of alerts resulting in anomaly detection and escalation can result in fewer incidents and improved mean time to resolve (MTTR), especially when integrated with the ITSM platform. Products and services are more available and less likely to suffer from poor performance or outages and disruption, costs are reduced, and overall productivity is improved.”

Gartner adds that there are several factors driving ServiceOps right now: “Business pressure for reliable and stable services drives the demand for incidents to be either eliminated before they can cause disruption or be diagnosed and resolved as quickly as possible.”

Advances in technology and ITSM are also helping drive ServiceOps. According to Gartner, other drivers include:

• The closer integration of monitoring and the service desk that creates a more seamless environment in which outages and disruptions can be more effectively managed.
• ITSM platforms featuring observability and correlation functions to identify incidents as part of the monitoring capabilities.
• ITSM platforms can now enrich events by applying the context of recent incidents and changes to impacted configuration items (CIs) to improve root-cause identification.
• The rise in the use of AI which enables the monitoring systems to swiftly correlate events and anomalies to present to technicians via the ITSM platform.

Obstacles to ServiceOps

According to the report, ServiceOps also faces obstacles. The report states: “AI-based monitoring services can take longer than expected to understand the context of the services, the traffic, and the environment creating delays in improving availability.”

Gartner explains that ServiceOps requires “Improved diagnostics and resolution times require time and effort to achieve due to dependencies including effective service mapping, the degree of automation and the quality of monitoring. This requires a concerted effort to achieve a holistic approach. Accurate service mapping and comprehensive, up-to-date configuration management databases are necessary to support end-to-end incident processes if enhanced availability and resilience is to be achieved,” and “automated responses need to be carefully chaperoned to ensure that their decisions match the use case.”

Gartner also explains, “Many monitoring and service desk teams operate in silos, making it difficult to create a seamless experience.”

Generative AI

This year, Gartner included generative AI in its report, noting the “interest in how AI underpins ITSM has gained momentum due to generative AI applications like ChatGPT.” The report categorizes generative AI as hitting the “Peak of Inflated Expectations,” as shown in the chart above. But Gartner anticipates it will reach a plateau in just two to five years.

What is Generative AI?

As Gartner defines it, “Generative AI technologies can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. Generative AI has profound business impacts, including on content discovery, creation, authenticity and regulations; automation of human work; and customer and employee experiences.”

Why is it important?

According to Gartner: “Generative AI exploration is accelerating, thanks to the popularity of Stable Diffusion, Midjourney, ChatGPT, and large language models. End-user organizations in most industries aggressively experiment with generative AI. Technology vendors from generative AI groups to prioritize delivery of generative-AI-enabled applications and tools. Numerous startups have emerged in 2023 to innovate with generative AI, and we expect this to grow. Some governments are evaluating the impacts of generative AI and preparing to introduce regulations.”

What is the business impact?

Gartner notes the business impact of generative AI in the report, explaining, “Most technology products and services will incorporate generative AI capabilities in the next 12 months, introducing conversational ways of creating and communicating with technologies, leading to their democratization. Generative AI will progress rapidly in industry verticals, scientific discovery, and technology commercialization.”

Among the drivers for generative AI, Gartner identifies: “Machine learning (ML) and natural language processing platforms are adding generative AI capabilities for reusability of generative models, making them accessible to AI teams.” Generative AI industry applications are growing, impacting creative work in marketing, design, music, and content. Generative AI is expected to disrupt software coding, and when “combined with development automation techniques, it can automate up to 30% of the programmers’ work,” according to Gartner.

Obstacles to generative AI

Generative AI poses many obstacles. Gartner states that “democratization of generative AI uncovers new ethical and societal concerns. Government regulations may hinder generative AI research.” The report adds that, “hallucinations, factual errors, bias, a black-box nature, and inexperience with a full AI lifecycle preclude the use of generative AI for critical use cases.”

Gartner also explains that the potential use of AI for nefarious purposes represents an obstacle, explaining, “Sadly, it will also become a security and societal threat when uses for nefarious purposes. Responsible AI, trust, and security will be necessary for the safe exploitation of generative AI.”

BMC’s approach to ServiceOps

BMC addressed ServiceOps when we combined our service management and operations management capabilities under one solution, the BMC Helix platform. Putting artificial intelligence (AI) and machine learning (ML) at the core of BMC Helix, we created a complete, integrated software-as-a-service (SaaS) digital transformation engine that converges ITSM and ITOM. BMC Helix modernizes processes and workflows for both service management and IT operations teams and empowers organizations to accelerate DevOps; leverage intelligence for faster, better decision-making; and strengthen collaboration.

We believe that ServiceOps removes organizational silos between service and operations management functions, opens new collaboration channels, elevates experiences, and boosts productivity for teams  by:

• Unifying data and workflows throughout hybrid cloud environments, minimizing manual, repetitive processes, and increasing the speed toward desired business outcomes.
• Spending less time reactively responding to incidents and proactively resolving them before they impact the business through integrated, cross-platform AI/ML, intelligent automation, and predictive service management in concert with other BMC Helix solutions.
• Automating processes and reducing silos to foster collaboration across IT, lines of business, product teams, and customers.

BMC’s approach to Generative AI

BMC HelixGPT is our answer to the next level of efficiency that enterprise operations will require with near real-time processing of operational data from infrastructure, applications, and end users. We believe that generative AI can establish a complete view of reality using this data from disparate sources, while ticket, incident, and change data inform how human beings have been responding to issues native to the environment and the business. BMC HelixGPT integrates all of these traditionally siloed data sources to derive actionable insights for anomalies and autonomous resolutions for mundane issues.

We developed BMC HelixGPT to ensure that the insights provided are human-relatable and still specific to the enterprise, so answers are based on organizational expertise, hence the term “expert of your systems.” We constrain our GPT with a domain- and tenant-specific model that continuously learns resolutions from subject matter experts (SMEs) in the organization.

BMC is excited to be a part of this transformational time in IT management and operations technology with our ServiceOps and generative AI offerings, and we will continue to innovate and integrate them with our BMC Helix portfolio.

*Gartner, Hype Cycle for ITSM, 2023, By Siddharth Shetty, Chris Laske, 14 July 2023.

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