AI Workload Design: Siddhesh Naik of IBM Discusses Automation by Design

Explore the insights of Siddhesh Naik, Country Leader of Data, AI & Automation software at IBM India & South Asia, as he discusses the critical factors in designing systems for AI workloads.

Aanchal Ghatak
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AI Workload

In the landscape of enterprise AI, the demand for resilient and scalable systems is paramount. Automation solutions play a critical role in empowering organizations to efficiently tackle AI workloads. By leveraging advanced AI technologies like machine learning and natural language processing, businesses can streamline operations and unearth valuable insights from their data. With automation solutions at the forefront, strategic partnerships with leading technology providers such as IBM provide organizations with the expertise and tools necessary to execute effective AI initiatives.


Welcome to an insightful discussion on Automation by Design, featuring Siddhesh Naik, Country Leader of Data, AI & Automation Software at IBM India & South Asia. Join us as we delve into critical considerations for designing systems supporting AI workloads, navigate challenges in scaling AI-driven automation, explore the significant benefits of AI-powered automation, and discover IBM's innovative approaches to implementing enterprise automation initiatives.

What are the critical factors organizations should consider when designing systems to support AI workloads?

Technology is a fundamental source of competitive advantage. A Right technology architecture can give companies the opportunity to bring products to market more quickly and fully leverage innovations like generative AI. Today, even though 95% of CEOs are pursuing a digital-first strategy 60% of organizations are not yet developing a consistent, enterprise-wide approach to generative AI.


Critical factors organisations should consider to support AI (Traditional / Gen AI) workloads:

· AI is multi-model and Hybrid Cloud based. No one model fits all. Run the model where the workflows, applications and data live.

· Governance needs to be addressed at the CEO and Board levels to scale AI responsibly.


· Data matters to move from pilot to production. Laying the right data foundation for AI Powered Automation.

· Scale for Value – It is critical to pick the right use cases and deployment for good ROI.

Enterprises that create, run and modernize their digital infrastructure and applications on integrated, event-based automated systems powered by AI (what is known as intelligent automation), will be the ones best positioned to optimize the value of future initiatives and innovations. To realize these benefits businesses must be intentional in their approach to creating integrated IT environments that run on automated operating models to be open, continuous, and allow for speedy innovation – i.e. they must adopt an automation by design approach.


What are the challenges with scaling AI-driven automation workloads?

As businesses look at exploring and applying intelligent automation across functions, if not done strategically it’s easy for automation initiatives to fail to get off the ground or not deliver much return on investment. A few notable challenges organizations should keep in mind.

There are still many companies that lack the right people and skillsets to scale intelligent automation. This gives us a tremendous opportunity to take full advantage of their existing talent by reskilling while also finding ways to augment their workforce digitally with automation tools tailored to their job profiles.


Another critical aspect is data, as it is a key component for effective automation and create insights. However, if the data is siloed, difficult to use and access due to being disconnected and/or untrusted it will render any automation initiative useless.

We have seen many instances of businesses starting with what’s easy to automate, but not necessarily what’s most beneficial to achieve the overall objective. The lack of a holistic strategy and the skills to effectively manage change will always hinder scaling automation. Having the right insights, software tools and expert support to successfully implement these projects is a must. 

What are the significant benefits that organizations can derive from AI powered automation?


Intelligent automation offers organizations with significant benefits by streamlining operations, enhancing decision-making, and improving customer experiences. By leveraging advanced AI technologies like Gen AI, machine learning and natural language processing, organizations can automate routine tasks such as data analysis, customer service, and supply chain management. This leads to increased efficiency, reduced costs, and improved responsiveness to market changes. Automation can also have a positive impact on team dynamics by liberating employees from mundane tasks and empowering them to engage in strategic, higher-value initiatives. These benefits put together create significant business value which is why we are seeing the acceleration of intelligent automation adoption. An IBM Institute of Business Value study found that 92% of C-suite executives expecting to digitize their organization’s workflows and leverage AI-powered automation by 2025.

How is IBM helping clients implement enterprise automation initiatives? Please provide some relevant use cases?

We are working towards helping businesses cut to the core of the complexity to focus on what matters and build a system that can maintain that focus at scale. Grounded in commitments to trust and transparency, we have a complete set of offerings – from fit-for-purpose IT operations, application management and integration products, to IBM Consulting services. We have invested in organic R&D innovation with a strong pipeline from our research labs as well as strategic acquisitions like Turbonomic, Instana and Apptio.


We’re seeing three core use cases emerge for intelligent automation.

First is making systems more proactive for IT and network operations – by increasing system observability, optimizing performance and cost, and managing assets and incidents to proactively avoid risks and mitigate impact to users and the business. Like Apptio’s solutions intelligently structures vast amounts of technology-spend and enterprise-operational data to deliver actionable insights that a CEO, CFO and CTO can use to work better together when optimizing technology investments.

Second is making business processes more efficient by integrating data and processes and applying AI and automation technologies to create intelligent workflows that span the organization – with things like Process Mining, Robotic Process Automation, API management, among others.

Finally, making people more productive by using intelligent automation tools to automate repetitive tasks. This empowers people with time to focus on higher value work and more meaningful interactions.

For example, in HR, IBM watsonx Orchestrate can help optimize HR processes like automating payroll, better managing employee data, and automating key hiring and onboarding processes.

AI Workload