Leaders Should Optimize IT I&O For Digital Business

by Sohini Bagchi    May 14, 2018

mishra

Enterprises are increasingly moving towards a digital future that’s making traditional infrastructure and operations [I&O] models obsolete. This in turn is challenging I&O leaders to bring fresh ideas that deliver business outcomes. In addition, data vulnerabilities in mobile environments and DevOps require I&O leaders to collaborate with other teams. At the recent Gartner Infrastructure, Operations Management & Data Center Summit 2018, held in Mumbai, DD Mishra, Research Director, Gartner explains to CXOToday the vital connections between digital technologies, relationships and cultural influences, with a focus on I&O role and leadership.

 CXOToday: What are the key takeaways from this year’s Gartner Infrastructure, Operations Management & Data Center Summit?

 Mishra: The key takeaways are the understanding around power of connectivity which revolves around leadership, partnership and technology. Connectivity is a powerful enabler of speed, scale and agility. I&O’s ability to participate and contribute to their organizations digital initiatives needs I&O leadership, not just management. The summit also explained that why is it important to make I&O digital business platform-ready. There were insight on how a shift towards “Platform Thinking” – ecosystems, data-driven trust, resource-orchestration and bimodal IT – will transform I&O. There are leadership capabilities I&O leaders will need to build and the summit has focused on that aspect as well. The “job” of CIO and the I&O leader must grow and evolve as digital business spreads, and disruptive technologies, including smart machines and advanced analytics, reach critical mass. The complication for many I&O leaders is that the path forward is unclear. The summit gave an opportunity to understand what powerful practices should CIOs and IT leaders adopt to position themselves and their organizations to face the coming challenges and opportunities.

CXOToday: Artificial intelligence and machine learning are often used interchangeably. This perception often leads to confusion. Can you explain the difference?

Mishra: Artificial intelligence is a technology that appears to emulate human performance typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialog with people, enhancing human cognitive performance (also known as cognitive computing) or replacing people on execution of no routine tasks. Applications include autonomous vehicles, automatic speech recognition and generating and detecting novel concepts and abstractions (useful for detecting potential new risks and aiding humans to quickly understand large bodies of ever changing information. Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks and natural-language processing), used in unsupervised and supervised learning, that operates guided by lessons from existing information. AI often uses machine learning concepts to deliver the expected outcome.  Though they are used together many a times, they cannot be used interchangeably as they are different.

CXOToday:  What are the areas in IT Infrastructure management where AI can play a big role?

Mishra: AI enabled by ML plays a big role in managing operations, driving efficiency, reducing cost and improving quality of IT operations. AI plays important role in driving Workplace services, Data Center Services, Monitoring,  Cloud, Network and Cybersecurity space. Today AI and ML are part of the mainstream delivery of most managed services providers. There are many interesting use cases available for IT Infrastructure Management.

CXOToday:  What are the key challenges to the adoption of AI within organizations, especially those having a strong legacy system? How can organizations deal with it?

Mishra: Competency and skills are considered the No 1 challenge , followed by lack of strategy, Identification of use case, funding, security and privacy concerns, complexity of integrating AI to existing infrastructure and lack of insights into measurement of value from AI. Sometimes the legitimate excitement about AI’s transformative power leads to unrealistic expectations within the organization. The extravagant hype fuels a deepening skepticism. It requires patience to change management approaches. Using proof of concept where applicable, can help in generating visibility around the various options. Going through some established use cases can be another option. Identification of right use case and AI technology solution is the key to address future disappointment. Gartner also receives number of enquiries from its client who want deeper insights which can help them in identifying right solutions. Close to half of the organizations have already adopted or are in the process of adoption. Many organizations are dependent on their service providers to drive AI based capabilities.

CXOToday: How can enterprises successfully scale DevOps adoption in I&O?

Mishra: Securing Support of various IT Functions is the top barrier to DevOps adoption followed by compliance requirements, availability of tools and skills, Security concerns, HR restrictions in redefining roles etc. DevOps implementation is a change management exercise across the board and a change in mindset is required.  Composition and organization of the team is important when it comes to DevOps. Automation plays a key role in DevOps. Identification of low hanging opportunities gives the ability to execute and learn and spread the early good news. Driving engagement across the team and changing to culture of collaboration becomes a key enabler. Early adopters report improvement in Speed of Delivery, Quality of releases, reduction of cost and skills development and team engagement.