CIO CIO

specifically about analytics, right, which was what we’re here to talk about today, as we’ve discussed quite a lot already, the underlying data quality infrastructure issue that’s real. That’s top of mind. I think that exists everywhere. Really interesting hearing Qiraat talk there about, in essence, ROI Right? Return on Investment. I think that’s a real challenge for a lot of organizations around these projects. I love the way keyra framed it. As you know, don’t worry about the FOMO kind of thing, like, like, drill down to the value. At the same time, organizations are worried about being left behind and from an innovation perspective. But I do think that issue of ROI is increasingly becoming a space where some organizations can see AI helping right data preparation, in and of itself, can consume a huge amount of time and resources due to those difficulties in finding, accessing, cleaning, transforming, sharing data efficiently. I think the increasing number and complexity of data sources coupled with the need to access them across distributed ecosystems, again, both the guys spoke about that that demands significant resources and expertise, and I’m starting to hear it buyers think maybe AI can help with some of that complexity, like applying AI to an imperfect system, as I mentioned earlier. Um, other things I think IT teams are often overwhelmed by the rising requests for self serving data access and integration, varying data requirements from different users, complicating the process further. So again, starting to see some opportunities for AI supported data platforms to help like reduce some of the challenges around data preparation and management, incompatible data types, formats, aging data. These things all pose obstacles to effective data access and collection. Skills Gap, which Qiraat hit on, I think the data related skill gaps are further hindering the development of robust data management as well as AI related skills gaps. It’s another area where actually, I think organizations are starting to think about AI supported data platforms, or potentially agentic AI helping to winnow data into insights. Again, there’s a risk involved, because you’re not doing it through human insight, but potentially it could be helpful and work. But with all of these, these pieces, all of these challenges, I think there is one underlying challenge that that we see and pretty much everywhere, which is, you know, you have the question of, can it be done? And after you answer, and this is where Qiraat was coming in, there are the questions of, will it work and should we do it? Because I think. Think the other questions that I’m hearing a lot, the other challenges that I’m hearing a lot are this year as opposed to last year. And Keith, you and I have spoken about this many times. The other challenge with AI applied to analytics is, okay, we can generate insights, but will those insights help us? Can we trust them? And then there’s the big question of, how are we managing the risks? Because some of those risks are unforeseen. So it definitely is all of the above, from what what the guys were saying before, plus some others, Keith Shaw source

While she wasn’t sure how it would land, it grabbed the attention of the CIO, who had never seen this approach before, and opened the dialogue for Schulze to be a candidate. She decided to push past any insecurities or fears, and go for a position she didn’t necessarily feel totally qualified for, but ended up landing the job. Schulze knows not everyone feels comfortable stepping out of their comfort zone, but as a leader, she wants to set that example for her employees. She identifies opportunities for growth and advancement, regardless of background or experience, and helps them tap into their potential. She understands it’s difficult for women to break through the boys club mentality that can exist in tech, and the challenge to fight stereotypes around women in IT and STEM careers. In her own career, Schulze had to apply herself extra hard to prove her worth and value, even when she had the same answers as her male counterparts. But she never got discouraged or deterred from tech, focusing instead on positive role models and mentors to help guide her. source

Next up: Figure out which alternatives are both best and most likely to be accepted by most of the group. Schedule a second round of one-on-one conversations, whose purpose is to nudge everyone toward the most likely alternative — the one most likely to be sufficiently agreeable to everyone involved. Yes, this is a lot of work. Consensus decision-making is, as noted, expensive and time-consuming, which is one reason it should be saved for when maximal buy-in is more important than any other aspect of choosing a direction. Consensus playbook, part 2: The meeting Now is the time for a meeting — a consensus check, consensus check because everyone involved is close enough to the same preferences that the meeting’s energy is best expended getting everyone to commit, in public, that this is what they agree to. Again, that’s agree to not agree with. And a significant part of the meeting is documenting, for the record, for each member of the group, how it is, if they don’t agree with the chosen alternative but do agree to it, why they are okay with it even if it isn’t, from their perspective, perfect. source

Q. What is the next wave of AI and Gen AI in Indian companies, and how will CIOs and tech leaders of companies stay prepared for it? Do you see faster adoption and implementation of AI and Gen AI in dynamic B2C industries like airlines and travel? Suresh: The ongoing tech transformation needs a proactive approach. I believe developing proprietary language models tailored to specific business needs can reduce reliance on external providers, mitigating geopolitical risks. Robust data governance frameworks are also crucial for the foundation for effective AI implementation. Moreover, investing in upskilling initiatives will equip teams with the necessary skills to manage and innovate with AI technologies. AI and Generative AI are rapidly enhancing efficiency and customer experience in B2C industries like airlines and travel. Digi Yatra is leading this evolution, deploying AI-powered facial biometric technology to streamline passenger verification and reduce wait times at airport touchpoints. AI-driven multilingual chatbots are being integrated to assist travelers with onboarding and real-time support. As Digi Yatra expands, AI will play a key role in document verification and fraud prevention, simplifying international travel processes. These advancements will create a seamless, contactless, and privacy-first journey for passengers, reinforcing Digi Yatra’s commitment to innovation and operational excellence in air travel. Q. The facial-recognition technology-based check-in service at airports, called Digi Yatra, could be implemented at hotels and public places like historical monuments, according to Digi Yatra Foundation. Has there been a prototype for this use-case developed and discussions ongoing with various government agencies such as the Ministry of Tourism? Suresh: Digi Yatra’s contactless biometric solutions have the potential to extend beyond airports, supporting a more integrated and secure travel ecosystem across India. While the platform is currently under enhancements at airports with additional e-gates at various touchpoints to ensure smoother passenger flow, its application can be explored in other sectors where identity validation is essential. By integrating Digi Yatra across multiple travel and public spaces, India can establish a tech-driven, globally benchmarked travel ecosystem that is efficient, secure, and traveler-friendly. For travelers, it would mean a hassle-free travel experience across the board. For the travel industry, integrating Digi Yatra can drive greater operational efficiency by automating ID checks and reducing manual verification processes, making the whole end to end experience much more satisfying for the end customers. The government and tourism ministry can leverage this to ensure security and a seamless travel experience. With data-driven insights, they can plan infrastructure, allocate resources efficiently, and improve overall travel management. source

In an era when artificial intelligence (AI) and other resource-intensive technologies demand unprecedented computing power, data centers are starting to buckle, and CIOs are feeling the budget pressure. To address these issues, IT organizations are increasingly migrating workloads to the cloud to gain operational efficiency and agility. There are many challenges in managing a traditional data center, starting with the refresh cycle. Server equipment, power infrastructure, networking gear, and software licenses need to be upgraded and replaced periodically. Purchasing, deploying, provisioning, and maintaining all of these pieces is expensive, creating a complex budgeting puzzle that’s difficult to manage. In addition, enterprise IT must build its infrastructure to manage a maximum load. Unfortunately, for most of the year, a significant portion of equipment may go unused — waiting for peak usage times — such as when an auto manufacturer launches a new model. How cloud eases IT challenges When organizations migrate their workloads to cloud platforms, this burden shifts dramatically. In this new paradigm, the underlying hardware becomes transparent to users. Hyperscale cloud providers upgrade and replace equipment behind the scenes without affecting workloads. Just as important, IT pays only for the resources it’s using, and when it needs to scale up, it can easily burst to accommodate dramatically higher loads. Then there’s the cost of supporting servers. “For every kilowatt-hour of required server power, traditional data centers may require up to 80% more power for cooling and peripherals,” says Matthias Schorer, head of the Research and Insights Platform at Google Cloud. Because cloud providers operate at such a massive scale, they can achieve significantly better power usage effectiveness. Not only does this reduce costs but it also translates to a reduced carbon footprint, helping organizations meet sustainability goals. Migrating workloads to the cloud can also accelerate new development. Take, for example, IT’s plans to deploy a new AI-powered application, which, like many other AI workloads, is hungry for highly performant hardware. IT must determine the resources required; send requests for proposals; negotiate with suppliers; await delivery; and, finally, deploy and provision the new gear. This could take weeks or, more likely, several months to accomplish. Cloud platforms eliminate these delays, empowering IT to spin up new infrastructure rapidly without complex negotiations or capital expense approvals. This agility is particularly valuable for AI initiatives, especially since they often require rapid scaling. Making cloud even easier The partnership between Broadcom and Google Cloud has made it simple to achieve these goals. IT can nondisruptively extend its on-prem environments to the cloud and run its VMware workloads in Google Cloud VMware Engine without downtime or rearchitecting. In addition, the partnership enables VMware license portability between on-prem and Google Cloud environments, providing the enterprise with significant flexibility to move VMs wherever they’re needed. Admins can maintain a consistent operational view, using the same tools IT has already deployed for on-premises management, which eliminates the need to retrain on new management systems. Additionally, when new updates or patches are available for accessing new functionality, Google manages these requirements for customers as part of the service. “By moving from a do-it-yourself model to a service model in the cloud, IT frees itself from the burden of managing and maintaining a data center,” says Schorer. “This enables the enterprise to apply the operational savings to future innovations, such as actually determining how to take advantage of AI — instead of spending so much time and money deploying and supporting it.” Find out how easy it is to migrate your virtual workloads to the cloud to achieve greater operational efficiency with Broadcom and Google Cloud. Find more information by clicking here. source

Enterprise analytics in 2025: AI and analytics convergence and focused utility In 2025, BI dashboards are dead and AI is moving the user experience from query, response and decision support to agent-based planning and execution with validated accuracy, automated process execution, adaptability and business impact. Enterprises are moving past experimentation, enabling specialized tools and systems of intelligence to address challenges at scale. The melding of AI with enterprise analytics is happening now as enterprise data platform vendors like Snowflake and Databricks recognize that they must differentiate beyond data aggregation and cleansing to systems of intelligence that support systems interaction and engagement. The industry itself is also shifting away from generalized AI solutions that are now commodities toward focused, utility-based applications that address in specific challenges in specific industries like healthcare, manufacturing, finance and telecommunications with specific solutions.  We are seeing evolve with Agentic AI solutions from SAP, Salesforce and Microsoft to name but a few that will move beyond data as insight to data as action.  Data and analytics leaders will need to evolve how they view the role of enterprise analytics in the Age of AI.  Every business initiative will expect access to organizational data and this will be problematic if data strategies don’t offer flexible, reliable and governed approaches to accessing information diverse data stores.  Effective governance can clearly drive adoption of intelligent analytics throughout the business.  source

CEOs and boards of directors are tasking their CIOs to enable artificial intelligence (AI) within the organization as rapidly as possible. As a result, many IT leaders face a choice: build new infrastructure to create and support AI-powered systems from scratch or find ways to deploy AI while leveraging their current infrastructure investments. For those enterprises with significant VMware deployments, migrating their virtual workloads to the cloud can provide a nondisruptive path that builds on the IT team’s already-established virtual infrastructure. “Think about this choice in terms of your own home, imagining your core business applications as the very foundation of your house,” says Ken Bocchino, Group Product Manager at Google Cloud. “Just as you wouldn’t demolish that house to start from scratch to build a new kitchen, you look for ways to expand and modernize the existing VMware infrastructure while preserving the integrity of the original.” Infrastructure challenges in the AI era It’s difficult to build the level of infrastructure on-premises that AI requires. The networking, compute, and storage needs — not to mention power and cooling — are significant, and market pressures require the assembly to happen quickly. AI workloads demand flexibility and the ability to scale rapidly. For many organizations, building this capacity on-premises is challenging. Cloud platforms offer purpose-built infrastructure on demand, but there are IT concerns that this involves expensive refactoring, along with what could be a time-consuming and tricky migration. However, organizations don’t have to build entirely new applications. New functionality, including AI capabilities, can be developed with cloud-native services while remaining interconnected with existing infrastructure elements. A true hybrid approach The partnership between Broadcom and Google Cloud provides enterprises with a strategy for maintaining their VMware operational models and integrating cloud-native services. Google Cloud VMware Engine enables enterprise IT to nondisruptively extend their on-prem environments to the cloud and easily run workloads in Google Cloud — without having to make any changes to the architecture. There’s no downtime, and all networking and dependencies are retained — as are other benefits (see this IDC Business Value study). IT teams maintain operational consistency by using their familiar on-premises tools to manage cloud workloads, eliminating retraining needs. Once migration has occurred, the journey toward AI begins, and it can be broken down into three basic steps — each of which can be a stopping point if the organization’s needs are fully met: Front door modernization. Organizations frequently begin by enhancing how users access applications. Users can take advantage of cloud-native load balancing and security capabilities such as Google Cloud Armor, which protects against distributed-denial-of-service (DDoS) attacks and provides a web application firewall (WAF). In this way, IT can employ the cloud’s ubiquitous access and security features without having to refactor and re-network their applications. Application layer evolution. The next step often involves rethinking specific elements of the application stack, potentially developing new components that use cloud-native services while maintaining connections to existing systems. But this is not necessary to achieve AI enablement. AI and analytics integration. Organizations can enable powerful analytics and AI capabilities by linking VMware-hosted data with services such as BigQuery and Vertex AI. In fact, this can be done without needing to refactor or develop new components, if that’s not something IT wants to undertake. “With Google Cloud VMware Engine, it’s easy to integrate Google’s Vertex AI directly into the VMware environment,” says Myke Rylance, client solution architect at Broadcom. “It’s like adding high-tech ‘smart rooms’ in your house, where both the old and new structures communicate and work together seamlessly. In Google Cloud, IT has all that it needs to scale up quickly to enable AI with their existing virtual infrastructure.” Find out how easy it is to incorporate AI into your virtual workloads with Broadcom and Google Cloud. Find more information by clicking here. source