Latest news with #CIOs
Forbes
5 hours ago
- Business
- Forbes
The Utility CIO's Dilemma: Finding Clarity In Data With AI
Abhay Gupta is cofounder and CEO of Bidgely, evolving energy analytics for utilities with the power of data and artificial intelligence. The modern utility faces a multifaceted mission stretching far beyond keeping the lights on. Today, its purview extends to a complex web of initiatives aimed at fostering energy efficiency, electrification and ambitious decarbonization goals. Since these initiatives are often provided funding, the challenge for utilities isn't always the scarcity of resources but, rather, a fundamental question: With so many critical pathways forward, where do we begin, and how do we determine which programs to prioritize first? While these questions can feel subjective, the answers may lie within data. The majority of utilities have already made investments in smart meters, and many are now bolstering their IT ecosystems by integrating data warehouse platforms (e.g., Snowflake, AWS Redshift) to create vast data lakes, or enterprise data platforms, that house the millions of data points they collect each day. However, the raw and unprocessed data in these enterprise data platforms doesn't inherently provide utility CIOs with clear guidance on where to allocate funds for maximum impact. How can CIOs optimize their existing smart meter and data warehouse investments to make this data truly valuable across the business? Transforming Raw Data Into Smart Data With AI By layering AI onto raw data, utilities can derive much more granular and meaningful insights about the energy being consumed across the territory. Specifically, by training AI algorithms to extract behind-the-meter insights, utilities can gain valuable intelligence into usage for not only standard home appliances but also electric vehicles (EVs), solar and battery storage. The best part is that utilities can easily add AI software to their existing cloud platforms to become one cohesive application. In my opinion, the next evolution lies in embedding these powerful tools directly within the data platforms themselves so that these insights are immediately accessible and unified to further amplify value. The integration of GenAI takes this capability a step further. Now, CIOs and other executives can directly interact with their data by asking natural language questions like, "Which of my customers have the highest grid impact based on their household energy use?" or, 'How many of my customers have Level 1 EV chargers?' Utilities can ask similar questions about households with pool pumps or those that show signs of inefficient or degrading appliances. This AI layer on top of enterprise data platforms unlocks a dual advantage for CIOs, providing comprehensive use case lists that fuel both foundational infrastructure development and targeted customer engagement. Connecting Insight With Programmatic Action From a programmatic standpoint, this is the tip of the iceberg for utility CIOs to leverage specific program funding aimed at managing shiftable loads and improving efficiencies. Common programs include: • Heat Pumps: Identifying homes with inefficient existing HVAC systems allows for targeted upgrade outreach and program enrollment. • Pool Pumps: Pinpointing customers with single-speed pool pumps makes them prime candidates for upgrades to more flexible variable-speed models. • EV Charging: Identifying customers with Level 1 chargers allows for targeted Level 2 charger upgrades. Similarly, analyzing each customer's charging patterns to detect on-peak charging enables targeted outreach for incentivized load-shifting. For example, EVs highlight the limitations of static load shifting, where shifting charging from peak hours (e.g., 5 p.m. to 9 p.m.) to off-peak times (e.g., after 11 p.m.) can create new demand peaks. To address this, utilities are realizing they need a more dynamic approach to demand flexibility that integrates efforts across multiple initiatives to achieve wider impact. Integrated Program Management By automating routine program management (customer outreach, data collection, performance monitoring), AI tools free utility staff from administrative burdens to manage more programs concurrently without increasing operational complexity. Because AI can also seamlessly integrate data from different programs and systems, utilities gain a more holistic view of their overall portfolio of initiatives and enable integrated program management. By understanding the interdependencies between programs, CIOs can better identify opportunities for synergy and optimization across multiple efforts. For example, AI can evaluate how participation in a smart thermostat program impacts the effectiveness of a time-of-use rate program. Or, let's say you've just invested millions of dollars to get a distributed energy resource management system (DERMS) solution up and running. Knowing which customers have EVs or inefficient appliances would be extremely helpful in driving load-shifting strategies using that DERMS solution. With an AI layer, you gain the ability to precisely target and engage the right customers, unlocking the full potential of your DERMS investment. With multiple programs underway simultaneously, CIOs can use these same AI tools for real-time monitoring and evaluation to identify potential issues early on and/or make data-driven adjustments to optimize program effectiveness across the board. Despite all of this, AI's widespread integration remains a work in progress. While many are already embracing these benefits, some utilities I speak with remain hesitant for a few reasons. Chief among these are traditional organizational cultures, where resistance to change prevents investing significant upfront costs in a perceived "nascent" technology. In an inherently risk-averse industry, this is not uncommon, and it's something we witnessed at the start of the smart meter transition. As with smart meters, however, the more willing the industry becomes to accept digital advancements, the faster these results start speaking for themselves. There's also a critical talent gap of scarce and highly specialized AI data scientists and engineers that utilities feel they need to fill before integrating AI. Through GenAI, however, we're learning utilities can upskill their current workforce to turn any employee (even those without data science backgrounds) into AI experts. Instead of needing to write code or understand intricate data structures, employees can ask simple, conversational questions to analyze critical data insights. The AI Advantage In the complex landscape of utility initiatives and resource allocation, CIOs are discovering that AI is a powerful enabler to make strategic prioritization decisions while also providing the tools to efficiently manage a diverse portfolio of programs. Ultimately, utilities are better positioned to pursue a more comprehensive and effective approach to energy management and customer engagement. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Channel Post MEA
14 hours ago
- Business
- Channel Post MEA
NTT Data: CEOs And CISOs Not Aligned On GenAI Adoption
NTT DATA has launched its new report, 'The AI Security Balancing Act: From Risk to Innovation,' highlighting the opportunities and risks AI presents in cybersecurity. The findings show a misalignment among C-Suite leaders when it comes to business goals and operational readiness for GenAI deployment. The report, which includes data from an NTT DATA survey of more than 2,300 senior GenAI decision makers, comprising 1,500 C-Suite leaders across 34 countries, found that while CEOs and business leaders are committed to GenAI adoption, CISOs and operational leaders lack the necessary guidance, clarity and resources to fully address security risks and infrastructure challenges associated with deployment. The C-Suite disconnect Nearly all (99%) C-Suite executives are planning further GenAI investments over the next two years, with 67% of CEOs planning significant commitments. In parallel, 95% of CIOs and CTOs report that GenAI has already driven, or will drive, greater cybersecurity investments, with organizations ranking improved security as one of the top three business benefits realized from GenAI deployment in the last 12 months. Yet, even with this optimism, there is a notable disconnect between strategic ambitions and operational execution with nearly half of CISOs (45%) expressing negative sentiments toward GenAI adoption. More than half (54%) of CISOs say internal guidelines or policies on GenAI responsibility are unclear, yet only 20% of CEOs share the same concern – revealing a stark gap in executive alignment. Despite feeling cautious about the deployment of GenAI, security teams still acknowledge its business value. In fact, 81% of senior IT security leaders with negative sentiments still agree GenAI will boost efficiency and impact the bottom-line. Organizational operations not ready for GenAI NTT DATA's research further reveals a critical gap between leadership's vision and the capabilities of their teams. While 97% of CISOs identify as decision makers on GenAI, 69% acknowledge that their teams lack the necessary skills to work with the technology. In addition, only 38% of CISOs say their GenAI and cybersecurity strategies are aligned compared to 51% of CEOs. Adding to the complexity, 72% of organizations surveyed still lack a formal GenAI usage policy and just 24% of CISOs strongly agree that their organization has a robust framework for balancing risk with value creation. Legacy tech limiting GenAI adoption Beyond internal misalignment, 88% of security leaders said legacy infrastructure is greatly affecting business agility and GenAI readiness, with modernizing IoT, 5G and edge computing identified as essential for future progress. To navigate these obstacles, 64% of CISOs are prioritizing co-innovation with strategic IT partners rather than relying on standalone AI solutions. Notably, security leaders #1 top criteria when assessing GenAI technology partners is end-to-end GenAI service offerings. 'As organizations accelerate GenAI adoption, cybersecurity must be embedded from the outset to reinforce resilience. While CEOs champion innovation, ensuring seamless collaboration between cybersecurity and business strategy is critical to mitigating emerging risks,' said Sheetal Mehta, Senior Vice President and Global Head of Cybersecurity at NTT DATA, Inc. 'A secure and scalable approach to GenAI requires proactive alignment, modern infrastructure and trusted co-innovation to protect enterprises from emerging threats while unlocking AI's full potential.' 'Collaboration is highly valued by line-of-business leaders in their relationships with CISOs. However, disconnects remain, with gaps between the organization's desired risk posture and its current cybersecurity capabilities,' said Craig Robinson, Research Vice President, Security Services at IDC. 'While the use of GenAI clearly provides benefits to the enterprise, CISOs and Global Risk and Compliance leaders struggle to communicate the need for proper governance and guardrails, making alignment with business leaders essential for implementation.'
The Sun
2 days ago
- Business
- The Sun
C-Suite misalignment slows GenAI adoption
KUALA LUMPUR: NTT Data, a global leader in digital business and technology services, yesterday launched its new report The AI Security Balancing Act: From Risk to Innovation, highlighting the opportunities and risks AI presents in cybersecurity. The findings show a misalignment among C-Suite leaders when it comes to business goals and operational readiness for GenAI deployment. The report, which includes data from an NTT Data survey of more than 2,300 senior GenAI decision makers, comprising 1,500 C-Suite leaders across 34 countries, found that while CEOs and business leaders are committed to GenAI adoption, CISOs (chief information security officers) and operational leaders lack the necessary guidance, clarity and resources to fully address security risks and infrastructure challenges associated with deployment. Nearly all (99%) C-Suite executives are planning further GenAI investments over the next two years, with 67% of CEOs planning significant commitments. In parallel, 95% of CIOs (chief information officers) and CTOs (chief technology officers) report that GenAI has already driven, or will drive, greater cybersecurity investments, with organisations ranking improved security as one of the top three business benefits realised from GenAI deployment in the last 12 months. Yet, even with this optimism, there is a notable disconnect between strategic ambitions and operational execution with nearly half of CISOs (45%) expressing negative sentiments toward GenAI adoption. More than half (54%) of CISOs say internal guidelines or policies on GenAI responsibility are unclear, yet only 20% of CEOs share the same concern – revealing a stark gap in executive alignment. Despite feeling cautious about the deployment of GenAI, security teams still acknowledge its business value. In fact, 81% of senior IT security leaders with negative sentiments still agree GenAI will boost efficiency and impact the bottom-line. NTT Data's research further reveals a critical gap between leadership's vision and the capabilities of their teams. While 97% of CISOs identify as decision makers on GenAI, 69% acknowledge that their teams lack the necessary skills to work with the technology. In addition, only 38% of CISOs say their GenAI and cybersecurity strategies are aligned compared to 51% of CEOs. Adding to the complexity, 72% of organisations surveyed still lack a formal GenAI usage policy and just 24% of CISOs strongly agree that their organisation has a robust framework for balancing risk with value creation. Beyond internal misalignment, 88% of security leaders said legacy infrastructure is greatly affecting business agility and GenAI readiness, with modernising IoT, 5G and edge computing identified as essential for future progress. To navigate these obstacles, 64% of CISOs are prioritising co-innovation with strategic IT partners rather than relying on standalone AI solutions. Notably, security leaders top criteria when assessing GenAI technology partners is end-to-end GenAI service offerings. 'As organisations accelerate GenAI adoption, cybersecurity must be embedded from the outset to reinforce resilience. While CEOs champion innovation, ensuring seamless collaboration between cybersecurity and business strategy is critical to mitigating emerging risks,' said Sheetal Mehta, senior vice-president and global head of cybersecurity at NTT Data Inc. 'A secure and scalable approach to GenAI requires proactive alignment, modern infrastructure and trusted co-innovation to protect enterprises from emerging threats while unlocking AI's full potential.' 'Collaboration is highly valued by line-of-business leaders in their relationships with CISOs. 'However, disconnects remain, with gaps between the organisation's desired risk posture and its current cybersecurity capabilities,' said Craig Robinson, research vice-president, security services at IDC. 'While the use of GenAI clearly provides benefits to the enterprise, CISOs and Global Risk and Compliance leaders struggle to communicate the need for proper governance and guardrails, making alignment with business leaders essential for implementation.'
Business Upturn
3 days ago
- Business
- Business Upturn
NTT DATA Research Reveals C-Suite Misalignment Over GenAI Adoption
London, United Kingdom: Nearly half of CISOs have negative sentiments about GenAI rollouts, despite CEO optimism CEOs are all-in on GenAI, but CISOs warn that security gaps and aging infrastructure are holding back progress Alignment requires stronger governance and dedicated investment NTT DATA , a global leader in digital business and technology services, today launched its new report, 'The AI Security Balancing Act: From Risk to Innovation,' highlighting the opportunities and risks AI presents in cybersecurity. The findings show a misalignment among C-Suite leaders when it comes to business goals and operational readiness for GenAI deployment. The report, which includes data from an NTT DATA survey of more than 2,300 senior GenAI decision makers,comprising 1,500 *C-Suite leaders across 34 countries, found that while CEOs and business leaders are committed to GenAI adoption, CISOs and operational leaders lack the necessary guidance, clarity and resources to fully address security risks and infrastructure challenges associated with deployment. The C-Suite disconnect Nearly all (99%) C-Suite executives are planning further GenAI investments over the next two years, with 67% of CEOs planning significant commitments. In parallel, 95% of CIOs and CTOs report that GenAI has already driven, or will drive, greater cybersecurity investments, with organizations ranking improved security as one of the top three business benefits realized from GenAI deployment in the last 12 months. Yet, even with this optimism, there is a notable disconnect between strategic ambitions and operational execution with nearly half of CISOs (45%) expressing negative sentiments toward GenAI adoption. More than half (54%) of CISOs say internal guidelines or policies on GenAI responsibility are unclear, yet only 20% of CEOs share the same concern – revealing a stark gap in executive alignment. Despite feeling cautious about the deployment of GenAI, security teams still acknowledge its business value. In fact, 81% of senior IT security leaders with negative sentiments still agree GenAI will boost efficiency and impact the bottom-line. Organizational operations not ready for GenAI NTT DATA's research further reveals a critical gap between leadership's vision and the capabilities of their teams. While 97% of CISOs identify as decision makers on GenAI, 69% acknowledge that their teams lack the necessary skills to work with the technology. In addition, only 38% of CISOs say their GenAI and cybersecurity strategies are aligned compared to 51% of CEOs. Adding to the complexity, 72% of organizations surveyed still lack a formal GenAI usage policy and just 24% of CISOs strongly agree that their organization has a robust framework for balancing risk with value creation. Legacy tech limiting GenAI adoption Beyond internal misalignment, 88% of security leaders said legacy infrastructure is greatly affecting business agility and GenAI readiness, with modernizing IoT, 5G and edge computing identified as essential for future progress. To navigate these obstacles, 64% of CISOs are prioritizing co-innovation with strategic IT partners rather than relying on standalone AI solutions. Notably, security leaders #1 top criteria when assessing GenAI technology partners is end-to-end GenAI service offerings. 'As organizations accelerate GenAI adoption, cybersecurity must be embedded from the outset to reinforce resilience. While CEOs champion innovation, ensuring seamless collaboration between cybersecurity and business strategy is critical to mitigating emerging risks,' said Sheetal Mehta, Senior Vice President and Global Head of Cybersecurity at NTT DATA, Inc. 'A secure and scalable approach to GenAI requires proactive alignment, modern infrastructure and trusted co-innovation to protect enterprises from emerging threats while unlocking AI's full potential.' 'Collaboration is highly valued by line-of-business leaders in their relationships with CISOs. However, disconnects remain, with gaps between the organization's desired risk posture and its current cybersecurity capabilities,' said Craig Robinson, Research Vice President, Security Services at IDC. 'While the use of GenAI clearly provides benefits to the enterprise, CISOs and Global Risk and Compliance leaders struggle to communicate the need for proper governance and guardrails, making alignment with business leaders essential for implementation.' Download the full report here , and visit our website to learn more about NTT DATA's AI services for cybersecurity. Methodology The report is based on insights from 2,300 senior GenAI decision-makers across 34 countries. 68% of respondents were from the C-suite, including CEOs, CISOs, CIOs, CTOs, CDOs, COOs, CCOs, CFOs, CHROs, and CSEs. 27% held Vice President, Head of, or Director-level roles, while 5% were senior managers or specialists. This research was independently conducted for NTT DATA by Jigsaw Research, a global strategic insight agency. About NTT DATA NTT DATA is a $30+ billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long-term success. As a Global Top Employer, we have experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are also one of the leading providers of digital and AI infrastructure in the world. NTT DATA is part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future. Visit us at View source version on Disclaimer: The above press release comes to you under an arrangement with Business Wire. Business Upturn takes no editorial responsibility for the same. Ahmedabad Plane Crash
Forbes
13-06-2025
- Business
- Forbes
What AI Agents Are Getting Right – And Wrong
Agentic AI promises to transform IT operations, but many platforms still fall short. Here's what's ... More working, what's hype and how CIOs can separate fad from reality. For many CIOs and technology executives, AI's promise was straightforward: smarter, faster and more efficient IT operations. The technology was envisioned as a game-changer, capable of reducing operational costs, automating mundane tasks, enhancing system reliability and freeing up human resources for much more important work. But ask them today, and you might hear frustration rather than enthusiasm. That's because the reality on the ground is starkly different from the optimistic projections that have headlined the news so far, primarily due to the complexities involved in effectively integrating AI into IT operations. It's a challenge that was front and center at Agentic AI Demo Day, where executives gathered to explore how autonomous agents can help streamline operations — but only if the underlying complexity is addressed first. Despite the global operational intelligence market valued at $3.2 billion in 2024, according to IMARC Group, and projected to reach $6.8 billion by 2033, growing at a CAGR of 8.8%, enterprises are still grappling with real-world barriers to effectively implementing AI in their IT operations. At the heart of it all is one major hurdle — untangling the operational complexity that prevents AI agents from delivering on their promise. The big question, though, is: How can they move past this complexity and harness the true power of AI? According to Andy Thurai, industry analyst at Field CTO, a major problem for enterprise IT today is that many organizations still run their IT operations through 'manual incident management processes,' a reality he described as 'shocking.' A 2024 report from the Uptime Institute found that nearly 60% of enterprises suffered major outages and downtimes tied to escalating IT complexity. One joint report by Splunk, a Cisco company, and global research institute Oxford Economics estimated the yearly global cost of such downtimes to be $400 billion. That's a huge cost when you think about the sheer numbers and it shows why enterprises are now scrambling to simplify the long-standing inefficiencies in IT. And in that scramble, many technical decision makers have bought into the AI hype and deployed AI tools which didn't fully solve their operational problems. While traditional machine learning and GenAI tools have addressed specific operational tasks — like forecasting or summarization — they still fall short when it comes to cross-domain workflow automation and real-time system orchestration. 'AI tools have tackled the easy parts,' Thurai said during Fabrix's Agentic AI Demo Day. 'But they haven't solved the fundamental workflow problems at the heart of IT operations.' Instead, many organizations have adopted fragmented point solutions that generate too much noise and too few insights. 'AI solutions promised to streamline operations, but instead, companies ended up with fragmented tools producing too much data and too few actionable insights,' Thurai explained during a recent webinar. He noted that one of the biggest pain points today is 'alert fatigue,' where IT teams are overwhelmed by excessive system alerts, diminishing their effectiveness and responsiveness. Thurai's sentiment is rooted in facts, with a report by McKinsey noting that while 92% of companies plan to grow their AI investments over the next three years, just 1% of surveyed C-Suite leaders describe their organizations as 'AI mature' — meaning AI is fully embedded into their operations and driving positive business outcomes. Many organizations face data overload from numerous sources, increasing rather than reducing existing operational pressures. As Thurai explained, this problem stems from the fact that modern enterprises rely heavily on intricate, microservices-based architectures. Systems at companies like Netflix, Uber and Amazon manage thousands of interdependent services simultaneously, dramatically increasing operational complexity. When incidents occur, traditional monitoring tools struggle to quickly pinpoint root causes, resulting in delayed resolutions that can cost millions in downtime and lost productivity. To address these shortcomings, the industry is gradually shifting toward agentic AI — also called agentic AIOps when applied to IT environments — which are so-called autonomous agents capable of independent action, reasoning and adaptive decision-making without constant human oversight. These agentic systems are particularly suited to IT operations precisely because they can operate independently, detecting and resolving incidents autonomously. While much is still being understood about how these agentic systems behave at scale and experts continue to call for companies to prioritize safety in building or deploying AI agents, they could potentially mitigate human error, reduce response times and directly address IT departments' alert fatigue. As Thurai noted in the webinar, organizations can achieve unprecedented efficiency, resilience and proactive management across their IT environments by orchestrating intelligent agents that can analyze, predict and act autonomously Companies are beginning to explore how these autonomous systems can be deployed effectively. For example, — which offers a modern intelligence platform for the agentic AI era — recently showcased practical ways businesses can deploy AI agents for operational intelligence during its Agentic AI Demo Day. The company's platform enables businesses to build customized AI agents tailored to specific operational scenarios, from anomaly detection to real-time event management. The anomaly detector agents demonstrated at the event can autonomously identify KPI deviations, automatically open trouble tickets and dynamically adjust system capacity. Event intelligence agents also showed capabilities in real-time alert correlation and executing closed-loop remediation. In practical terms, this means that AI agents — like Fabrix's solutions demonstrated — have a strong potential to significantly reduce operational costs and improve overall system reliability for organizations. However, the adoption of advanced autonomous systems isn't without hurdles. isn't the only player in this emerging space. Cisco has also introduced AI-native observability tools that incorporate agent-like behaviors to automate root cause analysis and AI observability. Similarly, Dynatrace is layering AI agents into its Davis AI engine to enhance multi-domain remediation across cloud-native environments. These developments reflect a broader move toward intelligent automation — though each vendor is taking a different route. Still, these agentic systems remain in early phases. Critics note that many so-called agentic platforms are still rule-based at their core, lacking the true autonomy and reasoning needed to adapt across diverse workflows. Even Fabrix's approach, while promising, is still evolving and may require customization for complex enterprise environments. As competition heats up, the key differentiator may not be the platform itself — but how well it balances adaptability, trust and enterprise-grade integration. Thurai warned that without robust guardrails, autonomous AI could exhibit unpredictable, or 'stochastic' behaviors. Companies must invest not only in agentic platforms but also in frameworks ensuring security, observability and ethical AI practices. 'Implementing guardrails and quality controls are essential,' Thurai said. 'Without proper oversight, you risk AI that doesn't just hallucinate — these systems can confidently produce inaccurate outcomes, leading to significant operational risks.' That message was echoed by multiple speakers at the Agentic AI Demo Day, including Cisco and IBM executives, who emphasized the need for enterprise-grade controls like embedded testing, persona-based access governance and auditable AI execution paths. These capabilities, they argued, are non-negotiables for agentic platforms that aim to operate autonomously at enterprise scale. Another significant challenge enterprises face is the severe shortage of skilled IT professionals. Korn Ferry predicts a global shortage of up to 85 million tech workers by 2030. This skill gap could further worsen already challenging operational issues, forcing enterprises to rely increasingly on automation and AI-driven solutions. Autonomous agents could be helpful in this regard, providing a critical lifeline that fills talent gaps and performs routine and even complex tasks previously managed by overstretched human teams. For now, the road to fully autonomous AI operations remains under construction. Enterprises considering this journey must prepare carefully, ensuring that their investments in agentic AI are matched with a thorough understanding of potential pitfalls with rushing to deploy AI, as well as a disciplined approach to implementing AI. Despite these challenges, the potential rewards — reduced downtime, increased operational efficiency and substantial cost savings — make agentic AI an investment worth serious consideration. But as Thurai noted, agentic AIOps — which describes the application of autonomous, decision-making AI agents within AI-powered IT operations — is still in the very early stages and only a few vendors offer it. In the next year, he added, 'we'll probably see too much vendor snake oil coming out of the market saying, 'Oh, we're an AI agent platform,' when they really aren't.' The big message, according to Thurai, is that as we enter into a new agentic era for AI applications, choosing the right vendor could be the deciding factor between scalable automation and another failed AI deployment. 'The major difference between choosing the right vendor and wrong vendor, especially in IT ops, is not just about the platform,' he said, 'but the capabilities that can and should be expandable by agents.'
