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We can’t know the potential or limitations of new technology, but we can empower people throughout our organizations to think proactively about solutions that utilize AI, and to educate and empower their peers. Act as guides and cheerleaders for AI experiments and projects at all levels.  The agentic org chart  How should IT leaders think about job roles within their organizations with the rise of generative and agentic AI? In our recent interview Salesforce’s Marc Benioff discussed how he is focusing less on hiring customer service professionals in favor of AI agents, whilst reskilling existing customer service people to become account managers, where he needs more humans.    Within IT and strategy some organizations have a named individual or team with overall responsibility for leading AI projects and solutions. They cascade out lots of technical training for existing infrastructure-, connectivity-, data center-, and end-user supporting staff.   source

Traditional sourcing processes are often slow, manual, and hindered by inefficiencies such as data silos and limited visibility into supplier performance and risk. Since the pandemic, supply chain teams have been laser-focused on building more resilient and agile supply chains, and many are now turning to autonomous sourcing tools. This enables process improvements and surfaces insights that drive smart decision-making in an ever-evolving landscape. Automation transforms sourcing by streamlining Request for X (RFx) creation (this is an umbrella term referring to various types of information requests), optimizing supplier selection and awarding, accelerating negotiations, enhancing collaboration, and enabling real-time data analysis. In a more dynamic economy increasingly impacted by digitization — that’s operating against a wider backdrop of uncertainty caused by trade wars and more — these advantages are critical for businesses. For these reasons, organizations are increasingly looking to autonomous sourcing as a must in the digital transformation of the procurement function. What is autonomous sourcing? Autonomous sourcing is the use of AI and automation to manage, optimize, and accelerate the sourcing process with minimal human intervention. Traditional sourcing tools have long helped sourcing managers ensure a steady, cost-effective supply while managing supplier risk. However, they’ve fallen short in delivering real-time insights into supply, supplier, and supply chain risks — which are crucial for agile decision-making. “COVID brought back focus on the importance of removing constraints, driving for a more resilient supply chain, and trying to foster innovation with suppliers,” says Raj Aggarwal, director of global product marketing at GEP. “But the technology that has been in existence has not really been able to provide real-time insights into suppliers, their performance, and changing costs.” Autonomous sourcing through a unified data model can deliver real-time supplier and market intelligence information that can be used to rapidly adapt to changing circumstances such as the impact of potential tariffs on pricing. Autonomous sourcing: Use cases and benefits Leading procurement teams are using autonomous sourcing with minimal human intervention to manage activities like three bids and a buy and tail spend. For more complex scenarios — like high-risk supplier negotiations — it’s simplifying the process as much as possible but still requiring human input. “As we move to more agentic AI, the orchestrator agent will oversee the process, assign tasks to sub-agents, and bring that back to the user in a concise manner,” Aggarwal continues. By managing tasks without humans, autonomous sourcing reduces procurement costs while accelerating sourcing cycles. Beyond cost savings, autonomous sourcing delivers substantial benefits, including: Accuracy and consistency. With automation and AI, sourcing decisions are based on standardized data and processes, which minimize human error and bias. As a result, autonomous sourcing ensures consistency in supplier evaluation, pricing analysis, and compliance. Better supplier relationships. Autonomous sourcing improves supplier relationships by enabling faster, more transparent communication. The end result is improved collaboration, trust, and long-term partnerships that deliver value to both sides. Increased visibility. By centralizing data and procurement processes, autonomous sourcing gives teams real-time visibility into supplier performance, sourcing activity, and market trends. This transparency enables proactive decision-making, risk mitigation, and strategic planning. Sustainability. Autonomous sourcing also supports sustainability — which is key as 80% of customers are willing to pay more for sustainable products, per PwC. [1]“Maybe emissions have been an issue with a supplier,” Aggarwal offers as an example. “I can’t just go off and find another supplier because it would disrupt my current supply.” Using the collaborative capabilities of the technology, both parties can set emissions goals and work together to achieve them over a mutually agreed timeframe. Learn more about autonomous sourcing and how it can transform your procurement function. [1] PWC, Consumers willing to pay 9.7% sustainability premium, even as cost-of-living and inflationary concerns weigh: PwC 2024 Voice of the Consumer Survey, May 2024   source

Decentralization. AI initiatives often need centralized data lakes, while domain-driven models emphasize decentralized ownership. Complex governance. Ensuring data quality, lineage and compliance becomes more challenging as data is federated across domains but consumed centrally by AI models. On-demand data access. AI systems require real-time data access and adaptability, which can collide with the more fixed, process-centric nature of traditional EA frameworks. How can modern EA bridge the gap? According to Dharani Pothula, “Enterprise architects need to establish robust data pipelines, enforce data quality standards and implement governance frameworks that allow AI to operate effectively without compromising security or compliance.” Rather than being fundamentally incompatible, the shift to AI-centric data models is sparking a transformation in EA itself by default if not by design. Leading analysts and practitioners emphasize that EA must evolve from rigid, recurring reviews and static models to a more dynamic, real-time and outcome-focused discipline.  Foundational and adaptive architecture opportunities for EA are many, but they demand evolutionary steps, flexibility and a responsiveness less focused on rigid constructs, frameworks and organizational structures. As I mentioned earlier, the notion of embedding or federating EA directly into business functions connects the function to business realities and the art of the possible. This notion of “infused EA” means we need a truly agile variant of EA as a practice.  Modern EA must support both global oversight and semi and fully autonomous business domains fluidly, frameworks for data sharing, AI governance and cross-domain collaboration.  AI-based data governance can also automate data quality checks, metadata management and compliance monitoring, helping EA teams manage the increased complexity of AI data flows.  Composability and cloud-native architectures are well paired to enable a shift toward modular, API-first and AI-Ops-based cloud-native designs, which are better suited to the demands of AI and real-time analytics. The difference is observable, intelligent and dynamic enterprise architectures.  Adaptive architecture is no longer just an aspirational “slideware” exercise. AI enables real-time monitoring, analysis and adaptive enterprise architectures, moving away from static documentation and toward living, evolving models.  What about agent technology and what it means for EA? Capability mapping has long factored prominently into EA in terms of strategic alignment and transformation, roadmaps, and mergers and acquisitions, to name a few. The exercises, however, can be lengthy analysis efforts involving complex, orchestrated stakeholder alignments across multiple business units. The process, tooling and outcomes are challenging at best given the demand on time, analysis, documentation and communication and stakeholder engagement.  source

Four years ago, the company had just five data scientists dedicated to AI. Now it has 100 who’ve completed four key tracks. But the training isn’t limited to any specific group — it was extended to people across the company who don’t necessarily want to become hardcore specialists. So people from HR, finance, manufacturing, and other lines of business who have a desire to learn and invest in themselves enroll, and even if they have no programming background, they’re taught Python to get a feel for how to create an AI application. Vishal Gupta, CITO, Lexmark Lexmark Volunteers from within the company attend three-hour classes four nights a week for a year, and are assigned mentors within Lexmark and given projects, which not only complement the programs, but target business objectives within the company. According to Gupta, nobody has dropped out of the course and very few participants have left the company. “People are happy to have the opportunity to invest in themselves and apply what they learn,” he says. So far, six cohorts of people have gone through Lexmark’s training program, helping the company not only develop a talent base, but find the use cases for AI. “After somebody from manufacturing, customer service, sales, or any other business area completes the course, they know exactly which problems they can bring to us to solve with AI,” says Gupta. source

Mills said he turned to Ironside Group to apply advanced analytics to its Sassy platform. As part of the solution, data is moved into AWS QuickSight, which gives account managers of HS Brands’ customers a vast array of dashboards to view data collected. The solution reduces those account managers’ time burden for analysis while improving the quality of HS Brands’ service, which not only helps its customers with their businesses but helps HS Brands get paid faster and grow their customer base, Mills said. HS Brands and Ironside worked together to develop the Q Business proofreading application, which in addition to saving money, enabled the company to redirect its proofreaders toward identifying other ways to apply AI techniques to company workflows and grow the business more. “AI is exploding everywhere, and like everyone, we’ve got to get involved. This is going to be a major, major jump forward for us,” Mills said, adding that, although HS Brands is in the early stages of AI use, he plans to apply Q to QuickSight to enable account managers to grow and manage more accounts. “It’s going to save money. It’s going to allow me to bid differently. It’s allowed me to walk into the door with an account and show them that we’re playing at a different level and that we’re going to be more accurate, faster, and cheaper. It checks off a lot of boxes for HS Brands. “ source

CIOs and CSOs are emerging as strong custodians of infra, data, apps, clouds and devices at their organizations in 2025. Their roles have expanded to accommodate new challenges, with risk management, securing AI-enabled technology and emerging technologies being added to their plate. As data increases at the rate of 30 to 40%, the challenge for CIOs and CISOs is the fact that cybersecurity budgets are not increasing to protect the security data, says Kash Shaikh, President & CEO, Securonix. Especially in the SIEM space, many CISOs do not want to compromise their security posture, yet at the same time, maximize their budgets and its ROI, he adds. Data Pipeline Management is an effective data routing solution, such as Securonix’s Cyber Data Fabric, that will be vital for managing Security Information and Event Management (SIEM) costs and optimizing analytics, while allowing CIO and teams to segregate different tiers of data and reduce the overall cost. Artificial Intelligence has sharpened both edges of the sword, as organizations are better equipped to defend against cybersecurity conundrums that are finessed to be deadly, wide-ranging and impact operations and market reputation. You need AI to fight AI, says Kash. We are increasingly looking at leveraging GenAI, where our solutions provide more automation capabilities for our customers, as well as preventing the threat from AI,” he adds. The AI Story In 2024, there was a lot of interest for evaluating technology like AI, but either the technology was not there in terms of ROI, providing the desired efficiency, or CIOs and CSOs didn’t realize the best way of capitalizing on the trend. 2025 is turning out to be pivotal as AI matures in successful use cases, product innovations by vendors and more buy-in by companies. Securonix has introduced EON, integrating innovations such as insider threat detection through psycholinguistics, exemplifying the industry’s shift towards AI-driven solutions. ​ In State of the CIO Study 2025 by Foundry, almost 30 percent of the CIO respondents said they will pay a premium for AI-infused security. Kash responds, there are forward-looking CIOs and CISOs embracing AI, and GenAI but the organizations might have concerns, compliance issues etc. and they don’t want their data to be exposed. In some cases, tech vendors leverage an algorithm or LLMs to use customer data to provide more insights to them. “We provide some functionalities of AI that are included without charging a premium as it will be tied to the level of coverage or the entitlement they have. But for scaling purposes of an AI infused solution, there will be a premium to pay by the organization.” Securonix platform reinforces AI, and the solutions use both the traditional learning algorithms, as well as GenAI. The India Market, Rise of MSSP With the increasing complexity of cyber threats, the role of Managed Security Service Providers (MSSPs) evolving in the cybersecurity landscape will become more prominent, supporting hybrid SOC models and handling advanced threat responses as per Kash. Artificial Intelligence is expected to enhance Security Operations Centers (SOCs), improving efficiency and reducing analysts’ workload. India is critical to US headquartered Securonix, as nearly 50% of the company’s employees are based out of India.  “Both from the perspective of customers and the business, as well as our own presence, India is our center of excellence. At our R&D centers in Pune and Bengaluru (in India). we almost doubled our engineers’ count for development in the last year,” informs Kash. One of the largest banks in India is a Securonix customer, including marquee customers across verticals as the company continues its focus on both the enterprise, large enterprise and MSSP space. A major challenge for the US, and other regions, is that CISOs and CIOs manage their SOCs, where SIEM is deployed, and are facing a shortage of talent. “They request for MSSP model which provides the skillsets, 24 by 7 coverage and MSSP that can support. Working with all the three hyperscalers, we have almost 25% of our business globally through MSSP model,” says Kash. Securonix is investing in product innovation, go-to-market initiatives, and dedicating resources in major regions, including India, with laser focus on managed security service providers.  “This year, with entrenched plans to develop AI, we will hire AI skillsets in India. The vision over next three years is to have 75% of our global business through MSSP,” he adds. CIO and CSO as Business Strategist The CIO – CSO – LOB equation is becoming critical in the evolving threat landscape. 56% of LOBs perceive CIOs as business leaders, and 50% of CIOs view their role as business leaders as per 2025 State of the CIO Study by Foundry. CIOs are the enablers of the business in terms of technology, whether it is e-commerce that they are responsible for, digitizing the infrastructure, or launching an app as they create new business models within the company.  CSOs, on the other hand, protects the brand, which is a business aspect, as their number one priority. If you get compromised, not only the business is impacted, but your brand is also impacted, says Kash. Kash suggests, “While selecting a SIEM vendor, cost only may not be the best strategy because technologies like SIEM require very specialized, well-established vendors.” The forward-looking CIOs and CISOs need to embrace AI, and GenAI to drive efficiencies, change the business model and go to the board for budget approvals, he says. CISOs and CIOs should take risks with AI and invest in AI to help their businesses and empower them in their role, says Kash. The best way to make sure that the product works on a scale, especially for larger enterprises, is to select a vendor that has scale, like Securonix for example. Another challenge arises if the organization gets compromised using single vendors, like the exposure with a cloud security vendor and the blue screens across enterprises few years ago. “A CIO and CSO is taking too much risk. Use open technology to make sure you’re not putting all your eggs

The rapidly evolving AI ecosystem, where new products and services seem to appear daily, presents CIOs and IT purchasing leaders with increasingly challenging decisions, in part because of uncertainty about where the AI market may ultimately be headed. One major debate, with implications for CIOs and IT buyers, is whether AI will primarily be a product or a feature after the AI market sorts itself out. On the one side are AI pure-plays offering niche and often task- or industry-specific point solutions, as well as AI generalists such as OpenAI, Meta, and Google, whose standalone large language models (LLMs) can be integrated with other IT systems. On the other side, IT vendors such as Salesforce, ServiceNow, and many cybersecurity providers are rapidly adding AI features to enhance and transform their core products. source

The intentions behind the project-to-product shift itself are valid, but too many organizations make the move without fixing the underlying problems, and set themselves up for the same challenges they’re trying to solve. Getting the results you want from your strategy isn’t actually about choosing between project or product. It’s about making sure you have the right approach, people, and alignment to get your strategy delivered in a way that actually moves the needle. So don’t abandon project thinking altogether. Rather, ensure whatever delivery approach you choose is actually set up for success. That’s where most organizations fail, not because they’re managing projects vs. products, but because they never fixed the broken systems that prevent execution from working in the first place. Strategy execution is like chess. Every piece has a role, and every move matters. If there isn’t focus on achieving the business goals, you won’t win — you’ll just move pieces around until you lose. If everyone in your organization isn’t aligned and strategically moving toward the final outcome, strategy execution turns into a disjointed effort with no real progress. source

As leaders in engineering, we are ultimately responsible for the quality of our product, the most important factor for user happiness and business success. Although feature deliverables are a regular focus, the complexity of modern software means that small performance deficiencies can lead to substantially larger impacts. This reality required my teams to persist through architectural endeavors, and the tightest scrutiny I experienced on my teams was often in team culture. Leading a group of highly skilled and intellectual engineers requires more than just engineering competence; it also means guiding systems and people to be flexible. If you are leading a large organization or gearing up to lead for the first time, scaling these behaviors is a challenge I vividly remember. I have managed connected engineers building (time-critical) financial systems, and with our systems, if we had an issue for more than 30 minutes, where even brief downtime could have serious consequences. I want to share something important (while keeping the underlying meaning unchanged) that you should find useful wherever your software’s stability and performance is essential. This article presents ideas in four categories – focusing on a reliability culture, the deploy trade-off, resilient teams, and sustaining progress. source

이렇게 AI 에이전트를 실제 업무에 도입하고 활용하는 것이 가시화되면서, IT, 연구 개발, 영업팀이 성장할 것으로 전망했다. 인사 담당 임원들은 단기적으로는 데이터 과학자나 기술 기획자 등을 직원들의 역할을 다시 지정할 계획이며, AI 활용 능력이 근로자에게 가장 필요한 기술이라고 생각하는 것으로 조사됐다. 하지만 조직에서 에이전트 AI를 완전하게 구현한 조직은 15%에 불과하며, 73%의 직원들은 AI 에이전트가 자신의 업무에 어떤 영향을 미칠지 알지 못한다고 설문에 답변했다. 또한 75%가 AI 에이전트와 같은 디지털 인력으로 인해 조직에서 소프트 스킬에 대한 필요성이 높아질 것으로 예상했다. 세일즈포스의 CPO(Chief People Officer)인 나탈리 스카디노는 “모든 산업은 직무를 재설계하고, 재교육하고, 인재를 재배치해야 하며, 모든 직원은 디지털 노동 혁명에서 성공하기 위해 새로운 인적, 상담원 및 비즈니스 기술을 배워야 한다”라고 전헀다[email protected] source