# C-Suite Published articles for C-Suite. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to deliver real data sovereignty DevFeed: [How to deliver real data sovereignty]() Original publisher: [Read original article]() Author: Mary Branscombe Published: 2026-11-09T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [digital sovereignty](), [Cloud](), [Confidential Computing](), [Shared Responsibility Model](), [Data Management]() Tags: [aws](), [c-suite](), [c-suite-careers-cio-data-center-data-center-management-data-governance-data-integration-data](), [careers](), [cio](), [cloud](), [cloud-regions](), [confidential-computing](), [control](), [costs](), [customer-data](), [data](), [data-center](), [data-center-management](), [data-governance](), [data-integration](), [data-management](), [data-privacy](), [data-residency](), [data-sovereignty](), [dependencies](), [edition-us](), [encryption](), [encryption-keys](), [eu](), [europe](), [google](), [it-leadership](), [it-management](), [it-strategy](), [language-en](), [microsoft](), [operational](), [oracle](), [privacy](), [security](), [sovereignty](), [storytype-feature]() ### AI overview Data sovereignty requires control over access and encryption keys, not just EU data residency. Cloud providers offer regional storage, audited support workflows, customer-approved access, and confidential computing, but a US parent company may still face legal demands; customer-managed keys and access controls can reduce provider control while adding staffing and operational costs. Private or disconnected infrastructure offers more control but demands greater ongoing security and maintenance resources. Strict staff-location rules can sharply raise costs and constrain connectivity, collaboration, and resilience. The article recommends setting controls according to each workload's risks and requirements, classifying data and mapping where it flows, then balancing sovereignty with operational needs rather than applying maximum isolation everywhere. ### Source excerpt Changing where your data is stored isn't enough to deliver sovereignty. You also need to implement the technologies that enforce real control. Local providers are keen to promise alternatives but US ones are making the effort to give customers more options without losing access to the depth of services they offer. Actually turning the promise of sovereign cloud regions into reality means dealing with encryption keys, access workflows and confidential computing, which also means more costs, dependencies, and operational responsibilities. The obvious concern with using US cloud services is the CLOUD Act, which allows the US government to request access to customer data from a US-owned service, even if it's held in the EU. Those requests are supposed to go through the criminal court system. "When it lands in Europe it headbutts with GPDR and you're involved in legal wrangling as to whether or not that data can be released or not," IDC research director Rahiel Nasir says. He views the US Foreign Intelligence Surveillance Act (FISA) as more problematic because it doesn't go through the normal criminal courts. "That's an executive order," he adds. "They'll just take whatever they need." The increasing number of sovereign public cloud regions promising data residency and local operations can't help with that. Controlling encryption keys Under its EU Data Boundary, Microsoft stores and processes EU customer data and even support logs, at least for core cloud services, inside the EU. AWS has similar commitments for its European Sovereign Cloud, claiming no critical dependencies on infrastructure outside the EU. And like Salesforce, it employs EU-based support staff for its EU operating zone. Google and Oracle have similar dedicated sovereign regions in Europe, and some of the providers plan to offer similar models in other jurisdictions. For admin and support, for example, most tasks are performed through automated, audited workflows, and Microsoft personnel don't have stand ## Smartsheet's new AI tool tracks corporate strategies, suggests fixes DevFeed: [Smartsheet's new AI tool tracks corporate strategies, suggests fixes]() Original publisher: [Read original article]() Author: Matthew Finnegan Published: 2026-11-05T18:55:47Z Content type: news Language: en Sources: [Computerworld]() Topics: [Artificial Intelligence](), [AI Integration Strategies](), [task management](), [Multi Agent Systems](), [Tool]() Tags: [agents](), [ai](), [ai-tool](), [application](), [applications](), [artificial-intelligence](), [artificial-intelligence-it-strategy-productivity-software](), [budget](), [c-suite](), [capacity-planning](), [data-quality](), [edition-us](), [it-strategy](), [language-en](), [modelling](), [monitor](), [problems](), [productivity](), [productivity-software](), [progress](), [project](), [projects](), [reporting](), [smartsheet](), [storytype-newsanalysis](), [strategies]() ### AI overview Smartsheet's standalone vsActual uses AI agents and an Execution Graph linking tasks, projects, and strategic goals to help executives track strategy against operational results. It draws on connected business systems and work tools, including Slack conversations: intake agents build plans from existing documents, planning agents model capacity and scenarios, and drift agents flag budget overruns, delays, or resource conflicts and can recommend actions. Agents can update third-party systems when permitted, but human approval is required by default. The article cautions that output quality depends on operational and financial data and says organizations should establish guardrails, testing, and ways for people to intervene before allowing autonomous work. vsActual is in early access, with general availability planned for early 2027; pricing will be usage-based, with details undisclosed. ### Source excerpt Work management software vendor Smartsheet has launched a new tool to help executives oversee company-wide strategies, with AI agents that monitor progress, identify problems, and recommend fixes. vsActual, announced Wednesday at Smartsheet's Engage event, is a standalone "AI-native" application, separate from the company's core work management software. It pulls data from a variety of business applications, including Oracle, Salesforce, SAP, and Workday, as well as work management tools such as Smartsheet itself. vsActual uses AI agents that carry out tasks on behalf of users, either in response to natural language prompts or running automatically in the background. An intake agent uses existing documents, such as presentation decks and spreadsheets, to help create a "plan of record" that includes goals, timelines, and budget and capacity limits. A planning agent helps with capacity planning and scenario modelling, and can be invoked when a plan needs to change. A drift agent can continuously look for data that highlights problems such as budget overruns, schedule delays, and resource conflicts. To do this, vsActual analyzes information from a range of connected applications, including conversations in Slack. vsActual agents can also recommend corrective actions, such as allocating additional staff to a project to address a predicted delay. Smartsheet Other agents include a data quality agent and a reporting agent that keeps users up to date on any changes across projects. "vsActual is aimed at the C-Suite, specifically the CFO and COO, who are often battling the issue of budget creep and timeline drift," said Liz Miller, vice president and principal analyst at Constellation Research. Tracking the gap between strategies and actual outcomes -- a concept that Smartsheet describes as "execution drift" -- is a common challenge for executive teams, said Miller, and is typically only revealed in PowerPoint slides during quarterly planning sessions, or via emailed Excel spr ## The open compute revolution: Why the C-suite is redefining the modern data center DevFeed: [The open compute revolution: Why the C-suite is redefining the modern data center]() Original publisher: [Read original article]() Author: HPE and AMD Published: 2026-11-04T21:15:03Z Content type: article Language: en Sources: [CIO]() Topics: [High-Performance Computing](), [clean energy data centers]() Tags: [ai](), [amd](), [analytics](), [architectures](), [artificial-barriers](), [business](), [c-suite](), [collaboration](), [community](), [components](), [compute](), [corporate-sustainability](), [cost](), [cycles](), [data](), [data-analytics](), [data-center](), [decoupling](), [design](), [edition-us](), [language-en](), [open](), [storytype-brandpost]() ### AI overview The article presents Open Compute as a way for enterprises to reduce dependence on proprietary data center systems while improving agility, costs and energy efficiency. It says open hardware standards allow modular upgrades and component choice; HPE adds enterprise support and life cycle management. Standardized parts and component-level upgrades may lower maintenance and refresh costs, while AMD EPYC processors are described as enabling workload consolidation and lower licensing costs. Rack-level power distribution and airflow design, paired with efficient silicon, are presented as ways to improve energy use. Suggested first steps are auditing upcoming refreshes, benchmarking alternatives for total cost of ownership, prioritizing workload density and tracking efficiency gains against sustainability goals. These are the article's claims; the text provides no quantified results. ### Source excerpt For years, the enterprise data center operated under a predictable, if restrictive, playbook. Siloed, proprietary hardware stacks dictated infrastructure life cycles, leaving CIOs locked into vendor ecosystems. Every hardware refresh meant navigating premium pricing, constrained supply chains, and artificial limitations on interoperability. Today, that playbook is being rewritten. As AI, massive data analytics, and intense computational demands reshape corporate strategies, C-level executives are turning to Open Compute infrastructure. Originally pioneered by hyperscalers to manage massive scale efficiently, open hardware design has graduated to the enterprise. By decoupling software from proprietary hardware and embracing collaborative, community-driven standards, open compute is no longer just a technical preference--it is a strategic business imperative. For the C-suite, transitioning to an open infrastructure model solves three critical pain points: accelerating technological agility, optimizing total cost of ownership (TCO), and meeting aggressive corporate sustainability targets. Here are the three primary reasons why open compute infrastructure has become vital for IT strategy, and how leaders like HPE and AMD are making it more accessible for the enterprise. 1. Shattering vendor lock-in to accelerate enterprise agility In a rapidly shifting market, operational agility is a primary competitive advantage. Traditional, proprietary data center architectures restrict a company's ability to pivot. If a specific vendor faces supply chain disruptions or falls behind in processing power, the enterprise suffers. Open Compute infrastructure dismantles these artificial barriers. By adhering to open-source hardware specifications, enterprises gain the freedom to mix, match, and upgrade components without replacing their entire infrastructure. This modularity means that hardware can keep pace with software innovations. This is where the collaboration between hardware pione ## How CIOs can retain control as AI writes more code DevFeed: [How CIOs can retain control as AI writes more code]() Original publisher: [Read original article]() Author: Mark Samuels Published: 2026-11-04T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [AI-assisted coding](), [agentic-coding](), [qualitygates](), [developer-productivity](), [Learning]() Tags: [agents](), [ai](), [ai-code](), [ai-coding](), [ai-coding-tools](), [ai-powered-coding](), [artificial-intelligence](), [artificial-intelligence-c-suite-careers-cio-code-editors-developer-development-tools-emerging](), [c-suite](), [careers](), [cio](), [cios](), [code](), [code-editors](), [control](), [developer](), [development-process](), [development-tools](), [edition-us](), [emerging-technology](), [guidelines](), [it-leadership](), [it-management](), [it-strategy](), [language-en](), [policies](), [process](), [productivity](), [research](), [roles](), [software-development](), [state-of-code-developer-survey](), [storytype-feature](), [survey]() ### AI overview AI coding is boosting output, but trust and review gaps make governance and engineering foundations essential. Sonar's survey of more than 1,100 developers found 72% of users of AI coding tools use them daily; AI accounts for 42% of committed code, while 96% do not fully trust it and only 48% always verify it. The article's examples show different controls: Ordnance Survey's developer checks an agent's work in detail; Chase emphasizes automated engineering pipelines and quality and security gates; TF1 documents agent processes and defines when staff may use or release agents; WalkMe guides non-developers and tests their applications for security risks. Leaders also describe automation as a way to scale review without adding excessive manual work, alongside training, clear responsibilities, and investment in experienced engineers and security skills. The excerpt presents these as ways to pursue productivity while controlling quality and risk. ### Source excerpt AI is writing more code than ever. Software specialist Sonar's 2026 State of Code Developer Survey, based on more than 1,100 professional developers, found that 72% of those who've tried AI coding tools now use them every day, and that today, AI now accounts for 42% of committed code, a number that's expected to reach 65% by next year. But while developers embrace AI-enabled coding services, many professionals are uncertain about how their businesses will cope with this influx of AI-generated code. Sonar's research additionally discovered that 96% of developers don't fully trust it, and only 48% always verify it before committing. In fact, 38% of developers told the researchers that reviewing AI code requires more effort than analyzing code written by human colleagues. That's a pattern recognized by Tim Chilton, a managing geospatial consultant at UK mapping agency Ordnance Survey (OS). "There's truth in the headlines that AI can get carried away, and developers spend more time fixing the code that's been created than not," he says. While that's not a pattern he sees at the OS, Chilton says his organization is cautious about how developers use AI coding tools, and conversations with other digital leaders suggest a similar approach. AI-enabled coding is indeed almost ubiquitous, and it can allow developers to reach new, higher levels of productivity, but more doesn't necessarily mean better code. To ensure that such coding services produce business benefits, CIOs must take control of the development process. They should exploit agents carefully, set professional responsibilities, and establish guidelines, policies, and organization-wide training. Exploiting the agents Snowflake co-founder and president Benoît Dageville suggests there are two ways to view AI-powered coding. One is the scary way, where AI takes over your tasks without human input. The second is the efficient way, where AI helps you do the same things as before, but at a fraction of the cost. He's a big ## Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI Officer DevFeed: [Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI Officer]() Original publisher: [Read original article]() Author: Marc Schmitt Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [AI Strategy](), [ai-governance](), [Artificial Intelligence]() Tags: [ai](), [ai-governance](), [artificial-intelligence](), [c-suite](), [cs-ai](), [cs-cy](), [cs-lg](), [econ-gn](), [executive-leadership](), [governance](), [integration](), [organization](), [q-fin-ec](), [roles]() ### AI overview The paper explains why organizations choose different structures for integrating AI into executive leadership. It identifies three features of AI systems--distributed accountability for judgment, upstream governance, and non-stationarity--and proposes three responses: concentrated extension of an existing mandate, distributed extension across roles, or creation of a dedicated Chief AI Officer (CAIO). Four propositions address when a CAIO emerges, which configuration an organization adopts, when that role is effective, and how configurations change. An archival census finds that executive AI leadership has spread sharply since 2023; role creation and mandate extension are advancing at nearly equal rates, adoption is concentrated where digital leadership structures already exist, and executive tenures challenge claims that the CAIO is a transient role. ### Source excerpt arXiv:2407.10247v4 Announce Type: replace-cross Abstract: The integration of Artificial Intelligence (AI) into corporate strategy has become critical for organizations seeking to maintain competitive advantage in the digital age. Although organizations increasingly rely on AI as a strategic and organizational resource, existing C-suite roles remain only partially equipped to govern, integrate, and leverage it coherently at the enterprise level. Organizations vary in their responses. Some create a dedicated Chief AI Officer (CAIO), others extend existing mandates into hybrid roles, and still others coordinate AI through federated structures. This paper develops a role-design theory to explain this variation. I identify three properties that distinguish AI systems from earlier cross-cutting enterprise technologies, distributed accountability for judgment, upstream governance, and non-stationarity, and three configurations through which organizations respond: concentrated extension, distributed extension, and role creation. Four propositions specify when a dedicated CAIO emerges, what form an organization's response takes, when the role is effective, and how configurations evolve. Drawing on the first archival census of executive AI leadership, I find that the role has diffused sharply since 2023, that role creation and mandate extension are advancing in near parity, that adoption concentrates where digital leadership structures already exist, and that executive tenures contradict practitioner accounts of a transient role. The paper contributes to research on executive leadership, AI governance, and the design of AI-driven organizations. ## The moat is dry - Part 2: A roadmap for rebuilding DevFeed: [The moat is dry - Part 2: A roadmap for rebuilding]() Original publisher: [Read original article]() Author: Steven Goodman Published: 2026-10-29T09:05:00Z Content type: opinion Language: en Sources: [CIO]() Topics: [ai-governance](), [trust](), [Zero Trust]() Tags: [2017](), [agents](), [ai](), [ai-risk](), [attacks](), [board](), [breach](), [c-suite](), [c-suite-careers-cio-cso-and-ciso-network-security-roles-security](), [careers](), [cio](), [contributor](), [cso-and-ciso](), [cyber-risk](), [edition-us](), [governance](), [language-en](), [network-security](), [roadmap](), [security](), [storytype-opinion](), [technology](), [trust]() ### AI overview The article argues that AI-enabled attacks exploit trust and that CISOs often lack the authority and budget influence to manage that risk. It recommends giving security independent reporting access to the CEO and board, assigning the CISO accountability for AI governance, and involving business, research, legal, finance, and technology leaders through a standing risk council. It also proposes board tests for a CISO's technical ability and judgment; accountable owners, least privilege, monitoring, and a kill switch for each AI agent identity; and reporting tested detection, containment, and recovery times. Technology teams should implement basics such as encryption, multifactor authentication, asset inventories, and network segmentation, with the CISO verifying progress and reporting gaps. The article frames board time as a budget and trade-off discussion, while presenting CIO-CISO independence and collaboration as complementary. ### Source excerpt In Part 1, I argued that the moat is dry. When NotPetya hit Merck in 2017, it didn't climb a wall. It came in through trusted vendor software, the routine traffic every perimeter is built to admit, and Merck put the damage at more than $1.4 billion. Its insurers then tried not to pay, and after that fight, underwriters started asking much harder questions. AI has since made attacks like that cheaper and faster. In IBM's 2026 Cost of a Data Breach report, one in four malicious breaches was AI-enabled, and breaches that took more than 200 days to find and contain cost about $1.3 million more than those that didn't. Deepfaked executives, software that can be talked into things and AI agents holding credentials are attacks on trust first and technology second. Yet the people we hold responsible are set up to lose. Some 64% of CISOs still report into IT leadership, only 10% influence financial decisions, and only a quarter say board discussions of cyber risk run longer than 30 minutes. They are accountable for the breach without budget authority, and they check the work of the people they report to. The organizations that feel safest about the next two years are not the ones with the most tools. They are the ones where leadership understands AI risk, someone clearly owns it, and the security team has the authority to act. That is a governance problem, and it has a governance fix. A roadmap for reconstruction 1. Take security out of IT, and do it before the breach No company lets the finance department audit its own books. That is why internal audit reports to the board's audit committee rather than to the CFO. Security deserves the same independence for the same reason: the people who build and run the systems should not vouch for them. The CISO's job is not to run technology. It is to govern how people, partners and machines behave around the things the organization cannot afford to lose, and to tell leadership the truth about that risk. That does not make the job less ## The moat is dry - Part 1: Why the old security model failed DevFeed: [The moat is dry - Part 1: Why the old security model failed]() Original publisher: [Read original article]() Author: Steven Goodman Published: 2026-10-29T09:00:00Z Content type: opinion Language: en Sources: [CIO]() Topics: [Security](), [ai security](), [prompt injection](), [ai-governance](), [shadow AI](), [trust]() Tags: [accounting-software](), [ai](), [backdoor](), [c-suite](), [c-suite-careers-cio-cso-and-ciso-network-security-roles-security](), [careers](), [cio](), [contributor](), [cso-and-ciso](), [data-breach](), [deepfake](), [edition-us](), [ibm](), [impersonation](), [it](), [language-en](), [malware](), [network-security](), [risk](), [roles](), [scam](), [security](), [storytype-opinion]() ### AI overview The article argues that AI has made cybersecurity a business risk centered on trust and governance, while many organizations still rely on perimeter defenses and leave CISOs accountable without budget authority. It cites IBM's 2026 breach report: one in four malicious breaches was AI-enabled, averaging $6 million; shadow AI appeared in 43% of breached organizations, and AI-system breaches often entered through compromised APIs, plug-ins or misconfigured cloud workloads. Its examples include NotPetya's entry through trusted vendor software and ransomware spreading from Johnson Controls' Asia offices. For research settings, it recommends pre-approved sandboxes and tools, monitoring data movement, classifying research by value, and targeted controls modeled on Science DMZs. It says fixed requirements such as export controls and federal research-security rules still apply. The supplied text frames these findings as a diagnosis and previews a separate eight-step roadmap in Part 2. ### Source excerpt In June 2017, a piece of malware called NotPetya got into Merck & Co. without climbing a single wall. It arrived through a backdoor in accounting software supplied by a Ukrainian vendor, the kind of trusted, routine traffic every perimeter is built to let through. It spread to some 40,000 of the drugmaker's computers, and Merck put the damage at more than $1.4 billion. Then several of Merck's insurers argued they owed nothing, because the attack was attributed to Russian military intelligence and their policies excluded acts of war. The fight ended in a confidential settlement in January 2024. Two lessons sit in that story. The moat didn't matter, because the threat came in with the traffic the moat was designed to admit. And the people who price risk for a living were the first to notice and the first to send the bill. That was 2017, when attackers still needed humans at the keyboard. Increasingly, they don't. Here is the argument of this two-part series in two sentences. Security stopped being a technology problem with business consequences; it is now a business risk with a technology component. Most organizations are still organized the other way around, and the person they hold responsible for that risk sits inside the IT department, reporting to the people whose work they are supposed to check. What AI actually changed IBM's 2026 Cost of a Data Breach Report, based on 602 breached organizations, found that one in four malicious breaches was AI-enabled, a 56% jump in a single year. Those breaches averaged $6 million, about $1 million above the global average of $4.99 million. In the U.S., the average breach now costs $11.5 million. You don't need a computer science degree to understand what changed. Three things matter: The con got a better voice. Deepfake impersonation was the most common AI-driven attack in IBM's data, at 45%. The oldest scam in business, the urgent wire transfer, now arrives in the CFO's actual voice on a video call. Your software can be talk ## What happens when vendors take the lead on agentic AI DevFeed: [What happens when vendors take the lead on agentic AI]() Original publisher: [Read original article]() Author: Maria Korolov Published: 2026-10-28T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [Agentic AI](), [AI for customer service automation](), [Artificial Intelligence](), [human review]() Tags: [agentic](), [agentic-ai](), [ai-stack](), [allows](), [apis](), [artificial-intelligence](), [artificial-intelligence-budgeting-c-suite-careers-cio-data-management-data-quality-emerging-t](), [budgeting](), [building](), [c-suite](), [careers](), [cio](), [connect](), [contact-center](), [customers](), [data-management](), [data-quality](), [edition-us](), [emerging-technology](), [forrester](), [hotel](), [it-leadership](), [it-management](), [it-strategy](), [language-en](), [like](), [money](), [restaurants](), [revenue](), [roi](), [storytype-feature](), [tasks](), [user-experience](), [vendor-management](), [vendors-and-providers](), [virtual](), [waiting]() ### AI overview The examples show vendors can provide ready-made agentic AI for focused tasks, while deployment still requires governance, cost control and employee adoption. Fertitta Entertainment used PolyAI call agents that connect to APIs to handle reservations; it reports abandoned calls fell from 40-50% to under 10%, attrition dropped 50%, and automated reservations generate 3,000 bookings and $600,000 monthly. Fogo de Chão reports all calls are now answered and bookings rose 10%; it also uses agents for repetitive internal work with human review. AltaClaro combines Verbit's agent platform with Credal for model flexibility and sensitive-data protection. The article cautions that agents can expose information through existing sharing permissions, production token costs can rise, and training alone may not change habits. It recommends readiness around information governance, workflow integration, ongoing adoption support and flexibility as tools and pricing change. ### Source excerpt There's no point building your own agentic AI stack for a non-core function when a vendor already has a product ready to go that meets your needs. For non-tech companies in particular, building from scratch can take time, money, and expertise they don't have. Take for example, Fertitta Entertainment, with more than 600 Landry's restaurants and the Golden Nugget hotel and casino. After the pandemic, the company had problems adequately staffing its call centers, says its director of contact center operations Brian Jeppesen. "The agents didn't want to come back," he says. The virtual assistants didn't work because the user experience was bad, and with the incoming call volume, 40 to 50% of them were abandoned. "Everybody wants AI, but nobody wants BS," Jeppesen says. "I talked to my CFO. This is what we're abandoning. This is the potential revenue we're losing. I can't throw bodies at it anymore." Agentic AI has upped their game for the virtual assistants. Instead of only answering simple questions like check-in hours, it allows the company to connect APIs and carry out more complicated tasks, like making reservations. "My abandonments fell to under 10%," says Jeppesen. "From waiting 45 minutes to an hour, they can get their questions immediately answered, or immediately transferred and taken care of." And turnover went down. Call centers typically see 100% turnover, says Jeppesen. Not because everyone quits, but because the bottom 25% quit and need to be replaced over and over again. Now that bottom tier doesn't have to be rehired, while the people who stay are the most knowledgeable. "They give a better experience for my customers," he says. "And it reduced my attrition by 50%." Forrester did a total economic impact study for the specific vendor that Fertitta used, PolyAI, and the ROI came back at 391% over the course of three years, with a payback period of less than six months. Jeppesen says he reviewed his own numbers and found that the automated reservations syst ## In good company: 10 CIOs every CIO should follow DevFeed: [In good company: 10 CIOs every CIO should follow]() Original publisher: [Read original article]() Author: Linda Rosencrance Published: 2026-10-26T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [Artificial Intelligence](), [responsible-ai](), [it modernization](), [human review](), [agentic workflows](), [AI for customer service automation]() Tags: [ai](), [ai-adoption](), [ai-governance](), [ai-strategy](), [c-suite](), [c-suite-careers-cio-industry-it-leadership-it-strategy-markets-networking-technology-industr](), [careers](), [chatbot](), [cio](), [cios](), [cloud](), [cloud-modernization](), [cybersecurity](), [digital-transformation](), [edition-us](), [follow](), [governance](), [industry](), [it-leadership](), [it-strategy](), [language-en](), [linkedin](), [markets](), [networking](), [responsible-ai](), [scaling](), [services](), [storytype-feature](), [technology-industry]() ### AI overview The CIOs highlighted here connect technology leadership to business and public outcomes, emphasizing that AI adoption depends on governance, workforce readiness, validated processes, and measurable value. Examples include Denver's multilingual Sunny chatbot; Fletcher Previn's focus on leadership, learning time, and incentives that support adoption; and Kellie Romack's account of ServiceNow internal AI use cases growing from 15 to more than 200. Mike Blandina stresses human review of AI outputs, data lineage, permissions, and productivity measures. Sears Merritt describes capping AI vendor contracts at 12 months and defining pilot metrics before scaling, while Kimberly Basile says counting agents obscured results and that automating an unvalidated process was a mistake. Other featured leaders stress responsible AI in regulated insurance, linking technology investment to business value, and oversight as AI agents enter enterprise software. The source is an excerpt and ends during its list of ten CIOs. ### Source excerpt CIOs are dealing with a lot of change, from the rapid rise of AI and cloud modernization, to cybersecurity and workforce transformation. Many are sharing what they're learning as they tackle these challenges, including what's working, what isn't, and how they're implementing new technologies in their organizations. Here, a select group of tech leaders reveal the CIOs they follow and why. Suma Nallapati, chief AI and information officer, City and County of Denver I follow Suma Nallapati primarily through public technology and AI leadership discussions. She regularly discusses AI adoption, responsible AI, digital transformation, technology strategy, AI governance, human-centered technology, and using AI to improve public services. I find her perspective particularly valuable because she connects AI strategy with practical organizational and citizen outcomes. She also emphasizes responsible and human-centered AI rather than viewing AI simply as a technology implementation exercise. One initiative I found particularly interesting was Denver's use of AI through its multilingual Sunny chatbot and other city services. Her current role includes scaling AI across areas such as resident engagement, internal automation, permitting, and emergency response while maintaining governance, transparency, and public trust. - Sachin Gupta, senior IT manager, Arrow Electronics Jennifer Hartsock, chief information and digital officer, Cargill I follow Jennifer Hartsock through the CIO community, as well as through her thought leadership, interviews, and LinkedIn. I especially enjoy hearing her perspective in longer-form conversations where she talks candidly about the realities of leading technology and transformation in a large, complex organization. Hartsock talks about many of the issues CIOs are navigating right now, including AI, digital transformation, technology operating models, talent, and leadership. A consistent theme in her perspective is connecting tech decisions back to the ## Building a trust architecture for your new team DevFeed: [Building a trust architecture for your new team]() Original publisher: [Read original article]() Author: Martha Heller Published: 2026-10-21T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [engineering-leadership](), [trust]() Tags: [advice](), [approach](), [architecture](), [benefit](), [board](), [building-trust](), [button](), [c-suite](), [c-suite-careers-cio-employee-experience-enterprise-architecture-it-leadership-it-management-i](), [cadence](), [careers](), [challenge](), [cio](), [confidence](), [connect](), [conversations](), [customers](), [edition-us](), [employee-experience](), [enterprise-architecture](), [investment](), [it-leadership](), [it-management](), [it-strategy](), [language-en](), [roi](), [storytype-interview](), [team](), [technical](), [technical-debt](), [trust]() ### AI overview Cengage CIO Ken Grady argues that a new leader must earn trust through shared experience, regardless of résumé. His trust architecture uses three currencies: technical respect and useful expertise, investment judgment grounded in ROI or NPV, and attention to employees' human concerns. He builds these through steady conversations and monthly forums: an open Enterprise Architecture Review, a Steps-to-Green portfolio review focused on helping at-risk projects, and anonymous Q&A with senior leaders. The aim is a safe environment where staff can raise problems, challenge norms, and suggest improvements--including stopping a release over quality concerns--while the CIO remains responsible for leading. Grady advises adapting to what each person needs: learning and connection with the team, practical help for stakeholders, and precise confidence for the board. Trust takes time and requires empathy across all three relationships. ### Source excerpt CIOs bring a lot to their new jobs: decades of leadership, transformation, pattern-recognition, and outcomes. But none of that matters in your new job. During a time of generalized uncertainty, your team -- and every stakeholder -- needs to trust you more than anything. But how do you build that trust in a new environment, and as quickly as possible? For Cengage CIO Ken Grady, the best approach is an architectural one. What is a trust architecture, and why did you develop it? I've been a CIO for almost 20 years, and when I joined Cengage six months ago, while I had roughly 7,300 days of evidence that I knew how to do this job, not one person in that building had ever seen any of it. The proof existed on my résumé. The trust did not. On day one as a new CIO, you have no shared experience with your team, so you have no trust. Your team might have respect for your background, and hope you'll succeed, but personal trust is something you must build from scratch. So how do you do that when the currency of trust for your team can shift depending on the situation? Establishing trust with the people in your new business requires an intentional approach, an architecture. What are the different currencies of trust? The first is technical currency. My team needs to know that as a new CIO, I'll respect their technical expertise, have enough of my own to be helpful, and engage in healthy technology discussions. To solve for that, I sat down and led an open conversation early on about whatever was on their minds, and I showed them that their technical expertise is valued. The second is investment currency. Can I, a technology leader, speak to ROI or NPV? Can I explain why we should invest in this technology, or address this technical debt, or move from this version to that one? My team needs to respect my ability to guide our investments. The third is human currency, which addresses the questions people are afraid to ask. Why did we move our offices? Why are we incentivized this way ## Autonomy creep: how AI quietly gains more authority than CIOs intend DevFeed: [Autonomy creep: how AI quietly gains more authority than CIOs intend]() Original publisher: [Read original article]() Author: Pat Brans Published: 2026-10-21T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [Agentic AI](), [Artificial Intelligence](), [least privilege](), [Access Control](), [identity and access management](), [human review](), [Multi Agent Systems](), [agentic workflows]() Tags: [1password](), [access-control](), [access-control-artificial-intelligence-c-suite-careers-cio-data-governance-data-management-em](), [actions](), [agent](), [agents](), [ai](), [ai-agent](), [ai-systems](), [artificial-intelligence](), [automatically](), [autonomy](), [c-suite](), [careers](), [cio](), [cios](), [corporate](), [danger](), [data](), [data-governance](), [data-management](), [edition-us](), [emerging-technology](), [enterprises](), [identity](), [identity-and-access-management](), [identity-security](), [it](), [it-governance](), [it-leadership](), [it-management](), [it-strategy](), [language-en](), [lifecycle](), [nancy-wang](), [organizations](), [security](), [storytype-feature]() ### AI overview AI agents can quietly gain more authority as tasks expand, permissions accumulate, and human approvals fade. The excerpt describes the risk as especially easy to miss when systems perform reliably: NatWest says it reduces checks deliberately, with stakeholder and risk manager involvement and continued monitoring, while warning against approval becoming a rubber stamp. Suggested controls include defining each agent's task, permitted tools, success evidence, and prohibited actions, then using a separate verifier; placing enforceable limits in enterprise identity and access controls rather than relying only on model instructions; reassessing permissions as they change; and using task-based authorization with short-lived credentials instead of standing privileges. The excerpt also calls for named ownership and lifecycle responsibility, business accountability for process outcomes, and system-level visibility and traceability as agents interact. It notes that governance for multi-agent systems remains an emerging problem. ### Source excerpt Few CIOs are likely to suddenly decide to give an AI agent broad authority over corporate systems. The more likely path to autonomy is far less dramatic. An agent starts with a narrow task. It proves useful, so another system is connected. It then needs more data, so another permission is added. Then human approval becomes a bottleneck, so the organization removes it, and exceptions that once went to people start being handled automatically. Each decision may be sensible in isolation. Taken together, however, they can leave an agent with considerably more authority than anyone originally envisioned. That's the danger of autonomy creep. And as enterprises move from AI systems that recommend actions to agents that execute them, CIOs need to govern not only what those agents were originally designed to do, but what they have gradually become capable of doing. It starts with small permissions Jim Taylor, president and chief product and strategy officer at identity security company RSA, sees a familiar pattern. Organizations typically create an agent for a specific purpose and give it the permissions necessary to accomplish the task. Later, the job expands. "They just give it tasks, permissions; it just sits there and accumulates things," he says. "Over time, it develops potentially toxic combinations. It gets too much access and can see too much data. It can do too many things." width="1240" height="827" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Jim Taylor, RSA RSA Humans accumulate permissions, too, of course, but organizations have developed processes around employees joining, changing roles, and eventually leaving. Agents may not have the same natural lifecycle. "They never give permissions back," Taylor says. Nancy Wang, CTO of 1Password, argues that CIOs should treat each expansion in an agent's responsibilities as something more consequential than another feature. "Every time you expand the scope of the agent's effective authority, you're essentially adding ## 9 tips for future-proofing your IT leadership career DevFeed: [9 tips for future-proofing your IT leadership career]() Original publisher: [Read original article]() Author: Mary K. Pratt Published: 2026-10-19T10:01:00Z Content type: article Language: en Sources: [CIO]() Topics: [engineering-leadership](), [balancing technical and leadership roles](), [shadow AI](), [AI Integration Strategies]() Tags: [agents](), [ai](), [c-suite](), [career](), [careers](), [careers-cio-it-leadership](), [cio](), [edition-us](), [influence](), [it-leadership](), [language-en](), [leadership](), [mit-sloan](), [need](), [skills](), [storytype-feature](), [teams](), [work]() ### AI overview The article outlines nine strategies for future-proofing CIO careers, including developing a transformational mindset, connecting IT decisions to business value and risk, staying technically informed, guiding teams through change, preparing for work involving AI agents, and strengthening leadership skills. ### Source excerpt To succeed today, CIOs must be strategic partners. It's no longer something to work toward achieving. "What used to be an aspiration, having a seat at the table, that's now needed," says George Westerman, a senior lecturer at the MIT Sloan School of Management, founder of the Global Opportunity Forum, and co-chair of the MIT Sloan CIO Leadership Award. "AI has made it more important for the CIO to be able to exercise influence and give advice, because now you have a major technology that every C-suite person is wondering about and feels they need to use, and, given its transformative nature throughout the organization, this is where the skills of a CIO who is a strategic partner can be really useful." That, though, may not be enough for CIOs in the future. The CIOs of tomorrow will be crafting the vision of what is possible for the organization, advising their C-suite colleagues on integrating agents into their teams, and navigating high-pressure situations, says Westerman and other IT leaders. That will require even more of the CIO. To deliver on such expectations and ensure career longevity, CIOs and executive advisers cite these nine strategies for future-proofing IT leaders' careers. 1. Cultivate a transformational mindset CIOs going forward must have "the willingness to look at the situation and think about what's better," Westerman says, explaining that this goes beyond improving how existing processes operate to remaking and transforming the organization into something new. To achieve that, CIOs of the future will need to be visionary and adaptable, with the decisiveness to pivot quickly and the ability to "convince other people to come with you," Westerman says. IT executives who have or who cultivate those traits now will be "ready for whatever tomorrow brings," he adds He and others note that some CIOs are already demonstrating these capabilities, for example, by developing a deep understanding of AI so they reimagine what's possible for their organization ## True data sovereignty isn't where it's kept, but who has access to it DevFeed: [True data sovereignty isn't where it's kept, but who has access to it]() Original publisher: [Read original article]() Author: Mary Branscombe Published: 2026-10-19T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [digital sovereignty](), [Access Control](), [Cloudflare Access](), [Advantages and Disadvantages of Cloud Service Models](), [Disaster Recovery](), [Sovereign AI]() Tags: [access-control](), [access-control-c-suite-careers-cio-compliance-data-management-data-quality-gdpr-government](), [access-management](), [ai-models](), [c-suite](), [careers](), [cio](), [cloud](), [compliance](), [data-centers](), [data-management](), [data-quality](), [data-residency](), [data-sovereignty](), [edition-us](), [eu](), [gdpr](), [governance](), [government](), [hyperscalers](), [identity-and-access-management](), [infrastructure](), [it-operations](), [language-en](), [laws-and-regulations](), [markets](), [providers](), [regulated-industries](), [security](), [sovereign-cloud](), [storytype-feature]() ### AI overview The article argues that data sovereignty depends on who can access data and under what legal authority, not just where it is stored. It describes the role of identity and access controls, audit trails, data handling rules, and recovery capabilities, while noting that organizations often combine local and global cloud providers. ### Source excerpt Regulated industries have been dealing with the equivalent of sovereign data for a long time. And as the geopolitical situation makes it more broadly relevant, it doesn't make it any easier to deal with. From concerns about the reach of the US CLOUD Act and what telemetry or sensitive business data gen AI models might collect, to implications of the US government taking part ownership of a range of technology companies and the impact of EU regulations, the state of the world means more organizations need to think about where data is located. The result is a flood of announcements from hyperscalers about new EU cloud regions, as well as pitches from regional and private cloud providers. The promised sovereignty may be anything from a local availability zone to more sophisticated approaches to ownership structure, compliance frameworks, where logs are stored, or where support staff work. Actual data sovereignty involves legal, technical, contractual, governance, and infrastructure questions, but all too often, what's on offer is what IDC research director Rahiel Nasir calls sovereignty washing. "When you look under the lid, all they're really offering is a solution for data residency and data localization, keeping data in country," he says. "That's part of sovereignty, but not all of sovereignty." In his experience, in-country data centers are the first thing organizations ask for to support data localization and data residency, but they also require enhanced controls over identity and access management. That's critical because data sovereignty is as much about who has access to the data and why, as where it lives. "It's really important the only people who touch my data in what is supposedly a sovereign cloud environment are those I give permission to," he adds. "Because it's my data, and I'm the only one who can give the authority to grant access to it. That also applies to support and admin staff." Companies want control through where they store data but also to re ## New US CIO appointments, October 2026 DevFeed: [New US CIO appointments, October 2026]() Original publisher: [Read original article]() Author: CIO Published: 2026-10-14T10:00:00Z Content type: news Language: en Sources: [CIO]() Topics: [engineering-leadership]() Tags: [2026](), [c-suite](), [c-suite-careers-cio-industry-it-leadership](), [careers](), [cio](), [edition-us](), [industry](), [it-leadership](), [language-en](), [mit](), [october](), [october-2026](), [storytype-feature](), [tech](), [ucla](), [us](), [warner-bros](), [workplace-technology]() ### AI overview This monthly roundup names CIO appointments at organizations including Centene, Mars, BD, Trader Joe's, and Laserfiche. It highlights executives' previous roles and technology responsibilities across areas such as application development, infrastructure, cybersecurity, data, AI, and digital transformation. The page also lists CIO appointments from prior months. ### Source excerpt Movers & Shakers is where you can keep up with new CIO appointments and gain valuable insight into the job market and CIO hiring trends. As every company becomes a technology company, CEOs and corporate boards are seeking multi-dimensional CIOs and IT leaders with superior skills in technology, communications, business strategy, and digital innovation. The role is more challenging than ever before -- but even more exciting and rewarding! If you have CIO job news to share, please contact me here! Bradley Bolivar joins Centene as CIO width="1240" height="828" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Bradley Bolivar, Centene Centene Centene Corporation is a healthcare enterprise committed to helping people live healthier lives. It provides access to affordable, quality-focused healthcare, innovative programs, and health solutions. Bolivar was most recently SVP and CIO for Fannie Mae, where he led enterprise technology strategy across application development, infrastructure, cybersecurity, data, AI, and workplace technology. Previously, he was SVP of enterprise engineering and architecture at Warner Bros. Bolivar holds a BS from MIT and an MBA from UCLA Anderson School of Management. Mars welcomes Varun Kakaria as CDIO width="1240" height="829" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Varun Kakaria, Mars Mars Inc. Mars, founded in 1911, is a global food production company specializing in confectionery, pet care, and food products. Kakaria joins Mars from Reckitt, where he served as CIO for North America, following his role as global head of commercial technology, where he led tech and digital roadmap initiatives across sales, revenue growth management, pharmacy, and medical functions. Earlier, he served as CIO - South Asia at Reckitt and had leadership positions at Philips Lighting, PepsiCo, and PwC. BD welcomes Arthur Hu as CIDO width="1240" height="828" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Arthur Hu, BD BD BD is a global medical technology c ## How to move fast with AI and still maintain customer trust DevFeed: [How to move fast with AI and still maintain customer trust]() Original publisher: [Read original article]() Author: Linda Rosencrance Published: 2026-10-14T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [AI Chat](), [human review](), [observability]() Tags: [ai](), [ai-responses](), [ai-systems](), [artificial-intelligence](), [artificial-intelligence-c-suite-careers-cio-digital-transformation-emerging-technology-it-lead](), [c-suite](), [careers](), [cio](), [customer-trust](), [digital-transformation](), [edition-us](), [emerging-technology](), [it-leadership](), [it-management](), [it-strategy](), [language-en](), [risk-management](), [security](), [storytype-feature](), [trust]() ### AI overview The article explains how companies can deploy customer-facing AI while protecting customer trust. It discusses testing with small groups, human review, observability, rollback and shutdown controls, data permissions, ongoing monitoring, and clear accountability for AI failures. It also notes the operational, legal, and reputational costs of inaccurate AI responses. ### Source excerpt Companies are moving fast to put AI into chatbots, virtual assistants, and other customer service tools. But for CIOs, that comes with a risk: if the technology gives customers the wrong information, makes biased recommendations, or mishandles data, it can immediately damage the trust companies have worked hard to build. Unlike conventional software, gen AI doesn't always give the same answer to the same question. An answer can sound confident and convincing while still wrong. And when customers rely on AI for information about their finances, insurance, or health, a mistake can have much greater consequences. Sumit Johar, CIO at cloud-based software company BlackLine, understands that inaccurate AI responses hurt the customer experience, force companies to spend money and resources to fix problems, damage the brand, and create regulatory risks. AI systems can also expose confidential information, making it important for companies to understand the data sources behind their responses. "An AI failure creates a massive trust issue around the core solution being provided," Johar says. "In the financial sector, bad AI advice or outputs can compromise balance sheet accuracy and lead to severe legal and personal liabilities." For Citizen Health CTO Daniel Wang, for instance, the stakes are particularly high because the company's users include rare disease patients and caregivers. Citizen Health works to ensure that important information provided by its AI can be traced back to the original source, such as patient records, clinical information, or medical studies. The company also reminds patients to talk with their healthcare providers before making medical decisions. Wang says one of his biggest concerns isn't just that AI can make a mistake, but that a wrong answer can sound right. "An AI system can present an answer that sounds caring, informative, and authoritative, even when the underlying information is inaccurate," he says. That risk has affected how Citizen Health ## Spend vs. debt: what's the bigger security risk? DevFeed: [Spend vs. debt: what's the bigger security risk?]() Original publisher: [Read original article]() Author: Carl Friedmann Published: 2026-10-14T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [Security](), [ai security](), [Tech Debt](), [Security, Identity, & Compliance]() Tags: [ai](), [artificial-intelligence](), [artificial-intelligence-c-suite-careers-cso-and-ciso-emerging-technology-it-leadership-it-mana](), [c-suite](), [careers](), [cso-and-ciso](), [edition-us](), [emerging-technology](), [it-leadership](), [it-management](), [it-strategy](), [language-en](), [risk](), [security](), [storytype-interview](), [tool-sprawl]() ### AI overview Security leader Selim Aissi discusses how organizations can turn security spending into operational risk through tool sprawl, complexity, and insufficient automation. He argues for measuring resilience through tested recovery, effective controls, and data protection, and recommends explaining cyber risk to boards in terms of business impact. The discussion also considers security risks from AI and cloud expansion. ### Source excerpt With a career built at companies like Visa, Ellie May, and Blackhawk, Selim Aissi, a 2026 CSO hall of fame inductee, understands how boards and executives demand a clear justification for cyber security investment, especially considering today's difficult economic climate.But getting security leaders to communicate tech debt, operational fragility, and long-term cyber risk in business terms the board can actually act on isn't always straightforward. A lot of organizations add tools, and more platforms and controls in response to new threats. Yet that doesn't necessarily address underlying complexities. Security investment can actually increase operational and strategic risk. "I think there's a tipping point once a reasonable depth of defense is in place," he says. "When there's a reasonable level of automation of security controls and cross integration of security tools, anything beyond that could potentially be overspend and could potentially add risk to the entire organization." So the challenge is to know when an environment exists and functions without the need for a sudden budget spike. "All of the KRIs and KPIs in place should be able to indicate the level of resiliency," he adds, "in addition to other controls such as making sure there are processes that would allow for detection of, let's say, API abuse, cloud-level abuse, and, increasingly these days, AI as it adds yet another threat vector." Understanding risk also lies in recognizing the distinction between organizations that are genuinely resilient, and those that appear mature on paper, focused on fine-tuning policies, compliance, governance, and frameworks. "These are all important but they shouldn't be the main focus," he says. "The first step is knowing where the data is and once in transit, how it needs to be protected with the most updated protocols." Aissi expands on how organizations unintentionally create risk through tool sprawl and operational complexity, how boards should think about long-ter ## AI governance's real gap is accountability, not technology DevFeed: [AI governance's real gap is accountability, not technology]() Original publisher: [Read original article]() Author: Robert L. Mitchell Published: 2026-10-07T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [ai-governance](), [responsible-ai](), [AI Integration Strategies](), [human review]() Tags: [ai](), [ai-assisted](), [ai-governance](), [artificial-intelligence](), [artificial-intelligence-c-suite-careers-cio-emerging-technology-it-governance-it-leadership-i](), [c-suite](), [careers](), [cio](), [deploy](), [emerging-technology](), [governance](), [it-governance](), [it-leadership](), [it-management](), [it-strategy](), [risk-management](), [security]() ### AI overview The article argues that enterprise AI governance depends more on clear accountability, ownership, and redesigned workflows than on tools alone. It describes how autonomous, multi-step AI systems raise challenges for monitoring, human oversight, and tracing errors, and offers examples of organizations assigning responsibilities and building controls into operations. The piece also discusses governance gaps in AI management platforms and argues that governance designed alongside adoption can support responsible scaling. ### Source excerpt Most enterprises govern AI the way they would any other IT rollout: deploy dashboards, institute policies, and conduct periodic reviews. Having the right AI governance tools in place and addressing gaps is important, but IT executives should be more concerned about rethinking workflows and establishing ownership. "Accountability usually breaks down first," says Beena Ammanath, executive director of the Global Deloitte AI Institute. "Teams deploy AI faster than their organizations can define who owns risk, approvals, monitoring, and outcomes," she adds, which results in inconsistent use, fragmented controls, and limitations in scale. Enterprises need to redesign jobs and workflows, not just the tooling, yet 84% of organizations the Institute surveyed haven't adjusted jobs for AI, and just 21% have a mature AI governance framework in place despite rapid adoption, according to Deloitte's 2026 State of AI in the Enterprise report. width="1240" height="828" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Beena Ammanath, Global Deloitte AI Institute Global Deloitte AI Institute "Technology can provide visibility and controls, but it can't replace leadership," says Rajesh Arora, chief data and analytics officer at Principal Financial Group. "The organizations that scale AI most effectively are the ones that treat accountability as a business responsibility, not a technology responsibility." One year ago Principal was focused on building its ethical and responsible AI framework, its inventory, and governance processes to support responsible adoption. Today the firm has assessed nearly 200 use cases. "Governance is increasingly embedded in those investment and operating decisions, rather than applied as a separate review at the end," he says. Costly, compounding mistakes Some organizations have already run into issues with respect to inadequate governance. A 2024 Stanford RegLab study found that AI-assisted research tools offered by LexisNexis (Lexis+ AI) and Thomson Reuter ## 3 best practices for CIOs to assess AI ROI DevFeed: [3 best practices for CIOs to assess AI ROI]() Original publisher: [Read original article]() Author: Nicholas D. Evans Published: 2026-10-05T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [finops](), [Enhancing AI model performance](), [AI Integration Strategies](), [it modernization](), [cloud cost management](), [digital experience monitoring]() Tags: [ai-roi](), [ai-strategy](), [artificial-intelligence](), [artificial-intelligence-budgeting-c-suite-careers-cio-emerging-technology-it-leadership-it-ma](), [budgeting](), [c-suite](), [careers](), [cio](), [compute-costs](), [digital-transformation](), [efficiency](), [emerging-technology](), [it-leadership](), [it-management](), [it-strategy](), [roi-and-metrics]() ### AI overview The article outlines three practices for assessing AI initiative ROI: manage investments as a portfolio aligned with organizational ambition, assess full costs against business benefits, and measure quantitative and qualitative outcomes. It recommends focused pilots, consistent value metrics, and tracking projected versus actual costs and benefits. ### Source excerpt Most CIOs can easily describe their AI initiatives, but struggle to demonstrate and report on AI value and ROI. This is evidenced by Gartner's AI ROI Vendor Sentiment survey, which found that 27% of respondents strongly agree that ROI from AI initiatives is difficult to quantify. In addition, only one in four had a clear process to evaluate success of AI initiatives. In the digital transformation era, compute costs were relatively cheap, and the focus was less on FinOps and more on user experience, and the classic software attributes of availability, reliability, scalability, performance, and security. AI-native and AI-enabled applications still bring these considerations, along with a host of new ones, such as accuracy, validity, explainability, safety, and all the other trust attributes like those in the NIST AI RMF. But as AI scales, it's quicky becoming a FinOps exercise, too. I've discussed techniques CIOs can apply to improve AI value and ROI such as architectural decisions related to controlling efficiency at the data layer, fine-tuning the application layer, and closing the gap between AI strategy and governance. Here, we'll look at three best practices for CIOs looking to formalize their process to evaluate the effectiveness of their AI initiatives through approaches, frameworks, and techniques that support measurement and ROI reporting across the entire AI portfolio. 1. Take a portfolio approach in line with AI ambition The first step to AI value is to take a portfolio approach and look across your entire AI estate and how it supports your AI strategy and broader organizational strategy. This means thinking about your AI innovation ambition and how you'd like to move the needle year-over-year by adjusting your percentage of spend across quick wins versus must haves, internal (employee-facing) versus external (customer-facing) applications, near-term versus long-term initiatives, and cost savings and productivity versus revenue generation. This AI innovatio ## Who should own AI? I started with an incomplete answer DevFeed: [Who should own AI? I started with an incomplete answer]() Original publisher: [Read original article]() Author: Christine Park Published: 2026-10-02T09:00:00Z Content type: opinion Language: en Sources: [CIO]() Topics: [engineering-leadership](), [ai-governance]() Tags: [ai](), [artificial-intelligence](), [artificial-intelligence-c-suite-cio-cso-and-ciso-it-leadership](), [c-suite](), [cio](), [company](), [contributor](), [cso-and-ciso](), [governance](), [it-leadership](), [security](), [team]() ### AI overview A chief transformation officer reflects on who should lead AI transformation in a company. The article argues that one executive should be accountable for connecting strategy, governance, technology, and organizational change, while business functions share responsibility for execution and outcomes. It emphasizes redesigning workflows, setting clear decision paths, and helping employees and managers adapt. ### Source excerpt Several months ago, I was invited to an event for CTOs. Just looking around the room, I could see someone had made an assumption about my title. I am a chief transformation officer, not a chief technology officer. I stayed anyway to see how the other type of CTO thinks about AI. When a CISO got onstage and said, "This is our time," my immediate reaction was cringe. I remember thinking, AI is going to change jobs, workflows, skills and how people experience work. Tools and controls alone will not produce transformation. The real challenge is redesigning how work gets done. I knew from the beginning that AI would require partnership across technology, security, legal, finance and the business. But I saw people and operating-model change as the center of the work, with technical and governance teams enabling it. After working to operationalize AI across a company, I can see what I missed. Why I started with people and operating models My original assumption was based on my own experience managing through change. Technology creates possibility; operating models create value. Access is not adoption. A team can run an impressive pilot that never becomes part of how the function operates. Experimentation means very little if leaders have not defined the business outcome, how the workflow will change or how the team will sustain it once the pilot ends. People leaders understand job design, learning, manager behavior and the emotional reality of change. People resist initiatives that feel disconnected from the work they are accountable for every day. Organizations also tend to budget for technology and not for transformation, treating licenses, APIs and model usage as the whole investment. The real cost includes the time people need to learn and build fluency, and the clarity managers need about what good use looks like. A technically strong AI program can fail because the organization never changes around it. AI raises the floor, but transformation raises the ceiling. The l ## What CIO Phil Dundas says helps technology executives lead at enterprise scale DevFeed: [What CIO Phil Dundas says helps technology executives lead at enterprise scale]() Original publisher: [Read original article]() Author: Dan Roberts Published: 2026-10-01T09:30:00Z Content type: article Language: en Sources: [CIO]() Topics: [engineering-leadership](), [balancing technical and leadership roles](), [AI Infrastructure]() Tags: [c-suite](), [c-suite-careers-cio-it-leadership](), [careers](), [cio](), [context](), [cto](), [foundation](), [information](), [it-leadership](), [leadership]() ### AI overview AlTi Global CTO Phil Dundas describes how technology executives can grow into enterprise leadership. He discusses building trust and sound data foundations for AI, delegating while retaining accountability, choosing which decisions require close involvement, and giving teams enough context to act independently. His examples also highlight the need to adapt technical solutions to business users and to create a team culture where people can question one another and raise concerns safely. ### Source excerpt New technology executives often discover the skills that made them successful early in their careers can be the very things that limit their success as a CIO, particularly as the scope, complexity, and expectations of the role expand. Few leaders understand that evolution better than AlTi Global CTO Phil Dundas, my recent guest on the Tech Whisperers podcast. Leading technology inside some of the world's most complex financial organizations, Dundas has repeatedly had to rethink what his job requires of him. That has meant learning when to get into the details and when to stay out, replacing control with context, creating genuine accountability and empowerment without leaving people unsupported, and measuring his success less by the problems he solves personally than by the capability of the organization he builds around him. Those lessons are particularly relevant as CIOs take on broader mandates and lead organizations that operate with increasing speed. Navigating this environment requires a leadership system that enables good decisions, accountability, innovation, and execution to happen at scale. That starts with trust, which Dundas sees as the foundation for client relationships and innovation. Here, Dundas shares some of the hard-earned shifts that helped him make that transition, as well as the leadership practices he believes separate true enterprise leaders from strong technology executives. Dan Roberts: Trust is a thread through everything you do. How does it shape the way you approach innovation, especially with AI? Phil Dundas: Wealth management is a trust business. I love the line that trust arrives on a bicycle and leaves in a sports car. It takes years to earn and moments to lose. The relationship between a client and their advisor sits at the center of that trust, and it is underpinned by the data and information the client sees. So client trust depends on data trust, and data trust depends on the people and the discipline behind it. I'm impatient to ## How CIOs should evaluate alternative clouds DevFeed: [How CIOs should evaluate alternative clouds]() Original publisher: [Read original article]() Author: Bill Doerrfeld Published: 2026-09-30T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [cloud cost management](), [Security, Identity, & Compliance](), [AWS Local Zones](), [Cloud Data Loss Prevention]() Tags: [ai-workloads](), [artificial-intelligence](), [artificial-intelligence-budgeting-c-suite-careers-cio-cloud-computing-cloud-management-compli](), [budgeting](), [c-suite](), [careers](), [cio](), [cloud](), [cloud-computing](), [cloud-management](), [compliance](), [cost](), [data-management](), [data-quality](), [data-sovereignty](), [deployments](), [edge](), [emerging-technology](), [it-leadership](), [it-management](), [it-operations](), [it-strategy](), [object-storage](), [risk](), [vendor-management](), [vendors-and-providers]() ### AI overview Alternative clouds, or neoclouds, can offer specialized benefits such as lower-cost object storage, improved edge performance, dedicated GPUs, and localized data sovereignty. Enterprise leaders remain cautious, weighing providers' maturity, compliance, security, resilience, costs, and continuity with existing cloud infrastructure. The article says adoption is likely to proceed on a per-workload basis. ### Source excerpt Alternative clouds have emerged amid major cloud providers such as AWS, Google Cloud, and Microsoft Azure, and often specialize in niche areas like AI-native computing, and sovereign and edge deployments, while professing to provide cost reductions and best-of-breed performance. "Many organizations are rebalancing away from all-in hyperscaler strategies to avoid lock-in and concentration risk," says Kyle Campos, CTPO at CloudBolt, a cloud management platform. "At the same time, alternative clouds are often purpose-built for specific advantages." To him, benefits include lower-cost object storage at scale, better edge performance for latency-sensitive apps, and faster access to dedicated GPUs for AI workloads. Data sovereignty is also pushing adoption of localized providers. But as alternative clouds, also called neoclouds, make headlines and amass considerable funding rounds, their use in enterprise environments remains relatively nascent. While enterprise executives see the potential in using alternative clouds, especially for GPU workloads, many are skeptical, taking a balanced approach to assessing maturity, costs, and whether it's worth sacrificing continuity with existing clouds. Smartsheet CIO and CISO Ravi Soin is one of these executives taking a measured approach. "We'll grow our use of alternative clouds, but within a clearly defined perimeter," he says. "That discipline is what separates considered adoption from sprawl." For Soin, many neoclouds still fail early in the evaluation process, especially for regulated workloads where a mature compliance posture is non-negotiable. width="1240" height="827" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Cloud Computing Survey Foundry In order to fully evaluate alternative clouds, the platforms must be treated with the same vigilance as any other IT acquisition. "This remains consistent across any tech stack: fit, form, and function," says Nigel Gibbons, director and senior advisor at the NCC Group, a cybersecur ## Driving ROI in your AI initiatives DevFeed: [Driving ROI in your AI initiatives]() Original publisher: [Read original article]() Author: Vasant Rao Published: 2026-09-30T09:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [Artificial Intelligence](), [cloud cost management](), [Finance](), [ai-governance]() Tags: [ai](), [artificial-intelligence](), [artificial-intelligence-budgeting-it-leadership-it-management-roi-and-metrics](), [budgeting](), [c-suite](), [contributor](), [costs](), [enterprise](), [governance](), [it-leadership](), [it-management](), [return-on-investment](), [roi](), [roi-and-metrics]() ### AI overview The article argues that enterprises should assess AI initiatives by their business value and implementation costs as pressure grows to demonstrate returns. It recommends disciplined budgeting and governance, including separating AI training and access costs from ROI tracking for business-approved use cases. ### Source excerpt Over the last four years, artificial intelligence has captured the imagination of technology and business leaders worldwide, holding out the promise of revamped business models, new growth avenues and efficiency gains. In the past 12 months, however, enterprise conversations have undergone a sharp strategic pivot. As the initial AI hype cycle cools, corporate boards and CFOs are asking harder questions regarding implementation costs and bottom-line return on investment (ROI). Given the scale of capital involved -- with Gartner projecting global AI spending by enterprise and vendors to hit $2.59 trillion -- the C-suite must take a rigorous look at how AI initiatives are scoped, budgeted and governed. A recent survey of 500 senior US and UK finance leaders showed that 79% (4 out of 5) of large enterprises missed their AI budgets in the past 12 months. In another survey, 85% of enterprises said that they missed AI forecast by greater than 10% and a quarter of them said that they missed it by 50% or more. These numbers are staggeringly high. The silver lining, however, is that 6% of large enterprises have reported an EBIT impact of greater than 5% driven by an enterprise-wide implementation of AI. This indicates that there is a clear path to ROI, provided one runs a disciplined, prudent and efficient governance model for all AI initiatives. Simply put, an enterprise can increase the ROI on AI investments by addressing the two components of the equation appropriately: Defining and amplifying the business value (numerator) Defining and optimising the cost of AI implementation (denominator). Defining and amplifying business value An enterprise must focus on chasing value, not chasing AI. The board has an important role to play here -- it needs to reconsider whether it is asking the right questions. By aggressively pushing for an "AI everywhere" strategy, the board unwittingly shifts the focus to proliferation of AI tools and use cases rather than tangible business value. Pres ## 6 steps to weigh compromise and priorities for AI sovereignty DevFeed: [6 steps to weigh compromise and priorities for AI sovereignty]() Original publisher: [Read original article]() Author: Martin De Saulles Published: 2026-09-28T10:00:00Z Content type: article Language: en Sources: [CIO]() Topics: [digital sovereignty](), [LLM security](), [self-hosted databases vs managed services]() Tags: [ai](), [ai-sovereignty](), [artificial-intelligence](), [artificial-intelligence-c-suite-careers-cio-emerging-technology-it-leadership-it-management-i](), [c-suite](), [careers](), [cio](), [costs](), [emerging-technology](), [it-leadership](), [it-management](), [it-strategy](), [llms](), [vendor-management](), [vendors-and-providers]() ### AI overview The article outlines trade-offs in achieving enterprise AI sovereignty, including the operational costs and lock-in risks of open, hybrid, and proprietary approaches. It recommends separating application and model layers with orchestration frameworks such as LangChain or LiteLLM so underlying models can be changed with fewer code rewrites. ### Source excerpt From an enterprise perspective, AI sovereignty means complete control over the entire AI stack. For businesses with the human, technical, and monetary resources, this may be possible. For the vast majority, however, external help is required. The danger, as with any vendor relationship, is being locked in to a supplier or technology. With a mature technology that performs discrete tasks, that may not be a problem. But with an emerging and rapidly evolving one like AI and its ability to impact multiple processes across an enterprise, difficulties could arise. Gartner recently predicted that AI platform lock-in will increase from 5% now to 35% next year. So how do you avoid being in that high percentile? 1. Be clear of the trade-offs As might be expected, there's an inverse relationship between lock-in risks and operational overheads, including upfront costs, longer-term licensing fees, and internal talent requirements. Relying on open-source models, applications, and self-hosting is the lowest risk in terms of lock-in, but it requires a significant investment in AI engineering skills and infrastructure. In the middle, a multi-cloud or hybrid API approach provides high flexibility to swap underlying models, but relies on managing the abstraction layer. The fastest way to AI deployment is contracting a proprietary all-in-one platform requiring less operational overhead, but risks longer-term vendor dependency. 2. Break down the stack A key priority to reduce AI lock-in is decoupling that application layer from the model layer. Deploying orchestration frameworks like LangChain or LiteLLM creates a controllable layer between the software applications an enterprise already uses, and the underlying AI models. The backend LLMs can then be swapped around as needed with minimal code rewrites. Model-agnostic architecture at this level ensures new models can be brought in as older ones become obsolete. The one certainty in AI at the moment is rapid change. 3. Keep an open minds ## Workforce economics emerges as AI reshapes the C-suite DevFeed: [Workforce economics emerges as AI reshapes the C-suite]() Original publisher: [Read original article]() Author: Cheryl Knight Published: 2026-09-23T21:18:50Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [AI Strategy](), [Artificial Intelligence](), [ibm]() Tags: [ai](), [ai-transformation](), [alignment](), [artificial-intelligence](), [c-suite](), [cfo](), [chro](), [client-zero](), [cube-event-coverage](), [culture](), [enterprise](), [enterprise-ai](), [finance](), [growth](), [hiring](), [human-capital](), [ibm](), [ibm-s-transformation-edge-a-c-suite-reinvention-series-2026](), [ibm-s-transformation-edge-a-c-suite-reinvention-series-hosted-by-thecube-and-ibm-2026](), [ibmtransformationedgeseries](), [jim-kavanaugh](), [john-furrier](), [news](), [nickle-lamoreaux](), [nyse-ibm](), [productivity](), [skills](), [strategy](), [thecube](), [thecube-research](), [transformation](), [workforce-economics](), [workforce-strategy]() ### AI overview An article examines IBM's concept of workforce economics, in which AI-driven changes to workflows and operating models bring finance and human resources into closer alignment. IBM executives discuss workforce planning, hiring, reskilling, productivity, and reinvesting capacity in growth and employees. ### Source excerpt The economics of enterprise transformation are pushing finance and human resources into much closer alignment as companies decide where artificial intelligence fits, where people create the most value and how productivity gains should be reinvested. At IBM Corp., that convergence is creating what SVP and CFO Jim Kavanaugh (pictured, left) calls a new discipline of [...] The post Workforce economics emerges as AI reshapes the C-suite appeared first on SiliconANGLE. [Next page]()