Information Week

Amit Basu walked into a bank to become a pioneering software developer, only to set sail years later for the greater freedom offered by the maritime industry. “Ironically, it wasn’t the domain but the technology that drew me in. While banks were still running older systems, this company was deploying the latest in database platforms. That technical edge attracted me and, as it turns out, anchored me,” says Basu who is now CIO and CISO at International Seaways, which owns and operates a large fleet of seagoing vessels to transport crude oil and refined petroleum products worldwide.   But Basu hasn’t spent his career chasing the latest new shiny thing in tech. The lure that catches his attention is the opportunity to solve increasingly more difficult business problems. He enjoys puzzling out a solution that is usually part tech but rarely just tech.  Grabbing Opportunity With Both Hands Basu joined a large commercial bank in India in the late 1980s as a software developer right at the onset of banking computerization in the country. He was part of a pioneering team that designed and implemented core banking applications for branch operations.  “It was my first encounter with the direct impact of technology, writing programs that real users depended on to serve real customers. Watching my code in action, powering everyday banking transactions, instilled in me a belief that has guided me ever since: I don’t want to be just a technologist; I want to be a solution provider enabling business to excel. And for that, I need to understand what the business needs, in their language,” he says.  Related:Unum Group CIO on How to Prioritize Learning and Relationships If there’s one industry where one can learn the universal language of business, it is banking and finance. And Basu learned it well. In banking IT, through the mid-1990s, before stepping into the intriguing challenges presented to him in an organization that built software for international banks. While there, he worked on real-time, network-based banking systems designed using relational databases.   Amit Basu, International Seaways “The early challenges of optimizing system performance on constrained hardware, and ensuring data security and integrity, pushed me into learning the intricacies of relational databases. I learned database administration skills, experimenting with configuration and performance tuning inside out, and that expertise became my launchpad into the bigger world,” he says.  From there, he segued to an American bank as a database administrator and moved to the US in 1994. A year later, he made another move: this time to another industry rather than another country. He kept the position of database administrator, but this time as a contractor.  Related:How CIOs Can Work With CFOs on Sufficient Project Funding “Maritime, in those days, was a greenfield for IT, and I took the opportunity with both hands. One of my early successes was implementing the first computers and LAN onboard ships, connecting to shore over satellite and developing data replication systems that crew onboard used to enter data,” Basu explains.   Moving Up and Onward Basu has spent the last three decades in Maritime IT. He transitioned from contractor to a full-time role in 1998 and over time progressed into leadership roles, first as deputy head of IT, then CIO, and today, CIO and CISO at International Seaways.   Over the years, he engineered multiple digital transformations, starting with ERP, then data warehouse and business analytics, cloud computing and SaaS, creating multi-layered cybersecurity frameworks, and now, the AI revolution.   His secret to always moving up in his career? “I’ve always remained focused on creating value for the business and delivered technology that helps the business achieve its goals and gain competitive advantage,” he says.  Yes, we can all agree that is a smart way to progress in IT. But what did that look like from Basu’s point of view? It’s a long and eventful story, he says, but it begins and ends with a mentor who can help you build a strong foundation.  Related:Why Cloud Efficiency is Driving More IT Spending (Not Less) “I entered the world of IT as a rookie developer, knowing only the syntax of some computer languages but nothing about software development. The person who transformed that beginner into someone capable of designing complex business systems was my senior at the bank, Mr. Narayana Bhat. He taught me the ABCDs of application design and development with clarity and purpose. Whatever I’ve built over the years rests on the foundation he gave me, and for that, I remain deeply grateful,” Basu explains.  Career Highlights Several milestones stick out in Basu’s mind as well. The first was one of many filed under the requirement to “just make it work!” Most of the time, that’s much easier said than done.   In one early example, he led a team that successfully deployed an online network-based retail banking solution for a bank in Indonesia. “The entire system ran on hardware with just 32 MB of memory and limited CPU, but we made it work because we believed that with smart design and efficient databases, even the toughest constraints could be overcome,” he says.  He points to another proud moment in 2013 when he championed one of the earliest cloud adoption strategies for the sector. You may recall that in those early days, many scoffed at the notion of cloud computing. But his willingness to step up and embrace it paid off later in an unexpected scenario.   “That decision proved invaluable. In 2020, during the global health pandemic, our CEO, Ms. Lois Zabrocky, publicly acknowledged on national television that the company’s ability to remain 100% productive from day one was made possible by the foresight to move to the cloud years earlier. That was a deeply gratifying moment, both personally and professionally,” he says.  In recent years, he has focused extensively on building a multi-layered cybersecurity framework to protect the company from increasingly sophisticated adversaries. He takes pride in what has been accomplished so far to secure the

Cloud bills that consistently exceed budget forecasts have become the new normal for enterprise technology leaders. Despite aggressive cost optimization efforts, 83% of organizations are spending more on cloud services than anticipated — with the average overspend reaching a staggering 30%. This persistent pattern isn’t a failure of management or forecasting. It’s a manifestation of a 160-year-old economic principle that perfectly explains our modern cloud challenge.  When Efficiency Accelerates Consumption: Jevons Paradox Reborn  In 1865, British economist William Stanley Jevons observed something counterintuitive during the Industrial Revolution. As coal-powered steam engines became more efficient, the total consumption of coal dramatically increased rather than decreased. This became known as “Jevons Paradox“: when technological progress increases the efficiency of resource use, we end up consuming more of that resource, not less as intuition might suggest.  Today, we’re witnessing this same paradox playing out in enterprise cloud computing, but with even greater intensity.  The Evidence: What 300 CIOs Revealed About Cloud Economics  In our recent survey of 300 enterprise CIOs, we uncovered compelling evidence of Jevons Paradox in action. While 80% of organizations report cost savings from their cloud deployments compared to traditional on-premises alternatives, 4% have been exceeding their cloud budgets significantly. Only 2% of organizations came in under budget.  Related:Bentley Systems CIO Talks Leadership Strategy and AI Adoption This contradiction isn’t just theoretical. One financial services CIO explained how they reduced per-transaction costs by 42% through cloud migration, yet their total cloud spend has doubled over three years as they process significantly more transactions and launch services in the cloud that weren’t possible before.  Why Cloud Amplifies the Paradox: Two Accelerating Forces  Two powerful forces in modern cloud environments accelerate this paradox beyond anything Jevons could have imagined:  1. Cost efficiency transformation: Cloud resources continue to become more affordable on a per-unit basis. What may have once required millions for a company to invest in capital investment for on-premise hardware that depreciated over five years now converts to flexible operational expenses that can scale with business needs. The costs for infrastructure in the cloud continues to decline — in 451 Research’s Cloud Price Quarterly, Q1 2025, the firm’s Cloud Price Index found that between Q4 2024 and Q1 2025,  on demand list prices dropped sharply for several infrastructure resources, consistent with long-term trends, e.g., database storage decreased nearly 25% quarter over quarter and NoSQL databases decreased 40% quarter over quarter. This general deflationary trend reflects the continuing race to drive cost-per-unit down.  Related:Policy Matters: Navigating the Brave New World of Immigration 2. Consumption agility: Unlike the original Jevons scenario that focused solely on cost efficiency, cloud computing introduces unprecedented deployment speed. When a new market opportunity emerged in pre-cloud environments, IT teams spent months procuring and configuring hardware. Today, development teams deploy new capabilities in minutes.  As a retail CIO told me, “Before cloud, launching a new customer analytics platform took six months and a seven-figure budget. Now my teams can experiment with new services for thousands of dollars per month and scale only what works. We’re getting significantly more value but spending more overall.”  From Cost Control to Value Creation: The Leadership Challenge  As a technology executive, I see this paradox playing out across our industry. Business and IT leaders regularly launch new cloud-based services and play “whack-a-mole” with unexpected cost spikes as innovation accelerates. The difference between organizations that struggle with cloud economics and those that thrive isn’t about spending less — it’s about generating more business value from each dollar spent.  Related:Empathy: The Strategic Differentiator for CXOs in Tech This explains why 56% of CIOs report that their CEOs and boards support current spending levels and would approve further increases, while 43% acknowledge concerns about cloud costs. The executives who understand the paradox recognize that optimizing simply for the lowest spend often means sacrificing innovation and competitive advantage.  Strategic Approaches: Beyond Basic Cost Optimization  While Jevons Paradox explains the pattern we’re seeing, it doesn’t mean organizations should simply accept uncontrolled cloud spending. The most successful enterprises are implementing sophisticated approaches that balance optimization with innovation; including:  1. Implementing business-aligned FinOps: Move beyond technical metrics to business outcomes. One healthcare technology company we work with doesn’t just track cloud cost per instance — they measure cost per patient served and revenue generated per cloud dollar spent.  2. Optimizing application efficiency: Look beyond infrastructure. Most enterprises only focus on right-sizing instances or reserved capacity purchases, missing additional opportunities. At Azul, we’ve seen organizations further reduce cloud compute resource consumption by 50% by optimizing their application runtime environments, particularly for Java workloads that power most enterprise applications.  3. Creating developer economic awareness: Many organizations discover that developers are unintentionally creating costly architectures. One financial services company implemented a “bill of materials” approach where teams forecast the cloud resources needed before deployment, creating accountability without restricting innovation.  4. Embracing continuous optimization: Cloud economics isn’t a one-time effort. One retail client implemented automated monitoring that alerts when spending patterns deviate from expected business metrics, allowing them to quickly identify both wasteful spending and unexpected business opportunities.  The Future of Cloud Economics: What CEOs and Boards Need to Understand  As AI workloads grow exponentially and enterprise cloud adoption similarly accelerates, expect Jevons Paradox to come into play even more intensely. Organizations deploying generative AI solutions today report computing requirements growing at rates that eclipse any previous technology wave. The CIOs who will succeed in this environment aren’t those focused narrowly on cost reduction but those who maximize business value from cloud investments.  The enterprises that thrive will shift from treating cloud as a technology expense to viewing it as a business accelerator with measurable ROI. Board-level discussions must evolve from “How can we reduce cloud spending?” to “How can we maximize the business value generated from each cloud dollar?”  After all, in today’s ever-changing technology world, the goal isn’t to use less cloud — it’s to create more value from it.  source

Dashboards are expensive. Beyond the investment in a particular platform, companies sink thousands into the time it takes for specialists to build the dashboard. The hundreds of staff hours spent in meetings about the metrics. That’s not cheap. Add in the opportunity cost of delayed decisions while waiting for the perfect dashboard to be built.   For all that investment, you’d expect a pretty good return. So, then why is it that so many BI dashboards gather dust?   You know your dashboard is working well when you see usage metrics in acceptable ranges and frequencies. It’s doing its job when you see decision makers reference dashboard data in meetings. And, you know the dashboard is engaging when you get requests for updates, adjustments and additions.   However, most of the time? You get crickets from the business side. Silence!   I’ve consulted hundreds of companies about their data visualization, resolving pain points and introducing solutions, and “dashboard underwhelm” is one of the most common issues that pops up, regardless of industry. In those conversations, I’ve seen patterns that predict a dashboard’s lifespan. Watch for what follows.  Trying to Make too Many People Happy  The developers tasked with dashboard construction likely consulted with multiple departments, and roles within those departments, to get input on what the dashboard should report. The problem is that everyone needs different information to do their jobs well.   Related:Why Master Data Management Is Even More Important Now The developers — who naturally want to please their supervisors — then make a dashboard where they’ve tried to cram in everyone’s hopes and dreams. These dashboards invariably become both visually overwhelming and actionably underwhelming because any given viewer has to dig through irrelevant visuals to get to what matters to them. People will dig only so far before they give up.  To do dashboarding right, you need different metrics in different layouts for different audiences. CEOs don’t want to widget and drop-down their way to the bottom line. They want interpretation of the key indicators that matter the most to them.  Managers, on the other hand, likely need those drill-down menus so they can investigate issues. A dashboard suitable for management will inherently be more complex.    Requiring Too Much Insider Knowledge  The best clue that a dashboard is likely to die a quick death: a set of unrelated numbers in a large font up at the top. Depending on your industry, you might refer to these as your key performance indicators KPIs) or big ass numbers (BANS). Whatever you call them, they lose your audience fast.  Related:InformationWeek Podcast: Proving Tech Investment’s Company-wide Value Let’s use an example. Monthly Active Users: 4,283,912  The only people who can make sense of that number are the people who are so close to the data for monthly active users that they can look at 4,283,912 and tell you whether that’s good, bad, or same as yesterday. Interpreting that number takes a lot of insider knowledge. It’s only a helpful way of reporting for your power users.   Everyone else needs context so they can make meaning out of that number. They’ll need to see the history of monthly active users to determine how it’s trending. They’ll need to see how that number relates to the annual goal.   Without that context, the number is just decoration. It’s something to glance at, not something to act on. And when your dashboard doesn’t lead to action, it stops getting used.  A Dashboard Is the Wrong Container for the Data Dashboards came of age before our lives revolved around screens. They were initially meant for C-suite executives to get the high-level view of their KPIs while marching from one meeting to the next.   We don’t operate like that anymore, but we’ve still clung to the notion that dashboards should entirely fit within one screen. This design parameter leads to cramped displays that people don’t want to use.  Related:Experian’s Lintner Discusses AI Transformation at the Credit Bureau Yet at the same time, the demand “We need a dashboard!” has become popular — so much so that we task developers with creating these because it sounds sexy even if it isn’t actually the right container for the data.   Perhaps some of your audiences would benefit more from a static one-page handout with contextual narrative so the reader better understands the information displayed. Maybe the right container looks more like a website where there’s a mix of images and explanations that can convey the meaningful interpretation of the data. People scroll these days. It’s ok if the information spans beyond one screenshot — so long as that information is informative and useful.   Dashboards don’t automatically improve decision-making just because they look slick or use the latest BI platform. If they’re visually intense, overly generic, or hard to interpret, they will be ignored, no matter how much they cost.   But when dashboards are thoughtfully designed, aligned with audience needs, and built for real-world use instead of trendiness, they become a powerful tool. So, before you launch another dashboard project, ask: Who is this really for? What decisions will it support? And, is a dashboard even the right solution? The answers to those questions will save your organization a lot of time and money.  source

The United States has long been the world’s go-to destination for business and technology innovation. From Silicon Valley to Boston’s biotech hub, American companies have lured exceptional talent from every corner of the globe. However, that draw is now fading. New barriers for work visas and green cards are prompting top talent to weigh opportunities elsewhere.  This potential brain drain won’t arrive in a flood. It’s more likely to occur as a slow drip that extends over years. Authorities may continue to block renewals for some IT workers on H-1B, O-1, EB-1A, or National Interest Waivers through onerous restrictions, high fees, and aggressive rhetoric. Meanwhile, future talent — typically international students on F-1 visas — may accept jobs or launch startups elsewhere.  One thing is clear: CIOs, caught in the crosshairs, must adapt. Without top-tier IT talent, “We may never see some of the breakthroughs that these people would have brought to the US,” says Jeff Le, managing principal at consultancy 100 Mile Strategies and a visiting fellow at the National Security Institute at George Mason University.  The takeaway? CIOs cannot treat immigration casually. It must become part of a broader business playbook. This means rethinking and reworking internal policies, experimenting with new tools and technologies, and finding ways to keep pace with abrupt changes in policy.  Related:Why Cloud Efficiency is Driving More IT Spending (Not Less) Beyond Skills Immigration critics as varied as Bernie Sanders and Steve Bannon view H1-B visas merely as a cost-cutting tactic or a way to undercut domestic wages. What’s often overlooked is that companies hiring foreign workers face increased compliance costs, legal fees, and administrative overhead. In addition, employers must file a Labor Condition Application affirming that an employee will not lower wages or negatively impact working conditions for equivalent US workers.  To be sure, most CIOs turn to foreign talent for highly specialized IT skills rather than cost savings. Top-tier talent helps companies — from banks and retailers to healthcare and aerospace companies — innovate and boost revenues. This, in turn, fuels broader economic gains for both the company and the country. The American Immigration Council reports that immigrants or their children founded 46% of all Fortune 500 companies. In 2023, these firms generated $8.6 trillion in revenue and employed over 15 million people worldwide.  “Restrictive policies choke the supply of niche skill sets that domestic pipelines can’t fill at scale,” states Patrice Williams-Lindo, CEO of Career Nomad and a former senior leader at Deloitte and KPMG. Curtailing immigration also creates incentives for CIOs to over-automate and push remaining staff past the breaking point, she argues.  Related:Bentley Systems CIO Talks Leadership Strategy and AI Adoption This isn’t to say that there isn’t room for improvement. Some companies, particularly IT staffing firms, have manipulated and abused the H1-B system, and some so-called “essential” workers aren’t so essential. There’s also a fundamental problem with the way the H1-B lottery takes place. Anyone can apply — so the highest-skilled workers don’t necessarily prevail. Some firms have flooded the system with applicants.  Yet these problems don’t negate the value of foreign workers in the US economy. Research shows that the H-1B labor force complements US workers rather than displacing them. “There has been a lot of emphasis on STEM education in K through 12,” says Julie Gelatt, associate director of the US Immigration Policy Program at the Immigration Policy Institute (MPI). “But the underlying demographics do not support the notion that the US can produce enough people with the required skills,” she says.  Green Cards, Red Tape A shortfall in homegrown talent might be manageable if the US had a tangible strategy for filling the gap. Unfortunately, a lack of training programs, technical institutes, and a general disinterest in STEM careers don’t bode well. “The number of people admitted to the US on high skill visas is perpetually short of the demand,” says Giovanni Peri, a professor of economics at the University of California, Davis, who studies labor issues.   Related:Empathy: The Strategic Differentiator for CXOs in Tech The current cap on H1-B visas is 85,000 per year, a number that hasn’t changed since the program launched in 1990 — despite population growth and huge changes to the economy. For fiscal year 2025, USCIS reported receiving nearly 480,000 registrations for the lottery, which translates to an 18% acceptance rate. In addition, there’s currently a backlog of 1.8 million green card applicants.   Reforms have floundered. In December 2024, the Biden Administration finalized a rule to revamp the selection process and modernize H1-B rules. This included tightening eligibility, addressing fraud and making the process more equitable. Earlier ideas — such as prioritizing applicants based on the highest wage levels — sparked controversy over potential harms to small businesses. While USCIS continues to use a random selection lottery that rewards volume over merit, the Trump administration has revised the idea of basing H1-B visas on wages. In mid-August 2025, it reportedly proposed a rule change.  Work in Progress Perhaps it may be difficult to see what comes next, but CIOs must develop a plan. Immigration restrictions pose a fundamental risk to talent pipelines, workforce stability, and future innovation. Meanwhile, the United Kingdom, Canada, Australia and other countries are turning America’s problem into an opportunity by actively courting foreign scientists, researchers and tech workers with streamlined visa programs and startup-friendly policies.  Loren Locke, an immigration attorney based in Atlanta, urges CIOs to fully embrace high-value employees who choose to self-petition for green cards under EB-1A or National Interest Waiver (NIW) categories. “CIOs can support this by helping them build qualifying case profiles,” she says. This includes highlighting articles, books, patents, conference appearances, and other signs of expertise. At the same time, it’s essential to provide internal mentorship and legal guidance, she adds.  Mitigating uncertainty and volatility is crucial, Le says. A big piece of the puzzle is establishing a comprehensive IT staffing and immigration strategy. This includes directly connecting digital initiatives with immigration. “CIOs must align their strategies with CHROs

Overhauling an enterprise’s technology resources can call for more than a budget and a bright idea to streamline and innovate. Getting the “buy-in” among stakeholders across the organization is a vital aspect of investments in new technology. That can be a complicated ask, especially if the introduction of new tech appears to be at odds with those stakeholders’ interests.   In this episode of the InformationWeek podcast, Michael Leland, field CTO for Island, and Diane Ma, US finance strategy and global business services practice leader for Deloitte, discussed how leaders might encourage organizations to embrace a new tech investment. Which stakeholders within the organization are vital to get on board for the overhaul to work? What happens if the new tech exceeds the budget? How should the rollout and training of staff be approached? How should the impact of a tech overhaul be assessed? Ma and Leland shared their ideas on those questions, and more — including taking their turns with tabletop exercises. source

This session reveals how budget optimization can satisfy C-Suite demands, advance your career, and boost IT performance. source

Master data management (MDM) has always been important and quite frankly, we’re all sick of hearing about it after three decades. For this and other reasons, some enterprises are unable to get their data houses in order, which is critical now, given the widespread use of AI and data analytics. In short, businesses that want to be competitive better prioritize MDM sooner rather than later.  Customer service, internal efficiency, and automation are still important, but AI introduces a new dimension, and a new level of urgency to this, according to Graeme Thompson, CIO at AI-powered enterprise cloud data management solutions provider Informatica. “It’s one thing to miss out on the opportunity to automate an internal process. It’s a completely different and much more serious thing to miss out on being able to have an AI-assisted customer experience or a fraud detection process.”  One challenge with MDM is that it’s not as sexy as the application-layer stuff, so it can be difficult to allocate the necessary resources to make it happen. While MDM tools can help, there also needs to be a process change, which requires a different mindset.  There is a mindset shift that must happen to get people to buy into the cost and the overhead of managing the data in a way that’s going to be usable, Thompson says. “It’s knowing how to match technology up with a set of business processes, internal culture, commitment to do things properly and tie [that] to a business outcome that makes sense,” he says. “[T]he level of maturity of some good companies is bad. They’re just bad at managing their data assets.”  Related:InformationWeek Podcast: Proving Tech Investment’s Company-wide Value Some enterprises, such as cruise ship companies, are unable to recognize customers across different cruise lines because their data is still siloed. The result is failing to recognize customers across cruise lines and missing out on substantial financial opportunities. Meanwhile, insurance companies are streamlining the claims process by prioritizing data quality.  Graeme Thompson, Informatica “[MDM] has very real business consequences, and I think that’s the part that we can all do better is to start talking about the business outcome, because these business outcomes are so serious and so easy to understand that it shouldn’t be hard to get business leaders behind it,” says Thompson. “But if you try to get business leaders behind MDM, it sounds like you want to undertake a science project with their help. It’s not about the MDM, it’s about the business outcome that you can get if you do a great job at MDM.”  Related:Why Your BI Dashboard Underwhelms CIOs must also make sure stakeholders understand the cost of failing to act, such as following versus leading an industry, providing substandard customer experiences and risking compliance audits and legal action.  Delaying MDM Is a Recipe for Disaster  Some CIOs are facing serious technical debt when it comes to MDM.  “Everyone wants to bypass the MDM phase. Let’s just get the data right for this one project, and then inevitably, [it leads] to other problems,” says Doug Gilbert, CIO and chief digital officer at business and digital transformation service and solutions provider Sutherland Global. “You’ve taken that contextual understanding, and now you’re doing AI, blindly follow[ing] that data and recommendations for you. Before, you could do a kind of quasi master data management around one or two projects and not think about it holistically.”  Through 2026, Gartner expects organizations to abandon 60% of AI projects unsupported by AI-ready data. “Organizations that fail to realize the vast differences between AI-ready data requirements and traditional data management will endanger the success of their AI efforts.”   This puts the importance of data governance and MDM front and center.  “I see two challenges going forward to put in a master data management strategy and structure because the very nature of [AI] systems is supposed to be autonomous. You must make sure that [the data] feeding it is always clean,” says Gilbert. “I do MDM because we go through so many different audits. It was painful, but I have less breakage, and my systems require less maintenance. I get proper AI outputs and proper predictions when I’m doing analytics. More importantly, my auditability is very easy to prove out because we have the proper controls in place.”  Related:Experian’s Lintner Discusses AI Transformation at the Credit Bureau Louis Landry, CTO at cloud and analytics data platform provider for AI Teradata, says in the last five to six years, organizations have walked away from rigorous data governance practices and the desire to automate everything. Instead, they’re having AI agents react to the data they have without that rigorous data governance.  “It definitely feels that we don’t necessarily want to talk about [MDM], but it’s very important and very necessary for the future we’re all planning to live in,” says Landry. “What I’ve seen over the last several years is when you’re talking about data quality and data governance, folks might be willing to spend money on a technology tool, but they’re not willing to spend money on the process and people that are associated with it, and a lot of this is a people problem.”  In older organizations, MDM maturity tends to be unevenly distributed. The core data tends to be fairly well organized and managed, but the rest isn’t. The age-old problem of data ownership and a reticence to share data doesn’t help.   “The notion of data mesh [is] I’ll manage this piece, and you manage that piece. We’ll be disconnected but we can connect, and you can use it, but don’t mess with it. It’s mine,” says Landry. “We’ve known for decades that value acceleration comes when you integrate all this stuff so you can see inventory with customer data, sales data with revenue data — the stuff where magic starts to happen when you bring all these things together. The most advanced organizations have subject matter experts for specific domains. It really improves the overall quality and accessibility of

Ruth Sleeter, CIO of engineering software company Bentley Systems, began her career in software product development. She managed product teams at NetApp before entering more senior roles that leveraged her digital strategy skills, notably at Lenovo.  She then made the leap to the C-suite, serving as CIO at Deutsche Bank. She returned to Lenovo as CIO before moving to roles at Sonos and Axon. In March 2025, she landed in her current position as CIO at Bentley. Here, she shares her thoughts on the importance of systems thinking and the delicate process of integrating AI into the workforce.  How did your early interest in technology develop? My dad was also a software engineer. I grew up in the Bay Area just as Silicon Valley was starting. I suppose I was surrounded by it and didn’t know it at the time.  I started my undergrad promising myself I would not be a music or a computer science major, because that’s what I did a ton of growing up. Regardless, I was really good at engineering and systems thinking. I took a class in discrete math in the computer science department and fell in love. I ended up getting an undergraduate degree in computer science. I was super lucky growing up, especially as a girl. I had all of these great people who encouraged me to do math and science. It was just a very natural fit to be a software engineer. That’s how I started my career as a software engineer for semiconductor software automation.  Related:Policy Matters: Navigating the Brave New World of Immigration Did you gain any formative insights during your education? In computer science, you start with data structure, which is just systems thinking — let’s break a big problem down into small parts and think about reusable components. That anchor — thinking and strong systems design — was very intuitive to me. People ask me how I get through my day to day, with the breadth of the information that I have to take in. The CIO role is pretty interesting. We get to spend a lot of time on strategy but at the same time, we have to make sure we’re building the right internal products. That same systems thinking that was inculcated in earning my degree, that type of thinking that sparked my interest, is exactly the type of thinking that I love doing now.   How did your early roles help you to develop the skills you have deployed as a CIO? I think it was my desire to try new things. I started out as a software engineer. I got into management kind of by accident during the dot com boom. I was organized and articulate. So, I spent some time learning how to manage software product teams, which is incredibly important in what I do today.   Related:Empathy: The Strategic Differentiator for CXOs in Tech Then I got the opportunity to be customer-facing. If I had advice for anybody who wants to be in these types of leadership roles, it would be to spend time customer-facing. I look at my role as a customer-facing role — learning about customer empathy and how to communicate strategy and approach and understand customers’ pain and how you’re going to solve it was crucial in my career.  How has the role of the CIO in the C-suite evolved since your first CIO position? The thing that’s really important for a CIO to be thinking about is that we are a microcosm for how all of the business functions are trying to execute the tactics against the strategy.  What we can do across the portfolio is represent the strategy in real terms back to the business. We can say: These are all of the different places where we’re thinking about investing. Does that match with the strategy we thought we were setting for ourselves? And where is there a delta and a difference?  Let me give you some insight into that and then help with the discussions around strategic enablement across our highest priorities. That gets you into a strategy conversation. I see myself as a strategic leader — being able to bubble up where there may be either support or inconsistencies in how we’re executing against our strategy and investing.  Related:From Promise to Practice: How IT Leaders Can Turn AI Hype into Tangible Value The new challenge — and it is a real challenge — is shepherding organizations through AI adoption. Creating a very flexible approach to this problem is really important. That is the new part of the remit that is particularly energizing — a change of this magnitude in how people work every day has not happened in quite some time.  How did you go about learning business strategy when you first entered the C-suite? When I got my first CIO role, there was all of this conversation about business process. That was the part that I had to learn and figure out how to map into these broader, strategic conversations. I had my first internal IT role at Deutsche Bank, where we really talked about product model a lot — thinking about our internal IT deliverables as products.   When I moved to Lenovo, we had very rich business process and transformation conversations because we were taking the whole business through such a foundational change. I was able to put those two things together.  It was a marriage of several things: running a product organization; marrying that to the classic IT way of thinking about business process; and then determining how that becomes representative to the business strategy.   Has the experience of being a woman in a male-dominated field changed? It’s changed tremendously. Early in my career, I was very lucky. I did not even want to be seen as a woman in technology, because I didn’t understand why it would matter, which was a wonderfully naive place to come from at the time. I hope that’s how women feel now. I

Ask just about any IT leader and they’ll tell you AI offers tremendous potential for improving productivity by helping reduce and eliminating toil — including automating help desk tasks, streamlining incident responses, summarizing reports, and giving workers time back on administrative busywork. These use cases are real, and so are the projected returns. According to McKinsey’s latest projections, generative AI could add up to $4.4T in annual global economic value.   But if you ask your average employee, that promise hasn’t quite been realized yet. New findings from GoTo’s 2025 Pulse of Work Survey reveal that 62% of employees believe AI is significantly overhyped, and 86% say they aren’t using it to its full potential.   Despite ongoing investment and growing access to tools, AI’s impact in many workplaces remains somewhat obscure and difficult to quantify.   Access Isn’t the Issue. Alignment Is.  The reality is that most organizations don’t have an AI problem — they have an execution problem. AI tools are increasingly available and embedded in platforms workers already use, from IT support software to productivity suites. However, less than half of IT leaders say their company has a formal AI policy, and nearly half admit they aren’t actively measuring the ROI of their AI investments.   Related:Budget-Smart Tech for CIO-CFO Alignment Meanwhile, employees are ill equipped; 87% say they haven’t been properly trained on how to use AI tools, which means lack of awareness, low adoption, misuse, or missed opportunities.   This training and skills gap, combined with the lack of policy, objectives, and measurement of outcomes, fuels skepticism and slow adoption. Gartner predicts that at least 30% of AI projects will be abandoned by year’s end, largely due to unclear business objectives, high implementation costs, or unreliable data. To compound the matter, only a small share of organizations report feeling prepared to manage AI-related risks such as data privacy, bias, and ethics.  IT Must Lead the Transition  The challenge of AI adoption offers a valuable opportunity for CIOs and IT leaders to move the technology from experimental toolsets into core operating procedures.   There is no doubt that AI is transformative and there are several examples of productivity improvements especially in the areas of making knowledge more readily available, performing analysis or summaries from conversations or sessions and translating ideas into functional prototypes with vibe coding. However, AI’s true potential is realized not in isolated pilot projects, but when it’s integrated across workflows, departments, and business goals. That kind of cross-functional integration requires a coordinated effort across departments, but IT must lead the way.   Related:How Immigration Crackdowns Are Changing IT Talent Management Three practical steps can help:   1. Establish a clear AI policy and governance model  Without a well-communicated and well-documented policy, AI quickly becomes a free-for-all. There are similarities to the early days of cloud adoption where we faced challenges around sprawl and lack of cost control at the time.  IT leaders must define not just how AI should be used, but also how it shouldn’t. A clear policy will outline use cases, ethical guidelines, data handling procedures, and compliance expectations.   While this might seem obvious, over a third of employees report they are using AI for sensitive tasks that involve confidential company data, personnel matters, or high-stakes decision making, which can contribute to major security or liability risks.   Organizations with an AI policy are also significantly more likely to report productivity gains, faster service delivery, and stronger employee confidence in using AI.   2. Prioritize practical training   AI training can’t be a one-off webinar buried in a knowledge base or a 30-minute introductory session with teams. To be effective, it must be embedded into everyday processes. Scenario-based training gets employees using the technology, learning how to make it work best for them, and drives faster adoption while building trust.   Related:Ways a CIO Might Derail an AI Strategy Inadvertently These trainings are well worth it: Employees who receive AI training during onboarding or upskilling programs are three times more likely to use those tools regularly and effectively.   3. Go beyond cost savings when measuring ROI   Traditional ROI models often don’t or can’t account for the productivity gains resulting from AI. Are help desk tickets being resolved faster? Are employees spending less time recapping meetings or manually handling service requests? These are the kinds of metrics that technology leaders should track and report on to validate continued investment.   New KPIs such as “hours saved per employee per month,” or “reduction in repeat support requests,” can help quantify AI’s impact on operational efficiency, even before cost reductions are viable.   Culture Will Drive AI Adoption  The reality is, many employees want to use AI, but don’t feel empowered or supported to do so productively.   This insight points to an important reality: AI transformation is as much about culture as it is about technological knowledge. Organizations that foster experimentation and collaboration within a governance framework will have an easier time scaling AI across their teams.   IT leadership can play a critical role here by creating cross-functional AI councils, championing internal success stories, and advocating for continuous learning.   Productivity Over Promises  The AI landscape is evolving quickly, and the tools will only become more powerful. But if companies can’t turn that power into usable, measurable improvements in daily workflows, they’ll fall short of expectations, potentially wasting millions.   Leadership must drive the shift from AI hype to AI habit. By prioritizing alignment between people, tools, policies, and strategy, they can unlock the productivity that AI promises. The stakes are high, since businesses and employees that use AI effectively will replace those that don’t.    source

Since the 1980s, technologies like ITIL (Information Technology Infrastructure Library) and ITSM (IT service management) have been on the scene. Their goals were to improve IT’s service culture, yet adoption has been uneven. What’s working and what’s not — and how can CIOs take best advantage of these technologies to improve service?  ITIL and ITSM  ITIL is a framework of 34 practices developed to assist IT in aligning its activities and strategies with the business. There are seven ITIL guiding principles:  Progress iteratively with feedback  Collaborate and promote visibility  Think and work holistically  Keep it simple and practical  In contrast, ITSM focuses specifically on the service elements of ITIL (i.e., delivering technology solutions and support to users).   ITSM emphasizes:  Aligning IT with the business, with the help of metrics tracking  Engendering interdisciplinary-team collaboration   Co-developing application between IT teams and users through methodologies like DevOps  Knowledge sharing and continuous improvement   Customer-centric service process and self-service  Rapid processing of user requests and faster incident response and resolution   Very large enterprises and companies in highly regulated industries tend to be the ones that formally adopt ITIL, but the collective emphasis of ITIL and ITSM on service, coupled with user demands for better IT service, have made almost every company CIO cognizant that the IT service culture must improve.  Related:From Promise to Practice: How IT Leaders Can Turn AI Hype into Tangible Value How Technology Improves IT Service  CIOs understand that pep talks about service in staff meetings only go so far — and that there are some IT staff members (e.g. system programmers, DBAs, and others who are highly technical) who are just not user-oriented. Despite this, CIOs are using new technologies that transform IT processes into being more service-oriented.   Here are five key technologies that are improving IT service:  1. Help desk  Help desk solutions now come with process automation, such as the auto generation of help desk request tickets and automated updates on work in process that flow directly from the help desk to users. There are also built-in metrics that measure factors such as how long a help desk request has been open, what the mean time to response for help desk requests has been, etc. Help Desk software has omnichannel integration, so a user can communicate with Help Desk personnel by phone, through chat, or go through standard systems communications. Help desk personnel can screen-share and work in real time with users on problem resolution. Help desk solutions have come a long way since the days of users booking their requests, and then waiting to hear from IT.  Related:Budget-Smart Tech for CIO-CFO Alignment 2. DevOps  From application inception, through design, development, prototyping, change management, testing and launching, users and IT now collaborate on development teams, giving everyone transparent access to project work. This is a departure from the traditional waterfall development of applications, where users handed system requests to IT and then IT went away into design and development phases that went on for months without the users knowing how a new application was progressing.  3. Self-help portals  Easy to use, point and click online portals that list services and enable users and customers to serve themselves without having go to other people to get things done, have the ability to exponentially increase IT’s service capabilities and reach. The key is designing these portals for both functionality and ease of use. Portals must be also be rendered “thoughtful enough” to handle the exceptions to every process and to rapidly route requesters to persons who can help. Common IT tasks found in self-help portal service catalogues include, but are not limited to, requests for new software and hardware, requests for new passwords or password resets; requests for onboarding new employees that include giving them user IDs, passwords and access privileges; and knowledge base FAQs that assist users with IT self-help.  Related:How Immigration Crackdowns Are Changing IT Talent Management 4. Para-user IT tools  No-code and low-code application development gives users tools of their own to develop applications, often with no or minimal support from IT. In this way, users can create applications without having to wait for IT services. IT still has a “service hand” in this process. It must be available for users when they are stumped by a low- or no-code problem, or when additional IT help is needed to integrate an app with underlying IT infrastructure.   5. Process integration and automation  A heavy machinery manufacturer was able to automate its requisition, approval and PO issuance process from days to minutes. It did it with the help of IT integration technologies like ETL (extract, transform, load), which bridged the integration gap between disparate systems in purchasing, accounting and other company departments. IT then automated many of the repetitive processes in requisitioning, ordering, and the approval process. This saved time for employees and improved their work environment. Unsurprisingly, IT’s service reputation also improved. E-commerce companies have seen similar gains from IT automation in online ordering, shipping and returns, because effectively streamlined and automated processes please customers and build loyalty.   Lagging Service Areas  While technology has advanced service initiatives for IT, there are still areas that continue to underperform. Here are four of them:  1. IT and business alignment  More CIOs now sit at the corporate strategic table, but there are still CIOs in mid- to small-sized companies who function “heads down,” worrying more about day-to-day operations than about the value technology is delivering to the company, or the caliber of service IT is providing.  2. Focus on value  The number and the velocity at which IT projects must be completed often obscures the reasons why they were undertaken in the first place — and the business value they were expected to produce.  Too often, a project completes, and IT then moves on to the next project — without stopping to examine if a project really delivered the business value that was intended. Users can be that way too — but upper management and the board are not.   3. Thinking and working