Information Week

Today’s CIOs move faster than their predecessors given the ever-increasing speed of business and technology innovation. In addition, what’s happening on the geopolitical stage is changing — sometimes intra-daily — creating uncertainty and complicating everything from planning and strategy to evaluation and execution.   “What we see with our clients [and] our work inside our company are things that we cannot control like tariffs and changes to decisions on countries you can and cannot operate from. [These decisions] are happening at alarming frequency with no control,” says Niranjan Ramsunder, UST Niranjan Ramsunder, CTO at digital transformation solutions company UST.  “For example, you find that one day a decision is [made]. Next day it is rolled back. The third day it is coming back. The fourth day it is reduced in impact. Most of our clients’ approach is, ‘Let’s take a breath.’ Maybe we’ll lose some temporary margins, but there is no change of business model until we get clarity about where [the situation] stabilizes.”  Other CIOs are trying to conserve cash as much as possible because they can’t anticipate what’s coming. That can be a death sentence when it comes to smaller businesses.  Bigger companies can weather that storm for some time, Ramsunder says, adding that companies don’t change their prices quickly; they don’t change their services quickly; they don’t change their sourcing of items and services quickly, [and] they are waiting for things to happen. “So the first response to the volatility is, ‘Let us see where it ends. Let me not make any big decision right now. Let me conserve cash. Let me not do a project that completely reimagines my business and let me just see whether I can hold on [to] what I have for some more time and survive because the innovative companies burning cash are struggling.’” Related:Organizations Every CIO Should Consider Joining Because of the current geopolitical climate, UST is being impacted by restrictions on travel visas, and there is concern about whether employees will be stopped at airports.  “All those things are day-to-day issues that are bothering us. That is addressed by let’s wait and see where it goes,” says Ramsunder.  Samir Datt, global technology strategy and architecture leader at global consulting firm Protiviti, also sees hesitation among CIOs when it comes to hiring. On one hand, they want to staff up, and on the other, they’re facing a skills shortage best addressed by variable staffing. Meanwhile, they’re delaying the start date of some projects.  “As a consulting organization, we’re talking about a project that we’re going to help with, and then we’re asked to wait one more month before we start so everything is in order. And then, let’s wait a few more weeks,” says Datt. “Then we start, and there’s a pause for some reason. Similarly on technology purchases, be that a major system implementation or hardware to support it or move to the cloud. There’s that emphasis and more scrutiny on the business value. There’s [also] the question of what happens if we wait. How much longer can we continue the way we are and just see where things shake out in the market?”  Related:Former CISO of Costco, Disney, and Now Exec at Axonius Talks CISO Strategies Samir Datt, Protiviti During the pandemic, many organizations ran Monte Carlo simulations so they could be prepared for several different scenarios. So, instead of having a Plan B, companies had many plans (B, C, D, E, etc.). The unusually high frequency of change in 2025 is proving more difficult to navigate, however.  “Our ability to respond at speed has become the crucial differentiator,” says Ramsunder. “My biggest learning is that my organization is ready to turn on a dime and really respond quickly to those changes, [but] there’s no way I can predict and prepare and be ready, because what we’re doing may be completely different from what we have to do.”  Pandemic Lessons Learned Can Help Now  Related:Digitalization — Is It Time for Humans to Intervene? BlackLine CIO Sumit Johar says the current geopolitical situation is disrupting access to hardware, software, and skill sets, but that organizations are better prepared to navigate the current uncertainty than they were when COVID hit.  “A lot of individuals and companies that I know, are far more prepared this time than they were [during] the COVID time in terms of having the diversity of skill sets and suppliers,” says Johar. “At times I hear about laptops getting stuck at Customs or something that we ordered is taking much longer, but the diversification we built over time is allowing us to at least move things around and run our business. So far, it’s been manageable thanks to all the lessons we learned during COVID times, but you never know. Things are moving so fast, we’re just keeping fingers crossed that it does not get worse.”  When the pandemic hit, CIOs and other leaders were forced to revisit the basics of how to structure their organizations, and the lessons were clear: Organizational structure affects risk, as does the structure of partner relationships.  “You cannot have a single point of dependencies. The diversification we built in the last three or four years since COVID is helping us right now,” says Johar. “My team is far more geographically distributed than it was five years back. The same goes with my supplier landscape. I have more than one supplier for everything I do.”  Meanwhile, Blackline is constantly reevaluating in the context of today’s hyper volatility, and Johar participates in CIO councils, which he considers very important.  Sumit Johar, BlackLine “When it comes to changing dynamics, [such as] AI and driving innovation, there are several things that people like me are dealing with right now. There is an impact on how you hire people, staffing, how to structure your organization,” says Johar. “There is an impact on risk. I’m also responsible within my organization for managing the risk of data, privacy and security, and AI is bringing a new dimension to that risk. It’s

In today’s uncertain economic environment, enterprise CIOs and CTOs are under tremendous pressure to do more with less. Budgets are tightening. R&D spend is under scrutiny. Hiring freezes mean teams must be stretched further. Add in the disruption and promise of AI, and technical leaders are operating in an environment that demands boldness, innovation, and speed.  Despite today’s headwinds, the mission of technical leaders remains unchanged. They still must scale transformative initiatives, deliver measurable business value, and keep their organizations nimble and resilient. The key lies in striking the right balance between innovation and efficiency, making smarter “build vs buy” decisions, and organizing for AI-driven productivity without sacrificing long-term progress.  Know What Matters and What Doesn’t  In economic downturns, technical debt can quietly become your biggest liability. CEOs don’t want to hear about tech debt. They want outcomes. That’s why ruthless prioritization and good hygiene are critical. Focus on what matters most and say no to anything that doesn’t directly tie to business value.  That doesn’t translate to trading quality for quantity. Reserve that mindset only for early-stage proofs of concept. Focus on fewer, more impactful projects that align with strategic priorities. Every new tool or process should pass the litmus test of solving a real business problem while integrating logically, scaling sustainably, and delivering value in a measurable way. Identify and prioritize near-term wins that drive clear business value, like those that improve reliability, reduce cost, and unlock growth.  Related:Organizations Every CIO Should Consider Joining The key is treating your roadmap like an investment portfolio. Resilience is created by a diversified blend of essential maintenance, incremental improvements, and long-term innovation.   Balance the Now and the Next  Rather than shelving innovation during lean times, technical leaders should rebalance. Identify and prioritize near-term wins that drive clear business value, but don’t abandon foundational investments like modernization and infrastructure upgrades. If the business case still holds, find a sustainable way to keep making progress, even if it’s at a slower pace.  Technical leaders should also improve productivity by finding what makes the company more efficient and the product improvements more agile. This means doing the right work in the right way and with the right people. In today’s world, that requires a framework to experiment with AI and apply what you learn quickly, and a team that’s trained and empowered to use AI tools, such as code generation, AIOps, observability and more.   Related:Former CISO of Costco, Disney, and Now Exec at Axonius Talks CISO Strategies Your customers won’t wait for you. If you go dark while a competitor finds ways to deliver more value, you risk losing market share and credibility.   Double Down on Developer Efficiency and Smart Partnering  Be strategic about what your team builds. When the lift is light, the team can learn, or if the capability is a core differentiator, building is advantageous. Otherwise, keep a bias towards buying, especially when speed matters. Buying not only can increase time-to-value but also give you leverage by freeing up teams to focus on what only your organization can uniquely do. Moreover, buying also helps with learning cheaply for an eventual build as you learn more about the problem complexity and your needs.  Make thoughtful choices about where to invest your team’s energy and where external partners can accelerate your goals. Build only when it gives you a lasting competitive edge. Otherwise, buy and customize to get value faster.   Technical leaders should also avoid long-term vendor lock-in during times of budgetary scrutiny and rapid technological change. Technology changes every few months. Choose partners that give you short-term wins and long-term options. The best ones will evolve with you and help you pilot new approaches quickly without big commitments. Look for composability, flexibility, and transparency.  Related:Digitalization — Is It Time for Humans to Intervene? Define the Future, and Build an Organization That Gets You There  The structure of your organizational design should follow your company’s strategy. Start with a clear vision of your future-state organization, then work backwards. If AI will change how your product works or how value is delivered, your squad structures should reflect that. Be prepared to reshape teams, roles and processes ahead of the change you are seeking   It’s also important to show up through strong leadership. Create clarity and protect focus. Make sure your team knows their work is essential and show them the impact they’re making. Celebrating wins is equally important as rapid experimentation and agility.  My team recently led a full-scale platform modernization while maintaining our legacy system. This wasn’t just a migration; it was a balancing act of speed, continuity and quality that required what I consider a heroic effort. Engineers were stretched thin across systems, racing to deliver business outcomes while building for the future.  This experience proved it’s all about the people. You need high-caliber IT and engineering leaders who can drive forward under pressure and make the hard calls. The right talent makes efficiency and innovation possible simultaneously.  The CIO or CTO who wins in times of economic uncertainty is the one who focuses the most on value, velocity, and vision. Technical leaders should emerge with a sharper team, a more resilient platform, and a stronger sense of purpose. Prioritize well, invest wisely, and keep building.    source

Regardless of where you are in your career, AI is likely changing how you work and will likely continue to do so at an accelerating pace for the rest of your working life. To adapt to these changes, make the most of AI’s possibilities, and ensure workers have the skills they need to thrive. Organizations should revive and reimagine mentorship so employees at all stages of their careers can benefit.  It’s not that workers, by and large, are afraid of AI. A recent Deloitte survey reveals how both early-stage and experienced workers are adapting to this technology. According to the survey, over 80% of both early career and older workers say that AI has improved the quality of their work.   However, AI is impacting younger and older workers differently, and each needs different kinds of learning experiences to adapt to and thrive in an AI-driven world.   Consider how AI is increasingly automating the very foundational tasks from which early career workers learn and gain experience, for example, preparing reports, analyzing simple data sets, and note-taking in meetings. In the past, new hires gained experience and confidence by gradually taking on responsibility in a supportive, task-driven environment. With AI’s ability to perform many of these foundational tasks, early career workers need more support than ever in gaining the foundational experience and skills needed to develop and grow.   Related:How CIOs Are Navigating Today’s Hyper Volatility On the other hand, many tenured workers want a clear roadmap for learning how AI can improve their work quality and efficiency today. They need to learn how to infuse AI into their everyday work and use it to help guide their decision-making.  Luckily, early career and tenured workers have an excellent solution to these skills challenges — each other. Reimagining the mentorship relationship could mean that each group can teach the other critical skills.  Done correctly, it’s an effective solution to a very real challenge. But at the moment, both early career and experienced employees say they struggle to find the right mentorship opportunities. According to Deloitte’s survey, less than a third of early career workers have either formal or informal mentors, and only a fourth of tenured workers say they do.  The AI Generation Gap  Not surprisingly, the survey found that younger workers feel more comfortable with AI than older workers. In fact, one person interviewed said AI was “that first person you ask before going to a manager” for advice on a variety of topics.  Other findings from the survey underscore this age difference:  Related:The Top Nightmares Keeping CIOs Awake at Night 83% of early career workers use AI tools in their jobs, compared to 68% of tenured workers.  77% of early career workers believe AI will help them move up in their careers, compared to 56% of tenured workers.  As remote and hybrid work continues, there have been fewer opportunities for hands-on training and experiential learning, which has limited early career workers’ ability to acquire foundational skills. These include communication and emotional intelligence. For some, AI is compounding that problem. According to the survey, 28% of early career workers report having fewer opportunities for on-the-job learning due to AI, compared with 19% of tenured employees.  Since AI will likely supplant at least some early-stage skills that are (or were) fundamental to learning and building a career, like compiling economic and financial trends data, making pitch decks or spreadsheets comparing company valuations, early career workers need new ways to build the experience and skills needed to develop and grow.    The Positive Results of Two-Way Mentorship   In two-way mentorship programs, tenured workers teach their younger colleagues interpersonal “soft” skills that will help them be better teammates and develop leadership skills. (These skills are in demand — 38% of early career workers say they want to learn to communicate better, for example.) Mentorship programs have been shown to reduce skills gaps, and younger workers get to experiment in a safe setting.  Related:CTO Daniel Blanchard on Overseeing Tech Strategy for Several Hotel Chains On the other side, tech-savvy younger workers can help tenured employees learn the AI skills they need to blossom later in their careers.  (This is especially necessary since tenured workers are more likely to prioritize leadership development over improving their AI skills.)  When combining their experience with newfound AI savvy, older workers can better determine things like the most appropriate AI investments in their organization, how best to train workers, and how to improve productivity and deepen business insights.   Rebalancing Knowledge for a Changing Era  In an organization that endeavors to develop both tech and human skills, AI power users will gain the skillsets needed to advance their careers and their company’s AI efforts. While the two-way mentorship model is unconventional — at the moment — it could become the norm as more organizations adapt their workforces to ever-changing realities.   Consider a two-way mentorship model to keep your talent adaptable and prepared as technology continues to change how we work.  source

It’s bad enough that many CIOs experience relentless stress during work hours. Some leaders are also unlucky enough to drag stress into their sleep time, resulting in disturbing nightmares.  In a survey of 1,750 working US adults in the conducted by Each Night, a sleep resource platform, more than three-fifths of workers reported having a nightmare about their jobs. CIOs are no exception.  How does nighttime stress affect CIOs, and how do restless nights impact their work performance? Here’s how three leaders cope, and in most cases, learn from, their nightmares.  Ann Funai  My nightmare — and surely the No. 1 nightmare of most CIOs — would be flubbing anything related to regulations or revenue, says Ann Funai, CIO and vice president of business platforms transformation at IBM. “There’s also enormous pressure now to move fast with the latest-and-greatest in AI systems, but many important systems have been running safely and securely for decades, and any CIO whose ‘modernizations’ disrupt that will incur the wrath of the business and market.”  Funai notes that in corporate IT, the word “fast” is rapidly becoming weaponized. “Things are going faster and faster, and it’s still never enough,” she explains. Yet, for Funai, that can actually be a fun part of the job, exploring new technologies and business approaches. She warns, however that that there are still times when precision is what’s needed most, and when moving fast may actually lead to legal or other problems.  Related:CTO Daniel Blanchard on Overseeing Tech Strategy for Several Hotel Chains A good CIO lives with one foot in technology and the other in business, Funai says. “My nightmares help remind me of that.” She observes, for instance, that there are still many areas where AI can be used to free-up time, accelerate outcomes, and experiment. “Let’s call that ‘fun risk’” Funai says. “Yet in other key areas the acceptable amount of risk is zero, and we absolutely shouldn’t be trying new tech unless we know the systems, culture, and validation, and are all collectively ready for it.”  Carter Busse  Carter Busse, CIO at cloud integration platform provider Workato, admits to having nightmares about artificial intelligence. “With adoption on the rise, a nightmare that keeps me up is employees using non-sanctioned AI tools and unintentionally feeding sensitive data into unauthorized LLMs without understanding the risks,” he explains.  Something that consistently wakes Busse up at night are increasingly sophisticated phishing attacks. During daylight hours, he sees a consistently growing number of attacks, many attributable to AI. “These phishing attacks leave businesses increasingly exposed,” Busse states. “AI has lowered the barrier for bad actors to launch convincing attacks, and it’s happening constantly.”  Related:Why Companies Need to Reimagine Workplace Mentorship in the Age of AI In Busse’s nightmares, as in real life, threats target his organization’s most vulnerable entry point: people. “A well-crafted phishing email to someone in finance could lead to a fraudulent wire transfer or, worse yet, unauthorized access to our production systems,” he explains. “Even if you’re not a high-profile target, a single misstep could bring your operations down, and the business with it.”  A steadily growing number of risks, as well as nightmares, are pushing Busse to evolve. “We’re investing in AI-driven, data-triggered automation that can detect suspicious behavior and respond instantly, such as shutting down access when phishing is detected,” he says. “We’re also focused on applying the SOAR framework — Security, Orchestration, Automation, and Response — to proactively manage threats using our platform.”  Mike Vance  Mike Vance, is a veteran CIO, having served in the role at Steak ‘n Shake, handbag designer Vera Bradley, and Matilda Jane Clothing. “Market uncertainty is something we’re all dealing with right now,” he says. “As a CIO, you’re constantly wondering what’s coming next and how it’ll impact your organization.”  Related:Tech Leadership Questions Potential Impact of Tariffs on Healthcare Many technology professionals feel like they’re in a holding pattern, Vance says. “Meanwhile they’re missing real opportunities to make their operations better and more efficient, inadvertently overlooking opportunities to optimize variables within their sphere of influence.”  But there’s a bright side, too. “Uncertainty can actually be the perfect time to step back and ask, ‘What can I control here?’,” Vance explains. He suggests focusing on cleaning up current processes, improving efficiency, and making your team more agile. “By concentrating on controllable factors, CIOs can position their organizations for flexibility and resilience even in the midst of uncertainty.” And, therefore, will likely sleep better at night.  source

Daniel Blanchard says he was a “typical tech geek” at the beginning of his career at Boeing, where he was manager of systems administration. There he worked on programs for the Army, Navy, National Institute of Standards and Technology, and the Defense Advanced Research Projects Agency.  These days he’s chief technology officer at IHG Hotels & Resorts, where he leads the vision and strategy for an organization with more than 6,000 hotels globally. They include InterContinental Hotels & Resorts, Crowne Plaza, Holiday Inn, and Kimpton Hotels and Restaurants. As CTO, he ensures that technology runs well with service-level agreements and tracking other metrics. Prior to IHG, he spent more than 25 years in IT business management, strategy, transformation, architecture governance, and program management.  “As you would expect, there are similarities and differences between running technology for an airline and a hospitality company,” Blanchard says. “On one hand, both organizations leverage solutions for reservation systems, loyalty systems, operational systems, and so on. And both organizations’ technology strategies focus on providing systems and support for travelers and for colleagues to help them keep the business up and running.”  In an email interview, Blanchard talks about his career as a tech leader and his strategies on everything from cloud migration to maintaining agility and a growth mindset.  Related:The Top Nightmares Keeping CIOs Awake at Night From Transportation to Hospitality  Blanchard has worked in multiple industries, from airlines to manufacturing to government and hospitality.  Before IHG, Blanchard worked at Delta Air Lines as vice president for IT infrastructure and reliability. He led IT reliability transformation across Delta worldwide. Before that, he worked for more than 20 years at Marriott International.  “At Delta, I always took very seriously the role I played in helping people travel for big life events like an interview for a new job, a business opportunity, a relative’s graduation, or a long-saved trip of a lifetime,” Blanchard says. “The trust people put in us to get them where they needed to be inspired me to do my best to provide high reliability and consistent IT services to get them there.”  Throughout his career, Blanchard has taken an interest in planning and control systems, from defense contracting to mission-critical IT management in airlines and now the relationship management and strategic deployment of the advanced technology in hospitality.  “All are great industries and challenges filled with opportunities to learn and be a better leader,” Blanchard says. “I really would not change my experience working in a variety of industries for anything.”  Related:Why Companies Need to Reimagine Workplace Mentorship in the Age of AI Overseeing the Tech of Multiple Hotel Chains  Blanchard supports more than 100 applications globally throughout IHG. He started at the company in February 2020 as the COVID-19 pandemic took hold of the world. He oversaw the tech strategy for the company’s hotel chains during the health crisis.   Recently Blanchard oversaw IHG’s cloud migration, moving the organization’s entire environment to the cloud. He was mindful to complete the work at a pace that keeps guests and owners happy. Now he’s worked on rolling out IHG’s Guest Reservation System and Revenue Management System to the organization’s hotels globally.  Dan Blanchard “I can’t tell you how proud it makes me when a potential owner actually wants to migrate to our technology because they can see the value to their business,” Blanchard says. “That is a great feeling.”  These days, Blanchard is busy overseeing control metrics that keep IHG running smoothly. Some of the metrics are industry standard, and others he and his team have developed over the years.  “While these metrics are critical, the most important thing I’ve found is to have a single person responsible for each of them — whether they are control metrics (where a more junior member of the staff may be accountable) or results metrics (where one of my direct reports will lead),” Blanchard explains. “Sometimes there are inputs and dependencies that are out of an individual team member’s control. But in a dynamic and fast-paced environment like the hospitality industry, a clear and single accountability model is critical to developing leaders who understand how to navigate these difficult situations while still achieving the overall objective.”  Related:Tech Leadership Questions Potential Impact of Tariffs on Healthcare At IHG, Blanchard has instituted an “asset-light model,” in which his team doesn’t control day-to-day operations, policies, or procedures of the franchised hotels. However, they still work closely together to deliver a “consistent brand experience for guests.”  Blanchard cites four key areas that are integral to his role: working with the chief information security officer to keep systems reliable and secure, communicating a viewpoint throughout the organization on architecture and innovation, building a technical environment to deliver products rapidly throughout the company, and focusing on organizational and business management.  In hospitality, Blanchard sees the role of IT in helping hotel staff address customers’ preferences. IHG recently signed a deal with Genesys, which offers an AI-powered, cloud-based experience orchestration platform, to boost the guest experience. IHG’s largest-ever customer experience center transformation consisted of migrating thousands of guest services agents as well as thousands of toll-free numbers across eight global sites.   “Some guests want a touch-free transaction where they can pass the desk and go straight to their room without talking to anyone,” Blanchard says. “Others want a high-touch approach where their every need is taken care of by staff that has the information and systems available to deliver superior customer service.”  Agility and a Solid Digital Experience  Key skills and challenges as an IT leader include maintaining a high level of agility and effective communication.   “The bigger challenge is to maintain a high level of agility — and not just in the sense of adopting an agile development methodology but rather being light on your feet and nimble to provide solutions to evolving business needs,” Blanchard says.   For Blanchard, he bases his IT decisions on how hotel owners can drive revenue and provide a solid digital experience for guests. In addition, he plans

Virtually every current C-level IT executive spent many years toiling as a tech staff member, eventually becoming a team leader, and finally arriving as a CIO or another C-level executive. The journey to senior leadership isn’t a one-time activity, says Karla Eidem, North America managing director for the Project Management Institute, a non-profit project management professional organization. “It’s the culmination of not just your performance, but also of your visibility and relationships,” she observed in an online interview. “It’s about what you deliver, but also how you engage across the organization and how others perceive your leadership,” Eidem says. Gaining that Business-Leader Mindset For any IT professional who aspires to become a CIO, the key is to start thinking like a business leader, not just a technologist, says Antony Marceles, a technology consultant and founder of software staffing firm Pumex. “This means taking every opportunity to understand the why behind the technology, how it impacts revenue, operations, and customer experience,” he explained in an email. The most successful tech leaders aren’t necessarily great technical experts, but they possess the ability to translate tech speak into business strategy, Marceles says, adding that “Volunteering for cross-functional projects and asking to sit in on executive discussions can give you that perspective.” Related:Should CIOs Start Hiring for Attitude? Your personal brand also matters, Eidem says. Build a reputation for working with people outside of your department, with the goal of being top-of-mind when executive opportunities arise. Leaders can elevate their presence by leading strategic, cross-functional projects, Eidem says. “These initiatives often put you in front of other departments, senior stakeholders, and decision-makers,” she says. “It’s not only about the project — it’s about demonstrating influence, adaptability, and the ability to drive outcomes that matter to the business.” Build your understanding of business concepts, such as strategic decision-making, goal-setting, revenue streams, governance, and compliance, Archie Payne, president of IT recruiting firm CalTek Staffing, advises. “Learn how to align tech initiatives with business objectives by translating them into terms like ROI, competitive advantage, cost reduction, and productivity improvement,” he says in an online interview. “Enrolling in an MBA program or an executive program focused on digital leadership or business strategy can both accelerate and validate your learning,” he said. Related:Forget the Career Ladder, AI Demands a Loop Mentors and Peers CIOs rarely have solo success stories; they’re built up by the teams around them, Marceles says. “Colleagues can support a future CIO by giving honest feedback, nominating them for opportunities, and looping them into strategic conversations.” Networking also plays a pivotal role in career advancement, not just for exposure, but for learning how other organizations approach IT leadership, he adds. Don’t underestimate the power of having an executive sponsor, someone who can speak to your capabilities when you’re not there to speak for yourself, Eidem says. “The combination of delivering value and having someone champion that value — that’s what creates real upward momentum.” Networking and Connections Count “I’ve heard the phrase, ‘your network is your net worth,’ and I couldn’t agree more,” Eidem says. “Networking isn’t about collecting contacts or business cards. It’s about building genuine, trust-based relationships — the kind that expand your thinking, challenge your assumptions, and open doors you didn’t even know existed.” One of the best ways to build connections is by joining a professional community, Eidem says. “When you surround yourself with people who share your values and your goals, you not only grow your skills — you grow your circle.” Related:An IBM CIO Approaches AI With Both Optimism and Caution A community can become a source of learning, mentorship, and even career opportunities. “When you show up consistently, contribute, and connect authentically, that’s when the real magic of networking starts to happen,” she adds. Networking is necessary for serious career progress, Payne says. There’s no guarantee a CIO role will be available within your current company, and an active professional network can open the door to external executive opportunities. “A strong network is a source of learning and knowledge in other ways as well, such as helping you stay current on industry trends and develop the skills and competencies most needed for executive leadership,” Payne says. Building relationships outside of IT can also accelerate the shift from tech-thinking to business-thinking, Payne says. Partnering with leaders in marketing, finance, operations, and other areas strengthens your understanding of those functions and how technology can help these individuals achieve their goals. A New Beginning Moving into the C-suite requires a shift from technical execution to strategic leadership, Eidem says. “This means knowing how to inspire action, communicate clearly, navigate ambiguity, and work exceptionally well with people,” she explains. “Leadership at that level is less about having all the answers yourself and more about shaping the environment where others can succeed.” The biggest mistake Marceles sees is ambitious IT professionals staying within their comfort zone. “Being great at code or infrastructure won’t get you into the CIO seat if you can’t manage people, build consensus, or think long-term,” he says. “You need to be just as comfortable in a boardroom as you are in a dev environment.” Marceles also stresses the importance of operating with curiosity and humility. “The moment you stop learning or begin thinking you’ve ‘arrived,’ you’re already falling behind.” source

For over a century, the metaphor for career success has been a ladder. Each rung was a new title, a bigger paycheck, a tangible step forward. But in the age of AI, that metaphor is cracking. Ladders presume stability, predictability, and linear growth. AI brings none of those. Instead, it brings disruption, redefinition, and constant reinvention.  The real image for the modern career? A loop.  In an AI-shaped economy, your value is no longer measured by how high you climb, but by how effectively you re-enter the learning loop, again and again. Master something, apply it, let it be disrupted, then adapt, re-learn and reapply. That’s not failure; It’s the new normal.  From Hierarchy to Hybridity  AI isn’t slotting into our existing systems; it’s redrawing the blueprint. It’s flattening org charts, dissolving job boundaries, and remapping how work gets done. Today’s marketing analyst might need to understand prompt engineering. Tomorrow’s software engineer might be collaborating with an AI product manager. Roles are becoming hybrid by necessity, not by design.  This means the traditional cadence of education → job → promotion → retirement is increasingly obsolete. Degrees still matter, but they’re becoming tickets to the first loop rather than guarantees of long-term relevance. Lifelong learning has gone from a professional virtue to a professional survival skill.  Related:Should CIOs Start Hiring for Attitude? The End of ‘Set It and Forget It’  Most enterprises still treat learning and development like a one-off event: a training session here, a conference there. But with AI evolving so quickly, that approach just doesn’t cut it anymore. Learning needs to be continuous, not just something you do now and then.  Forward-looking CIOs are already shifting from training programs to learning cultures. They’re investing in cross-functional cohorts, project-based upskilling, and real-time feedback systems. They know that adaptability now outperforms specialization. In fact, according to LinkedIn’s 2024 Workplace Learning Report, adaptability is the most in-demand skill of the moment. That underscores the need for technology professionals to continuously evolve and embrace lifelong learning.  We don’t need more credentials. We need more flexibility.  The Hidden Cost of Linear Thinking  There’s also an emotional aspect to consider. The ladder model rewards predictability and penalizes pivots. On the other hand, loops normalize detours. That mindset shift could unlock a more sustainable model of growth, especially in a world where burnout is epidemic and job tenures are shrinking.  Related:How IT Leaders Can Rise to a CIO or Other C-level Position But for this to work, we need to destigmatize career “resets.” We need to stop asking “Why did you leave?” and start asking “What did you learn?” IT leaders should be hiring for curiosity and reinvention, not just credentials and tenure. In an AI future, the ability to adapt is the credential.  Reinvention as a Team Sport  If we want people to adapt, we can’t leave them to do it alone. Institutions need to evolve, too.  Educators must think beyond the degree. What if universities offered “career loop subscriptions” — flexible, modular learning tracks designed around emerging technologies, giving people the freedom to pivot, re-skill, and explore new paths over time?  Enterprises must rethink career pathing. Instead of promoting based on time-in-seat, what if they rewarded successful skill reinventions? What if job architectures allowed for horizontal loops where an engineer can explore design, or a data analyst can enter product strategy without derailing their progress?  Policy needs to catch up, too. If we want a truly adaptive workforce, we need to support portable benefits, tax-deductible reskilling, and public-private partnerships that make reinvention accessible, not just aspirational.  Related:An IBM CIO Approaches AI With Both Optimism and Caution Case in Point: Tech’s Self-Disruption  Even in the tech world — the birthplace of today’s AI revolution — leaders are having to relearn the rules. Engineering orgs are reconfiguring roles to account for AI co-pilots. Designers are adjusting to AI as a creative partner. Meanwhile, product managers are running sprints alongside data scientists and AI ethicists, an unfamiliar but increasingly essential collaboration.  The smartest CIOs I know aren’t asking, “How do I level up?” They’re asking, “What loop am I in, and what’s next?”  This isn’t just the future. It’s now. And the enterprises that win won’t be the ones with the steepest ladders. Instead, they’ll be the ones building the best loops.  Building Your Own Loop  So how do you apply this mindset to your own career in technology leadership?  Audit your loops. Look at the past five years of your work. Where did you stretch? What new tools, skills, or mindsets did you pick up? These loops are your real growth stories.  Diversify your inputs. Read outside your industry. Attend events outside your job function. Cross-pollination fuels reinvention.  Make reinvention visible. Document your learning process. Share it and teach others. In the loop model, your learning path is part of your portfolio.  Measure growth differently. Don’t just track promotions or compensation. Track projects launched, skills acquired, and people mentored. The best loops leave impact.  The Future Isn’t Vertical — It’s Circular  AI didn’t kill the career ladder. It just made it irrelevant for most technology leaders. In its place is something more dynamic, more human and, ultimately, more empowering.  We’re all loopers now. If we embrace that, we can build a world of work that doesn’t just survive AI but thrives because of it.  source

Change is often hard, and when it comes to technology it can risk becoming chaotic. Jason Noel, CTO for EY Americas Consulting, is navigating such tech tumult in his role — which was established to help the consulting giant get its arms around emerging technologies such as AI. He faces such questions as how does new tech change and accelerate the business. As EY helps its clients sort out the AI-enhanced world, Noel says, introspective considerations also surfaced. “How do we use AI to improve the value of what we’re bringing to our clients immediately?” he asks. Furthermore, Noel says there was a need to find a strategic direction in relation to the workforce evolving and the business developing along with technology. In this video interview, he discusses his marching orders in this role as well as establishing a strategy based on capabilities rather than products in order to establish resilience in the face of rapid-fire change. source

Skills shortages abound for critical positions in the IT job market, and now there is the threat that many young people might opt for career paths other than IT, given the looming threat of elimination that AI poses for entry-level jobs.  Should CIOs Consider Hiring for Attitude?  There are all kinds of ways to describe “attitude,” but at its most elementary level, the Cambridge dictionary defines it as, “The way you feel about something.”  As a CIO, I looked for “attitude” in my staff. I was seeking out individuals with a positive, “can-do” approach to their work, and an earnest desire to succeed as a team member and as a technical talent.  There were times when I was willing to take chances on attitude—like the time I decided to engage a lessor skilled, but highly enthusiastic and motivated junior person to take over a critical technical role on a project when the senior person who had been assigned was demonstrating lethargy and disinterest.  The experiment was fraught with risk, but it worked.  Will ‘Hiring Attitude’ Solve the IT Skills Shortage?  Hiring individuals with a “can-do” attitude and raw but undeveloped talent won’t solve every critical skills need on an IT staff — but it can help.  Senior database analysts, software engineers, and network and security specialists generally have five to six years of experience in order to gain the technical know-how that makes them experts at what they do. Individuals at intermediate skill levels have three to four years under their belts, and junior staffers average from six months to two years’ experience.  Related:How IT Leaders Can Rise to a CIO or Other C-level Position It’s also important to consider the time it takes to find these skilled individuals in the job market, and to weigh that against the idea of training someone internally.  At present, it takes an average of 41 days to hire an IT professional and 62 days to hire an engineer. The timeline starts when a company posts a new position and ends on the day that the newly hired employee begins work. This timeline doesn’t necessarily show the timelines needed for hard-to-fill positions, such as a data scientist, a senior systems programmer, a data base administrator, or a CISO. For these positions, some CIOs I’ve spoken with have shared that it takes months and even years to find someone.  CIOs are facing other workplace realities, too. The turnover rate for technology workers in 2025 is estimated to be between 20-25%, which is the highest turnover rate for any industry sector, and the direct and indirect costs of replacing an employee who leaves in 2025 is projected to be between half to four times the amount of that employee’s salary. These findings, coupled with actual “on the ground” experiences of hiring IT talent, suggest that alternative strategies like “hiring for attitude” could be a valid approach.  Related:Forget the Career Ladder, AI Demands a Loop How Do You Do Hire Attitude and Convert It into Skills?  We all want enthusiastic employees who will make positive contributions to their work teams and demonstrate “can do” attitudes — but in the end, they must be able to do the jobs that they are assigned to. If you choose to hire for attitude, how do you convert raw talent and enthusiasm into a skillset that benefits IT?  Here are three key strategies:  1. Develop your talent prospecting approach  If you want to find a “diamond in the rough,” you have to know how to look for it.  Many of the “can do” high attitude employees on your staff will initially be “poor ore” from a skills standpoint — but what IT leaders should look for is both the “can do” attitude and the native ability of an employee to learn an IT discipline quickly.  You might have a “can do” employee who is a great team member, but who has already shown that they have only moderate to low raw talent for the skills you need. This is not the best person to invest in for IT technical talent development. On the other hand, a junior maintenance programmer you have on staff is independently coding and developing automated robots at home. He exhibits natural skills, aptitude and enthusiasm for robotics, and might be a great candidate to develop for a factory automation role.  Related:An IBM CIO Approaches AI With Both Optimism and Caution 2. Stress training and development in your IT culture — and then see if it can be done!  It’s not enough to find raw and energized talent in your workforce. You have to foster and develop it. The choice IT often makes is to send individuals off to seminars and certification programs. However, it’s only through real work on projects that employees can apply what they’ve learned and gain confidence in their skills.  To make this happen, project assignments should be aligned with skills development, and the senior members of staff must be committed to serve as on the job mentors for the junior workers. This can be easier said than done, as not all senior staff members will be willing to share their knowledge, and some are just bad teachers.   This is why CIOs and IT leaders should first evaluate their senior technical staff’s ability and willingness to train and mentor, before moving forward with programs to train and break in raw talent.  3. Assign junior staff member real work projects  If you have strong mentoring skills in IT senior staff, there is no better proving ground for junior employees than the world of real work on projects. This is how junior staffers “cut their teeth,” learn from their mistakes, and develop confidence and skills.  Hiring raw talent with attitude is an IT strategy for developing the skills base that IT needs for the future. It can never replace being able to use a highly skilled technical person that you either have on your staff or are able to recruit, but it can be a complementary

To err is human, so GenAI errors may simply be a sign of an imperfect, almost human-like, technology. Still, whether generated by humans or AI, errors are always a good thing to avoid.  GenAI errors aren’t just frequent, but common, warns Matt Aslett, director of research, analytics, and data with technology research and advisory firm ISG. “Anyone using GenAI, either personally or professionally, should be aware that GenAI models are designed to produce a realistic replication of the content on which they have been trained, rather than a factual representation,” he observes in an email interview.  Large language models (LLMs), for example, are trained to generate written content that’s grammatically valid, based on the statistical predictability of the next word in a sentence, Aslett explains. “LLMs have no semantic understanding of the words generated,” he notes. “As such, there’s no guarantee that the content generated will be factually accurate.”  GenAI and large language models have an uncanny ability to sound very accurate, confident, and knowledgeable, says Mike Miller, a senior principal product leader at Amazon Web Services. “They can sound eloquent and converse in language that feels authentic,” he observes in an online interview. “Catching errors from GenAI can be difficult, because if you ask GenAI how it came up with an answer, it might give you a reasonable-sounding explanation that could still be made up or false.”  Related:Smart AI at Scale: A CIO’s Playbook for Sustainable Adoption Embrace Verification  GenAI models should never be used in isolation, Aslett advises. “Users should always verify the factual accuracy of both the content generated by GenAI and its cited sources, which could also be a fabrication.”  Individuals must ultimately rely on their own knowledge to assess the accuracy of content produced by GenAI and identify errors, Aslett says. Enterprises, meanwhile, can apply validation models to assess a GenAI model’s output and then compare the content against approved data and information sources to identify likely errors.  GenAI mistakes can be addressed in several ways, says Satish Shenoy, global vice president, technology alliances and GenAI at business process automation firm SS&C Blue Prism. “These techniques vary, including logging and auditing to predictive debugging to using LLMs as a judge, or even placing a human-in-the-loop,” he states in an email interview. “Governance and guardrail frameworks are also being used in conjunction with the LLMs to catch generative AI errors.”  Danger Ahead  Given GenAI’s inherent lack of accuracy, decisions should never be based solely on its output, Aslett says. “There’s a risk that could result in an organization making costly business decisions based on erroneous information.” Additionally, enterprises disseminating insights generated by GenAI run the risk of regulatory fines and reputational damage if the information proves to be inaccurate.  Related:Navigating Generative AI’s Expanding Capabilities and Evolving Risks There are many examples of GenAI errors, Aslett observes, For example, Air Canada’s chatbot providing a customer with inaccurate information. He also notes that lawyers have been fined for submitting court filings incorporating inaccurate information, such as citing legal cases that never existed.  Improving Accuracy  The best approach to improving GenAI accuracy is by adopting a variety of processes, Aslett advises. “This could include training a model on its own data and information, although that’s potentially costly in terms of training and maintaining the model,” he says. Another approach is prompt engineering, in which a user instructs the model to use only specific data or information when generating its response. “This is a short-term solution that only applies to the individual prompt as the additional information is not retained by the model,” he cautions.  Related:How Companies Are Making Money from AI Projects Miller advises using automated reasoning, a scientific discipline that leverages mathematics and logic to prove theorems or facts. “We use automated reasoning to generate policies or procedures and guidelines,” he says. “Automated reasoning provides higher confidence in correctness than traditional testing methods, although it still depends on underlying assumptions about component behaviors and environmental models.”  Once a GenAI error has been detected, begin tracing the problem, Shenoy suggests. Start by analyzing the error and the potential factors that led to its occurrence. “Fixing the model could involve tuning or training it,” he notes. In some instances, the model may need to be tweaked. “It’s also important to bolster any governance and control frameworks that are in place to minimize errors from slipping through the cracks.” Additionally, to avoid future errors, it may be necessary to test the data and the process involved. “If humans are involved in any part of the process, they should also be trained.”  Correctness Counts  Checking GenAI for correctness is essential since it allows enterprises and customers in various industries to use AI in applications where safety, financial, or health information is provided to customers, Shenoy says.  source