Information Week

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

Searching for a CIO position with an organization that focuses on critical public issues rather than the bottom line? Then consider a government CIO post. Seeking a job as a government CIO offers a chance to make a real impact on the lives of citizens, says Aparna Achanta, security architect and leader at IBM Consulting — Federal. CIOs typically lead a wide range of projects, such as upgrading systems in education, public safety, healthcare, and other areas that provide critical public services.  “They [government CIOs] work on large-scale projects that benefit communities beyond profits, which can be very rewarding and impactful,” Achanta observed in an online interview. “The job also gives you an opportunity for leadership growth and the chance to work with a wide range of departments and people.” Seeking mission-driven work is an growing differentiator not just for existing CIOs, but for next-generation, executive-level leaders, says Pat Tamburrino, Jr., chief operating officer at the non-profit NobleReach Foundation and former chief human capital officer for the U.S. Department of Defense. In an email interview, he notes that having the ability to make positive change is something today’s workforce is actively seeking. “According to a 2024 Deloitte report, 89% of millennials and 86%t of Gen Z expressed desire for a sense of purpose in their job,” Tamburrino says. Related:How IT Leaders Can Rise to a CIO or Other C-level Position How to Start Your Government CIO Job Search Begin your government CIO job search by either gaining experience in government IT or working closely with tech-related public-sector projects, IBM’s Achanta recommends. “Apply for leadership roles in technology departments, such as a deputy CIO position or an IT manager,” she says” Also helpful is building a comprehensive understanding of government systems, laws, and procurement processes, as well as having a mix of technical and leadership skills, she adds. “Therefore, networking with government officials and staying informed on digital transformation trends and policy can open doors to CIO roles.” A key decision is deciding whether to seek a federal CIO position or to focus on a state or local post. “This may vary depending on interests and goals,” Achanta says.  “For example, federal CIO roles often involve complex systems and large budgets, but more bureaucracy,” she says. “On the other hand, state-level CIOs have more room for innovation, since they typically deal with smaller, regional challenges. “Local government CIOs are more hands-on and can see the direct impact of their work and see their impact on the community.” Related:Forget the Career Ladder, AI Demands a Loop Consider the Downsides Government CIOs often face challenges their corporate counterparts rarely or never face.  “Being a government CIO might mean dealing with slow processes and bureaucracy,” Achanta says. “Most of the time, decisions take longer because they have to go through several layers of approval, which can delay projects.” Government CIOs face unique challenges, including budget constraints, a constantly evolving mission, and increased scrutiny from government leaders and the public.  “Public servants must be adept at change management in order to be able to pivot and implement the priorities of their administration to the best of their ability,” Tamburrino says. Government CIOs are often frustrated by a hierarchy that runs at a far slower pace than their enterprise counterparts. “The fast-paced environment of innovation seen in the private sector can be challenging [for government CIOs] to keep up with, making it harder to move quickly or to try new ideas,” Achanta says. “It’s also common to experience budget limitations, making it difficult to get the newest technology or hire additional staff.” Politics can also influence decisions, making it hard to focus on what’s best from a management or technology point of view. Related:An IBM CIO Approaches AI With Both Optimism and Caution Working hard and being undercompensated for one’s value is an important drawback in the public sector, says Jeff Le, managing principal at 100 Mile Strategies, a public-sector tech consulting firm. Another important downside is the lack of sole decision-making authority, he observed in an email interview. Le notes that government IT leaders are often frustrated that critical decisions can require the blessing of up to three stakeholder groups: Political appointees to sign off on strategy issues; Budget guardians who approve all expenditures; Lawyers who need to study and sign off on risk mitigation. Given the number and complexity of issues faced by government CIOs, it’s not surprising that burnout is common. Final Thoughts The best way to approach public service is to connect with peers already holding government positions, Le advises. “Understanding how to navigate government culture and bureaucracy is also essential for long-term success.” source

Like many chief information officers, IBM CIO Matt Lyteson is approaching artificial intelligence both carefully and optimistically. In a recent online interview, he observed that no CIO can afford to switch focus on every new “it” thing, yet AI is certainly anything but a passing fad. “My approach to AI includes traditional AI, generative AI, agentic AI, and business automation — using any and all of these tools in concert to help transform IBM’s business into a digital operating model,” Lyteson explains. “While there are some incredible new AI capabilities, one thing hasn’t changed — it’s best to take a holistic view rather than simply deploy another tool.”  Lyteson notes that generative AI in particular has the potential to impact every business area. “Out of the box it can often be a blunt tool — a sledgehammer for the work of a chisel.” To gain full value out of generative AI, he believes that new adopters should focus on the specific business areas that are ripe for transformation.  An Initial Project  Lyteson’s first AI project was deploying generative AI, trained on the firm’s own data, to the entire employee population, and doing so in a secure, trusted way. “We already had a number of AI-powered assistants and automations, and users were familiar with these for specific tasks or functions,” he explains. While relatively simple and uncomplicated, the new project represented a significant step forward, providing a general, enterprise-wide solution with context-specific generative capabilities.  Related:EY Americas Consulting’s CTO Noel on Getting Close to Innovation Matt Lyteson The project helped Lyteson move forward on three basic goals. “First, we wanted to give IBMers a space to play — a safe, trusted, and secure environment to use and experiment with AI on our own business data,” he says. The approach was designed to minimize risk. It also helped Lyteson equip employees with a user interface that worked specifically for them. “This helped build on our cultural transformation at IBM, pointing employees toward how AI can augment their work, and signaling our expectation for this to happen day-to-day in every corner of the business.”  The approach, Lyteson notes, also established an important reference point for future expansion. “We’ve introduced additional assistants and agents since then [yet] it remains the single-entry point for employees looking to access the continually expanding knowledge and capabilities we’re rolling into the back-end technology.”  Effective Planning  Successfully implementing AI demands thoughtful intentionality at the outset, Lyteson says. “Our plan began with getting key stakeholders around a table and zeroing-in on crystal clear objectives,” he explains. “We didn’t allow for mission creep, and we also didn’t dive into complex environments with no specific plan.”  Related:Tech Company Layoffs: The COVID Tech Bubble Bursts Lyteson says project planning began in November 2023 and was completed in less than 90 days, resulting in the first iteration’s release. “Because the capabilities of the technology keep evolving, as does our acumen, we continue to iterate based on feedback and solving specific needs,” he notes. “For example, routing between different assistants.”  Expectations Met  Lyteson says that in many respects the project was about showcasing the incredible potential of generative AI to the full breadth of IBM’s employee — more than 200,000 team members doing all sorts of work worldwide. “That was a key first step to having AI be an enabler, and we have now deployed more and more AI-powered agents and business automations across the enterprise.”  “Thanks to an upfront commitment to intentionality, our project met initial expectations, and we’ve been able to continually learn and iterate since then,” Lyteson says. “A CIO’s work is a team sport, especially at large companies.”  Lyteson notes that it was also interesting to see how generative AI often spits out nondeterministic answers when some business functions absolutely require deterministic responses. “This was a key learning for us,” he says. “As a result, we took steps to help our various user bases understand [the concern] — proactively communicating, offering education sessions, and most importantly embedding the right messaging within the user interface itself.”  Related:How to Hold Efficient Team Meetings A Final Thought  A single project such as this one should be table stakes for CIOs, Lyteson says. “Learning to initiate AI projects, experiment rapidly, and capitalize on data-backed learnings is key to ongoing success,” he observes. “This was a nice example of all of those elements rolled into a single package.”  source

A global survey of 600 IT leaders highlights this concern, revealing that while the potential of Gen AI to transform work is widely recognized, the digital infrastructure in most organizations simply isn’t ready. This isn’t about minor tweaks; it’s about a fundamental need to rebuild. Even though 79% of IT leaders believe Gen AI has the potential to elevate the quality of work, fewer than half think their current digital workplace tools effectively support productivity, engagement, and innovation. 89% agree that only a foundational overhaul of digital infrastructure will enable Gen AI to truly deliver on its promise. This insight from Lenovo’s Work Reborn Research Series 2025, identifies both the opportunities and the critical challenges presented by Gen AI and highlights an urgent need for IT transformation. In order to realize Gen AI’s true value, organizations must rethink their operational frameworks, starting with their technology infrastructure. The Urgent Need for IT Transformation The need for IT transformation becomes clear when we consider the many opportunities Gen AI affords. This isn’t just another software upgrade; it’s a catalyst to redefine how teams collaborate, ignite creativity, and boost their productivity. Imagine breaking down global communication barriers with instant translations, freeing skilled employees from repetitive work to focus on groundbreaking ideas and streamlining complex processes with intelligent automation. Related:Navigating Generative AI’s Expanding Capabilities and Evolving Risks It’s no surprise that nearly half (49%) of IT leaders see improving the employee experience as a top priority. In fact, according to the 2025 CIO Playbook: It’s Time for AI-nomic’s, enhancing productivity is the leading business goal for organizations in 2025. However, the report also highlights significant hurdles faced by IT teams, particularly in delivering personalized experiences and automating IT support. While the majority of IT leaders (63%) understand the importance of a tailored digital workplace, the lack of adaptable tools leaves many organizations struggling to unlock productivity with a one-size-fits-all approach. And despite 61% recognizing the value of Gen AI-driven IT support, the actual implementation remains a challenge. The answer isn’t a simple patch; it requires a complete architectural shift. Gen AI needs to be embedded at the very core of a workplace IT strategy. When integrated seamlessly, AI can intuitively understand individual employee needs, automatically configuring their tools and workflows. Picture AI-powered IT support that anticipates and resolves issues before they even impact productivity. Related:How Companies Are Making Money from AI Projects To realize these gains, proactive change is needed. While Gen AI offers the potential to free up teams to focus on strategic innovation, it also gives organizations a chance to set themselves apart from the rest. In fact, 76% of IT leaders say that businesses not empowering employees with AI will lag in the next one to two years. Three Steps to an AI-Ready Workplace I believe IT leaders are at a pivotal moment, but they must move beyond incremental gains and strategically weave Gen AI into the heart of their organizations. This demands a strong partnership with executive peers, a shared vision, and the courage to lead significant change. “Reinventing Workplace Productivity” outlines three critical steps on this journey: 1. Simplify and Personalize the Employee Experience: Tailor digital tools, workflows, and applications to individual roles and working styles, enabling employees to leverage Gen AI for maximum productivity and innovation. 2. Automate IT Processes with AI: Utilize Gen AI to manage workplace devices and IT support, freeing up resources for higher-value tasks and ensuring seamless operations. 3. Transform Workflows for Value Creation: Re-engineer existing workflows and processes to fully realize the capabilities of Gen AI and drive innovation. Related:How to Avoid the AI Customer Experience Cliff By prioritizing personalization, intelligent automation, and reimagined workflows, companies can build a workplace that truly unlocks the potential of Gen AI. The Work Reborn Research Series 2025 is just getting started. Building a Gen AI-driven future requires strong leadership and a willingness to rebuild. Are you ready to take the necessary steps? source

Today’s tech leaders must simultaneously navigate nonstop change on all fronts from cloud shifts and AI disruption to rising security threats. To reveal important insights that only real-world experience can forge, InformationWeek is reaching out to top CIOs about what they’re facing now, how they’re handling it, what advice they have for aspiring chief information officers, and how they got where they are.   These conversations aren’t just about tools and platforms. Every aspect of a seasoned tech leader’s experience is touched on from their business strategy and leadership approach to the real-world lessons that come from building resilient tech organizations when the world itself seems to morph beneath their feet.  Today we’re talking with Gill Haus, managing director and CIO at Chase, the consumer and community banking division of JPMorgan Chase & Co. Haus leads one of the largest and most complex tech operations in financial services, where he’s focused on scaling innovation, modernizing legacy systems, and keeping millions of customers connected and secure every day.  Here is what he had to say.  What has your career path looked like so far?  I always knew I wanted to work in technology. Even in middle school and high school, my evenings were spent watching science fiction, eating pizza, listening to electronic music, and coding. I realized early in my career that I enjoyed solving problems and helping others solve problems together. This has been a self-fulfilling prophecy and enabled me to get the roles I’ve had in my career. I didn’t set out to become the CIO for Chase; I focused on solving problems, helping people around me, and learning, and that paved the way for the roles I held.  Related:Tech Company Layoffs: The COVID Tech Bubble Bursts What are the highlights of your career path?  I’ve had the chance to work with some amazing tech brands like eBay, PayPal, Capital One, and JPMorgan Chase. Each experience has been unique, but a standout moment was during the pandemic at Chase. We supported millions of small businesses through the Paycheck Protection Program. It was tough, with long nights, but the teamwork and positive impact were incredibly rewarding.  I’m also proud of modernizing our flagship chase.com website and mobile app, moving them fully to the cloud. Things move quickly here, and in the past few years of rapid transformation, every day feels like a highlight. We serve millions of customers, helping them achieve their dreams and goals through the ongoing modernization of our technology.  What are you excited about in your current role?  Related:How to Hold Efficient Team Meetings I’m thrilled to influence and steer the technology direction of a firm that touches people, businesses, corporations, and governments globally. The technology challenges at our scale are second to none, and it’s exciting to work at the heart of the economy, where money plays a central role in people’s lives. It’s exhilarating to be part of a place where we can deploy technologies — from cloud to mobile to generative AI — to millions of customers. Despite being a large company, our people make it feel like a small family, making it enjoyable to come to work every day.  Please tell us about your advocacy efforts for diversity, inclusion, and culture.  Everyone brings their own unique insights and perspectives, offering fresh ways of thinking. It’s important to tap into this dynamic, which is why we aim to attract the best and brightest from around the world to JPMorgan Chase. I make sure everyone’s voice is heard, including junior employees, by involving them in discussions like monthly business reviews, so they can learn and contribute. We foster a meritocracy where you can be yourself and thrive.  What emerging trends are you excited about in the industry?  I’m excited about the promise of generative AI tools and how they will likely compel teams to focus on the fundamentals, offering benefits beyond just writing software. These tools can generate code and reduce engineer toil, but they also emphasize the importance of foundational computer science principles. By addressing the time-consuming and error-prone processes that have slowed us down — through unit testing, component testing, end-to-end testing, performance testing, and software release — and focusing on automating these areas, we can adopt generative AI more easily. This focus will help us use these tools to solve complex problems quickly and with greater confidence. A fully automated end-to-end software delivery lifecycle is crucial for scaling these technologies, allowing engineers to focus on the right tasks and boost productivity.  Related:5 Smart Hardware Moves CIOs Are Making During Tariff Uncertainty What do you do for fun or to relax?  I’ve loved electronic music and computers since I was a kid, and that hasn’t changed. In my spare time, I dive into coding and check out the latest tech. I still DJ and hit the clubs now and then, because embracing what makes us happy is key to recharging. This balance helps me bring my best to work, sparking creativity, and getting things done effectively.  What advice do you want to give to young people considering a leadership path in IT?  Embrace curiosity and welcome change because change is unstoppable. Leaders who cherish change and guide others through it are rare, but they are our future leaders, especially as change accelerates. If you’re feeling scared, that’s a good sign — it’s a part of learning. Embrace the healthy dose of fear that comes with growth every day.  source

Ask technologists and enterprise leaders what they hope AI will deliver, and most will land on some iteration of the “T” word: transformation. No surprise, AI and its “cooler than you” cousin, generative AI (GenAI), have been hyped nonstop for the past 24 months.  But therein lies the problem.  Many organizations are rushing to implement AI without a grasp on the return on investment (ROI), leading to high spend and low impact. Without anchoring AI to clear friction points and acceleration opportunities, companies invite fatigue, anxiety and competitive risk. Two-thirds of C-suite execs say GenAI has created tension and division within their organizations; nearly half say it’s “tearing their company apart.” Most (71%) report adoption challenges; more than a third call it a massive disappointment.  While AI’s potential is irrefutable, companies need to reject the narrative of AI as a standalone strategy or transformational savior. Its true power is as a catalyst to amplify what already works and surface what could. Here are three principles to make that happen.  1. Start with friction, not function  Many enterprises struggle with where to start when integrating AI. My advice: Start where the pain is greatest. Identify the processes that create the most friction and work backward from there. AI is a tool, not a solution. By mapping real pain points to AI use cases, you can hone investments to the ripest fruit rather than simply where it hangs at the lowest.  Related:Navigating Generative AI’s Expanding Capabilities and Evolving Risks For example, one of our top sources of customer pain was troubleshooting undeliverable messages, which forced users to sift through error code documentation. To solve this, an AI assistant was introduced to detect anomalies, explain causes in natural language, and guide customers toward resolution. We achieved a 97% real-time resolution rate through a blend of conversational AI and live support.  Most companies have long-standing friction points that support teams routinely explain. Or that you’ve developed organizational calluses over; problems considered “just the cost of doing business.” GenAI allows leaders to revisit these areas and reimagine what’s possible.  2. The need for (dual) speed  We hear stories of leaders pushing an “all or nothing” version of AI transformation: Use AI to cut functional headcount or die. Rather than leading with a “stick” through wholesale transformation mandates or threats to budgets, we must recognize AI implementation as a fundamental culture change. Just as you wouldn’t expect to transform your company culture overnight by edict, it’s unreasonable to expect something different from your AI transformation.  Related:How Companies Are Making Money from AI Projects Some leaders have a tendency to move faster than the innovation ability or comfort level of their people. Most functional leads aren’t obstinate in their slow adoption of AI tools, their long-held beliefs to run a process or to assess risks. We hired these leaders for their decades of experience in “what good looks like” and deep expertise in incremental improvements; then we expect them to suddenly define a futuristic vision that challenges their own beliefs. As executive leaders, we must give grace, space and plenty of “carrots” — incentives, training, and support resources — to help them reimagine complex workflows with AI.  And, we must recognize that AI has the ability to make progress in ways that may not immediately create cost efficiencies, such as for operational improvements that require data cleansing, deep analytics, forecasting, dynamic pricing, and signal sensing. These aren’t the sexy parts of AI, but they’re the types of issues that require superhuman intelligence and complex problem-solving that AI was made for.  3. A flywheel of acceleration  The other transformation that AI should support is creating faster and broader “test and learn” cycles. AI implementation is not a linear process with start here and end there. Organizations that want to leverage AI as a competitive advantage should establish use cases where AI can break down company silos and act as a catalyst to identify the next opportunity. That identifies the next as a flywheel of acceleration. This flywheel builds on accumulated learnings, making small successes into larger wins while avoiding costly AI disasters from rushed implementation.  Related:How to Avoid the AI Customer Experience Cliff For example, at Twilio we are building a customer intelligence platform that analyzes thousands of conversations to identify patterns and drive insights. If we see multiple customers mention a competitor’s pricing, it could signal a take-out campaign. What once took weeks to recognize and escalate can now be done in near real-time and used for highly coordinated activations across marketing, product, sales, and other teams.  With every AI acceleration win, we uncover more places to improve hand-offs, activation speed, and business decision-making. That flywheel of innovation is how true AI transformation begins to drive impactful business outcomes.  Ideas to Fuel Your AI Strategy  Organizations can accelerate their AI implementations through these simple shifts in approach:  Revisit your long-standing friction points, both customer-facing and internal, across your organization — particularly explore the ones you thought were “the cost of doing business”  Don’t just look for where AI can reduce manual processes, but find the highly complex problems and start experimenting  Support your functional experts with AI-driven training, resources, tools, and incentives to help them challenge their long-held beliefs about what works for the future  Treat AI implementation as a cultural change that requires time, experimentation, learning, and carrots (not just sticks)  Recognize that transformation starts with a flywheel of acceleration, where each new experiment can lead to the next big discovery  The most impactful AI implementations don’t rush transformation; they strategically accelerate core capabilities and unlock new ones to drive measurable change.  source

According to a McKinsey survey, 61% of executives feel that at least half of the time they spend making decisions — much of it in meetings — was ineffective. Just 37% of respondents said their organizations’ decisions were both timely and high quality.  When it comes to creating effective and efficient meetings, preparation matters, says Peter Wood, CTO at tech recruitment firm Spectrum Search. He believes that the most high-quality and results-focused IT team meetings always start with one thing: clarity. “Everyone knows why they’re there, and that sets the tone for the entire session,” Wood explains in an online interview. “If you can’t sum up the purpose of the meeting in a single sentence, it’s probably not ready to happen.”  Justin Maynard, a vice president at IT consulting firm Resultant, stresses the need for detailed advance planning. “All of the normal things that characterize an efficient meeting need to be in place — clear objectives, an agenda shared in advance, and active engagement are all needed,” he says in an email interview.  Building Efficiency  Limit meeting participants to necessary stakeholders, advises John Russo vice president of healthcare technology solutions at healthcare software provider OSP Labs. He believes that meetings should be short, focused, and outcome driven. “We respect time by sticking to priorities and reserving discussions for items that truly require group alignment,” Russo says in an online interview. “It’s all about purpose and preparation.”  Related:5 Smart Hardware Moves CIOs Are Making During Tariff Uncertainty Russo says this approach works well, since it encourages participants to arrive prepared, allowing decisions to be made faster with a sense of accountability. “Especially in healthcare IT, where time is critical, this structure ensures meetings aren’t just check-ins — they’re strategic tools that drive momentum without wasting energy.”  Every meeting should have a clear reason to happen — whether it’s syncing up on a sprint, solving a specific problem, or aligning priorities, says Trevor Young, chief product officer at security and compliance specialist Security Compass. “Send out an agenda ahead of time so attendees can come prepared,” he advises in an email interview. “Don’t invite the whole world — just the folks who really need to be there.”  Wood stresses the importance of valuing participants’ time. “Most engineers I’ve worked with dread meetings that drag on or don’t lead to any actionable outcomes,” he says. “If everyone leaves the meeting knowing exactly what they need to do, or with a clear understanding that nothing has changed, then the meeting has done its job.”  Related:How CIO Kristy Folkwein Is Building an IT Team for ADM’s Digital Future Stay focused, Maynard recommends. “IT people always want to dig into the details and solve every issue,” he says. This can result in meetings that fall into a bottomless rabbit hole. “While you may solve one problem, you have wasted everyone else’s time.” Arranging a direct connection with the individuals best suited to solve the issue makes more sense.  Costly Mistakes  Trying to control every meeting aspect is a common trap, Russo says. “Leaders who dominate the conversation miss out on valuable input.” Also, holding meetings just for routine’s sake builds no value. “Empowering the team and knowing when not to meet is often more impactful.”  One of the biggest mistakes leaders make is assuming that speaking relentlessly means making an impact, Wood says. He believes that many meeting moderators talk too much. Meetings shouldn’t be a platform for long, windy monologues. “You’re there to diagnose, not lecture.” Asking the right questions and then listening is key. “Engineers, for instance, don’t need to be micromanaged — they need to feel trusted to tackle problems,” he states. “The way you run your meetings should reflect that trust.”  Related:The Pros and Cons of Becoming a Government CIO One simple rule is to avoid wasting time, Wood says. If a meeting doesn’t lead to any progress, end it. “This approach forces you to treat every meeting as something valuable and ensures that you’re always focused on moving forward.” He also recommends rotating meeting leaders, including giving junior team members a chance to offer their views. “This encourages ownership and brings in new perspectives.”  Closing Thoughts  You’ll feel it when a meeting drags, Young says. “People are zoning out, multitasking, or asking the same questions as last week.” If meetings always run long, lack clear outcomes, or leave participants wondering what to do next — it’s not doing its job. Additionally, if a participant says, “This could have been handled by email,” it’s a clear sign that the meeting was probably unnecessary.  Make reflection part of the process, Russo suggests. Regularly ask the team what’s working, involve all parties, and keep meetings dynamic. “A quick check-in every few weeks will prevent drift and reinforce a culture in which everyone values time and focus.”  source