Information Week

Kristy Folkwein accidentally took a class in college meant for computer science majors. She went to school with the intention of following in her mother’s footsteps as an executive secretary. But that class changed her path. “I loved it. I had an affinity for it, and that’s how I got into technology,” she tells InformationWeek at the beginning of our conversation.   Today, Folkwein is the senior vice president and CIO at ADM, a big player in human and animal nutrition. She speaks to InformationWeek about how she developed her career at three different companies and how she is working to transform ADM, a company built by acquisitions.   A Career in Three Chapters   Folkwein’s first job out of college was at Ashland, a specialty chemicals company. “[I] grew up through the ranks of IT project manager, leading projects, being an analyst. I did a little development in the early years. So, that’s where it all started,” she shares.   She considers her time at Ashland the first chapter of her career. As she worked her way up at that company, her IT leadership skills grew.   “I had the opportunity to work on an ERP transformation of SAP across five different business units,” Folkwein recalls. “I was the mini CIO for the distribution company and then the distribution company in Valvoline.” (Ashland would later spin off Valvoline.)  Related:The Pros and Cons of Becoming a Government CIO She spent 17 years at Ashland before moving on to Dow Corning. The leader of the company approached her about stepping in as CIO, and she accepted. That big change marked the beginning of the second chapter in her career.   “After 17 years and then going to a new company, I knew no one,” says Folkwein. “And so it was the change management influence, meeting people where they are, understanding that business, their experience is what made them who they were. I learned a lot about how you get things done. Empathy, how you execute.”   Kristy Folkwein She considers the eight years she spent at Dow as her years of honing her customer-facing skills. “How do we help our customers? How do we bring value through technology in new ways through different types of web portals? The way we work with our customers to streamline our processes … Those were my big learnings in those years.”  A recruiter with ADM approached Folkwein, and the timing was right. She was ready for the third chapter of her career.   “I felt like all of my experiences had prepared me to hopefully come to ADM and really make a difference in helping [it] to transform,” says Folkwein.   Building the Team at ADM  Related:Visa CISO Subra Kumaraswamy on Never Allowing Cyber Complacency ADM is an established company — in business for 120 years — and it is one that has grown through acquisitions. “As far as my team goes with ADM, we’ve had a bit of a journey on the talent side,” Folkwein shares.   Acquisitions bring plenty of talent together, but that talent is spread out across different functions, teams, and structures. Folkwein has worked to create a more central IT organization that focuses on consistent delivery of the technology and skills necessary to build ADM’s digital future. ADM also works with a managed services partner to support its legacy systems.   “We have a multi-region approach. We have people spread around the world to support our organization, and we’re currently in the process of standing up an India hub to continue to allow us to get the scale we need to [provide] the IT services our organization needs,” says Folkwein.   Over the past nine years that Folkwein has been with ADM, the team stands out as one of her greatest accomplishments.  “We’ve built an incredible IT team that is very committed to ADM,” she says. “While delivering daily operations, [we are] also leaning into how we deliver value … through technologies like generative AI.”   Technology Delivery and Transformation   Folkwein has a lot of the concerns that most CIOs can relate to: legacy technology and modern, evolving cyber threats.   Related:How a New CIO Can Fix the Mess Left by Their Predecessor “The way things were developed in decades of old, there wasn’t as much standardization, documentation, good IT practices. So, it keeps me up at night. Just keeping all of this running until we can get to more modern technology,” she says.   And then, there are the industry-specific challenges. Quality control in the nutrition space is essential, and Folkwein’s IT organization needs to ensure it has the capabilities to support the production, tracking, and delivery of quality products in the food space.   Right now, Folkwein and her team are invested in transforming ADM’s ERP system. She is always thinking about how to support effective business operations, manage the cost to serve, and provide value.   “At the end of the day, the big slogan is: We deliver what we commit,” she says.   For Folkwein, and all CIOs, AI is now a part of that equation. How can she use that technology to deliver value for ADM?   “We’re using AI to help us to create flavors, to be more productive, to provide information to help our people in the plant,” she says. “There’re so many different possibilities.”  As she considers all of the challenges and the goals her team is tackling today, data surfaces as the most important thing.   “Why do you put in in place a common ERP?” she asks. “Why is ADM, right now, trying to standardize and consolidate from acquisitions? It’s the data. Why is the data more important than ever? Because data is the fuel of AI, and we’re going to be all competing with capabilities through AI.”  source

The emergence of prompt engineering as a high-demand skill caught the attention of enterprise CIOs almost overnight. As AI adoption accelerated, organizations scrambled to bring in specialists capable of squeezing more value from large language models (LLMs). Salaries soared, and internal teams found themselves either vying to justify those costs or struggling to match the specialists’ results. For AI policy advisors and developers, the ability to adapt has become increasingly demanding. Prompt engineering has always ultimately hinged on clear communication and careful framing of the problem. That still holds true, yet prompt engineering is reaching a pivotal moment. As LLM use continued inside the enterprise, the discipline morphed into system-level context management, where reusable frameworks, memory integration, and orchestration pipelines replace handcrafted prompts. The discussion has moved past whether prompt engineers should be hired. The new question is how they can future-proof the AI workforce. The Rise — and Limits — of Prompt Engineering Prompt engineering exploded into the mainstream alongside ChatGPT’s debut. It promised fast, fine-tuned results without any model training, provided you knew the right words. For a brief period, prompt experts were indispensable. They could prototype LLM-powered tasks, document summarization, code generation, and data extraction, in a fraction of the time it once took. Related:How Companies Are Making Money from AI Projects Yet limitations surfaced quickly. Prompts proved brittle across use cases and tough to scale across business units, and relied heavily on individual expertise. The ability to reproduce and audit prompts was low. Truly, the prompt engineer was never meant to be the star of the show; it was a symptom of missing architecture. What CIOs Are Experiencing on the Ground CIOs soon faced a new budget dilemma: pay premium salaries for prompt engineers, place them somewhere between data science and IT, or find an alternative path to scalable AI. Industry trackers such as Levels.fyi reported total compensation approaching $335,000 for top prompt specialists, while startups and consultancies added to the bidding war. Business units launched shadow AI projects, intensifying internal demand. Even when prompt engineers delivered, their work was frequently locked away in personal notebooks and ad-hoc spreadsheets, making successful proofs of concept hard to replicate at scale. From Prompts to Platforms Prompt engineering is not disappearing; it is transforming. Enterprises are shifting from hand-crafted prompts to intelligent context frameworks, options that are inherently more scalable, consistent, and auditable. Retrieval-Augmented Generation pipelines, orchestration libraries such as LangChain, CrewAI, and DSPy, vector databases that store persistent memory, and new open standards like the Model Context Protocol (MCP) are leading the charge. Related:How to Avoid the AI Customer Experience Cliff These technologies encapsulate the context an LLM needs, turning prompts into modular function calls. As one CIO recently told me, “Prompt engineering is evolving into context architecture, and that requires systems thinking, not just clever phrasing.” CIO’s Options for Rewriting the AI Workforce Playbook With the mystique fading, enterprises are replacing large prompt-engineering teams with AI platform engineers, MLOps architects, and cross-trained analysts. A prompt engineer in 2023 often becomes a context architect by 2025; data scientists evolve into AI integrators; business-intelligence analysts transition into AI interaction designers; and DevOps engineers step up as MLOps platform leads. The cultural shift matters as much as the job titles. AI work is no longer about one-off magic, it is about building reliable infrastructure. CIOs generally face three choices. One is to spend on systems that make prompts reproducible and maintainable, such as RAG pipelines or proprietary context platforms. Another is to cut excessive spending on niche roles now being absorbed by automation. The third is to reskill internal talent, transforming today’s prompt writers into tomorrow’s systems thinkers who understand context flows, memory management, and AI security. A skilled prompt engineer today can become an exceptional context architect tomorrow, provided the organization invests in training. Related:Beyond Productivity: How to Cut Costs With Generative AI Where the Savings Appear Compensation: US salaries for prompt engineers range from roughly $175,000 to $335,000. By comparison, AI-platform engineers and context architects typically earn $150,000 to $240,000. Hiring a small, versatile platform team often costs less, while reducing dependency on a narrow specialty. Reusability: A prompt engineer may spend eight to 20 hours crafting a new use case, whereas a context architect working with RAG and MCP frameworks can often do the job in 2-6 hours. Across 20 use cases a year, the difference can translate to more than $36,000 in labor savings for a mid-size team. Tooling: Consolidating multiple prompt-specific platforms into a unified, self-hosted context framework can eliminate $30,000 to $100,000 in annual licensing fees. Operational efficiency: Standardized context injection patterns reduce errors, lower support tickets, and cut onboarding time. One CIO reported a 40% drop in internal AI support requests after moving to vector-based memory and automated system prompts. Overall, platform-oriented AI teams achieve higher cost predictability, easier scaling, and far greater enterprise reusability, typically at a lower total annual cost than a prompt-engineer-centric model. A Quick-Action Playbook for CIOs Audit existing prompt-engineering efforts, tools, teams, outcomes, and map where duplication or brittleness exists. Invest in frameworks that eliminate one-off prompt writing and make context reusable. Upskill analysts and developers so they can design context-aware systems, not just clever prompts. Standardize how context is delivered, through MCP, a similar protocol, or a custom approach with comparable audit trails. Measure success by reproducibility, user trust, and maintainability rather than the novelty of a prompt. Prompt engineering isn’t dead, but its peak as a standalone role may already be behind us. The smartest organizations are shifting to systems that abstract prompt complexity and scale their AI capability without becoming dependent on a single human’s creativity. For CIOs, the question is no longer, “Do we hire a prompt engineer?” Instead, it’s, “How do we architect intelligence into every system we build?” And that answer begins with context. source

For better or for worse, AI has been extremely busy transforming virtually every aspect of daily life. However, utilizing insecure AI tools can be far more dangerous than the typical wonky enterprise software. In this archived keynote session, Juliet Okafor, CEO and founder of RevolutionCyber, explains how to discover your entire AI ecosystem, assess any posture and model risks, and implement security at runtime. This segment was part of our live virtual event titled, “Generative AI: You’re Already Behind.” The event was presented by InformationWeek and ITPro Today on May 15, 2025. Watch the archived “Generative AI: You’re Already Behind” live virtual event on-demand today. source

Day one as CIO can mean opening up a box of leftover IT nightmares. Whether you’re an experienced or first-time CIO, getting started in a new post will be a challenge. To make matters worse, it turns out that the previous leader didn’t only drop the ball but left behind a total shambles that threatens to degrade or destroy IT performance. It’s now your turn to set things right.  Start the rebuilding process by understanding the full scope of the situation, advises Ravi de Silva, founder of compliance advisory firm De Risk Partners. “This means reviewing systems, vendors, policies, and personnel,” he explains in an online interview. It’s important to look at what’s broken, as well as what still works and why. “Before making changes, take a step back and assess the landscape,” de Silva says. “Decisions made without that context can do more harm than good.”  The assessment phase is your reconnaissance mission, says Zaira Pirzada, an IT leader at security threat exposure management services firm Hive Pro, and a former Gartner security and risk management analyst. “You can’t fix what you don’t understand, and assumptions will kill you faster than any zero-day exploit,” she warns in an email interview.  Seek and Study  Begin the reconstruction process with a comprehensive asset inventory — not just the obvious servers and workstations, but every device touching the network, Pirzada suggests. “I’ve seen CIOs get blindsided six months-in by discovering critical systems they didn’t know existed.” She adds that it’s imperative to extend research beyond configuration management databases (CMDB) and asset management tools. Pirzada also recommends extending the inquiry into security functions. “Cyber asset attack surface management (CAASM) tools will give depth and breadth to the digital asset landscape.”  Related:Visa CISO Subra Kumaraswamy on Never Allowing Cyber Complacency Pirzada advocates running comprehensive vulnerability scans. “Don’t just look at patch levels,” she says. Examine configurations, access controls, and the network architecture. “I’ve found domain admin privileges scattered like confetti across user accounts more times than I can count.”  Evaluate each system on four basic criteria: security supportability, business criticality, integration complexity, and replacement cost, Pirzada says. “A system handling customer data that hasn’t seen a security update in three years is a different problem than an isolated HR application used only during performance reviews,” she explains.   The new CIO should listen to IT teams, business stakeholders, and end-users to uncover pain points and achieve quick wins that will build credibility, says Antony Marceles, founder of Pumex, a software development and technology integration company in an online interview. Whether to rebuild or repair depends on the architecture’s integrity. “Sometimes, patching legacy systems only delays the inevitable, but in other cases smart triage can buy time for a thoughtful transformation.”  Related:AI Is Driving a Return to Tech Fundamentals, Says Chase CIO Build Support  Connect with your immediate peers — the CFO, COO, CISO, and legal counsel, de Silva suggests. “They’ve likely experienced the pain points and can give you a grounded view,” he says. “It’s also helpful to lean on internal audit or trusted outside advisors who can help pressure-test your early assumptions.”  Establish trust and clarity, de Silva advises. “People inside the organization will be watching closely to see how you lead, especially if the last CIO left things in disarray,” he says. Set expectations, listen to your teams, and communicate priorities clearly. “Focus on small but meaningful wins early to build momentum.”  Support can often come from unconventional corners, such as high-performing team leads, finance partners, or external advisors, all of whom may have experienced their own transitions, Marceles says. “The biggest mistake is trying to fix everything at once or imposing top-down change without context,” he notes. “A new CIO needs to balance urgency with empathy, understanding that cleaning up someone else’s mess is as much about culture repair as it is about tech realignment.”  Related:The Strategic Transition from CIO to CDO Your existing IT and security staff will be invaluable, even if they’ve been operating under poor leadership, Pirzada says. “They possess institutional knowledge about system dependencies, workarounds, and hidden issues that no documentation captures.” She also advises creating safe spaces for unlocking truth-telling. “Your team knows where the bodies are buried, but they need to trust that sharing problems won’t get them blamed for creating them.”  Final Thoughts  When you inherit a messy situation, it’s both a technical and leadership challenge, de Silva says. “The best thing you can do is lead with transparency, make thoughtful decisions, and rebuild confidence across the organization.” People want to see steady hands and clear thinking, he observes. “That goes a long way in these situations.”  Remember, too, that every inherited mess is also an opportunity, Pirzada says. “It’s a chance to build something better, establish new standards, and demonstrate the value that thoughtful IT leadership brings to an organization.” The key, she suggests, is approaching the challenge with the right combination of urgency, patience, technical expertise, and business acumen.  source

Executives see AI as a quick win, while practitioners know it’s a long road. So, who’s right? The growing disconnect between leadership and IT teams could be the difference between companies that thrive with AI and those that fall behind.   Having led digital transformation efforts for many years, I can say this kind of misalignment is nothing new. Executives often underestimate the complexity of new technology initiatives, while practitioners have a more grounded view of the challenges — though they may not always see the big-picture goals.  What’s different now is the scale and impact of the consequences arising from this growing disconnect between leadership and IT teams. As non-IT leaders take a bigger role in driving AI investments in 2025, the rapid shift to cross-departmental decision-making has proven messy. But given what’s at stake — with global players like DeepSeek driving competition — companies can’t afford to let this old disconnect linger. It needs to be fixed, fast.  So, who has it right when it comes to AI? Right now, the answer is no one.  Overcoming Common Fault Lines Bridging the AI divide between leadership and IT requires intentional alignment and execution. And with 77% of digital leaders planning to ramp up AI investments in 2025, the pressure to overcome common AI fault lines is higher than ever.  Related:Beyond Productivity: How to Cut Costs With Generative AI To maximize AI innovation, organizations must align leadership decisions with frontline realities, invest in workforce upskilling and bring practitioners into AI strategy discussions from the start.   1. Emotions, structure and siloed mindsets  Even the best-intentioned digital initiatives lose traction when stakeholders disagree. It’s no surprise the biggest obstacles to digital transformation efforts are siloed mindsets, particularly in complex business environments.  For example, executives may believe AI funding alone is enough to drive change, leaving practitioners without clear expectations, tools or support to make good on those resources. This approach overlooks the more practical realities practitioners face, e.g., fragmented workflows, legacy dependencies and cross-team misalignment.   Organizational emotions surrounding AI also slow the adoption of new AI tools. We can tackle these challenges through both an organizational change management (OCM) and emotional change management (ECM) lens, making sure we address both the practical and human sides of change.  To break down silos, leaders must acknowledge fear and uncertainty and foster interdepartmental collaboration early during AI decision-making processes. Maintaining in-the-weeds oversight throughout iterative adoption and scale cycles ensures AI initiatives remain integrated internally and in direct engagement with customers.   Related:How Will You Staff Your AI Workforce for the Future? Continuous conversation, feedback, design, refactoring and refinement help prevent siloed thinking from derailing AI-powered experiences. Without it, companies risk strategic drift and move further away from the factors that make AI successful: knowledge sharing and intersecting workflows.  2. Mismatched goals and metrics  Employees at different levels of the organization have different expectations for AI — especially leaders outside of IT. For example, leaders in marketing or finance may prioritize higher-level objectives tied to organizational ROI and growth, while IT practitioners measure success through operational improvements and tactical productivity gains.   Although those objectives naturally coalesce with the right executive leadership, many organizations struggle to align and integrate goals in a mutually compounding fashion.   This disconnect extends to confidence in investments, with 62% of C-suite leaders saying they’re confident digital transformation investments will deliver the expected ROI, compared to just 45% of line-level managers. Moreover, 42% of C-suite executives expect these transformation initiatives to deliver results within six months, yet only 19% of line-level managers share this expectation.  Related:Why Companies Need to Reimagine Their AI Approach Differing goalposts inevitably lead to pressure, unrealistic deadlines and false starts. Executives may grow impatient with slow AI results, while IT teams may hesitate to experiment and accelerate groundwork. The problem lies in operating like two separate groups rather than a single, unified AI team.  When introduced early, KPIs give leaders and IT teams a shared framework for AI alignment. For example, practitioners can show leadership why AI-driven success takes time, phasing deliverables for increased visibility while still advancing bottom-line goals. Conversely, leaders outside of IT can champion AI needs and surface new, more diverse use cases that reinforce investment value.   3. Talent shortages and upskilling gaps  AI investments stall without proper training, resources and talent. Training employees is the No. 1 driver of digital transformation success. Yet, nine out of 10 organizations report a lack of the necessary talent to implement AI effectively.   Organizations that lack robust IT assets and staff struggle to turn AI investments into tangible results. That’s when frustration kicks in — leaders see no progress, and IT practitioners are left without the tools AI innovations require to thrive.   It’s like buying a car without wheels and expecting it to take you where you need to go. You can turn up the sound system on your favorite playlist and rev the engine all you want, but you’re still going nowhere.  Again, a proactive approach to talent management can prevent this disconnect from derailing AI success. By acknowledging lapses in organizational knowledge, communicating where those talent gaps exist, and responsibly distributing and enabling upskilling, leaders can help IT teams invest in the resources to build a flexible, AI-ready workforce.   From there, both groups can collaborate on a plan to ensure IT teams evolve and thrive in a fast-changing AI landscape. For IT practitioners and leaders, this means integrating feedback loops driven by user insights and real-time AI performance data. Shared ownership enables stakeholders to regularly improve and refine processes, optimize staffing and L&D, and replicate successes.  By tapping into a third-party technology partner with deep expertise in workforce transformation and talent development, companies can champion a cohesive roadmap to drive AI success – especially in scenarios where stakeholders disagree.    Alignment Turns AI Divides into Global AI Leadership  The race for AI leadership is reshaping industries. AI leaders will shape the future of innovation, efficiency and economic growth — but getting there means bringing practitioners in early and prioritizing workforce upskilling.  

Building up an AI-enhanced workforce calls for more than telling staffers to use new tools and then hope for the best. Job roles can change, C-suite leadership may need new strategies, and the organization could face a different set of security and oversight concerns. In this episode of the InformationWeek podcast, Michael Fanning, CISO for Splunk; and Andie Dovgan, chief growth officer with Creatio, came together for a Breaking Bread session, tackling the tech and operations aspects of making AI part of the workforce. How should AI be vetted as it introduces new efficiencies — and redundancies — in the workforce? What roles could be affected by AI? What roles cannot be entrusted to AI-enhancement because of sensitivity or regulatory compliance? The session then shifts to a tabletop exercise where Dovgan and Fanning collaborate and respond to potential issues companies might see as they make AI a tool for the workforce. source

Greg Touhill, director of the Software Engineering’s Institute’s (SEI’s) Computer Emergency Response Team (CERT) division is an atypical technology leader. For one thing, he’s been in tech and other leadership positions that span the US Air Force, the US government, the private sector and now SEI’s CERT. More importantly, he’s been a major force in the cybersecurity realm, making the world a safer place and even saving lives.  Touhill earned a bachelor’s degree from the Pennsylvania State University, a master’s degree from the University of Southern California, a master’s degree from the Air War College, was a senior executive fellow at the Harvard University Kennedy School of Government and completed executive education studies at the University of North Carolina.  “I was a student intern at Carnegie Mellon, but I was going to college at Penn State and studying chemical engineering. As an Air Force ROTC scholarship recipient, I knew I was going to become an Air Force officer but soon realized that I didn’t necessarily want to be a chemical engineer in the Air Force,” says Touhill. “Because I passed all the mathematics, physics, and engineering courses, I ended up becoming a communications, electronics, and computer systems officer in the Air Force. I spent 30 years, one month and three days on active duty in the United States Air Force, eventually retiring as a brigadier general and having done many different types of jobs that were available to me within and even beyond my career field.”  Related:Mastering the Art of IT Task Delegation Specifically, he was an operational commander at the squadron, group, and wing levels. For example, as a colonel, Touhill served as director of command, control, communications and computers (C4) for the United States Central Command Forces, then he was appointed chief information officer and director, communications and information at Air Mobility Command. Later, he served as commander, 81st Training Wing at Kessler Air Force Base where he was promoted to brigadier general and commanded over 12,500 personnel. After that, he served as the senior defense officer and US defense attaché at the US Embassy in Kuwait, before concluding his military career as the chief information officer and director, C4 systems at the US Transportation Command, one of 10 US combatant commands, where he and his team were awarded the NSA Rowlett Award for the best cybersecurity program in the government. While in the Air Force, Touhill received numerous awards and decorations including the Bronze Star medal and the Air Force Science and Engineering Award. He is the only three-time recipient of the USAF C4 Professionalism Award.  Related:Tech Burnout: CIOs Might Be Making It Worse Greg Touhill “I got to serve at major combatant commands, work with coalition partners from many different countries and represented the US as part of a diplomatic mission to Kuwait for two years as the senior defense official at a time when America was withdrawing forces out of Iraq. I also led the negotiation of a new bilateral defense agreement with the Kuwaitis,” says Touhill. “Then I was recruited to continue my service and was asked to serve as the deputy assistant secretary of cybersecurity and communications at the Department of Homeland Security, where I ran the operations of what is now known as the Cybersecurity and Infrastructure Security Agency. I was there at a pivotal moment because we were building up the capacity of that organization and setting the stage for it to become its own agency.”  While at DHS, there were many noteworthy breaches including the infamous US Office of People Management (OPM) breach. Those events led to Obama’s visit to the National Cybersecurity and Communications Integration Center.   “I got to brief the president on the state of cybersecurity, what we had seen with the OPM breach and some other deficiencies,” says Touhill. “I was on the federal CIO council as the cybersecurity advisor to that since I’d been a federal CIO before and I got to conclude my federal career by being the first United States government chief information security officer. From there, I pivoted to industry, but I also got to return to Carnegie Mellon as a faculty member at Carnegie Mellon’s Heinz College, where I’ve been teaching since January 2017.”  Related:How Continuous Learning Paid Off for CTO Pravin Uttawar Touhill has been involved in three startups, two of which were successfully acquired. He also served on three Fortune 100 advisory boards and on the Information Systems Audit and Control Association board, eventually becoming its chair for a term during the seven years he served there.  Touhill just celebrated his fourth year at CERT, which he considers the pinnacle of the cybersecurity profession and everything he’s done to date.  “Over my career I’ve led teams that have done major software builds in the national security space. I’ve also been the guy who’s pulled cables and set up routers, hubs and switches, and I’ve been a system administrator. I’ve done everything that I could do from the keyboard up all the way up to the White House,” says Touhill. “For 40 years, the Software Engineering Institute has been leading the world in secure by design, cybersecurity, software engineering, artificial intelligence and engineering, pioneering best practices, and figuring out how to make the world a safer more secure and trustworthy place. I’ve had a hand in the making of today’s modern military and government information technology environment, beginning as a 22-year-old lieutenant, and hope to inspire the next generation to do even better.”  What ‘Success’ Means  Many people would be satisfied with their careers as a brigadier general, a tech leader, the White House’s first anything, or working at CERT, let alone running it. Touhill has spent his entire career making the world a safer place, so it’s not surprising that he considers his greatest achievement saving lives.  “In the Middle East and Iraq, convoys were being attacked with improvised explosive devices. There were also ‘direct fire’ attacks where people are firing weapons at you and indirect fire attacks where

IT didn’t start the fire but they’re sure feeling the heat. “A big reason for burnout is things outside of a CIO’s control — economic uncertainty, political tension, market shifts,” says Manuj Aggarwal, founder and CIO of TetraNoodle Technologies, an AI and automation company. “When that happens, companies lean heavily on their tech teams to fix things: cut costs, automate, and generate revenue fast.”  This hard push for IT to fix anything and everything isn’t smart or sustainable. Many CIOs know that but assume the burden anyway and push forward rather than push back.  “This strain cascades into performance. Overworked teams cut corners, increasing technical debt and security risks. Worse, CIOs themselves aren’t immune, often mirroring their teams’ unsustainable pace. This vicious cycle, where leaders, drowning in their own burnout, lack the bandwidth to recognize or address staff struggles, says Alex Kratko, founder and CEO of Snov.io, a platform that automates sales on LinkedIn.   The cycle of CIO accelerated burnout often pedals along, picking up momentum despite the growing futility of the effort.  “If the overarching culture of the IT division is highly reactive, then staff burnout is more likely. A highly reactive environment is characterized by unplanned work, shoulder tap requests, heroically putting out fires and a lack of strategic direction,” says Andy Miears, partner, at global technology research and advisory firm ISG.  Related:CERT Director Greg Touhill: To Lead Is to Serve Decades-long, persistent burnout now flames higher than ever under the auspices of CIOs who typically are more misguided heroes than cruel task-masters.   AI to the CIO’s Rescue?  Ah, but what about AI and automation? Surely these advanced technologies will soon lift the burden from the shoulders of CIO and IT staff members alike, right? Not exactly. Or at least not in the short term.  “The rush to implement AI is exacerbating tech burnout as CIOs grapple with unrealistic expectations from boards and shareholders. Under pressure to deliver ROI, many leaders prioritize speed over sustainability, greenlighting overlapping AI projects without aligning them with team capacity,” says Kratko.  Rightly or wrongly, AI is being touted as salvation for beleaguered companies struggling to survive in uncertain times. The C-suite demands IT “make it so” as if they are all captains in Star Trek’s Starfleet.  Meanwhile, IT tends to hope it’s a fix for the growing number of fires that they can’t seem to put out. But while efforts of transformation spring eternal, AI has yet to show up as a reliable rescuer.  Related:Mastering the Art of IT Task Delegation “One of the biggest challenges plaguing workforces today stems from transformation fatigue. This isn’t necessarily exhaustion from too many change programs, rather a frustration and weariness stemming from little to no meaningful progress stemming from those initiatives,” says Alex Adamopolous, chairman and CEO of Emergn, a technology services and business consultancy.   According to Emergn research, 58% of employees report feeling burnout from change initiatives, with 50% of employees blaming leadership failures and bosses who are out of touch with the concerns of their employees for transformation failures.  By all accounts, AI is the gorilla in the data center and it’s pounding out the biggest changes with little care about whether that change works for anybody concerned.   “Execution speed is increasing but operational design hasn’t caught up. Teams are pushed through AI rollouts and security shifts without recalibrating load or sequence. Engineers get pulled in too many directions without runtime control, while the CIO stays too abstract to resolve bottlenecks,” says Nic Adams, co-founder and CEO at 0rcus, a pen-testing software provider.  Developers echo those same concerns.  Related:How Continuous Learning Paid Off for CTO Pravin Uttawar “From my experience leading development teams through high-pressure digital initiatives, including AI implementation, the biggest contributor to burnout isn’t the technology itself, it’s the pace and expectations surrounding its rollout,” says Antony Marceles, founder at Pumex, a software development and technology integrations company.  “CIOs often unintentionally worsen burnout by underestimating the human toll of constant context switching, unclear priorities, and always-on availability. In the rush to stay competitive with AI-driven initiatives, teams are pushed to deliver faster without enough buffer for testing, reflection, or recovery,” Marceles adds.  In the end, it’s the panic surrounding AI adoption, and not the technology itself, that’s accelerating burnout. The panic is running hot and high, surpassing anything CIOs and IT members think of as normal.  “The pressure to adopt AI everywhere is real, and CIOs are feeling it from every angle — executives, investors, competitors. But when that pressure gets passed down as back-to-back initiatives with no breathing room, it fractures the team. Engineers get pulled into AI pilots without proper training. IT staff are asked to maintain legacy systems while onboarding new automation tools. And all of it happens under the expectation that this is just “the new normal,” says Cahyo Subroto, founder of MrScraper, a data scraping tool.   To Err Is Tech, to Burn Out Is Human  The focus on tech overshadows the needs of mortal humans. Which is an odd development, if you think about it, since tech is supposed to help or protect the people it serves and not the other way around. Somewhere along the way, the priority flipped.  “What gets lost is the human capacity behind the tech. We don’t talk enough about how context-switching and unclear priorities drain cognitive energy. When everything is labeled critical, people lose the ability to focus. Productivity drops. Morale sinks. And burnout sets in quietly, until key people start leaving,” Subroto says.  Relieving burnout requires flipping the priority back to human-first.   “To fix this, CIOs need to slow the rollout down — not in terms of strategy, but in terms of how it lands on people. That means setting clear phases. It means choosing fewer tools but supporting them properly. And it means protecting focus time for staff and for themselves. Because burnout doesn’t just erode performance. It breaks trust. And once that happens, even the best tech strategy falls apart,” Subroto adds.   source

Developers typically follow one of two paths: They rise through the ranks to principal engineer or pursue a software development or IT management path. Pravin Uttarwar, CTO at healthcare solution provider Mindbowser took the latter route, jumping directly from a developer role to CTO.   Uttarwar began his career with a Bachelor’s of Computer Science and a Master’s of Computer Applications before joining an IT consulting startup as the second employee.  “The job was not good in terms of pay, but I totally enjoyed the journey because I was doing so many things there like coding as a software engineer and handling client communications,” says Uttarwar. “The most important part was the ownership I was getting. The founders relied on me, so every night of late work was worth it.”  To Uttarwar, the freedom to do many types of jobs meant he had broader experiences than the average developer, which laid the groundwork for effective cross-functional communication and collaboration. After about five years at the company, he and his then-colleague, Ayush Jain, decided to start their own company, Mindbowser.  “I was bit hesitant about whether to start a company or not because there was no advisor, no playbook and no safety net. After talking with family members who encouraged me, I figured there was nothing to lose so no harm in trying,” says Uttarwar. “There’s the money aspect, which I believed would come, but more importantly, it’s a matter of making sure that what you’re doing today really helps others and you to become a better person. That continuous learning mindset set the tone for everything.”  Related:Mastering the Art of IT Task Delegation For example, during its 12-year history, the company has grown from 10 to 200 employees. Not bad for someone who has never been in an IT leadership role before.  “Luckily we had a few good clients and good employees for the first few years, and they are still with us,” says Uttarwar. “Over time, we built credibility with customers who refer new customers and leads. We also transitioned from a service business to a service and solution provider after about seven years. A couple of our solutions failed, so we got some advisors to better align our services and solutions offerings.”  Becoming an Effective IT Leader  Today’s IT leaders need both hard and soft skills, but it can be difficult to find the time for upskilling. Uttarwar and Jain — both Mindbowser founders — came to the US to study technology leadership simultaneously a few years ago while the business continued, back in India.   Related:Tech Burnout: CIOs Might Be Making It Worse “There was a turning point about four years ago when we were stuck, when we were trying to figure out what’s next. We knew we wanted to grow, but we felt we’d be more effective leaders if we took some executive-level classes. I chose the MIT Technology Leadership Program while my partner was at Berkeley. We were trying to figure out the market,” says Uttarwar. “At the time, Mindbowser had a mobile app, ecommerce solutions and we were focused on healthcare and retail. I think the toughest decision we had to make was to just focus on one thing. That’s how our healthcare journey started.”  Pravin Uttarwar That singular focus involved hiring healthcare subject matter experts and enabled the company to expand into various healthcare subsectors, like dentistry and home care.  “Our mentors were pivotal, but we realized there were certain things we had to learn on our own. Luckily, we had hired solid leadership. They made it possible for us to study at MIT and Berkeley, respectively,” says Uttarwar. “My co-founder and I consciously decided to take different leadership programs. I wanted to make sure I was aligned with people and what they want. I was fortunate enough to be in the MIT Technology Leadership Program with seasoned IT leaders when I was the junior person. I learned a lot from them, too.”  Related:CIO Chaos Mastery: Lessons from Vertiv’s Bhavik Rao For example, Mindbowser tries new things each year to retain people. Some of it has to do with innovation and some has to do with unlocking benefits based on tenure.   “If you spend three years, you get a nice gift. If you’re with us for five years you get money to buy a house. At 10 years, you receive an international tour with your family. These are small things, but they help us retain talent,” says Uttarwar. “Last year, we started an ESOP so they could earn stock options. As founders, it’s our way of giving back to these people who have spent a lot of time with us. We also try new things with customers to boost retention.”  Uttarwar constantly analyzes markets for what’s coming. Sometimes, he does a quick POC as an example of what Mindbowser should be doing. He also uses prototypes to customers who want to try new things.   The Biggest Challenges   Growth is what Uttarwar says is most challenging.  “It can be challenging to find new customers and delivering to customers sometimes. For example, at one point, we had multiple employees at a U.S. office, but that didn’t work out the way we hoped,” says Uttarwar. “So, this year, either I or my cofounder will focus on the US completely.”  In the meantime, most of the CIOs and chief medical officers (CMOs) Uttarwar talks to aren’t sure what to do with “new” technologies like GenAI.  “Stakeholders are putting pressure on CIOs and CMOs to do something with GenAI, so we’re trying to help them by bringing in new ideas, helping with change management, planning and execution,” says Uttarwar. “They’re focused on the AI aspect, but they need to do it in a responsible way. Worse, they have a lot of broken systems that are not speaking to each other, and that’s where they need to start first.”  Most healthcare CIOs have been tasked with building AI or building something on top of AI that will help make clinicians’

Task delegation is a critical IT leadership responsibility. Unfortunately, many IT leaders continue to hand assignments to individuals who are either unqualified or already busy handling other tasks.  Delegation isn’t just about offloading work; it’s also about transferring ownership, says Justice Erolin, CTO at software development company BairesDev. “Start with clarity, focusing on the why, what, and expected outcome, then assign tasks based on skills and stretch potential,” he recommends in an online interview. Make sure you’re taking advantage of your team’s strengths and interests and do regular check-ins. “It’s important to keep tabs on [team] progress, but don’t micromanage.”  Don’t just assign tasks, advises Hiren Hasmukh, CEO at IT asset management software provider Teqtivity. “Make sure team members understand how their work contributes to larger goals,” he states in an email interview. Also provide the appropriate tools and resources. “Nothing hinders productivity more than expecting results without [providing] proper support.”  Empowerment without clarity creates chaos, Erolin says. “Oversight without trust breeds micromanagement,” he adds. “The more we delegate well, the more confident and capable the team becomes.”  A Fine Line  Related:Tech Burnout: CIOs Might Be Making It Worse There’s a fine line between monitoring and micromanaging, Erolin says. “Conversations with your team should be about progress and challenges rather than time invested.” The team should know what “good” feels like. “Oversight then becomes a shared responsibility.”  Focus on results, recommends Trevor Young, chief product officer at cybersecurity firm Security Compass. “Use tools like Jira, Trello, or ServiceNow to keep an eye on progress without constantly checking in,” he advises in an online discussion. Daily stand-ups, progress dashboards, and milestone reviews will help keep things moving along. “Most important, create a culture of open communication.”   Another effective strategy is implementing clear metrics and KPIs that teams can self-monitor, Hasmukh says. “When everyone knows what success looks like, monitoring becomes about achieving shared goals rather than watching over shoulders,” he explains.  “Having metrics in place helps to avoid misalignment and ambiguity,” Erolin adds.  Avoiding Mistakes  The biggest mistake is dumping a task on a team or individual without supplying the full picture, Young says. “If people don’t know why something matters or how it fits into the bigger goal, they won’t be as effective.” Another common mistake, he notes, is micromanaging or completely disengaging from the team. “The sweet spot is somewhere in the middle; offer guidance and accountability but allow room for autonomy.”  Related:How Continuous Learning Paid Off for CTO Pravin Uttawar The biggest mistake is delegating without creating support or proper context, Hasmukh says. “Many leaders hand off tasks without explaining why they matter or how they connect to bigger objectives,” he explains. “This creates a disconnect that leaves team members feeling like order-takers rather than valued contributors.”  Yet another trap is false delegation, Erolin says. In this situation, the leader continues to own the outcome, either practically or emotionally. “Delegation isn’t abdication; it’s a transfer of ownership,” he observes. “Leaders must define success upfront and resist the urge to ‘fix’ mid-flight unless absolutely necessary.” To do otherwise means training the team to defer instead of lead.  The key is to delegate with intention, Young says. Assign tasks based on skills, experience, and potential for growth. “Be clear about expectations — what needs to be done, why it matters, and any constraints.” He also recommends using a framework, such as RACI, to define specific roles.  Young advises leaders to provide the tools and support necessary to allow team members to succeed. “If a task is repetitive, automate it,” he says. “People should focus on high-value work, not busywork.”  Related:CIO Chaos Mastery: Lessons from Vertiv’s Bhavik Rao The goal should be eliminating guesswork and keeping all parties aligned, Young says. “When tasks match a person’s skills and aspirations, they stay engaged and perform better.” Meanwhile, a clear structure prevents miscommunication while automation reduces the chance of human error. “Plus, when people understand the ‘why’ behind their work, they’ll take more ownership of it.”  Final Thoughts  Delegation is leadership at scale, Erolin says. “If I’m the only one thinking critically, solving problems, and driving outcomes, then I’m the bottleneck”. A leader’s job isn’t just to get things done but to multiply capacity. Delegation isn’t a transaction; it’s a transformation. “It doesn’t just lighten the load; it lifts the entire team.”  Effective delegation is ultimately about building trust and developing your team, Hasmukh says. It’s not just about distributing the workload. “It’s about creating growth opportunities,” he states. “Some of our best innovations came when team members were empowered to solve problems in their own way.”  source