Information Week

Ongoing trade tensions and shifting tariffs have left CIOs in a tough spot: how do you maintain stability while controlling costs? With the price of exports surging as companies rush to purchase before tariffs hit harder, reactive stockpiling is no longer sustainable.  In uncertain times, having a clear picture of your technology assets and how they’re being used becomes a powerful edge. Strategic CIOs take inventory, identify inefficiencies, and make every dollar count.  Start with Visibility: Know What You Own Before making new purchases, it’s essential to understand what’s already in your environment. Many organizations still track IT assets in spreadsheets, leading to inaccuracies and waste. A proper inventory system reveals opportunities to redistribute and repurpose hardware rather than overspend.  One of the organizations I’ve worked with quickly reassessed its technology assets upon anticipating new tariff laws. Their solution? They extended hardware upgrade cycles and provided refurbished devices to new hires. Simple changes that maintained productivity while significantly reducing costs.  Beyond budgeting concerns, poor asset visibility also introduces operational risks. Unused devices can lead to unmonitored endpoints, outdated software, or missed security patches. Visibility isn’t just a cost saver; it’s also a safeguard against compliance gaps and cybersecurity threats.  Related:How to Hold Efficient Team Meetings When you know what’s in use, underused, and outdated, you can prioritize purchases based on actual need, not fear.  5 Moves to Strengthen Your Hardware Strategy Forward-thinking CIOs are adopting these five tactics to build flexibility and resilience:  Extend Hardware Lifecycles Use predictive monitoring to catch failures before they happen. This allows for planned replacements, avoids emergency purchases, and squeezes more value from every asset.  Unite IT and Finance Data When IT and finance share data, decision-makers get a clear picture of total cost of ownership. This alignment leads to smarter choices between repair and replacement.  Prioritize Mission-Critical Systems Not all systems are created equal. Identify which assets are most essential to your operations and protect that budget, while trimming less critical areas.  Reassess Vendor Agreements Tariff-driven uncertainty is the perfect time to revisit contracts. With clear usage data and a tighter strategy, you can negotiate favorable terms more easily.  Automate and Optimize Use automation to reduce manual tasks, enabling your IT team to focus on innovation while controlling operational costs.  Related:How CIO Kristy Folkwein Is Building an IT Team for ADM’s Digital Future Stay Strategic, Not Reactive Cutting all new IT projects may feel safe, but often leads to stagnation. Instead, successful CIOs are maintaining strategic momentum while tightening spending elsewhere. Hardware decisions don’t happen in a vacuum. Collaboration with finance, procurement, and operations is essential.  Planning Ahead: Build Resilience Now Looking forward, resilient organizations are diversifying suppliers, opting for modular hardware upgrades, building device pools for redeployment, and exploring cloud solutions to reduce hardware dependence. Tariff uncertainty is challenging, but it also presents a chance to innovate and optimize. CIOs who take stock, invest in visibility, and act strategically can weather the storm while positioning their organizations for long-term success. Now is the time to shift from reactive responses to resilient frameworks. A proactive hardware strategy empowers CIOs to lead with clarity, agility, and confidence in the face of global uncertainty.  source

Cybersecurity is paramount in the world of digital payments. As senior vice president and CISO at Visa, Subra Kumaraswamy leads cybersecurity efforts at the payment card services giant with a philosophy that he and his team could always be doing more.  “Every day I wake up and say, ‘What I should do better?’” he tells InformationWeek. “Being pessimistic and being paranoid, P&P, meaning constantly look at this as ‘glass half empty.’ What else we should be doing to ensure we have a strong security posture?”  Before he stepped into the lead cyber job at Visa, Kumaraswamy built his career through many different roles at many different companies. He looks back at those experiences and forward to the ever-present need to manage and strengthen cybersecurity in his current position.   A Diverse Set of Roles  Kumaraswamy considers himself an engineer and a problem solver at heart. His first job was as a software engineer at the University of Notre Dame; he was figuring out how to offer internet services across the campus before the dot com boom began.   Since that first job, he has built experience at companies like Netscape, Sun Microsystems, eBay, and Intuit. He also spent time as an entrepreneur.   “In my journey, what defined me was the diversity … of roles,” says Kumaraswamy. “I was able to be a developer. I was able to be a data center architect. I was able to run services in the cloud, and I was able to be an entrepreneur. And all of this helped me to create much more of a holistic view.”  Related:How to Hold Efficient Team Meetings When he was at Netscape, the company was hit with a DDoS attack, the initial spark that got Kumaraswamy interested in cybersecurity. Throughout his career, he has focused on securing enterprises as they ride the waves of new transformative technology, whether that be the internet, the cloud, or now, AI.   Subra Kumaraswamy He was working as head of digital security at Apigee, a company that is now part of Google Cloud, focusing on API security. Then came a call from a recruiter.    “Visa was going through the whole transformation around creating open systems, opening up the platform to millions of developers using APIs,” Kumaraswamy recalls. “The hook was, ‘Hey, you can do this at scale.’ You can bring the same mindset, your passion, and all the experience … to one of the largest payment security payment companies in the world.”  He accepted the role in security engineering and security architecture in 2015. A decade later, he is leading cyber strategy as the company’s CISO.   Cyber Leadership at Visa  Related:5 Smart Hardware Moves CIOs Are Making During Tariff Uncertainty More than 1,000 people work in cyber at Visa, according to Kumaraswamy. “I’m really proud of the fact [that] the bench is very strong. We have top talent across multiple locations, not just in the US — across the globe,” he says.   That bench of talent works in six vertical functions within cybersecurity: governance, risk and compliance; access control and management; cyber engineering; cyber defense; cloud security; and security architecture and engineering.   Kumaraswamy works closely with Rajat Taneja, Visa’s president of technology. “I’m very fortunate to have a CTO who thinks cyber first,” says Kumaraswamy. “That sets the tone at the top. Saying that, ‘Hey, we do have to innovate in technology and payments. But if you don’t do cyber, well, nothing matters.’ It’s an existential threat for Visa.”  Avoiding Complacency   Gartner rates Visa’s cybersecurity maturity. “When I started my career path here at Visa in 2015, it was about 3.2 out of 5,” Kumaraswamy shares. “For the last two years, we’ve been given a score of 4.9 out of 5.”  While those numbers are a testament to Visa’s investments in cybersecurity, Kumaraswamy hardly sees them as a given. Cyber threats are constant and ever-changing.   Looking back at his years with Visa, Kumaraswamy recalls working through the aftermath of the Log4J zero-day vulnerability in 2021. He and his team spent four weeks sweeping hundreds of applications using Log4J and potentially open to attack.   Related:How CIO Kristy Folkwein Is Building an IT Team for ADM’s Digital Future “It was around the clock effort and literally hundreds of people, maybe thousands of people, in the company, were involved in the technology to make sure we mitigated this in a very short order,” he says. “I think that also gave us a lot of exposure to how we should think about the next Log4J.”  There will be, inevitably, more zero days and more cyberattacks. “When you wake up in the morning, [the] first thing you think about is, ‘Am I paranoid enough?’ Complacency is the enemy of security,” says Kumaraswamy.   Pushing Cybersecurity Forward  Kumaraswamy is always thinking about talent and technology in cybersecurity. Talent is a perennial concern in the industry, and Visa is looking to grow its own.   The Visa Payments Learning Program, launched in 2023, aims to help close the skills gap in cyber through training and certification. “We are offering this to all of the employees. We’re offering it to our partners, like the banks, our customers,” says Kumaraswamy.   Right now, Visa leverages approximately 115 different technologies in cyber, and Kumaraswamy is constantly evaluating where to go next. “How do I [get to] the 116th, 117th, 181th?” he asks. ”That needs to be added because every layer counts.”  Of course, GenAI is a part of that equation. Thus far, Kumaraswamy and his team are exploring more than 80 different GenAI initiatives within cyber.  “We’ve already taken about three to four of those initiatives … to the entire company. That includes the what we call a ‘shift left’ process within Visa. It is now enabled with agentic AI. It’s reducing the time to find bugs in the code. It is also helping reduce the time to investigate incidents,” he shares.   Visa is also taking its best practices in cybersecurity and sharing them with their customers. “We can think of this as value-added services to the

The chief digital officer (CDO) role has generated significant discussion in recent years, raising questions about its true value and the misunderstandings surrounding it. With the explosion of AI and the expectations it has placed on leaders, the role of the CDO is more relevant now more than ever. This observation highlights the potential for existing roles, such as CIOs, to evolve into CDOs.  The rapid evolution of technology management has expanded the expectations for leadership, meaning traditional CIO roles may no longer suffice in addressing the complexities of today’s macroeconomic environment and the era of AI. Organizational strategies must adapt, and CDOs are essential to drive this transformation.  Are you a CIO or in a similar position and curious about what transitioning to a CDO role would be like? Read on to see if adopting the CDO role would help your organization realize greater value.    Technology Meets Business   How is a CDO different from a CIO? Simply put: CIO role + strategic business vision = CDO. This elevated role not only covers IT operations but also focuses on cybersecurity, risk, and compliance. A CDO is responsible for strategizing how to enable, protect, and transform an organization through technology.  Related:How CIO Kristy Folkwein Is Building an IT Team for ADM’s Digital Future Being both a technology and a business leader, a CDO oversees a broader range of operations beyond just IT infrastructure. They position themselves to drive digital transformation by developing and executing a strategic vision that propels the business forward. When organizations add a CDO to the executive suite, CEOs and boards seek a leader who balances growth, risk, and protection.  In today’s competitive and fast-paced business environment, merely maintaining the IT status quo is insufficient. Organizations require a CDO to see the bigger picture, connect more dots, and make decisions based on the direct impact of IT on business outcomes. Transitioning from CIO to CDO involves moving away from daily operations to concentrate on how technology drives sales, enhances customer engagement, and fosters innovation. Since the CDO’s role encompasses enterprise risk and compliance, they must balance innovative ideas with the challenges of security. There’s also often more engagement with an organization’s board and risk audit committees to address how digital transformation impacts shareholder interests.    Bringing Value  A key way a CDO delivers value is by remaining closely aligned with business needs. By questioning existing processes and tools, a CDO ensures that the organization extracts the maximum value from its technology investments. The question “Is this really moving the needle?” underpins every decision made by a CDO.  Related:The Pros and Cons of Becoming a Government CIO The CDO also adds discipline to operations, ensuring that both existing and new tools are cost-effective, streamlining unprofitable workflows, and leveraging data to discover growth opportunities. A prime example is evaluating AI capabilities within organizational tools. When adopting AI, a CDO critically assesses whether the technology adds value, adheres to security and data privacy standards, and justifies the investment. Gartner predicts that by 2027 enterprise application costs will increase by at least 40% due to GenAI pricing, meaning balancing enablement, costs, and compliance will be increasingly difficult.  Once AI tooling is selected, a CDO collaborates with human resources and internal communications to effectively launch the technology. Unlike the traditional CIO model, which often is siloed, a CDO works early in the process with internal partners to enhance user experience through comprehensive communication, resources, and training — especially regarding the safe use of AI. By embedding controls within the technology framework, a CDO bridges agility and security to optimize the AI experience for both the business and its employees.  Related:Visa CISO Subra Kumaraswamy on Never Allowing Cyber Complacency Key for Success   Having transitioned from CIO to CDO at Dayforce, a leader in HCM technology, I can say with firsthand experience that it can be challenging to widen your mindset to think differently. It can also take time for your colleagues to view you differently and not just think of you as an “IT person.” Getting a bigger seat or voice at the table can be hard, but with the support of internal champions across the organization, I’ve made significant inroads during my transition to CDO.  Here are the top lessons I’ve learned along the way:   Trendspotting with discernment: Staying abreast of the latest technology trends is vital, but a savvy CDO must separate genuine innovations from the noise. Assess new technologies with a critical eye, ensuring that any adoption will help accelerate business priorities and drive tangible benefits to the bottom line.  Mind on the money: Speaking of the bottom line, a successful CDO keeps ROI at the forefront. If your background as a CIO leans heavily on technology knowledge, start amping up your understanding of your organization’s finances. Be a big-picture strategist rather than narrowly focusing on IT.    Aligning on AI: Getting buy-in from leadership on a value-driven, integrated AI strategy is critical — and it all starts with defining measurable goals with input from stakeholders across the organization. CDOs are often responsible for providing strategic counsel on the state of existing infrastructure, how operations will be redesigned, and what IT tools will drive workforce innovation, mitigate risks, and maximize ROI. Once departments are aligned to a shared strategy, the true work begins.   source

Some researchers spend all their professional lives at research facilities because they can spend all their time innovating without being bound by business constraints, such as time to market. According to Krishna Dubba, CTO and co-founder at event sponsorship platform provider CoVent, while both disciplines provide continuous learning opportunities, the intersection of two mindsets can serve businesses and their CIOs well.  Dubba grew into the role of a researcher after earning a series of IT-centric degrees, starting with a bachelor of computer science degree at Jawaharlal Nehru Technological University. Near the end of the program, he took the only AI courses available in 2004.   “At that time, I realized I needed to study AI more because it was so fascinating, and there was only one university offering a master’s degree in AI in India at that time. It was the University of Hyderabad, which is very research oriented,” says Dubba. “I did a lot of research at the end of the course, trying to find computer viruses using AI techniques.”  Next, he went to work for a hedge fund company working on algorithm development before deciding to go back to school at the University of Leeds to pursue a Ph.D. on a grant from The European Research Commission that covered all costs.  Related:How to Hold Efficient Team Meetings “The European Research Commission is a group of countries that work collectively on research. I got the opportunity to go to different countries and work with different universities,” says Dubba. “I was trying to analyze what is happening in videos using computer vision. It’s easy for humans to understand what is going on, but it’s very difficult for a machine.”  His Ph.D. work focused on “cognitive vision” that allows a machine to recognize objects and comprehend what’s happening in a video. One project involved an airplane at an airport. When a flight lands, a lot of activity ensues on the ground, at the gate, on the plane, and more. Using cognitive vision, the airline was able to identify process inefficiencies that could be used to lower costs and improve safety.  However, as a post-doctorate, Dubba chose a more difficult challenge: Robotic vision.  “During my Ph.D. we used fixed cameras to record video, but with a robot, you need ‘egocentric vision’ because the cameras move with the robot. So, as a post-doc, I could see that everything becomes a more complex understanding of what’s going on in a video,” says Dubba.   From Research Facilities to Startups  Many different industries can use cognitive and egocentric vision for their benefit, so Dubba went to work as a principal researcher at Nokia Tech’s Advanced Research Lab.  Related:5 Smart Hardware Moves CIOs Are Making During Tariff Uncertainty “At the time, Nokia had an eight-camera device, called OZO, that looked like the head of a duck and cost around $50,000. Its purpose was to capture 360-degree views. If you wore a headset, you could experience the video in 3D, meaning you could look up, down, or in any direction to explore it,” says Dubba. “It’s called, ‘presence capture,’ and there were a lot of problems with it because you have to stitch the video from eight cameras together.”  Next, he worked for Nokia Bell Labs solving problems in deep learning. Dubba worked in the Social Dynamics group that is charged with quantifying the unquantifiable.   “[Research facilities like Bell Labs] don’t ask, ‘How fast can we build or how much money can we make?’ They want to understand how it will change humanity, so most projects have a 10-year lifespan,” says Dubba. “We were trying to measure things like the emotion or health of a city, which is a very challenging problem, because it is hard to define and hard to measure, so you must use proxies. We used social media feeds as proxies. For example, a map app can tell you the fastest way to get to a destination easily because it is easy to define and measure, but it can’t tell you what your ‘happy path’ would be as it is vague, subjective, and hard to measure.”  Related:How CIO Kristy Folkwein Is Building an IT Team for ADM’s Digital Future Next, he went to work for automation company Blue Prism, first as a senior research scientist and later as a staff research scientist. At the time, the company was building robotics process automation solutions so organizations could automate business processes. When Dubba joined, the company had recently created an AI lab in London. His job was to set the strategy and recruit the researchers.  “It was fascinating — completely different from what I did before. As a researcher, I never had to worry about the business impact or justify the business case, so I learned how to determine the value of a research project from a commercial point of view,” says Dubba. “We also wrote a lot of patents, four of which were granted by the time I left. We also built a product called, ‘Capture,’ so fast, I realized I was working in an entrepreneurial environment. So, I started thinking that I could found a startup doing the same things.”  The first two companies Dubba co-founded were AI-powered life balance app provider Jeevi AI and enterprise-grade GenAI solution provider A2O. Both companies failed for a common reason: A lack of domain expertise.   “Jeevi AI had 25,000 customers, so we were monetizing the product, but we couldn’t make enough to sustain the business. At A2O, we built a chatbot that allowed users to ask questions on unstructured data and documents using LLMs. We also built a product called, ‘Insights’ that used the structured data users fed it so the users could ask data science queries using natural language,” says Dubba. “But we realized that we were not domain experts, we were all technologists.”  So, for CoVent, Dubba decided to co-found the company with a sales and marketing expert who happened to have the expertise it would take to shape a product that could sell. CoVent is an event sponsorship

Global business leaders are urging CIOs to implement generative AI (GenAI) at scale, in hopes that it will enhance organizational productivity and improve operating margins, especially in the face of budget constraints. Yet delivering measurable cost savings in the near term through productivity proves elusive.  The assumption is that incremental productivity gains from GenAI — faster code development, quicker report generation, swifter customer support — will translate smoothly into financial savings. Yet, despite persistent executive enthusiasm, CIOs struggle to realize meaningful bottom-line improvements from productivity-focused initiatives.   CIOs should adopt a GenAI strategy centered on financial efficiency to cut costs, save cash, reduce losses and risk, and boost near-term return on investment (ROI). Six specific tactics are detailed below. CIOs do not need to do all; they should start with the most feasible and impactful tactic for their organization and should acknowledge that this approach may reshape their GenAI strategy.  Pursue Cost Reduction Within IT  There are three tactics CIOs can use to implement a financial efficiency strategy within their own function and directly reduce the IT budget.  Outsourcing haircuts: Outsourcing constitutes over 13% of the average IT budget, presenting CIOs with opportunities to renegotiate contracts using GenAI.   Related:How Companies Are Making Money from AI Projects Although vendors face challenges in realizing productivity savings, competitive pressures drive outsourcers to offer 5%-20% price reductions, often mid-contract. To capitalize on this, CIOs should benchmark current supplier agreements, engage new suppliers for competitive pricing, and renegotiate or switch providers to achieve significant savings.   Reducing third-party variable spend: CIOs often use small, variable contracts with external specialists. CIOs have successfully cut costs by encouraging their staff to use GenAI for tasks typically handled by contractors. The aim is not to achieve productivity gains or complete project in-sourcing, but rather to incrementally reduce the reliance on external contractors. This approach is most effective in areas such as business analysis, PMO, translation, and regulatory document creation. By leveraging GenAI, internal staff can access on-demand expertise, reducing the need for these external engagements and cutting direct costs by eliminating third-party invoices.  Managed services recontracting: Traditional outsourcing providers rely on knowledge asymmetries and high switching costs to maintain price premiums. GenAI disrupts this by compressing the learning curve for new service providers, especially in contact centers, service desks, and application support.   Related:How to Avoid the AI Customer Experience Cliff Large language models help new vendors quickly acquire organizational knowledge, reducing risks and disruptions associated with switching providers. This gives CIOs stronger negotiating leverage, enabling them to seek competitive bids from lower-cost providers who can swiftly become competent. Switching suppliers often entails increased risk; however, leveraging GenAI strategically within the service desk can mitigate this by accelerating the learning curve and enhancing time-to-value. This reduction in risk makes transitioning to a new supplier more viable, and if the supplier offers lower costs, it can lead to significant savings for the CIO.  To effectively manage services recontracting, CIOs should first assess incumbent lock-ins by identifying key managed services contracts where existing providers leverage their institutional knowledge for price advantages. CIOs should invite competitive bids from challenger providers and compare these with incumbent costs, urging incumbents to match or beat the offers. By utilizing GenAI to flatten the learning curve for new vendors, CIOs can enhance their bargaining power and achieve direct reductions in IT service costs.  Related:How Will You Staff Your AI Workforce for the Future? Unlock Enterprise-wide Cost Reductions  There are three tactics CIOs can use to implement a financial efficiency strategy in nontechnology functions to reduce enterprise-wide budgets and save cash.  Working capital reduction: Excess working capital ties up cash that could be used for innovation or debt reduction and is a significant boardroom topic. CIOs can leverage GenAI-based predictive analysis to improve sales and accounts payable forecasts, reducing the need for idle working capital. By identifying patterns and utilizing unstructured data, and most importantly identifying new predictive factors, GenAI enhances forecast accuracy, enabling CFOs to reduce reserves and redirect funds to growth initiatives.  Improved forecasting directly reduces financial overhead. This approach offers immediate cost savings, especially in industries like insurance, aviation, and government, where capital reserves are crucial.  To reduce working capital, organizations should provide GenAI with comprehensive historical finance data to identify patterns and anomalies that enhance forecast accuracy. This enables CFOs to confidently adjust cash buffers, reducing reserves and freeing cash for growth or cost reduction. It’s crucial to track and evaluate the reinvestment of freed cash to assess the strategy’s effectiveness.  Revolving debt expense reduction: Revolving debt bridges cash flow gaps but incurs high interest costs. GenAI-driven cash flow forecasting helps CIOs and CFOs reduce reliance on expensive short-term financing by refining projections for strategic payment timing and reduced credit line usage, lowering interest expenses. Even small interest cost reductions can yield significant cash benefits without operational changes.  By integrating GenAI with enterprise planning systems, organizations can transform it into a strategic asset that frees funds for innovation and reduces operational costs. To implement this tactic, finance teams should map cash flow timing, deploy GenAI for precise forecasting, and track interest savings directly in the income statement.   Stronger contracts and revenue leakage reduction: Revenue leakage is a significant financial drain caused by weak contract terms, poor contract management, or unenforced pricing adjustments.   GenAI-assisted contract analysis provides a scalable solution by quickly identifying weak terms, ambiguous clauses, and invoice undercollection patterns. It can strengthen contracts to increase revenue and reduce losses, as demonstrated by an electronics manufacturer facing revenue loss due to inadequate pricing provisions.   CIOs should work with general counsel and finance teams to input historical contracts and performance data into GenAI, which can propose renegotiations and highlight anomalies. This leads to increased revenue and benefits the bottom line.   Takeaway CIOs should reposition GenAI as a strategic financial tool focused on measurable savings. By focusing GenAI investments on direct financial outcomes, GenAI becomes a powerful instrument for enhancing fiscal management and achieving near-term ROI, especially crucial in increasingly uncertain business environments.  source

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.