Information Week

Since 2001, Regent Surgical Health has developed and managed surgery center partnerships between hospitals and physicians. The firm, based in Franklin, Tennessee, works to improve and evolve the ambulatory surgical center (ASC) model.  Rusty Strange, Regent’s CIO, is used to facing challenges in a field where lives are at stake. He joined Regent after a 17-year stint at ambulatory surgery center operations firm Amsurg, where he served as vice president of IT infrastructure and operations.  In an online interview, Strange discusses the challenge he faced in building an entire IT department.  What is the biggest challenge you ever faced?  The biggest challenge I faced when I came to Regent was building an IT department from the ground up. As background, I was the first IT employee. At the time, we had no centralized IT structure — each ambulatory surgical center ASC operated with fragmented, non-standard systems managed by local staff or unvetted third parties. There was no cohesive strategy for clinical applications, data management, cybersecurity, or operational support.  What caused the problem?  The issue arose from rapid growth. The company was acquired, transforming into a high-growth organization overnight. Multiple ASCs were added to our portfolio over a short period, but we lacked the infrastructure to have sustainable success. There was no dedicated IT budget, no standardized software or hardware, and no staff trained to handle the increasing complexity of healthcare technology. This left us vulnerable to inefficiencies, security risks, and a lack of data to inform important decisions.  Related:Knowledge Gaps Influence CEO IT Decisions How did you resolve the problem?  I started by conducting a full assessment of existing systems across all locations to identify gaps and risks. I developed a multi-year plan to address foundational needs/capabilities, secured buy-in for an initial budget to hire our first functional area leaders, and partnered with a few firms that could provide us with the additional people resources to execute on multiple fronts. We standardized hardware and software, implementing cloud-based systems and a scalable network architecture. We also established policies for cybersecurity, business continuity, and staff training, while gradually scaling the team and outsourcing specialized tasks like penetration testing to additional trusted partners.  What would have happened if the problem wasn’t swiftly resolved?  Without a stable IT department, the company would have been unable to grow effectively. Important data would have been at risk and unutilized, potentially leading to violations and missed insights. Operational inefficiencies, like mismatched scheduling systems or billing errors, would have eroded profitability and frustrated surgeons and patients alike. Over time, our reputation as a first-class ASC management partner would have suffered, potentially stalling further growth or even losing existing centers to competitors.  Related:The Kraft Group CIO Talks Gillette Stadium Updates and FIFA World Cup Prep How long did it take to resolve the problem?  It took about 18 months to establish a fully operational IT department. The first six months were spent laying the foundation, hiring the core team, standardizing systems, and addressing immediate risks. The next year focused on refining processes, expanding the team, and rolling out core capabilities. It was a phased approach, but we hit key milestones early to stabilize operations and gain organizational buy-in/trust.  Who supported you during this challenge?  The entire leadership team was a critical ally, trusting the vision and advocating for the investments needed to achieve it. My initial hires were integral, they were able to adopt an entrepreneurial mindset, often setting direction while also being responsible for tactical execution. Our ASC administrators also stepped up, providing insights into their workflows and championing the changes with their staff. External partners helped accelerate implementation once we had the resources and process to engage them properly.  Related:CIO Angelic Gibson: Quell AI Fears by Making Learning Fun Did anyone let you down?  Not everyone was the right fit and not everyone in the organization was ready for the accelerated pace of change, but those were not personal failures, just circumstantial and provided learning opportunities for me and others in the company.  What advice do you have for other leaders?  Start with a clear vision and get fellow-executive buy-in early — without it, you’re facing a steep uphill climb. Prioritize quick wins, like fixing the most glaring risks and user pain points to build momentum and credibility. Hire a small, versatile team you can trust — quality beats quantity when you’re starting out. Be patient but persistent; building something from scratch takes time, but cutting corners will haunt you later. Communicate constantly — stakeholders need to understand why the change matters. Lastly, build a “team first” mindset so that individuals know they are supported and can go to others to brainstorm or for assistance.  Is there anything else you would like to add?  This experience reinforced the critical role technology plays in ASCs, where efficiency and patient safety are non-negotiable. It also taught me that resilience isn’t just about systems — it’s about people. It’s proof that even the toughest challenges can transform an organization if you tackle them head-on with the right team and strategy.  source

CEOs are increasingly honest about their IT knowledge deficiencies. Anyone who has worked in tech in the past several decades has a story or two about the imperious and dismissive attitude taken by the C-suite toward tech issues. It is a cost center, a gamble, an unworthy investment.  There are plenty of CEOs and other executives who still refuse to engage with the tech side of the business. But they are now viewed as dinosaurs — relics of an age where tech was a novelty. Now, CEOs and their cohorts have been compelled to acknowledge these errors. Many are attempting to correct them — both personally and on an organizational level.  A recent Istari survey found that 72% of CEOs felt uncomfortable making cybersecurity decisions. Respondents to the survey acknowledged the need to trust the knowledge of their tech counterparts — an encouraging finding for CIOs.   The difficulty of this shift is understandable. CEOs were initially only responsible for industrial operations and the money they produced. Following the Industrial Revolution, their responsibilities became largely financial. Now they must juggle both fiscal and technological aspects to remain competitive.   Strategic implementation of technology, both in expanding business and defending it against attackers, is increasingly essential. Doing so requires a working knowledge of tech trends and how they can be leveraged across the organization. This may be a difficult ask for people who come from strictly business backgrounds. Thus, it is incumbent upon them to both educate themselves and consult with their CIOs to ensure that informed decisions are made.   Related:Surgical Center CIO Builds an IT Department According to a 2021 MIT Sloan Management review, organizations whose leadership was savvy to new tech developments saw 48% more revenue growth. Now, when organizations seek a CEO, they increasingly ask whether their candidates possess the knowledge necessary to manage the risks and benefits of implementing new technologies such as AI while maintaining a strong security posture.   Here, InformationWeek explores the knowledge gaps that CEOs need to be aware of — and how they can fill them — with insights from Ashish Nagar, CEO of customer service AI company Level AI, and Susie Wee, CEO of DevAI, an AI company working on optimizing IT workflows.  What CEOs Don’t Know  Business-trained CEOs may lack many technological skills — an understanding of AI, how to best manage cybersecurity, and the ability to determine what infrastructure is a worthwhile investment. The narrow parameters of their training and the responsibilities of their previous roles leave many of them in the dark on how to manage the integration of technological aspects into the businesses they manage.   Related:The Kraft Group CIO Talks Gillette Stadium Updates and FIFA World Cup Prep “Technology is not their business. The technology is used to fortify their offer,” Wee says. “The question is, how can they use technology to compete while thinking first about their customers?”  Susie Wee, DevAI A 2025 report issued by Cisco offers intriguing findings about the feelings of CEOs on IT knowledge gaps. Of the CEOs surveyed, some 73% were concerned that they had lost competitive advantage due to IT knowledge gaps in their organization. And 74% felt that their deficiencies in knowledge of AI were holding them back from making informed business decisions regarding the technology.   “The arc of what is possible right now with these modern technologies, especially with how fast things are changing, is what I see as the biggest gap,” Nagar says. “That’s where it creates friction between technical leaders and the CEO.” CEOs who cannot connect the dots between the capabilities of nascent tech and what it may offer in the future do a disservice to their organizations.  According to Cisco, around 84% of respondents believed that CEOs will need to be increasingly informed about new technologies in coming years in order to operate effectively. However, other data from the report suggests that some CEOs view IT deficiencies as the responsibility of their teams — only 26% saw problems with their own lack of knowledge.  Related:CIO Angelic Gibson: Quell AI Fears by Making Learning Fun “Some are very scared — and actually frozen and not moving forward. They’re deciding to allow legal and compliance to put up gates everywhere,” Wee observes.  Other research, however, indicates that CEOs are taking ownership of their personal knowledge gaps — 64% of respondents to an AND Digital survey felt that they were “analogue leaders.” That is, they were concerned that their skill sets did not match the increasing integration of digital into all aspects of business. And some 34% said that their digital knowledge was insufficient to lead their companies to the next growth phase. The survey found that female CEOs were more nervous about their knowledge gaps — 46% thought they lacked the necessary technological skills.  “The buck stops with me. If anything goes wrong in cyber for whatever reason, customers will not excuse me because it is in an area I can say somebody else is looking after,” said one CEO who spoke with Istari.  One of their main complaints is the lack of usable data and how to obtain it. If they have structured data, many of them can adapt their existing skill sets around it and make effective decisions. But obtaining that information requires at least a general understanding of the landscape. If they can direct their subordinates to capture that data and massage it into a usable format, they can make more informed choices for their organizations.  How CEOs Can Bridge the Gap  CEOs are increasingly seeking tech training — 78% were enrolled in digital upskilling courses according to the AND Digital survey. Some CEOs are even engaging in reverse mentoring, where they form partnerships in which their subordinates share their skill sets in a semi-structured environment, allowing them to leverage that knowledge. Advisory boards and other programs that put CEOs in contact with their tech teams are also useful in facilitating upward knowledge transfer.   Digital immersion programs in which executives are embedded with their tech

IT and OT systems can seem worlds apart, and historically, they have been treated that way. Different teams and departments managed their operations, often with little or no communication. But over time OT systems have become increasingly networked, and those two worlds are bleeding into one another. And threat actors are taking advantage.   Organizations that have IT and OT systems — oftentimes critical infrastructure organizations — the risk to both of these environments is present and pressing. CISOs and other security leaders are tasked with the challenge of breaking down the barriers between the two to create a comprehensive cybersecurity strategy.   The Gulf Between IT and OT   Why are IT and OT treated as such separate spheres when both face cybersecurity threats?  “Even though there’s cyber on both sides, they are fundamentally different in concept,” Ian Bramson, vice president of global industrial cybersecurity at Black & Veatch, an engineering, procurement, consulting, and construction company, tells InformationWeek. “It’s one of the things that have kept them more apart traditionally.”  Age is one of the most prominent differences. In a Fortinet survey of OT organizations, 74% of respondents shared that the average age of their industrial control systems is between six and 10 years old.   Related:Surgical Center CIO Builds an IT Department OT technology is built to last for years, if not decades, and it is deeply embedded in an organization’s operations. The lifespan of IT, on the other hand, looks quite different.  “OT is looked at as having a much longer lifespan, 30 to 50 years in some cases. An IT asset, the typical laptop these days that’s issued to an individual in a company, three years is about when most organization start to think about issuing a replacement,” says Chris Hallenbeck, CISO at endpoint management company Tanium.   Maintaining IT and OT systems looks very different, too. IT teams can have regular patching schedules. OT teams have to plan far in advance for maintenance windows, if the equipment can even be updated. Downtime in OT environments is complicated and costly.   The skillsets required of the teams to operate IT and OT systems are also quite different. On one side, you likely have people skilled in traditional systems engineering. They may have no idea how to manage the programmable logic controllers (PLC) commonly used in OT systems.   The divide between IT and OT has been, in some ways, purposeful. The Purdue model, for example, provides a framework for segmenting ICS networks, keeping them separate from corporate networks and the internet.   Related:Knowledge Gaps Influence CEO IT Decisions But over time, more and more occasions to cross the gulf between IT and OT systems — intentionally and unintentionally — have arisen.   People working on the OT side want the ability to monitor and control industrial processes remotely. “If I want to do that remotely, I need to facilitate that connectivity. I need to get data out of these systems to review it and analyze it in a remote location. And then send commands back down to that system,” Sonu Shankar, CPO at Phosphorus, an enterprise xIoT cybersecurity company, explains.   The very real possibility that OT and IT systems intersect accidentally is another consideration for CISOs. Hallenbeck has seen an industrial arc welder plugged into the IT side of an environment, unbeknownst to the people working at the company.   “Somehow that system was even added to the IT active directory, and they just were operating it as if it was a regular Windows server, which in every way it was, except for the part where it was directly attached to an industrial system,” he shares. “It happens far too often.”  Cyberattack vectors on IT and OT environments look different and result in different consequences.   “On the IT side, the impact is primarily data loss and all of the second order effects of your data getting stolen or your data getting held for ransom,” says Shankar. “Disrupt the manufacturing process, disrupt food production, disrupt oil and gas production, disrupt power distribution … the effects are more obvious to us in the physical world.”  Related:The Kraft Group CIO Talks Gillette Stadium Updates and FIFA World Cup Prep While the differences between IT and OT are apparent, enterprises ignore the reality of the two worlds’ convergence at their peril. As the connectivity between these systems grows, so do their dependencies and the potential consequences of an attack.   Ultimately, a business does not care if a threat actor compromised an IT system or an OT system. They care about the impact. Has the attack resulted in data theft? Has it impacted physical safety? Can the business operate and generate revenue?   “You have to start thinking of that holistically as one system against those consequences,” urges Bramson.   Integrating IT and OT Cybersecurity  How can CISOs create a cybersecurity strategy that effectively manages IT and OT?  The first step is gaining a comprehensive understanding of what devices and systems are a part of both the IT and OT spheres of a business. Without that information, CISOs cannot quantify and mitigate risk.  “You need to know that the systems exist. There’s this tendency to just put them on the other side of a wall, physical or virtual, and no one knows what number of them exist, what state they’re in, what versions they’re in,” says Hallenbeck.   In one of his CISO roles, Christos Tulumba, CISO at data security and management company Cohesity, worked with a company that had multiple manufacturing plants and distribution centers. The IT and OT sides of the house operated quite separately.   “I walked in there … I did my first network map, and I saw all this exposure all over,” he tells InformationWeek. “It raised a lot of alarms.”  Once CISOs have that network map on the IT and OT side, they can begin to assess risk and build a strategy for mitigation. Are there devices running on default passwords? Are there devices running suboptimal configurations or vulnerable firmware? Are there unnecessary IT and OT connections?   “You start

As tech pros, the question isn’t just whether AI will disrupt our industries — it’s how we can leverage its power in a responsible, sustainable way.  At SXSW 2025, I had the privilege of serving on the “Innovation Unbridled: Balancing the Promise and Peril of AI” panel. The session provided a thought-provoking exploration of AI’s transformative potential, and the challenges tech leaders face when integrating AI into their operations. What made this panel particularly engaging was the diverse audience — attendees from all walks of life, asking tough questions that forced us to consider AI from ethical, practical, and social perspectives.  Here are six key takeaways to guide your AI journey:  1. AI should solve problems, not create them  AI must address real business challenges, not introduce new ones. Too often, organizations rush to adopt the latest tools and technologies without fully understanding their impact on existing processes. The result? More complexity, confusion, and inefficiency.  It’s crucial to ensure that any AI implementation directly addresses specific pain points within your organization. Whether it’s automating tasks, improving customer personalization, or enhancing decision-making, AI should add measurable value. When deploying AI, start by asking: How will this improve our business outcomes? What specific problem does it solve?  Related:Surgical Center CIO Builds an IT Department Actionable insight: Prioritize AI tools that seamlessly integrate with existing systems and processes. Use AI as a strategic asset to enhance productivity and deliver tangible results.  2. AI should upskill people, not replace them  It’s no secret that many fear AI will lead to widespread job displacement. While this concern is valid, the reality is that AI is designed to augment human abilities, not replace them. It can handle repetitive tasks, analyze vast amounts of data, and provide real-time insights, allowing employees to focus on higher-value activities that require creativity, empathy, and complex problem-solving.  The key is understanding that AI’s real value lies in enabling your team to work smarter, not harder. AI can help streamline operations and improve efficiency, but it should never be seen as a substitute for human ingenuity.  Actionable insight: Invest in upskilling and reskilling your workforce to ensure employees are ready for a future where AI complements their work. Offer training programs or collaborate with educational institutions for continuous learning opportunities.  3. Balancing open-sourcing AI with ethics  Related:Knowledge Gaps Influence CEO IT Decisions While open-sourcing AI has the potential to democratize access and drive innovation, it also raises important ethical concerns. How can we ensure that AI tools are used responsibly and safely? What measures need to be put in place to prevent misuse or unintended harm?  It’s vital to ensure that any AI system deployed in your organization follows strict ethical guidelines. Whether you’re using open-source models or proprietary tools, transparency, accountability, and safety should always be top priorities.  Actionable insight: Establish a robust AI governance framework within your organization, including security protocols, ethical guidelines, and regular audits. Collaborate with legal and compliance teams to create policies that protect both your business and customers.  4. AI’s role in reshaping industries  AI is transforming industries, from precision healthcare to environmental sustainability, by driving value, personalization, and innovation. To fully leverage AI’s potential, businesses must adapt their operating models and become more agile.  The challenge lies not only in adopting AI but also in fostering an environment where innovation thrives. This requires rethinking organizational structures, embracing cross-functional collaboration, and cultivating a culture of continuous improvement.  Related:The Kraft Group CIO Talks Gillette Stadium Updates and FIFA World Cup Prep Actionable insight: Build an agile organization that adapts quickly to AI advancements. Encourage cross-functional collaboration, experimentation, and view AI as an enabler of ongoing business transformation, not a one-off project.  5. Fostering a culture of support and growth  Workforce burnout is an increasing concern as businesses push employees to adopt new technologies and work longer hours to stay competitive. While AI can alleviate some repetitive tasks, leaders must prioritize creating an environment that nurtures employee growth and well-being.  Actionable insight: As you implement AI to boost efficiency, foster a culture of support and growth. Encourage flexibility, invest in employee development, and set realistic productivity expectations. Innovation should empower your team, driving both business and personal growth without compromising employee satisfaction.  6. AI regulation — balancing innovation with responsibility  AI is evolving rapidly, and the need for regulation is becoming more pressing. Strong guardrails are essential to ensure AI is developed responsibly and ethically. As tech pros, it’s our responsibility to stay ahead of the regulatory curve, ensuring that your AI initiatives align with emerging ethical standards. While regulation may evolve over time, embedding ethical considerations into your AI strategy now will help future-proof your business.  Actionable insight: Stay informed about AI regulation and collaborate with industry bodies to help shape the future of AI governance. This proactive approach will protect your organization from legal challenges and demonstrate your commitment to responsible innovation.  Closing Thoughts  AI must be used thoughtfully, serving both business goals and societal well-being. As tech pros, it is our responsibility to harness AI in ways that solve real problems, empower employees, and drive ethical innovation. By embracing these takeaways, you can position your organization to thrive in the AI era while staying true to your values and responsibilities.  source

Effective technology leadership today prioritizes people as much as technology. Just ask Angelic Gibson, CIO at accounts payable software provider AvidXchange. Gibson began her career in 1999 as a software engineer and used her programming and people skills to consistently climb the corporate ladder, working for various companies including mattress company Sleepy’s and cosmetic company Estee Lauder. By the time she landed at Stony Brook University, she had worked her way up to technology strategist and senior software engineer/architect before becoming director, IT operations for American Tire distributors. By 2013, she was SVP, information technology for technology solutions provider TKXS and for the past seven years she’s been CIO at AvidXchange.  “I moved from running large enterprise IT departments to SaaS companies, so building SaaS platforms and taking them to market while also running internal IT delivery is what I’ve been doing for the past 13 years. I love building world class technology that scales,” says Gibson. “It’s exciting to me because technology is hard work and you’re always a plethora of problems, so you wake up every day, knowing you get to solve difficult, complex problems. Very few people handle complex transformations well, so getting to do complex transformations with really smart people is invigorating. It inspires me to come to work every day.”  Related:Surgical Center CIO Builds an IT Department Angelic Gibson One thing Gibson and her peers realized is that AI is anything but static. Its capabilities continue to expand as it becomes more sophisticated, so human-machine partnerships necessarily evolve. Many organizations have experienced significant pushback from workers who think AI is an existential threat. Organizations downsizing through intelligent automation, and the resulting headlines, aren’t helping to ease AI-related fears. Bottom line, it’s a change management issue that needs to be addressed thoughtfully.  “Technology has always been about increasing automation to ensure quality and increase speed to market, so to me, it’s just another tool to do that,” says Gibson. “You’ve got to meet people where they’re at, so we do a lot of talking about fears and constraints. Let’s put it on the table, let’s talk about it, and then let’s shift to the art of the possible. What if [AI] doesn’t take your job? What could you be doing?”  The point is to get employees to reimagine their roles. To facilitate this, Gibson identified people who could be AI champions, such as principal senior engineers who would love to automate lower level thinking so they can spend more time thinking critically.  Related:Knowledge Gaps Influence CEO IT Decisions “What we have found is we’ve met resistance from more senior level talent versus new talent, such as individuals working in business units who have learned AI to increasingly automate their roles,” says Gibson. “We have tons of use cases like that. Many employees have automated their traditional business operations role and now they’re helping us increase automation throughout the enterprise.”  Making AI Fun to Learn  Today’s engineers are constantly learning to keep pace with technology changes. Gibson has gamified learning by showcasing who’s leveraging AI in interesting ways, which has increased productivity and quality while impacting AvidXchange customers in a positive way.  “We gamify it through hackathons and showcase it to the whole company at an all-hands meeting, just taking a moment to recognize awesome work,” says Gibson. “And then there are the brass tacks: We’ve got to get work done and have real productivity gains that we’re accountable for driving.”  Over the last five years, Gibson has been creating a learning environment that curates the kinds of classes she wants every technologist to learn and understand, such as a prompt engineering certification course. Their progress is also tracked.  Related:The Kraft Group CIO Talks Gillette Stadium Updates and FIFA World Cup Prep “We certify compliance and security annually. We do the same thing, with any new tech skill that we need our teammates to learn,” says Gibson. “We have them go through certification and compliance training on that skill set to show that they’re participating in the training. It doesn’t matter if you’re a business analyst or an engineer, everyone’s required to do it, because AI can have a positive impact in any role.”  Establish a Strong Foundation for Learning  Gibson has also established an AI Center of Excellence (CoE), made up of 22 internal AI thought leaders who are tasked with keeping up with all the trends. The group is responsible for bringing in different GenAI tools and deep learning technologies. They’re also responsible for running proofs of concept (POC). When the project is ready for production, the CoE ensures it has passed all AvidXchange cybersecurity requirements.  “Any POC must prove that it’s going to add value,” says Gibson. “We’re not just throwing a slew of technology out there for technology’s sake, so we need to make sure that it’s fit for purpose and that it works in our environment.”  To help ensure the success of projects, Gibson has established a hub and spoke operating model, so every business unit has an AI champion that works in partnership with the CoE.  In addition, AvidXchange made AI training mandatory as of January 2024, because AI is central to its account payables solution. In fact, the largest customer use cases have achieved 99% payment processing accuracy using AI to extract data from PDFs and do quality checks, though humans do a final review to ensure that level of accuracy.   “What we’ve done is to take our customer-facing tool sets or internal business operations and hook it up to that data model. It can answer questions like, ‘What’s the status of my payment?’ We are now turning the lights on for AI agents to be available to our internal and external customer bases.”  Some employees working in different business units have transitioned to Gibson’s team specifically to work on AI. While they don’t have the STEM background traditional IT candidates have, they have deep domain expertise. AvidXchange upskills these employees on STEM so they can understand how AI works. 

FICO Chief Analytics Officer Scott Zoldi has spent the last 25 years at HNC Software and FICO (which merged) leading analytics and AI at FICO is well known in the consumer sector for credit scoring, while the FICO Platform helps businesses understand their customers better so they can provide hyper-personalized customer experiences.   “From a FICO perspective, it’s making sure that we continue to develop AI in a responsible way,” says Zoldi. “There’s a lot of [hype] about generative AI now and our focus has been around operationalizing it effectively so we can realize this concept of ‘the golden age of AI’ in terms of deploying technologies that actually work and solve business problems.”  While today’s AI platforms make model governance and efficient deployment easier, and provide greater model development control, organizations still need to select an AI technique that best fits the use case.  A lot of the model hallucinations and unethical behavior are based on the data on which the models are built, Zoldi says. “I see companies, including FICO, building their own data sets for specific domain problems that we want to address with generative AI. We’re also building our own foundational models, which is fully within the grasp of almost all organizations now,” he says.   Related:Surgical Center CIO Builds an IT Department He says their biggest challenge is that you can never totally get rid of hallucinations. “What we need to do is basically have a risk-based approach for who’s allowed to use the outputs, when they’re allowed to use the outputs, and then maybe a secondary score, such as a AI risk score or AI trust score, that basically says this answer is consistent with the data on which it was built and the AI is likely not hallucinating.”  Some reasons for building one’s own models include full control of how the model is built, and reducing the probability of bias and hallucinations based on the data quality.    “If you build a model and it produces an output, it could be hallucination or not. You won’t know unless you know the answer, and that’s really the problem. We produce AI trust scores at the same time as we produce the language models because they’re built on the same data,” says Zoldi. “[The trust score algorithms] understand what the large language models are supposed to do. They understand the knowledge anchors — the knowledge base that the model has been trained on — so when a user asks a question, it will look at the prompts, what the response was, and provide a trust score that indicates how well aligned the model’s response is aligned with the knowledge anchors on which the model was built. It’s basically a risk-based approach.”  Related:Knowledge Gaps Influence CEO IT Decisions FICO has spent considerable time focused on how to best incorporate small or focused language models as opposed to simply connecting to a generic GenAI model via an API. These “smaller” models may have eight to 10 billion parameters versus 20 billion or more than 100 billion, for example.  He adds that you can take a small language model and achieve the same performance of a much larger model, because you can allow that small language model to spend more time reasoning out an answer. “And it’s powerful because it means that organizations that can only afford a smaller set of hardware can build a smaller model and deploy it in such a way that it’s less costly to use and just as performant as a large language model for a lot less cost, both in model development and in the inference costs of actually using it in a production sense.”  Scott Zoldi The company has also been using agentic AI.  “Agentic AI is not new, but we now have frameworks that assign decision authority to independent AI operators. I’m okay with agentic AI, because you decompose problems into much simpler problems, and those simpler problems [require] much simpler models,” says Zoldi. “The next area is a combination of agentic AI and large language models, though building small language models and solving problems in a safe way is probably top of mind for most of our customers.”  Related:The Kraft Group CIO Talks Gillette Stadium Updates and FIFA World Cup Prep For now, FICO’s primary use case for agentic AI is generating synthetic data to help counter and stay ahead of threat actors’ evolving methods. Meanwhile, FICO has been building focused language models that address financial fraud and scams, credit risks, originations, collections, behavior scoring and how to enable customer journeys. In fact, Zoldi recently created a focused model in only 31 days using a very small GPU.  “I think we’ve all seen the headlines about how these humongous models with billions of parameters and thousands of GPUs, but you can go pretty far with a single GPU,” says Zoldi.   Challenges Zoldi Sees in 2025  One of the biggest challenges CIOs faces is anticipating the shifting nature of the US regulatory environment. However, Zoldi believes regulation and innovation go hand in hand.  “I firmly believe that regulation and innovation inspire each other, but others are wondering how to develop their AI applications appropriately when [they’re not prescriptive],” says Zoldi. “If they don’t tell you how to meet the regulation, then you’re guessing how the regulations might change and how to meet them.”   Many organizations consider regulation a barrier to innovation rather than an inspiration for it.   “The innovation is basically a challenge statement like, ‘What does that innovation need to look like?’ so that I can meet my business objective, get a prediction, and have an interpretable model while also having ethical AI. That means better models,” says Zoldi. “Some people believe there shouldn’t be any constraints, but if you don’t have them, people will continue to ask for more data and ignore copyrights. You can also go down a deep learning path where models are uninterpretable, unexplainable, and often unethical.”  What Innovation at FICO Looks Like  At FICO, innovation and

IT leaders typically begin their careers by working on a team. Exhibiting their knowledge and skill, they rise through the ranks to become managers and executives. Yet for many leaders, that urge to do some hands-on work never really disappears. Unfortunately, that’s rarely a good idea.  As a technology and business leader, it’s crucial to maintain oversight of strategic and operational priorities, says Rebecca Fox, group CIO at cybersecurity consulting firm NCC Group. Actively contributing to day-to-day project delivery or operations limits the leader’s ability to focus on the broader direction, she observes in an email interview. “While occasional involvement in details may be necessary for decision support or critical interventions, the leader’s primary role is to delegate, inspire, and drive execution.” For leaders transitioning from a subject matter expert role, mastering this shift is critical for personal success as well as the organization’s growth, Fox advises. “The larger the organization, the more essential it becomes to prioritize leadership over operational tasks.”  Danger Zone  There are three key dangers lurking for senior leaders who become too involved as active project participants, Fox says. “Perhaps most important, the project team’s autonomy is undermined, leading to constant reliance on the leader for decision-making instead of driving outcomes independently.” Another risk is that critical responsibilities outside the project may be neglected, jeopardizing broader business success and operational excellence. “Finally, the leader’s role as a strategic business partner is diminished, as they become seen as part of the project rather than a leader with enterprise-wide oversight.”  Related:The Kraft Group CIO Talks Gillette Stadium Updates and FIFA World Cup Prep If you dive too deeply into specific projects, you risk losing sight of the overall direction your team needs to follow, warns Bill Bragg, CIO at AI technology developer SymphonyAI. “While your expertise is certainly valuable, your real strength lies in crafting strategy and growing your team and colleagues’ capabilities,” he says in an online interview. “Your goal is to remove obstacles and steer the ship toward success, growing the people and business together.”  Staying Both Above and Involved  Regular governance and trust in the delivery team is essential, Fox says. “Unless you’re a subject matter expert, active involvement should focus on two areas: ensuring that the right people are involved and validating that the project’s objectives remain relevant.” Effective governance should show when leadership intervention is necessary, such as resolving personnel issues or realigning objectives. “While cost pressures may tempt leaders to take on a contributory role without backfilling, it’s crucial to prioritize long-term project success by maintaining proper resources.”  Related:CIO Angelic Gibson: Quell AI Fears by Making Learning Fun There will be times when your expertise is crucial, or the team is short-staffed, Bragg says. “Recognizing these moments is vital to prevent burnout or mistakes within your team,” he advises. “Be sure to have an exit plan and know when to step back once the gaps are addressed.”  Participation should be as brief as possible, but as long as necessary, Fox explains. Projects and programs require clear organizational structures, and leadership involvement should last until they are established. “Leaders must also be willing to make tough decisions, such as pausing a project until the right resources are available or reallocating resources to meet business needs.”  An IT leader may not be involved in the daily activities of a project, but they should always demonstrate interest and support to their teams and peers, Fox advises. She believes that engagement comes from regular communication, visible support, and showing genuine interest in the team’s challenges and successes. “Leadership isn’t passive; it requires consistent effort to connect and inspire.”  Related:Breaking Down the Walls Between IT and OT Trust and Success  Leadership is primarily about creating the conditions for success, empowering teams, and ensuring alignment with strategic objectives, Fox says. “IT leaders must balance trust in their teams with timely interventions, focusing on outcomes over activity.” She feels that prioritizing leadership over direct contribution enables sustainable growth and operational excellence.  Maintain open communication and regularly meet with your team and other departments, Bragg recommends. “This builds trust and transparency, helping everyone understand how their work aligns with the company’s goals.” By sharing insights into strategies and priorities, the leader steadily builds a cohesive framework that highlights the value of team contributions. “Creating a cadence is important, as the group and staff events themselves become anchors for operationalizing the strategy and envisioning the future.”  A Final Thought  As an IT leader, your primary role is to steer the business technology strategy that empowers the organization’s goals, Bragg explains. “It’s crucial to foster strong relationships and open communication with leaders from every department to ensure that functional and product strategies move in the same direction,” he says. “With a bird’s-eye view of the company’s priorities, you’re in a unique position to drive alignment and facilitate the change that builds the strength to grow together.”  source

Neuromorphic computing is the term applied to computer elements that emulate the way the human brain and nervous system function. Proponents believe that the approach will take artificial intelligence to new heights while reducing computing platform energy requirements.  “Unlike traditional computing, which incorporates separate memory and processors, neuromorphic systems rely on parallel networks of artificial neurons and synapses, similar to biological neural networks,” observes Nigel Gibbons, director and senior advisor at consulting firm NCC Group in an online interview.  Potential Applications  The current neuromorphic computing application landscape is largely research-based, says Doug Saylors, a partner and cybersecurity co-lead with technology research and advisory firm ISG. “It’s being used in multiple areas for pattern and anomaly detection, including cybersecurity, healthcare, edge AI, and defense applications,” he explains via email.  Potential applications will generally fall into the same areas as artificial intelligence or robotics, says Derek Gobin, a researcher in the AI division of Carnegie Mellon University’s Software Engineering Institute. “The ideal is you could apply neuromorphic intelligence systems anywhere you would need or want a human brain,” he notes in an online interview.  Related:Breaking Down the Walls Between IT and OT “Most current research is focused on edge-computing applications in places where traditional AI systems would be difficult to deploy, Gobin observes. Many neuromorphic techniques also intrinsically incorporate temporal aspects, similar to how the human brain operates in continuous time, as opposed to the discrete input-output cycles that artificial neural networks utilize.” He believes that this attribute could eventually lead to the development of time-series-focused applications, such as audio processing and computer vision-based control systems.  Current Development  As with quantum computing research, there are multiple approaches to both neuromorphic hardware and algorithm development, Saylors says. The best-known platforms, he states, are BrainScaleS and SpiNNaker. Other players include GrAI Matter labs and BrainChip.  Neuromorphic strategies are a very active area of research, Gobin says. “There are a lot of exciting findings happening every day, and you can see them starting to take shape in various public and commercial projects.” He reports that both Intel and IBM are developing neuromorphic hardware for deploying neural models with extreme efficiency. “There are also quite a few startups and government proposals looking at bringing neuromorphic capabilities to the forefront, particularly for extreme environments, such as space, and places where current machine learning techniques have fallen short of expectations, such as autonomous driving.”  Related:How to Tell When You’re Working Your IT Team Too Hard Next Steps  Over the short term, neuromorphic computing will likely be focused on adding AI capabilities to specialty edge devices in healthcare and defense applications, Saylors says. “AI-enabled chips for sensory use cases are a leading research area for brain/spinal trauma, remote sensors, and AI enabled platforms in aerospace and defense,” he notes.  An important next step for neuromorphic computing will be maturing a technology that has already proven successful in academic settings, particularly when it comes to scaling, Gobin says. “As we’re beginning to see a plateau in performance from GPUs, there’s interest in neuromorphic hardware that can better run artificial intelligence models — some companies have already begun developing and prototyping chips for this purpose.”  Another promising use case is event-based camera technology, which shows promise as a practical and effective medium for satellite and other computer vision applications, Gobin says. “However, we have yet to see any of these technologies get wide-scale deployment,” he observes. “While research is still very active with exciting developments, the next step for the neuromorphic community is really proving that this tech can live up to the hype and be a real competitor to the traditional hardware and generative AI models that are currently dominating the market.”  Related:3 Ways to Build a Culture of Experimentation to Fuel Innovation Looking Ahead  Given the technology’s cost and complexity, coupled with the lack of skilled resources, it’s likely to take another seven to 10 years before widespread usage of complex neuromorphic computing occurs, Saylors says. “However, recent research in combining neuromorphic computing with GenAI and emerging quantum computing capabilities could accelerate this by a year or two in biomedical and defense applications.”  Mainstream adoption hinges on hardware maturity, cost reduction, and robust software, Gibbons says. “We may see initial regular usage within the next five to 10 years in specialized low-power applications,” he predicts. “Some of this will be dictated by the maturation of quantum computing.” Gibbons believes that neuromorphic computing’s next phase will focus on scaling integrated chips, refining and spiking neural network algorithms, and commercializing low-power systems for applications in robotics, edge AI, and real-time decision-making.  Gibbons notes that neuromorphic computing may soon play an important role in advancing cybersecurity. The technology promises to offer improved anomaly detection and secure authentication, thanks to event-driven intelligence, he explains. Yet novel hardware vulnerabilities, unknown exploit vectors, and data confidentiality remain critical concerns that may hamper widespread adoption.  source

Building a thriving tech company isn’t all about better code or faster product launches — you have to foster an environment where experimentation is the norm. Establishing a culture where employees can safely push boundaries encourages adaptability, drives long-term innovation, and leads to more engaged teams. These are critical advantages in the face of high turnover and intense competition.    Through my own process of trial and error, I’ve learned three key strategies engineering leaders can use to make fearless experimentation part of their team’s DNA.  Strategy #1: Normalize failure and crazy ideas A few months into my first job, I took down several production servers while trying to improve performance. Instead of blaming me, my manager focused on what we could learn from the experience. That moment gave me the confidence to push boundaries again. From then on, I knew that failure was not an end, but a steppingstone to future wins. It’s now a mindset that I encourage every leader to adopt.  Innovation is messy and risky — here’s how leaders can embrace the chaos and bold thinking:  Build a “no-judgment zone”: Before every brainstorming and feedback session, re-establish that there are no bad ideas. This might seem straightforward, but it can make the team feel safe, suggesting radical solutions and voicing their opinions.   Related:Breaking Down the Walls Between IT and OT Encourage “what if?” questions: Out-there ideas like “What would this look like if we had no technical constraints?” or “What would it take to make this 10x better instead of just 10%?” encourage teams to consider problems and solutions from a new perspective. Leaders should walk the walk by asking these same types of questions in meetings.  Celebrate the process, not just the outcome: Acknowledge smart risks – even if they don’t succeed. Whether it’s a shoutout in a team meeting or a more detailed discussion in Slack, take the time to highlight the idea and why it is worth pursuing.     Use failure to fuel future successes: If a project falls short of its goals, don’t bury it and move on right away. Instead, hold a session to discuss the positives and what can be done differently next time. This turns missteps into momentum and helps the team get more savvy with every experiment.   Strategy #2: Give experimentation a framework For experimentation to flourish, leaders must provide teams with the guidelines and resources they need to turn bold thoughts into tangible products. I suggest the following:  Allow for proof-of-concept testing: Dedicate space for testing in the product development lifecycle, especially when designing technical specifications.  Related:How to Tell When You’re Working Your IT Team Too Hard Make room for wild ideas: One of my favorite approaches is adding a “Crazy Ideas” section to our product or technical spec templates. Just having it there inspires the team to push boundaries and propose unconventional solutions.  Establish hackathons with purpose: At our company, we encourage hackathons that step outside our product roadmap to broaden our thinking. And don’t forget to make them fun! Let teams pitch and vote on ideas, adding some excitement to the process.  Use AI to unlock creativity: AI allows developers to build faster and focus on higher-order thinking. Provide the team with AI tools that automate repetitive tasks, speed up iteration cycles, and generate quick proofs-of-concept, allowing them to spend more time innovating and less on process-heavy tasks. AI also helps teams prototype multiple versions of a new solution, letting them test and adjust at speed.  I’ve seen these strategies produce incredible results from my teams. Our hackathons have led to some of our most important breakthroughs, including our first AI feature and the implementation of internal tools that have significantly improved our workflows.  Related:Today’s Technology Should Be Designed By and For All Minds Strategy #3: Test, learn, and refine High-performing teams know that experimentation isn’t failure — it’s insight in disguise. Here’s how to maintain a strong understanding of how each project is progressing:  Set clear success metrics: Experimentation works best when teams know what they’re testing for. The key is setting a clear purpose for each experiment and determining quickly whether it’s heading in the right direction. Regularly ask internal teams or customers for feedback to get fresh perspectives.  Share what works (and what doesn’t): Prioritize open knowledge-sharing across teams, breaking down communication silos in the process. Whether through Slack check-ins or full-company meetings, the more teams learn from each other, the faster innovation compounds.   Run micro-pilots: Leverage these small-scale, real-world tests with a subset of users. Instead of waiting to perfect a feature internally, my team launches a basic version to 5-10% of our customers. This controlled rollout lets us quickly gather feedback and usage data without the risk of a full product launch missing the mark.  Make experimentation visible: For example, host weekly “demo days” where every team presents its latest experiments, including wins, failures, and lessons learned. Moments like this foster cross-team collaboration, which is key to staying agile.  Most transformative technologies — from email to generative AI — probably sounded off the wall at first. But because the engineers behind them were allowed to push boundaries, we have tools that have changed our lives.  Leaders must create an environment where engineering teams can take risks, even if they sometimes fail. The companies that experiment today will be the ones leading innovation tomorrow.  source

Artificial intelligence promises to accelerate many of the tasks and functions that drive today’s business. Few organizations have realized its potential, however, mostly because AI capabilities are still relatively new and legacy architecture limits AI project scalability. Despite these limitations, chief information officers are under enormous pressure to deliver measurable, concrete return on AI investments, says executive recruiter Charley Betzig, managing director at Heller. In this Q&A with InformationWeek, Betzig discusses the CIO job market and how AI is influencing the CIO’s role. This interview has been edited for clarity and length. What do CIOs need to know about the job and the CIO job market? How is the CIO’s day-to-day role evolving? It’s all about AI — and it’s not just AI, in of itself, but it’s AI value creation. That affects every search, every CIO role. There are always different starting points, and every CIO role is a little bit different. But baseline, we are looking for CIOs who have created value using AI. Someone has to understand the business that they are walking into, understand the starting point, and then know how to build from that starting point to take the business where they want to go to achieve that value creation. Every business is starting on a different part of the spectrum. Some, to get value from AI, they are starting from a very primitive place in terms of data. You need to build a foundational data strategy to make sure data is clean and available so AI can be used to create that value. Related:Breaking Down the Walls Between IT and OT Charley Betzig Other organizations are further along, and you can start building those AI use cases more quickly. But it is really business acumen, to know the environment that you are walking into and how to move the organization forward from there. There is the cross-functional leadership — the IT function has evolved a lot over time. Early days, IT was more of a back-office function; it was a follower. Then you had this whole concept of IT as a leader — a CIO had to own all of technology in an organization. If the business owned any of it, it was bad, it was shadow IT. I think that is kind of going away too, especially with AI. There is kind of this notion of the CIO is sort of the sherpa, and the business is the one climbing the mountain. But the CIO is there guiding the way, putting the right guardrails in place, making sure everyone is moving in the right direction when pushing AI. But you need the business to be the ones who are out there driving these use cases because they know what they want. Related:How to Tell When You’re Working Your IT Team Too Hard What are companies looking for in a modern CIO? Is an MBA important or are there certain certifications that are proving more valuable? It’s always nice to have an MBA, but what I’m focused on is making sure CIOs have that right blend of technical chops and business acumen. Technical chops are the easiest thing to look for — we always look for CIOs that have a foundation in computer science or something like an information systems degree — those things point to that technical knowledge. If they have an MBA, then that is a plus for sure. But I more look to the education to make sure they have that technical foundation. Everything right now revolves around AI, but you still as CIO have to have that grounding in all of the traditional disciplines of IT. Whether that is systems, whether that’s infrastructure, whether that’s cybersecurity, you have to have that well-rounded background. Even as these AI technologies become more prolific, you must consider your past infrastructure spend, your cloud spend, that went into these technologies. How do you manage that? If you don’t have grounding in managing those costs, and being able to balance those costs with the innovation you are trying to create, that’s a recipe for failure on the cyber side. And AI is creating even more vulnerabilities from a cyber standpoint. Someone has to have that sort of foundation as well. You still have those classic disciplines you can’t forget about even as you’re searching for that shiny object. Related:3 Ways to Build a Culture of Experimentation to Fuel Innovation Are there certain CIO-specific skills that companies have a hard time hiring for? It goes back to that AI value creation — every company is trying to do that, and the hard part is it’s really a new thing. It’s not a ‘we can go out and we can recruit someone from Silicon Valley who is an AI pioneer and knows all about the sexiest different technologies that can be applied.’ Is that the best person to come into a manufacturing company in the Midwest and work with those employees on AI use cases to create value? It’s not. When we’re looking for skill sets, we’re looking for people who have actually taken those AI technologies and applied them within their organizations to create real business value — whether that is cost savings or top-line revenue creation, whatever those are. It’s hard to find those candidates, because there are a lot of those people who can talk the talk around AI, but when you really drill down there is not much in terms of results to show. It’s new, especially in applying the technology to certain settings. Take manufacturing: there’s not that many CIOs out there who have great examples of applying AI to create value within organizations. It’s certainly accelerating, and you’re going to see it accelerating more as we go into the future. It’s just so new that those examples are few and far between. There are certainly people out there who have done it, they are just not all over the place. What are CIOs looking for in both the organization and