Information Week

Ryan Knisley, chief product strategist for enterprise asset management company Axonius, began his career in the US Army. His goal was to work for the Secret Service, and after eight years in the Army, he did just that. Working for the Electronic Crimes Special Agent Program (ECSAP), he cultivated a range of skills that he would later apply to the private sector.   He went on to work for such companies as Walmart and PwC before stepping into the C-suite at Costco and then Disney. He intentionally limited his time in these roles but remains highly attuned to the responsibilities of the modern chief information security officer — he talks to CISOs across a variety of industries on a regular basis. Here, he shares his professional journey and his insights into the crucial responsibilities of the CISO.  Did you have an early interest in technology? Or did that develop later in your career?  I was playing college football and realized I was not going to go to the NFL. I had always wanted to be a Secret Service agent. My dad’s friend was a Secret Service agent. He said, “You won’t go from the frat house to the White House. You better join the military and do something special.” I told my dad and mom, “I’m quitting football. I’m going to drop out of college. I’m going to join the Army.”   Related:Ways a CIO Might Derail an AI Strategy Inadvertently I joined the army and stayed for eight years. During the last half of that time, I was a criminal investigations division (CID) special agent. I was exposed to forensic investigations in CID. When I got into the Secret Service, they were looking for people who had experience in digital evidence collection. I entered the Electronic Crimes Special Agent Program.  What kind of work did you do for the Secret Service?  I sat in the forensic lab and looked at digital evidence to support the prosecution of criminal cases that the Secret Service had taken on. My responsibility was to find the digital evidence to support those cases. Most of those were mundane investigations, such as bank fraud.   I was involved in some really large breaches. I happened to be the duty agent and answered the phone at the wrong time. I was involved in the case of Albert Gonzalez [the person who orchestrated the TJX and Dave and Busters attacks of 2007–08].   Why did you transition from the Secret Service to the private sector?  I thought I would retire from the Secret Service, but I got a call from my wife, who discovered she had cancer. We were 32 at the time and we had young kids. I was traveling a lot. I needed a more stable work life to help care for her. She is fine now. We’ve been married 25 years.   Related:Can Tech Transform Your Staff Into a Service Culture? But that was the catalyst. I got connected with a former Secret Service agent who was working at Walmart. That’s how I ended up there — it was my first private sector job out of government.  How transferable were your skills? Did you have to learn on the job?  I had a really strong technical foundation. I think the most challenging part for individuals who transfer from the government to private sector companies is they don’t often learn the language of the business. That has been a key to my success — explaining really complex technical and cyber issues in terms that non-technical businesspeople can understand and appreciate.  How did you end up in the C-suite? What led to your first CISO position?  I was a partner in PwC cybersecurity practice, advising Fortune 500 companies on cyber topics. PwC had been doing some work with Costco. One of the partners there asked if I knew anybody who would be a good CISO. I started consulting with them on candidates. Four or five months into that process, Costco came to me and said, “What about you?” Two weeks before that, I was at a conference and somebody said, “Would you be a CISO?” I said, “No, it’s a terrible job.” What it came down to was a great brand that really wanted to invest in transforming their cyber practice. I thought: These opportunities don’t come along that often. I better pursue this one.   Related:AI May Solve Its Own Talent Shortage — Here’s How When I joined, I made the promise to myself that I was not going to be a CISO forever. I’m going to work hard and help them through this transformation. Then I’m going to do other things.  CISOs sometimes observe that they have only recently been taken seriously in the C-suite. During your time as a CISO, did you see any changes in the value accorded to your position?  I certainly saw the evolution of the role as I came up through my career. A lot of the CISOs that I had worked with and for prior to that were very tactical. By the time I had gotten to the role of a CISO, I think the shift had been made to a more business-focused role. It continues to evolve even today. It depends on the industry that you’re in.  By the time I got there, it was considered a true C-suite role. I had a voice in the business. When I would talk to the board, I would talk about business problems, not “cyber problems.”  How did your experience as a CISO translate to your current role?  I always explain my role in three parts. The first part is spending time with customers and learning from them. The second piece is taking all of this customer feedback and working with our product teams to inform the roadmap and evolve the products. The last piece is being the voice back to the market — a champion for our product and platform.  What are some of the concerns you are seeing from the CISOs you speak with? 

Generative AI is reshaping productivity across industries, boosting workers’ output by an estimated 33% during the hours they actively use these tools and saving roughly 2.2 hours per week on a standard 40-hour schedule, according to a St. Louis Fed study. These aren’t hypothetical gains — they’re real-world shifts showing how AI can act not just as a technological tool, but as a workforce multiplier.  As enterprises scramble to expand their AI capabilities, many assume they need to hire waves of new specialists. But the more compelling opportunity may lie in using AI itself to close skill gaps, accelerate learning, and elevate existing teams. Could the solution to the AI talent shortage actually be more AI?  Forget the AI Skills Panic — Here’s What Really Matters  Many organizations instinctively assume that adopting AI demands an influx of new skills and specialized hires. But not every technological leap requires sweeping retraining. Historically, AI has quietly improved workplace tools — think spellcheckers, spam filters, or autocomplete — without demanding new expertise.  While some predicted that roles like “prompt engineer” would dominate the future, the reality has shifted quickly. Intuitive AI interfaces now guide users through tasks, reducing the need for specialized knowledge. The real challenge isn’t a lack of technical skill; it’s the friction of facing too many options too quickly without clear direction.  Related:Can Tech Transform Your Staff Into a Service Culture? This means that instead of focusing solely on recruiting external talent, organizations have an opportunity to empower their existing workforce by making AI tools more accessible, intuitive, and integrated into daily work.  How AI Lifts Underperformers and Supercharges Teams  AI’s greatest power in workforce development may lie in its ability to lift the floor, not just raise the ceiling. The St. Louis Fed study also found that workers using generative AI save, on average, 5.4% of their work time (roughly 2.2 hours) by streamlining tasks, improving workflow, and offering just-in-time support. Importantly, these gains are often most pronounced among lower performers, who benefit from AI’s ability to codify best practices and deliver them directly.  Companies are already seeing results. For example, 88% of organizations now use AI in recruitment, automating time-consuming tasks like résumé scanning, candidate fit prediction, and initial screening. This frees up HR teams to focus on strategic hiring decisions and improves time-to-hire — a critical factor in competitive industries.  Related:Entry-Level Cyber Talent Doesn’t Exist. Here’s How to Change That But AI’s potential extends far beyond hiring. When embedded thoughtfully, it can serve as an always-on performance coach, helping employees identify blind spots, track progress, and receive actionable feedback. This creates a more adaptive workforce that can grow and evolve in sync with shifting business needs.  Why Old-School Training Can’t Keep Up With AI  Traditional workforce development models, centered on formal training courses and workshops, are struggling to keep up with the pace of change. That’s why some industries, such as healthcare, are turning to AI-powered platforms that dramatically reduce time-to-competency. For clinical support roles, where the US is facing a projected shortage of over 100,000 positions by 2028, these platforms can cut training timelines to under four months — directly addressing one of the industry’s most urgent labor challenges.  By delivering targeted, personalized learning embedded in daily workflows, AI accelerates employee development in ways that traditional approaches simply can’t match. This not only improves return on investment but also helps organizations stay agile in fast-moving markets.  Ethics or Automation? The Risks Lurking in AI Development  Of course, integrating AI into talent development comes with risks. Governance and ethical oversight are critical, particularly when it comes to data privacy. Many AI tools default to aggressive data collection, and companies must ensure they’re using performance and development data responsibly and transparently.  Related:2026 Budgets: What’s on Top of CIOs’ Lists (and What Should Be) There’s also the risk of overreliance. Easy automation can tempt employees to hand off too much of their thinking, leading to lower-quality work, diminished curiosity, and reduced innovation. Leaders must balance the efficiencies AI offers with a commitment to keeping human engagement, creativity, and judgment at the center of their operations.  Want an AI-Ready Workforce? Start by Valuing Critical Thinking  Ultimately, the most valuable workforce skill in the AI era isn’t coding or prompt crafting; it’s critical thinking. As routine tasks become automated, organizations increasingly need employees who can frame smart questions, interpret complex outputs, and navigate ambiguity.  The companies that will thrive are those that foster cultures of curiosity, adaptability, and lifelong learning. AI can show teams not just what they’re doing well, but what they’re overlooking. But turning those insights into meaningful progress requires thoughtful leadership and intentional workforce development.    AI isn’t just a tool for doing more with less — it’s a catalyst for rethinking how we learn, grow, and create value together.  source

Organizations want actionable insights, but what elements are necessary to ensure the data is reliable? How can tech leadership and operations gather data quickly enough to remain relevant? In this podcast episode, Harry Folloder, chief digital and technology officer for Alorica; and Brooke Huling, chief product officer for Accruent, came together for a Breaking Bread session to discuss their perspectives on leveraging predictive data. What type of data is real and vital for forming campaigns and strategies? How do organizations balance security, privacy, and compliance with predictive data? How fresh must data be to deliver on predictive analytics? How can they balance the use of AI in this mix, especially if that data might be collected and kept by third-party AI? In a tabletop exercise that followed, they tackled scenarios to help a fictional company, Questionable Ideas, navigate the use of predictive data in ethical, secure ways — even when questionable ideas get introduced. source

While the world waits for artificial general intelligence to awaken and take over, internal and external AI resources continue to be leveraged for transformations. Combined with the intense competition for talent to develop future iterations of AI, the potential scale of transformation powered by the technology could be ubiquitous. Businesses have been through transformative times, from the Industrial Revolution to the dawn of the Digital Age. As a flurry of AI agents and tools emerges, what should CIOs look for in this next period of transformation? How does this era of change differ from cloud transformation? Saket Srivastava, CIO of Asana, and Pierre DeBois, founder and CEO of Zimana, spoke to these and more questions in the latest InformationWeek podcast. This includes concerns and opportunities that tech leadership and operations see with the technology. How should viable plans be formed that allow for exploration of new tech, while safeguarding resources and the business? Then they tackled the tabletop exercises, doing their best to steer the fictional company Questionable Ideas ‘R’ Us on a reasonable path for AI transformation. source

A recent CIO survey revealed nearly 9 in 10 companies experienced a breach in the last year and almost all CIOs (96%) say security coverage isn’t strong enough. CIOs face constant pressure to secure their enterprises, but there simply aren’t enough seasoned professionals to go around.  As a result, job listings often target only the most senior cyber experts, overlooking entry-level talent. This increases business risk, drives up costs, and leaves critical positions unfilled.  The Cyber Talent Shortage Is Now a Business Risk  There’s a global shortage of over 4 million cyber professionals, with two-thirds (67%) of organizations reporting a moderate-to-critical skills gap in cybersecurity. Jobs in this area hold a 28% vacancy rate.   The entry-level shortage is especially acute: Nearly one third of cybersecurity teams have no early-career professionals, and 62% of open roles are reserved for mid to senior positions.  Every unfilled seat is a vulnerability. Relying on poaching or consultants is short-sighted; building a pipeline of early-career talent is essential for long-term resilience.  Without an intentional strategy to engage entry-level talent, CIOs will continue to struggle with ineffective cybersecurity programs.   Breaking the Entry-Level Talent Stigma   Related:AI May Solve Its Own Talent Shortage — Here’s How Many CIOs shy away from entry-level hires, reluctant to invest in training or mentorship in high-stakes environments. But ignoring early-career talent leads to higher costs, turnover, and fragile teams.  Building a talent pipeline ensures future roles are filled, reduces long-term payroll costs, and gives teams access to fresh thinking and new perspectives — all critical for outpacing attackers.  With CIOs under pressure to safeguard their organizations, here’s why hiring only the most senior cyber talent can’t work:    Enough cyber security talent simply doesn’t exist, at all levels. If companies decided only to focus on mid-level and above hires, they still wouldn’t be able to meet demand.  Entry-level professionals can take on the more junior tasks to enable senior employees to focus on complex ones.  A sustainable pipeline ensures future needs are met, as senior talent leaves or retires. With senior-level talent being consistently poached, companies need an entry-level strategy to retain their institutional knowledge.  It’s cost effective. Onboarding early-career talent saves payroll costs and investing in their training yields greater retention rates. High consultancy costs to fill gaps have overrun budgets.   Related:2026 Budgets: What’s on Top of CIOs’ Lists (and What Should Be) Fresh talent brings fresh perspectives, creating a team with diversity of thought. Their unique backgrounds along with their willingness to take on new tasks brings important value.  3 Ways CIOs can Help Ensure Successful Entry-Level Cyber Talent  1. Redefine entry-level. The root of the entry-level cyber talent challenge lies in the misalignment of entry-level definitions and expectations in the industry. Many postings require a degree and three years of experience for junior roles, excluding most capable candidates.   Instead, define the baseline technical and soft skills needed for success and work with HR to prioritize these skills over credentials. For example, an SOC analyst needs hard skills such as a solid understanding of networking concepts and the ability to conduct log analysis techniques. They can obtain these skills outside of a traditional four-year college or enterprise through training. You’re also looking for them to possess soft skills: they should be able to demonstrate that they take direction well, are quick learners, and can pivot when needed.   When entry-level is defined by ability, not pedigree, more roles are filled faster, and critical risk gaps close sooner.  2. Build career pathways. Most organizations lack a clear roadmap for cyber talent. As the threat landscape shifts, roles evolve, and new skill sets are required. CIOs should clearly define advancement criteria for every level – both technical and soft skills – and promote from within whenever possible.  Related:CIOs’ Top Hiring Challenges Today, and How to Solve Them Supporting early-career programs builds loyalty and is also a retention strategy. Employees who see growth opportunities stay longer, reducing the cost and disruption of external hiring.  Companies with visible career pathways are stronger, more resilient, and less likely to lose top talent to competitors.  3. Embrace apprenticeships and other training. Traditional training programs often lag real-world needs. By the time employees finish, new threats have already emerged. Registered apprenticeship programs, shaped in partnership with the CIO, can address this gap directly.   CIOs should have a strong hand in shaping training to business needs, whether managed in-house or outsourced. They can also set clear KPIs for all training partners and ask for practical experience: hands-on labs, capture-the-flag exercises, mentorship, and measurable results. Lastly, they should hold their partners accountable to ensure new hires are ready to defend your business.   No single leader can close the entry-level cyber talent gap alone. But CIOs who redefine entry-level roles, build clear career pathways, and demand training outcomes will develop stronger, future-ready teams. Inaction is the greatest risk of all.  source

Good intentions can still lead to software, AI, and other IT resources that run amok within an organization. Implementations of new platforms can stack up in unintended ways, potentially going unnoticed until a logjam occurs. So, where should CTOs start to sort out layers of tech that may have been necessary one moment, only to jumble into digital sprawl down the line? How should business and operations leaders convey to tech leadership which resources remain essential and what amounts to clutter?    Margaret Dawson, CMO of Chronosphere, and Samya DasSarma, CTO of Iterable, responded to those questions and more in this episode of the podcast. They also collaborated in a round of “Question Ideas ‘R’ Us” tabletop exercises, where they tried to help the fictional company survive its latest IT misadventures. The gremlins of team Let’s Break Stuff and the COBOL Kobolds had other ideas… source

A significant part of transformation goes beyond the technology that serves as the catalyst of change. Adoption of a new resource that touches many departments can lead to organizational shifts within a company. Beyond the need to retrain teams, entire departments could shift in purpose or even be eliminated. Furthermore, business customers may require some preparation to integrate with new platforms for contracts and sales. What must CIOs take into account when mapping out how new technologies might cause ripples in the organization? How does the operations side of business adapt and find ways for leadership and team members to work under new directions in technology? Dan Carpenter, Amplitude’s CIO, spoke to these questions and others, then took on a few tabletop exercises where he spoke to the importance of humanity amid the potential for chaos when new tech is on deck. source

Finding top-quality IT talent is a daunting challenge in an era of rapidly emerging technologies, such as AI, quantum computing, nuclear-powered data centers, and advanced robotics. To find IT leaders and team members qualified to handle tasks in these and other hot-topic areas, CIOs must become more creative and persistent.  CIOs today are looking for IT talent that possesses skills in AI, automation, cloud technologies, cybersecurity, and data analytics, says Byron Love, cybersecurity program director at security and intelligence solutions provider Nightwing. “However, there’s fierce competition for this elite talent, causing a significant gap between talent requirements and the candidates available on the market,” he notes in an online interview.  Overcoming Challenges  Today’s biggest challenge is finding candidates with both deep technical expertise and broad systems knowledge, says Chase Snuffer, CIO at the Rayburn Electric Cooperative, a not-for-profit electric cooperative in Texas. “Too often, we encounter individuals who are highly specialized — strong in one vertical, such as cybersecurity or networking, but who lack exposure to integrated systems across a complex enterprise environment,” he observes in an online interview.  Related:2026 Budgets: What’s on Top of CIOs’ Lists (and What Should Be) Maintaining cultural alignment — and finding individuals who can thrive in an IT culture with adaptability and forward-thinking skills — is essential, Snuffer says. “We’re not a massive enterprise, but we move fast, and we invest early in modern platforms,” he states. “This can be a tough adjustment for candidates coming from slower-moving or highly segmented organizations.”  It’s hard to spot great talent when your recruitment teams are flooded with resumes, especially if you haven’t honed an efficient, skills-focused screening process, observes Justice Erolin, CTO at BairesDev, which specializes in software development, software outsourcing, and staff augmentation services. In an email interview, he notes that top tech talent is in high demand, which makes finding qualified job candidates an increasingly difficult challenge. “Big Tech is no longer the only place where great engineers can work,” Erolin says. “Companies of every size and in every industry need those specialists to bring their projects to life.”  Coping Tactics  Successful CIOs build high-performing teams that embrace collaboration, chemistry, and the trust necessary to weather future technology disruptions, Love says. “Yet they also face the dilemma of hiring personnel quickly to fill gaps or waiting to find candidates who fit both technically and culturally.”  Related:A Tech Leader’s Guide to Reputation Management “We found success by taking a proactive, pipeline-first approach,” Snuffer says. “Our internship program has been a real asset — three out of four interns we’ve hosted since 2017 now work here full time.” Snuffer adds that to attract a wider candidate pool, he’s also recruiting nationally and offering flexible, hybrid arrangements.  Snuffer believes that his proactive approach places practicality over perfection. “Rather than search endlessly for unicorn candidates, we’ve invested in upskilling and internal development,” he explains. “We cross-train aggressively, support continuing education, and rely on peer mentoring.”  Skills Matter  Look beyond the traditional tech profile and focus on a candidate’s abilities, Erolin advises. He suggests evaluating prospective talent based on what they can do. “Simulate real-world problems and assess their skills.”  Love recommends cultivating employee performance and satisfaction. Encourage and incentivize employees to refer candidates who are not only technically qualified but also fit strongly with the culture, he says. “Our technology managers are expected to be both technically proficient and effective people leaders with the emotional intelligence, cultural awareness, and team-building skills required to create cohesive teams capable of performing with resilience under pressure.”  Related:The Power of Optimization: Turning AI into Enterprise Efficiency Long-Term Planning  CIOs have to think long-term, Snuffer says. “It’s all about building a resilient, future-ready team that can scale with the business,” he explains. “Our role as CIOs is to cultivate an environment in which people grow, systems evolve, and IT becomes a strategic driver — not just a cost center.”  CIOs who treat hiring as a one-off transaction are missing the strategic dimension, Erolin warns. “Continuous investment in internal career tracks, team culture, and learning opportunities, can transform your organization from ‘just another job’ site into a top destination for top tech talent.”  Today’s CIO role demands not only technical expertise, but also strategic vision, Love says. He believes that strong leadership and having the ability to drive cross-departmental digital transformation is crucial. “Organizations that prioritize team cohesion and leadership development, alongside technical acumen, will be best positioned to attract and retain top talent and to weather future technology disruptions.” source

Reputation is one of the most valuable — and vulnerable — assets a tech company holds. Studies show that a high percentage of a company’s market value is tied to its public perception, and a single reputational incident incurs major costs in lost revenue, diminished trust, and long-term brand damage. For brands and tech executives alike, reputation management must be approached as a necessary strategic function, not an afterthought. It’s not enough to deliver innovative products; companies are expected to demonstrate transparency, ethical leadership, and operational integrity. In this environment, proactive reputation management becomes a competitive advantage and a safeguard against volatility.  People Buy from People — Not Just Companies  In the B2B tech world, where decision-makers are typically solving very specific pain points, buying decisions go well beyond product features and price points. Prospects are evaluating the people behind the brand just as much as the brand itself. Trust begins with a digital search, a LinkedIn scroll, or a ChatGPT query, and in that first moment of truth, perception is shaped not just by what’s said, but who is saying it.  This is why building the reputation of your executive leadership is equally important as building brand equity. Executives need to show up as thought leaders, consistently contributing meaningful insights and fostering trust long before the first sales conversation. In a world of increasing noise and content saturation, people gravitate toward credible, authentic voices. A respected leadership team may be the deciding factor in winning a deal or closing a sale.  Related:2026 Budgets: What’s on Top of CIOs’ Lists (and What Should Be) That trust is also reinforced through networks. Once a potential customer identifies your company as a contender, they’ll do what any smart buyer does — they ask around. They tap into peer networks and look at which companies are already using your product. That word-of-mouth credibility is irreplaceable, and it can’t be bought. It must be earned through consistently delivering value and cultivating positive experiences.  Reputation Is Built in Layers, and Can Be Lost in Seconds  Tech leaders must understand that reputation is multi-dimensional and fragile. It’s shaped by culture, leadership, customer service, your solution and the value it creates, as well as the smallest actions. A careless comment, a poorly handled support ticket, a sales person overselling capabilities, or an underwhelming customer experience can have outsized consequences. You could spend years building credibility and lose it in a matter of moments.  Related:CIOs’ Top Hiring Challenges Today, and How to Solve Them One of the most underappreciated tools in managing reputation is the feedback loop. Net Promoter Score (NPS) filters out mediocrity by acting as an honest gauge of how your customers perceive you. When you understand that the threshold for excellence is high and unforgiving, you start to build systems around exceeding expectations.  True reputation management also means making hard choices about which customers to serve. If a potential partner’s expectations don’t align with your capabilities or values, the best decision might be to walk away. Taking on misaligned customers for short-term gain leads to poor outcomes, miscommunications, and ultimately, reputational damage. Being selective isn’t arrogance — it’s strategic protection.  Beyond product and performance, companies enhance their reputations by fostering their customer community. This is especially powerful in tech, where many companies are solving similar challenges, sometimes in heavily regulated environments or spaces marked by ongoing disruption. Building a customer community encourages knowledge sharing and creates a support system where customers help one another. The value multiplies — and so does the loyalty.  Related:The Power of Optimization: Turning AI into Enterprise Efficiency But community doesn’t just happen. It needs to be intentionally built and led by people who genuinely care. That starts with a “customer-first” mindset and extends into a philosophy I call “plus one.” Plus one is about creating value beyond the transaction. If you’re meeting a customer and are stopping at a coffee shop on the way, offer to bring them their favorite drink. If you see someone in need on your way to a meeting, stop and help — and let your customer know why you’re running late. These human touches reinforce your values and leave lasting impressions.   When your customers see you as a partner who adds value across all dimensions, trust and loyalty deepen — and your reputation grows stronger. To safeguard and strengthen reputation, tech leaders should:  Encourage executives to regularly publish thought leadership content to build personal and brand credibility.  Monitor NPS and act on customer feedback to close experience gaps proactively.  Invest in digital listening tools to track brand mentions, sentiment, and competitive comparisons.  Develop customer communities that foster peer-to-peer support and loyalty.  Adopt a “plus one” approach of their own to customer engagement to enhance human connection and trust.  Be selective about customer partnerships to avoid misalignment and protect long-term reputation more than “quick” deals.  Actively manage online reviews and brand references to ensure consistency and accuracy across platforms.  Reputation isn’t just a public relations concern — it’s a leadership imperative. When managed with intentionality and heart, it becomes one of your greatest strategic assets.  source

AI continues to command attention, yet most organizations are frustrated by the gap between potential and real-world execution. Predictive models forecast demand or detect anomalies, but optimization answers the vital question: “What action should we take?” Without it, AI often stays in the lab. McKinsey’s 2025 report on AI adoption, The State of AI, reveals that firms embedding AI at scale are redesigning workflows and centralizing governance. They’re creating the structured infrastructure that elevates AI from experimentation to enterprise impact, especially when paired with optimization frameworks Expert Insight: Gurobi on Optimization in the Real World In a recent AI Think Tank Podcast discussion, Jerry Yurchisin, Sr. Data Scientist at Gurobi, highlighted that optimization is no longer niche, it’s central to modern decision systems. He explained that optimization bridges the gap between predictions and business outcomes by translating probabilistic insights into constrained, goal-driven recommendations.The big change isn’t the math, it’s the connection: Optimization brings clarity by making decision assumptions transparent. Each outcome can be audited, and each constraint traced back. That level of explainability is essential in modern governance regimes. Related:2026 Budgets: What’s on Top of CIOs’ Lists (and What Should Be) Optimization methods vary based on complexity. For scheduling and resource allocation in logistics or manufacturing, discrete approaches like integer programming are delivering fast, measurable results. One global airline cut crew scheduling costs by 12%, all while staying compliant with union rules. In sectors like finance or healthcare, convex optimization provides predictable and scalable decision frameworks. It supports portfolio balancing or risk scoring under constraints like fairness or regulatory limits. For more stubborn problems, like hyperparameter tuning in complex AI systems, enter derivative-free techniques like Bayesian optimization. One financial firm realized an 8% accuracy boost and cut model development cycles in half by adopting this approach. Embedding Optimization in the Enterprise To scale optimization, leaders must first identify decision domains suffering from inefficiency, complexity, or manual intervention, areas such as pricing, inventory, or workforce planning. These “hotspots” become the focus of cross-functional teams that define variables, objectives, and constraints. Gartner’s 2025 Magic Quadrant report for data science and machine learning platforms notes that market-leading tools, from Google Vertex AI to Databricks, now embed solver-based optimization as a core capability. This evolution enables AI platforms to not merely analyze, but decide, automate, and adapt in real time. Related:CIOs’ Top Hiring Challenges Today, and How to Solve Them Optimization creates inherent transparency. Each decision is derived from explicit objectives and constraints, exposing what was prioritized. This makes compliance and auditability easier in regulated industries like finance or healthcare, compared to opaque AI black boxes. Additionally, optimization supports adaptability. As business conditions shift, whether due to market changes or regulatory updates, models can be reoptimized quickly without a full rewrite, providing strategic agility. The Measurable ROI of Optimization The financial upside of optimization is clear. Organizations deploying it in operations often report cost reductions between 10–30%, while AI workflows gain 5–15% performance boosts and faster deployment cycles. Deloitte’s 2025 supply chain analysis emphasizes how AI, combined with decision frameworks like optimization, enhances forecasting, inventory alignment, and operational responsiveness. It shows that optimization is not just technological; it’s a tool for business-level transformation. CIOs and CTOs should elevate optimization to a strategic level: A core component of digital transformation, alongside cloud, governance, and AI ethics. Begin by cataloging decisions ripe for optimization. Pilot use cases in targeted domains can deliver quick wins and organizational confidence. Long-term success comes from cross-disciplinary teamwork and a feedback loop that keeps models aligned with business dynamics. Related:A Tech Leader’s Guide to Reputation Management While many chase the promise of AI, optimization quietly powers some of the world’s most effective decision engines. It transforms prediction into production and strategy into scale. With insights from optimization pioneers like Gurobi and current evidence from leading research, we can confidently say: In the AI revolution, optimization isn’t optional, it’s essential. Enterprises that embrace it now will shape the future, not chase it. source