Key Factors for Successful Technology Investments

Most enterprises waste millions on tech without seeing real impact. I learned this the hard way. Early in my career, I saw companies invest in cutting edge tools only to struggle with adoption, integration, and ROI. That’s when I developed a smarter, outcome-driven approach. Here’s the exact method I use to maximize ROI from technology investments:  Start with Business Outcomes, Not Features ↳ Define the measurable impact before picking the tech. What problem are you solving? What KPIs will prove success?  Ensure Alignment Across Teams ↳ IT, finance, and business leaders must be on the same page. Misalignment leads to wasted budgets and underutilized tools.  Adopt in Phases, Not All at Once ↳ Test, refine, and scale. A phased rollout prevents disruptions and maximizes adoption.  Measure, Optimize, Repeat ↳ Regularly assess ROI. What’s working? What needs adjustment? Continuous refinement drives long-term value. Tech alone doesn’t drive transformation—strategy does. How do you ensure your technology investments deliver real business impact? Let’s discuss. 👇 🔹 Follow me for more insights on digital transformation. 🔹 Connect with me to explore strategies that drive real impact. ♻️ Repost this to help your network. P.S.: Thinking about how to maximize your tech investments? Let’s chat. I’m happy to share insights on what works (and what to avoid).

After working with a number of organizations that have gone from AI crisis to competitive advantage, here's what I've seen separates success from disappointment: 1. Business Outcomes First, Technology Second Stop asking "How can we use AI?" Start asking "What business results do we need?" Leading with value creation gets you executive commitment. Leading with technology gets you pilot projects that often die. 2. Invest in People, Not Just Platforms The biggest barrier isn't technical - it's cultural. Organizations achieving significant improvements spend 10-15% of their budget on workforce transformation. Your people need to know not just HOW to use AI, but WHY and WHEN. 3. Don't Automate Yesterday's Problems Most processes were designed for information scarcity and human-only decisions. So before deploying any AI, ask: "If we were starting from scratch today, how would we solve this?" Adding AI to 10-year-old workflows is like putting a jet engine on a horse-drawn carriage. 4. Make Data Your Strategic Partner Traditional data sits passively in databases. "Intelligent data" understands context, validates itself, and prevents problems before they occur. This shift from "data management" to "intelligence orchestration" creates exponential - not linear - advantages. 5. Think Ecosystem, Not Just Efficiency While others focus on internal automation, successful organizations create network effects that benefit customers, partners, and suppliers. The pattern? Organizations that think exponentially, not incrementally, are building sustainable competitive moats while others optimize for yesterday's competition. What's your experience? Are you automating old processes or fundamentally rethinking how work gets done? #AI #DigitalTransformation #Leadership #Innovation #Strategy

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝘀 𝗣𝗿𝗼𝗳𝗶𝘁 𝗖𝗲𝗻𝘁𝗲𝗿: 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗠𝗮𝘁𝗵 𝗼𝗳 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗩𝗮𝗹𝘂𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 Most board conversations about technology still frame it as a cost center. This legacy perspective is increasingly dangerous in a market where technology-driven revenue streams now represent the primary growth engine for market leaders. After leading digital value creation initiatives across multiple enterprises, I've observed a fundamental shift in how successful organizations measure technology's contribution to enterprise value. 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗾𝘂𝗮𝘁𝗶𝗼𝗻: 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝘀 𝗖𝗼𝘀𝘁 For decades, execs evaluated technology through the lens of: • Cost reduction (improve efficiency) • Risk mitigation (maintain stability) • Capital expense management (minimize spend) This framework produced predictable outcomes: technology budgets constrained to 2-5% of revenue, innovation limited to incremental improvements, and strategic discussions focused on cost containment rather than value creation. 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗘𝗾𝘂𝗮𝘁𝗶𝗼𝗻: 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝘀 𝗩𝗮𝗹𝘂𝗲 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿 (business accelerator) Market-leading organizations now evaluate technology through a fundamentally different formula: 1. Revenue multiplication (over cost reduction) 2. Margin expansion (over operational efficiency) 3. Valuation multiple enhancement (over capital management) This framework produces dramatically different outcomes. When we implemented this model at one healthcare organization, technology investments shifted from 4% to 8% of revenue—while increasing EBITDA by 14%. 𝗤𝘂𝗮𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆'𝘀 𝗣&𝗟 𝗜𝗺𝗽𝗮𝗰𝘁 The organizations achieving exponential returns apply three specific calculations: 1. Revenue per digital channel: One financial services firm discovered their digital-first customers generated 2.8x higher lifetime value than traditional channels. This insight transformed their technology roadmap from cost management to revenue acceleration. 2. Margin by technology enablement tier: A manufacturing company segmented product lines by technology enablement level, revealing a direct correlation between digital capabilities and margin expansion—from 12% to 38% across tiers. 3. Valuation premium from technical architecture: Companies with modular, API-first architectures command 2-3x higher valuation multiples than legacy competitors—a metric now explicitly tracked in board-level technology reporting. Organizations that measure technology as a profit center outperform those that measure it as a cost center by 340% over a five-year horizon. This is not mere thought leadership! I've implemented this framework across multiple organizations, transforming technology's position from cost burden to value driver. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘝𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘰𝘸𝘯 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴.

In the US, enterprise tech spending has grown 8% annually while labor productivity has grown less than 2%. The tech spend to productivity relationship is showing up in mid-year budget discussions currently underway at most companies. The economics of IT/tech/digital/AI are (again) under a microscope. It was the same last year, and the year before that and every year prior for as long as I have been a professional adviser to CEOs, CFOs, and CIOs.  Tired of this Groundhog Day moment every year, we decided to dig into the economics of enterprise tech. There is some “new news” and some new insights on “old news”. The “new news” – what’s driving up costs: 1. Cyberattacks increased over 25% last year, resulting in a 15% increase in cybersecurity spend this year. While much of this is necessary, it doesn't correlate with an ROI a company can point to. 2. Increase in AI and geopolitical-related spending. On AI, most companies haven't seen value from their investments (only 1% describe themselves as “mature” in their AI deployments).     The new insights on “old news” are: 1. Indirect costs of product development (cloud/security services/tool licenses) can account for 80% of a product’s lifetime costs.   2. Incentive misalignment leads to poor decisions on enterprise tech spend and results in a 20-30% loss of value.  3. Companies pay an additional 10-20% to address tech debt on top of the costs of any project, creating a significant drag on productivity.  4. 5-10% of IT productivity improvements can be lost to vendors (for example, when providers don't pass along reduction in hardware costs).     Clearly, there's a need for deeper understanding and transparency into the economics of enterprise tech. In this new analysis with my colleagues Pablo Prieto, Ph.D., Jeffrey Lewis, James Kaplan, we lay out 4 ways to optimize these investments.  1️⃣Meter and measure: Track tech usage cost at a granular level to foster accountability and minimize tech debt, use models like FinOps. 2️⃣Treat everything as a product: Manage all technology initiatives as products with autonomous, accountable, and incentivized cross-functional teams (led by product managers) to ensure cost responsibility and value capture. 3️⃣Go big: Prioritize domains (end-to-end processes) over single use cases, leverage analytics to pinpoint and amplify initiatives with the most impact. 4️⃣Embrace and accelerate: Optimize agentic AI to modernize and rethink talent models with more flexible systems. In this season and beyond, the choices CEOs, CFOs and CIOs make now will be the cornerstone of success in an AI-driven future.  Looking forward to discussing this more with clients over the rest of the year to ensure 2026 decisions and priorities are better planned, executed, and value is fully realized. #NeverJustTech #McKinseyTechnology #TechEconomics #CIO #CFO https://lnkd.in/grFUuQks

Stop doing tech for tech's sake. Try this instead. Transform your technology initiatives with purpose-drive implementation. - Start with clear business objectives before selecting solutions. - Map technology choices to actual user needs and pain points. - Measure success through user satisfaction, not feature count Build human-centered tech solutions that last. - Design systems around user behaviors, not trending features - Create feedback loops to continuously validate user experience - Focus on solving real problems rather than chasing buzzwords Master the art of strategic tech adaption - Evaluate technology based on business impact, not market hype - Align tech investments with long-term organizational goals - Prioritize solutions that enhance human capabilities Remember: The best technology solutions start with understanding people, not products. #digitaltransformation #techwithHEART #humanize #strategy .orgSource Kevin Ordonez, AAiP

Innovation is only as valuable as the problem it solves. We live in an age where technological advancements move faster than our ability to strategically adopt them. It’s no longer a question of can we implement this? but rather, should we? The real challenge isn’t access to innovation. 𝐈𝐭’𝐬 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞. Discipline to pause before we purchase. Discipline to align tools with outcomes. Discipline to measure impact before we declare success. 𝐓𝐡𝐞 𝐃𝐫𝐢𝐯𝐞𝐫𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡 𝐏𝐚𝐫𝐚𝐝𝐨𝐱: • 𝐒𝐡𝐢𝐧𝐲 𝐍𝐞𝐰 𝐎𝐛𝐣𝐞𝐜𝐭 𝐒𝐲𝐧𝐝𝐫𝐨𝐦𝐞: The irresistible pull towards the ‘new’ and ‘novel’, often at the expense of sustained objectives and an overarching strategic vision. • 𝐅𝐞𝐚𝐫 𝐨𝐟 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐎𝐮𝐭 (𝐅𝐎𝐌𝐎): The anxiety that failing to adopt new technologies or trends could result in missed opportunities for growth or competitive advantage. 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤: • 𝟑𝟎% of App deployments fail • 𝟕𝟎% of Digital Transformation initiatives don’t meet goals • 𝟕𝟎%+ of manufacturers worldwide are stuck in pilot purgatory • 𝟓𝟖% of IoT projects are considered not to be successful • 𝟔𝟏% of manufacturers don’t have specific metrics to measure the effectiveness or impact of AI deployments 𝐀𝐝𝐯𝐢𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡-𝐂𝐮𝐫𝐢𝐨𝐮𝐬 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 1. 𝐀𝐬𝐬𝐞𝐬𝐬, 𝐃𝐨𝐧'𝐭 𝐀𝐬𝐬𝐮𝐦𝐞: Evaluate whether the technology fills a need or optimizes current operations before investing. 2. 𝐀𝐥𝐢𝐠𝐧, 𝐓𝐡𝐞𝐧 𝐀𝐜𝐭: Ensure that any new tech acquisition is in alignment with your strategic business goals. 3. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐨 𝐌𝐚𝐧𝐚𝐠𝐞: Develop clear metrics or KPIs to track the success and relevance of your technology investments. 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞 𝐨𝐧 𝐭𝐡𝐢𝐬 𝐭𝐨𝐩𝐢𝐜, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐬𝐨𝐮𝐫𝐜𝐞𝐬:  https://lnkd.in/eX89kQ6n ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Recently, I've been getting a lot of questions about "Return on Tech." 🙋🏼 Have I made the right choice? 🥇 Am I optimizing in terms of revenue and operating model? 💰 Am I receiving the expected value? #ReturnOnTechnology remains a pressing concern for leadership teams striving to stay both relevant and competitive. So, what does this mean for a bank’s bottom line? As a measure of the benefits and financial returns of your bank’s technology investments, various metrics ensure that your investments drive growth and efficiency. Three Key Components 📊 **Investments**   For instance, track digital spend per $1 billion in assets and digital spend as a percentage of total IT spend to gauge your commitment to digital transformation. 📈 **Adoption**   Measure how many customers use your digital services. Key metrics include active mobile banking users as a percentage of checking accounts and paperless statement users as a percentage of checking accounts. 📉 **Output**   Assess the business impact of these digital channels. Track metrics such as digital accounts opened as a percentage of total accounts and growth in digital account opening to see the tangible benefits of your technology investments. Did You Know? When over 50% of checking account holders use mobile banking, banks report a 25% reduction in operational costs (per a Cornerstone Advisors report on digital banking performance metrics). Below, I share a metrics framework from the report by Ron Shevlin and Elizabeth Gujral to ensure your tech investments deliver real value. Since banks need to balance the number and focus of metrics tracked, I invite you to drop a line in the comments if there are any you would add / remove 💯 #SmarterBanks #OwnTheOutcome #TechROI

Managing IT budgets is like trying to diet while living next to a bakery. It's tempting to overindulge but essential to resist! 😂🍩🍰 Strict financial management of delivering technology services is often overlooked in carve-outs or hold periods, yet it’s hugely important. Many IT leaders struggle to right-size investments while ensuring service quality and agility. According to Bain & Company, the median multiple on invested capital (MOIC) for carve-outs has fallen to 1.5x, slightly below the 1.7x average for other buyouts. https://lnkd.in/g24tEEtu For IT leaders and private equity investors, mastering this balancing act is critical. Here are the top considerations which seem like no-brainers, but you'd be surprised: ▪️Accurate Baseline Assessment: Know your current IT spend and service levels to avoid surprises. ▪️Align Budget to Business Priorities: Ensure investments directly support key operational goals during the hold period. ▪️Flexible Cost Structures: Leverage cloud and SaaS models to scale expenses with actual needs. ▪️Vendor Management: Negotiate terms that allow agility and avoid long-term lock-ins. ▪️Continuous Monitoring: Use dashboards and KPIs to track spend versus value delivered. Getting this right can drive operational efficiency and protect investor returns. Don’t let IT financial management be the “bakery” that distracts your carve-out diet! #privateequity #investor #TechFinance #ITLeadership #PortfolioManagement

New tech won’t fix a broken process. For pre-sales teams, adding AI or any other tool isn’t just about buying software—it’s about driving real impact. The teams that get this right follow three key steps: 1️⃣ Position the SE org for success Before evaluating new tools, SEs need to be seen as strategic drivers of revenue—not just a support function. Without this, securing budget and decision-making power becomes an uphill battle. 2️⃣ Define what the technology actually needs to do A tool isn’t valuable just because it “has AI.” A tightly scoped POC ensures the technology fits real workflows and answers key questions: → Does this actually save time, or just add complexity? → Will it scale with the team’s needs? 3️⃣ Measure impact post-adoption The best way to justify an investment? Hard numbers. Comparing the pre-technology state (how teams work today) with the post-technology outcome based on a POC experience (efficiency gains, time saved, and business impact) makes ROI crystal clear. At the end of the day, technology should create leverage—not just add to the stack. How does your team evaluate new tools before making an investment? 👇 Video excerpt from a podcast I recently did with the great folks at PreSales Collective.

♻️ The AI Infrastructure Dilemma: What Boards Need to Know About Technology Investment Cycles 🔹 In a packed keynote at NVIDIA GTC, Jensen Huang unveiled a roadmap (https://lnkd.in/ew4V34V2) that would make any CIO's head spin: Blackwell Ultra coming this year, followed by the Rubin architecture in 2026 promising 14x performance improvements. But here's the boardroom question that's not being addressed: When is the right time to invest in next-generation AI infrastructure, and when is "good enough" actually optimal? 🔹 As I observed Hewlett Packard Enterprise CEO Antonio Neri note during the conference, "Time to market is what really matters for enterprises" - not necessarily having the absolute bleeding edge. Even Ford Motor Company's director of AI acknowledged they'll "get a lot of use out of our Hopper GPUs the next few years." This is the strategic technology tension boards must navigate: ✅ The acceleration paradox: Waiting too long risks competitive disadvantage, but constant upgrades create implementation complexity. ✅ The ROI timeline: When organizational readiness lags behind hardware capabilities, your capital sits underutilized ✅ The talent equation: Infrastructure decisions directly impact your ability to attract and retain technical leadership 🔹 In my conversations with board chairs and lead directors across industries, the most successful are establishing technology committees and investment governance that balances innovation with operational stability. They're creating frameworks that prevent both overinvestment in unproven technologies and underinvestment in foundational capabilities. Technology governance can no longer be delegated. It must be a core board competency. 🔹 The Technology Committee Imperative Forward-thinking boards are establishing dedicated technology committees that advance organizational interests in several critical ways: ✅ Identifying and prioritizing relevant technologies: Developing methodologies for monitoring technologies along dimensions of proximity to core expertise, maturity, and time horizons ✅ Embracing risk: Creating frameworks that prevent both overinvestment in unproven technologies and underinvestment in foundational capabilities ✅ Shepherding core technologies: Ensuring existing investments deliver maximum value before rushing to next-generation solutions ✅ Counseling the board: Educating fellow directors on technological implications without technical jargon What frameworks is your board using to evaluate technology investment cycles? Would welcome perspectives from other directors navigating these waters. #CEO #KSgems #BoardOversight #CIO #CTO #CISO #CFO #AI #AgenticAI #BoardLeadership #TechnologyGovernance #AIStrategy Angela Yochem Manuj Aggarwal Mark Rogers Ralph Ward James Strock Nitin Gaur Tarun Khanna Nora M. Denzel Nicholas Donofrio Nabil Sakkab