How to Support Digital Transformation

𝗥𝗶𝗴𝗵𝘁 𝗻𝗼𝘄, 𝟳𝟰% 𝗼𝗳 𝘁𝗵𝗲 𝗙𝗼𝗿𝘁𝘂𝗻𝗲 𝟱𝟬𝟬 𝗮𝗿𝗲 𝘂𝗻𝗱𝗲𝗿𝗴𝗼𝗶𝗻𝗴 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀. 𝗨𝗽 𝘁𝗼 𝟵𝟱% 𝗼𝗳 𝘁𝗵𝗲𝗺 𝘄𝗶𝗹𝗹 𝗳𝗮𝗶𝗹. 𝗪𝗵𝘆? 𝗣𝗼𝗼𝗿 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. When I stepped in as CTO, it was clear that if our transformation was going to succeed, we had to improve execution. So, instead of chasing shiny tools or trendy models, we relentlessly focused on the basics. 🧱 Here’s my advice for anyone on this journey: 1️⃣ 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗳𝗼𝗿 𝗦𝗽𝗲𝗲𝗱 Standardization doesn’t limit creativity — it removes roadblocks. Certified pipelines, test plans, and frameworks eliminate chaos, helping teams deliver faster. 2️⃣ 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 You need rules, but only enforce the “no-regret” ones. This gives teams the flexibility to innovate solutions for different regions or customers. 3️⃣ 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Take it step by step and front-load complexity. Doing everything in parallel or saving the hardest for last will result in gridlock and deflating surprises. 4️⃣ 𝗧𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 Tech teams know a lot, but the business knows best. Demand clear requirements so you can build what's needed... and not bridges to nowhere. 5️⃣ 𝗜𝘁'𝘀 𝗮 𝗧𝗲𝗮𝗺 𝗦𝗽𝗼𝗿𝘁 They’re called ‘digital transformations,’ but they’re really business transformations. Everyone — not just tech — must own it. There's always more to do, but we’ve made huge strides this year:   ✅ Cut over four 40+ year-old mainframes to the cloud ✅ Migrated all North American mainframe pipelines to data fabric ✅ Closed data centers from Alpharetta to Australia ✅ Beat our all-time stability records ✅ Achieved our best-ever tech hygiene stats 𝗧𝗵𝗲 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀? We won’t be in the 95%. 𝗧𝗵𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗻𝗲𝘄𝘀? We’re now seeing the transformation benefits we envisioned at the start: AI innovation, model precision, next-gen services, enhanced resilience, and more. 🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘁𝗿𝗲𝗻𝗱𝘀—𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. What are digital transformation lessons you've learned? I’d love to know! 👇

🎯 The CIO's Organizational Playbook for the AI Era... I recently spoke with a CIO friend about how IT teams are changing. Our discussion made me think about what sets apart IT teams that succeed with AI from those that don’t. I looked over my research and reviewed my interviews with other leaders. This information is too valuable not to share: ✓ Build AI-Ready Capabilities 🟢 Establish continuous learning programs focused on practical AI applications 🟢 Implement cross-functional training to bridge technical/business gaps 🟢 Prioritize hands-on AI workshops over theoretical certifications ✓ Master AI Risk Management 🟢 Develop processes to identify and mitigate technical failures early 🟢 Create a strategic AI roadmap with clear risk contingency protocols 🟢 Align all AI initiatives with broader business objectives ✓ Drive Stakeholder Engagement 🟢 Build a cross-functional AI coalition (executives, HR, business units) 🟢 Communicate AI initiatives with transparency to reduce resistance 🟢 Document tangible benefits to secure continued buy-in ✓ Implement with Agility 🟢 Replace waterfall approaches with iterative AI development 🟢 Focus on quick prototyping and real-world testing 🟢 Ensure infrastructure scalability supports AI growth ✓ Lead with AI Ethics 🟢 Train teams on bias identification and mitigation techniques 🟢 Establish clear governance frameworks with accountability 🟢 Make responsible AI deployment non-negotiable ✓ Transform Your Talent Strategy 🟢 Enhance IT roles to integrate AI responsibilities 🟢 Create peer mentoring programs pairing AI experts with domain specialists 🟢 Cultivate an AI-positive culture through early wins ✓ Measure What Matters 🟢 Set specific AI KPIs that link directly to business outcomes 🟢 Implement continuous feedback loops for ongoing refinement 🟢 Track both technical metrics and organizational adoption rates The organizations mastering these elements aren't just surviving the AI transition—they're thriving because of it. #digitaltransformation #changemanagement #leadership #CIO

One of the most common questions I hear from executives at "traditional companies" is simple yet profound: “What should we do at the company level to start and accelerate our AI transformation?” After countless meetings and discussions with executives and boards, I’ve learned that simplifying the path forward is often the best way to build confidence and trust. In my experience, there are three essential steps every company should take: Step 1: Build a Comprehensive AI & Generative AI Transformation Roadmap Start by creating a clear, tailored roadmap that highlights the key AI and GenAI opportunities for your company. I encourage you to review the slide I’ve shared—and more importantly, build your own version. This will bring clarity to your executives, teams, and shareholders about your AI journey. Step 2: Implement an AI Transformation Office This office should go far beyond the technical aspects. It must address: Responsible AI, Regulatory requirements, IP protection, AI democratization and more... Its mission is to move your company from isolated POCs to sustainable competitive advantage. I led the creation of such an office as a Chief Digital Officer and co-authored a white paper on the topic—happy to share more if you're interested. Step 3: Prioritize Change Enablement Digital transformation made teams uncomfortable. We introduced the concept of “majors” and “minors” (e.g., major in marketing, minor in digital/data/analytics). With AI and agents, continuous adaptation is essential. Teams must be equipped and upskilled—not just for the company’s benefit, but to increase their own market value. Yet, I often see companies underinvesting in workforce transformation. Change enablement is no longer optional—it’s a critical capability for your company, your shareholders, and your employees. I love what Thomas Edison said: “Vision without execution is hallucination.” The vision is clear with these three steps. Now it’s up to you to make it happen. #AITransformation #GenerativeAI #DigitalLeadership #CIO #CTO #CEO #CLevelLeadership #EnterpriseAI #ResponsibleAI #ChangeEnablement #FutureOfWork #AIinBusiness #BoardLeadership #InnovationStrategy #TechLeadership #AIExecution

Let's be honest... most of us are living in digital chaos right now; Data, technology, and new product overload. How do you make sense of it all? Establishing your own set of Golden Rules Golden rules are the non-negotiable principles that offer a blueprint for success. In digital transformation, they are the critical load-bearing walls that support the entire structure of transformational change. Here are my 10 Golden Rules for Successful Digital Transformation: 𝟏. 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐄𝐧𝐝-𝐔𝐬𝐞𝐫 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: Always craft your digital interfaces and processes with the end-user in mind, ensuring that every interaction is intuitive, engaging, and satisfying. 𝟐. 𝐂𝐨𝐦𝐦𝐢𝐭 𝐭𝐨 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Foster a culture where ongoing education is valued, enabling your team to stay ahead of the curve by mastering new technologies and methodologies as they emerge. 𝟑. 𝐔𝐩𝐡𝐨𝐥𝐝 𝐃𝐚𝐭𝐚 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: Vigilantly guard your customer’s data as if it were your own, implementing robust security protocols and privacy measures to maintain trust and compliance. 𝟒. 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐀𝐠𝐢𝐥𝐞 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐢𝐞𝐬: Adopt a flexible and responsive approach to project management, allowing for rapid iteration and adaptation in the face of changing digital landscapes. 𝟓. 𝐁𝐫𝐞𝐚𝐤 𝐃𝐨𝐰𝐧 𝐃𝐚𝐭𝐚 𝐒𝐢𝐥𝐨𝐬: Encourage a collaborative environment where data flows freely between departments, enhancing decision-making and fostering a unified view of the business. 𝟔. 𝐂𝐨𝐧𝐝𝐮𝐜𝐭 𝐑𝐞𝐠𝐮𝐥𝐚𝐫 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Implement a rigorous testing regime to identify and address issues early on, ensuring that your digital offerings are resilient and reliable. 𝟕. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐅𝐮𝐭𝐮𝐫𝐞 𝐆𝐫𝐨𝐰𝐭𝐡: Anticipate the scalability of your digital solutions, ensuring that they can evolve and expand as your business grows and market demands shift. 𝟖. 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲 𝐑𝐞𝐯𝐢𝐬𝐞 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: Continually reassess and refine your digital strategies to stay relevant and effective in an ever-evolving technological ecosystem. 𝟗. 𝐄𝐧𝐠𝐚𝐠𝐞 𝐚𝐧𝐝 𝐈𝐧𝐯𝐨𝐥𝐯𝐞 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: Ensure that your leadership is actively involved in driving digital initiatives, setting a visionary tone and aligning digital goals with business objectives. 𝟏𝟎. 𝐌𝐚𝐢𝐧𝐭𝐚𝐢𝐧 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐭 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Cultivate an environment where communication is clear and open, establishing a foundation of transparency that builds trust and facilitates smoother digital transitions. Use this as a framework to write your own set of Golden Rules, and communicate them to EVERYONE who is a part of the transformation. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e_TGu_4D What else would you add to the list?

These people out in the field, they don't have a tech adoption problem. They have an alignment problem. That's right, this one might sting a little. When a digital transformation fails (in construction or otherwise), it’s almost never because the technology didn’t work. It’s because we (those tasked with executing) didn’t lead the change well. If you want your digital transformation to work, you don’t start with the tech. You must first start with alignment. And that doesn't happen by accident. How do we get our people aligned and rowing in the same direction? 1. Start with the why...then show the how. 2. Involve the right people...early and often. 3. Communicate...communicate again...then communicate differently. 4. Crawl, walk, then run. 5. Train like you actually want this to work. 6. Celebrate the wins. It's time to take a look in the mirror. Check how you are doing on each of these 6 things here: https://lnkd.in/gv-baxgx Welcome to TheEngiNerdLife. #businesschangemanagement #construction #contech #digitaltransformation #TheEngiNerdLife

Transformation Executives must speak the C-suite language: To drive successful change. Here's how to make them listen and support your transformation: ✅ Lead with Impact & Urgency The C-suite needs to know immediately: → Current transformation status → Critical risks or roadblocks → Required decisions from them → Business impact of delays ✅ Connect to Business Value The C-suite cares about: → ROI of transformation → Market competitiveness → Customer impact → Revenue implications Frame updates like: "This digital transformation phase needs X investment, which will reduce operational costs by Y% within Z months" ✅ Show Clear Progress Use executive-friendly visuals: → Transformation scorecard → Milestone tracking → Adoption metrics → Value realization dashboard ✅ Anticipate Strategic Questions Think like a CEO: → How does this affect our market position? → What's the competitive advantage? → Where are the biggest risks? → How does this impact our people? ✅ Focus on Strategic Outcomes Stay high-level: → Link to strategic goals → Focus on business outcomes → Highlight market implications → Show people readiness Transformation impact needs to be measured. Speak their language and make decisions easy for them.

17 advanced strategies that separate successful AI projects from failures: 1. create automation opportunity maps first   → Track every manual touchpoint for 2 weeks   → Score each task: frequency x complexity x impact   → Example: A client found 42% of tasks had negative ROI 2. baseline performance with precision   → Track 5 key metrics: time, cost, accuracy, throughput, satisfaction   → Measure for 30 days minimum   → Real case: Captured 2,300 data points across 3 departments 3. build process intelligence dashboards   → Monitor business process performance in real-time   → Identify bottlenecks before automation   → Result: Average 31% efficiency gain pre-automation 4. run parallel validation pilots   → Test AI solutions alongside existing processes   → Compare outcomes without disrupting operations   → Method: 2-week sprints with increasing complexity 5. implement hybrid intelligence workflows   → Design human-AI collaboration points   → Create clear handoff protocols   → Impact: 47% higher accuracy than full automation 6. establish quantitative success metrics   → Track leading & lagging indicators   → Set progressive milestone targets   → Framework: Weekly, monthly, quarterly KPIs 7. create AI feedback optimization loops   → Build in automated performance monitoring   → Set up continuous model retraining cycles   → Result: 28% improvement in first 90 days 8. develop precision escalation matrices   → Define confidence thresholds   → Create decision trees for edge cases   → Outcome: 94% reduction in critical errors 9. implement data quality pipelines   → Automate data validation   → Set up anomaly detection   → Impact: 3x faster time to value 10. create success metric hierarchies   → Link project KPIs to business outcomes   → Build automated reporting dashboards   → Result: 82% higher executive buy-in 11. develop role-based training programs   → Create persona-specific learning paths   → Include hands-on simulation modules   → Outcome: 91% adoption rate 12. build digital transformation playbooks   → Document every decision, success, and failure   → Create reusable process templates   → Impact: 64% faster subsequent deployments 13. implement data structuring protocols   → Standardize input formats   → Create data cleaning pipelines   → Result: 73% reduction in data prep time 14. establish governance frameworks   → Define roles, responsibilities, and controls   → Create audit trails and compliance checks   → Outcome: Zero compliance incidents 15. design scalable architectures   → Build modular components   → Plan for 10x growth minimum   → Impact: 89% lower technical debt 16. create security-first implementations   → Implement zero-trust architecture   → Regular penetration testing   → Result: No security breaches in 500+ deployments 17. quantify and communicate wins   → Create weekly impact reports   → Share success metrics company-wide   → Outcome: 3.4x higher project funding Give it a repost for your audience ♻️

Where do I start? This is arguably the question I’ve been asked the most by data leaders tasked with a large scale transformation initiative. The transformation could be a cloud migration, an ERP consolidation, or any large data-centric replatforming that involves a complex web of people, process, and technology. Quite often, many leaders have convinced themselves, or have been guided by a consultant, that taking a ‘bottoms up’ approach that starts with with an inventory of the data, often along with some form of a maturity assessment, is the right way to go. It’s not. The right way to go is to take an outcome-driven approach where you are rabidly focused on solving a very limited number of business problems. Each problem would have a well defined and limited scope, and would be accompanied by a business case where the financial benefits of that initiative are quantified, and aligned upon by your customers. Instead of focusing on all data, you’ll instead inventory, observe, govern, steward, master and integrate only the data needed to solve your immediate problem. Yes, some idea of the ‘future state’ must be defined and you need to ensure you’re building out an architecture that is scalable and flexible, but complete clarity on all aspects of every individual deliverable between now and that future state do not need to be defined. If you focus each of your phases around solving specifc problems, you will build the momentum and business support you need to get more funding, and slowly grow the program over time. Instead of taking a ‘framework driven’ approach that ensures your customers will have to wait 18+ months to see any value, your customers will get benefits now. Don’t be foooled into thinking that you need to catalog and govern everything in order to transform your data estate. You don’t. Focus on solving business problems and in time, you’ll catalog and govern what matters the most. What do you think? If you have different ideas on where to start, I would love to hear them? #cdo #datagovernance #datamanagement

𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗶𝗻 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Technology is evolving fast, and businesses must adapt to stay competitive. But rolling out new tools and systems isn’t enough—employees and customers need the skills to use them effectively. This is where training plays a critical role in digital transformation. Without proper training, digital adoption stalls, productivity drops, and frustration grows. Here’s how training helps organizations successfully embrace new technologies. 1️⃣ Training Reduces Resistance to Change People resist what they don’t understand. Employees often fear that new technology will complicate their jobs, and customers may struggle to adopt unfamiliar tools. ✅ Early training builds confidence, showing users the benefits of new systems. ✅ Step-by-step learning helps ease the transition, turning uncertainty into mastery. 2️⃣ Faster Digital Adoption = Faster ROI Investing in new technology is costly, but the real value comes from how quickly employees and customers start using it effectively. ✅ Interactive training programs ensure teams can integrate new tools into their workflows immediately. ✅ Customers who understand new features are more likely to use them, increasing retention and satisfaction. 3️⃣ Preventing Productivity Loss During Transitions When employees don’t receive proper training, productivity takes a hit. Confusion leads to errors, and support teams get overwhelmed with questions. ✅ On-demand learning resources allow employees to learn at their own pace without disrupting workflows. ✅ AI-driven training solutions deliver personalized learning paths, ensuring employees get the information they need when they need it. 4️⃣ Creating a Culture of Continuous Learning Digital transformation isn’t a one-time event—it’s an ongoing process. Companies that embed training into their culture keep their teams agile and adaptable. ✅ Regular training updates help employees stay current as technology evolves. ✅ Microlearning modules provide bite-sized lessons that fit into daily work schedules. 5️⃣ Ensuring Customers Can Leverage New Features Customers don’t always explore new features on their own, meaning valuable tools go unused. Training ensures they get the most out of your product, leading to better satisfaction and retention. ✅ Tutorials, webinars, and AI-powered guides make digital tools easier to navigate. ✅ Proactive customer education prevents churn and drives deeper product engagement. Training is the Key to Digital Success Technology alone doesn’t drive transformation—people do. When businesses prioritize training, they unlock the full potential of their digital investments. Want your digital transformation to succeed? Invest in training. Your employees and customers will thank you. 🚀 #training #digitaltransformation #education #innovation #technology #analytics

Digital transformation isn’t about digitizing what exists. It’s about transforming how decisions get made. I’ve seen this play out firsthand. A major supply chain team was rolling out a new planning system. Sleek interface, predictive analytics, cloud integration, the works. But most planners had quietly agreed to keep working in Excel. Why? Because the new system didn’t match how decisions were actually being made. It enforced a rigid process that ignored local constraints. It prioritized forecast accuracy but gave no room for last-minute intel from the field. It assumed decisions were deterministic, but in the real world, planners were navigating uncertainty every day: delayed parts, shifting priorities, factory overrides. Most organizations don’t have a technology gap. They have a decision gap. If you don’t explicitly design for how decisions get made (and under what uncertainty) no software will fix it. You’ll get faster wrong answers. The starting point for any transformation isn’t “What can we automate?” It’s: 🔍 What are the key decisions this process supports? 🔍 Who makes those decisions, and based on what information? 🔍 How do we measure quality of those decisions and business impact? 🔍 What constraints are hard (legal, physical) vs. soft (political, financial)? 🔍 What level of uncertainty is tolerable, and how is feedback captured? Once you have answers to those, you can begin designing policies: that is, repeatable strategies that guide decisions under uncertainty. Policies can be rule-based, optimization-based, or even learned from data. But they need to match how work actually gets done. So I ask you, do you want to build smarter systems? Then design them around decisions first, not data. Model policies, not point predictions. Embed feedback, not just KPIs. When you do that you move beyond just “going digital.” You reshape the core of how your business learns and adapts. That’s real transformation. If you’re working through a transformation and want to make sure the decisions come first, let’s connect. Happy to share frameworks or talk it through. #DigitalTransformation #DecisionArchitecture #DecisionIntelligence #OperationsResearch #SupplyChainStrategy #BitBros #Optimization