Key Factors for Successful Automation

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

Most automation fails. Not because of technology. But because it starts in the wrong room. I have led enterprise transformations that have delivered multimillion dollars in automation ROI and here is what I have learned: The secret isn’t in the tools. It’s in the truth. Here’s the 3-step playbook to turn automation into a revenue engine: ✅ Step 1: Follow the Bottlenecks Ask your teams: Where does work regularly fall through the cracks? These aren’t just annoyances; they’re hidden gold mines. Especially in finance, operations, and shared services. ✅ Step 2: Measure the Impact, Not the Effort Forget chasing “easy wins.” Instead, ask: → What’s the real cost of this inefficiency? → How much volume moves through it? → What’s the risk if it fails? High volume × high impact = high ROI. That’s your North Star. ✅ Step 3: Align With Business Goals The best automation doesn’t just improve a process. It accelerates the mission. Ask yourself: → Will this help us scale? → Improve customer experience? → Advance strategic priorities? 💡 Bottom Line: Automation isn’t an IT project. It’s a business investment. If you want ROI, focus less on the tools and more on the outcomes. Is your automation strategy driving measurable impact or just checking a box? P.S. If you could automate one process tomorrow, what would you pick? Share your comments below. --- 📌 Save to revisit later ♻️ Repost to help your network ➕ Follow Ganesh Ariyur for more insights on enterprise transformation. #DigitalTransformation #CIO #OperationalExcellence #EnterpriseTechnology #TransformSmarter

The Salesforce automation paradox: Companies with the most automated processes often have the least efficient operations. Why? Because they automate existing processes instead of reimagining them. Effective Salesforce automation requires a different approach: 1. Start with the desired business outcome 2. Map the current process and identify friction points 3. Reimagine the process from first principles 4. Automate the reimagined process 5. Measure results against business outcomes For one client, this approach reduced a 27-step sales process to 8 steps while increasing conversion rates by 35%. The most valuable automation isn't the one that saves the most clicks, it's the one that delivers the most business impact. What business outcome would you most like to improve through automation? #ProcessAutomation #SalesforceEfficiency #BusinessTransformation #WorkflowOptimization #SalesforceConsulting #DigitalEfficiency

"We need to automate this process." Famous last words I've heard in countless tech organizations. Most automation initiatives fail not because of bad code, but because of narrow thinking. After 20+ years of leading global tech teams, I've witnessed a pattern that costs organizations millions: Here's why systems thinking transforms automation success: 𝟭. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗗𝗼𝘁𝘀: 𝗧𝗵𝗲 𝗥𝗶𝗽𝗽𝗹𝗲 𝗘𝗳𝗳𝗲𝗰𝘁 ➝ That "simple" deployment automation triggered unexpected security alerts - until we included security teams in early planning, turning alerts into preventive measures ➝ The "efficient" ticket routing created support bottlenecks - before we mapped customer journey touchpoints and transformed it into a seamless flow ➝ The "smart" code review process slowed cross-team collaboration - until we understood team dynamics and built bridges instead of checkpoints Each time, the technical solution was solid. The systems understanding wasn't. 𝟮. 𝗧𝗵𝗶𝗻𝗸 𝗶𝗻 𝗖𝗶𝗿𝗰𝗹𝗲𝘀, 𝗡𝗼𝘁 𝗟𝗶𝗻𝗲𝘀 ➝ Map dependencies by interviewing stakeholders across departments ➝ Follow the ripple effects by shadowing work across teams ➝ Consider second and third-order impacts through scenario planning 𝟯. 𝗕𝘂𝗶𝗹𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗼𝗼𝗽𝘀 ➝ Start small with pilot programs, but monitor wide-ranging impacts ➝ Gather feedback from unexpected places - from maintenance to marketing ➝ Adjust based on system behavior, not just metrics - study the stories behind the numbers 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: One of our teams reduced deployment failures by 70% not by writing better scripts, but by understanding the entire deployment ecosystem. They mapped every touchpoint, from dev handoffs to customer experience impacts, before touching a single line of code. When you master systems thinking, you don't just build better automation—you build better organizations. 𝗬𝗼𝘂𝗿 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Before your next automation project, spend one hour mapping potential impacts across teams, processes, and customer experiences. What hidden connections did you uncover? Share a time when systems thinking prevented an automation failure in your organization 👇 #TechLeadership #SystemsThinking #AutomationStrategy

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

Just read a fascinating piece by Tetiana S. about how our brains naturally "outsource" thinking to tools and technology - a concept known as cognitive offloading. With AI, we're taking this natural human tendency to a whole new level. Here's why organizations are struggling with AI adoption: They're focusing too much on the technology itself and not enough on how humans actually work and think. Many companies rush to implement AI solutions without considering how these tools align with their teams' natural workflow and cognitive processes. The result? Low adoption rates, frustrated employees, and unrealized potential. The key insight? Successful AI implementation requires a deep understanding of human cognition and behavior. It's about creating intuitive systems that feel like natural extensions of how people already work, rather than forcing them to adapt to rigid, complex tools. Here are 3 crucial action items for business leaders implementing AI: 1) Design for Cognitive "Partnership": Ensure your AI tools genuinely reduce mental burden rather than adding complexity. The goal is to free up your team's cognitive resources for higher-value tasks. Ask yourself: "Does this tool make thinking and decision-making easier for my team?" 2) Focus on Trust Through Transparency: Implement systems that handle errors gracefully and provide clear feedback. When AI makes mistakes (and it will), users should understand what went wrong and how to correct course. This builds long-term trust and adoption. 3) Leverage Familiar Patterns: Don't reinvent the wheel with your AI interfaces. Use established UI patterns and mental models your team already understands. This reduces the learning curve and accelerates adoption. Meet them where "they are"" The future isn't about AI thinking for us - it's about creating powerful human-AI partnerships that amplify our natural cognitive abilities. This will be so key to the future of the #employeeexperience and how we deliver services to the workforce. #AI #FutureOfWork #Leadership #Innovation #CognitiveScience #BusinessStrategy Inspired by Tetiana Sydorenko's insightful article on UX Collective - https://lnkd.in/gMxkg2KD

Exactly a year ago, we embarked on a transformative journey in application modernization, specifically harnessing generative AI to overhaul one of our client’s legacy systems. This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic business objectives Reflecting on this journey, here are the key lessons and outcomes we achieved through Gen AI in application modernization: [1] Assess Application Portfolio. We started by analyzing which applications were both outdated and critical, identifying those with the highest ROI for modernization.  This targeted approach helped prioritize efforts effectively. [2] Prioritize Practical Use Cases for Generative AI. For instance, automating code conversion from COBOL to Java reduced the overall manual coding time by 60%, significantly decreasing costs and increasing efficiency. [3] Pilot Gen AI Projects. We piloted a well-defined module, leading to a 30% reduction in time-to-market for new features, translating into faster responses to market demands and improved customer satisfaction. [4] Communicate Success and Scale Gradually. Post-pilot, we tracked key metrics such as code review time, deployment bugs, and overall time saved, demonstrating substantial business impacts to stakeholders and securing buy-in for wider implementation. [5] Embrace Change Management. We treated AI integration as a critical change in the operational model, aligning processes and stakeholder expectations with new technological capabilities. [6] Utilize Automation to Drive Innovation. Leveraging AI for routine coding tasks not only freed up developer time for strategic projects but also improved code quality by over 40%, reducing bugs and vulnerabilities significantly. [7] Opt for Managed Services When Appropriate. Managed services for routine maintenance allowed us to reallocate resources towards innovative projects, further driving our strategic objectives. Bonus Point: Establish a Center of Excellence (CoE). We have established CoE within our organization. It spearheaded AI implementations and established governance models, setting a benchmark for best practices that accelerated our learning curve and minimized pitfalls. You could modernize your legacy app by following similar steps! #modernization #appmodernization #legacysystem #genai #simform — PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies.

What’s it going to take for you to trust network automation? Or AI? This is how Damien Garros opened his talk “Building Trustworthy Network Automation, From Principles to Practice” at #AutoCon3 from the Network Automation Forum. Damien points out that engineers don’t trust a black box. They need to know what’s going on inside. Therefore, when we build automation systems, we have to do more than make it work. We have to add functionality that fosters trust. Move from a system that *I* would use to a system that *we* would use. Damian’s Six Principles To Build Trust 1️⃣ Predictable 2️⃣ Manageable 3️⃣ Transparent 4️⃣ Simple 5️⃣ Reliable 6️⃣ Human Friendly From there, Damien defined 3 design principles that make a virtuous cycle of trust building. 1️⃣ Idempotency. Running the job gets the same result every time. Without idempotency, we have to build additional logic. 2️⃣ Dry runs. Gives operators the chance to test, review, and approve before executing. 3️⃣ Transactional. Make changes all or nothing. If there's a failure , roll back to where you started. Building on that virtuous cycle, Damien moved on to more key ideas. 1️⃣ Declarative (WHAT - focused on outcomes) versus imperative (HOW - focused on specific actions) making the point that you really want declarative functionality in your system. Declarative systems tend to be simpler to implement and easier to roll back, fostering trust. 2️⃣ Version Control. With version control, changes can be prepared off to the side, validated to have no risk, and reviewed. Once past those steps, the change can be integrated into the main automation environment. Version control fosters trust. 3️⃣ Testing. To test a system, it’s got to be broken down into modules small enough to test without too much complexity. Testing is a tradeoff, though. Not enough tests are insufficient. Too many tests weigh you down. But you must test to foster trust. Unit tests. Integration tests. End to end tests. And then automate that testing. Damien brought his talk together asking, "What are some practical patterns to build trust?" 1️⃣ Don’t reinvent the wheel. Integrate with existing tools when you can. Build something new only if you have to. But...pick your tools carefully. Will the tools work with the system you’re designing? For example, do they work idempotently if idempotency is a core design principle you’re committed to? 2️⃣ Damien also advocated for classifying your data. Know what it is and does, as that will help you process that data in your workflows correctly. 3️⃣ Finally, Damien suggested providing safe default options for your tools. For instance when using Ansible, call out safe playbooks explicitly, ensure default values are safe, and activate diff mode by default. A great talk from Damien, one of folks behind the FOSS project InfraHub by OpsMill. Follow Network Automation Forum to be notified when all of the talks from #AutoCon3 are posted to YouTube.

Why Your Automation Project might be Doomed before it has even begun... After working with countless small businesses on process automation, one thing has become painfully clear: The number one mistake is trying to automate broken processes. 🚫 Here’s the truth: no matter how fast you make something broken go, it’s still broken. The solution? Start with the basics: 1️⃣ Map your processes, step by step. Understand what your process looks like now and define what it should look like. Visual tools like Miro or putting it on "paper" can help you visualize inefficiencies. 2️⃣ Identify bottlenecks that exist now. Find what’s slowing you down before you bring in automation. (Otherwise, you’re just speeding up the chaos.) 3️⃣ Automate for the greatest impact. Focus on areas that will create the biggest leverage for your team and business. 4️⃣ Continuously improve. Once automation is in place, regularly revisit and refine your processes to address new bottlenecks and opportunities. When done right, automation doesn’t just save time and money—it transforms your business. 💡 Here’s an example: We helped a client significantly reduce their onboarding time from 10 days to 2 hours by using Make to integrate Stripe payments, automated emails, and Tally onboarding forms. The result? Their team could focus on service and growth rather than repetitive onboarding admin tasks. Are your automations solving the right problems? Or do you need to rethink the process entirely? #automation #businessgrowth #processimprovement #efficiency #smallbusiness

8 out of 10 businesses are missing out on Ai. I see this everyday in my calls. They jump straight to AI tools without understanding their processes first. Then wonder why their "automations" create more problems than they solve. Here's the proven framework that actually works: STEP 1: MAP YOUR PROCESSES FIRST Never automate a broken process. → List every touchpoint in your workflow → Identify bottlenecks and time-wasters → Note who handles each step → Find communication gaps Remember: You can only automate what you understand. STEP 2: START WITH HIGH-ROI TASKS Don't automate because it's trendy. Focus on what saves the most time: → Data entry between systems → Client onboarding workflows → Report generation → Follow-up sequences One good automation beats 10 fancy tools that don't work together. STEP 3: BUILD YOUR TECH FOUNDATION Most companies use 10+ disconnected tools. AI can't help if your data is scattered everywhere. → Centralize data in one source (Airtable works great) → Connect your core systems first → Then layer AI on top STEP 4: DESIGN AI AGENTS FOR SPECIFIC PROBLEMS Generic AI = Generic results. Build precise agents for precise problems: → Research and data analysis → Customer support responses → Content creation workflows → Internal process optimization Each agent needs specific inputs and defined outputs. STEP 5: TEST SMALL, SCALE SMART Don't automate your entire business at once. → Start with one small process → Get team feedback → Fix bottlenecks as you go → Scale what works Build WITH your team, not without them. The biggest mistake I see? Companies hire someone to build exactly what they ask for. Instead of finding someone who challenges their thinking and reveals what they're missing. Good automation is just process optimization. Nothing more. The result? → 30+ hours saved per month on onboarding → Delivery time cut in half → Capacity increased by 30% → Revenue multiplied without adding team members Your competitors are stuck switching between apps. You'll be dominating with seamless systems. Follow me Luke Pierce for more content on AI systems that actually work.