Proven Automation Techniques for Success

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

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

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

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.

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 ♻️