Tips for Improving User Adoption of Technology

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.

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘀𝗮𝘆𝘀 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝘁𝗿𝗮𝗰𝗸 “𝗧𝗶𝗺𝗲 𝘁𝗼 𝗩𝗮𝗹𝘂𝗲.” But no one ever explains how. So let’s break it down. First, forget the word “value.” It’s vague. It’s subjective. It’s hard to measure. Instead, ask: 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝙢𝙚𝙖𝙨𝙪𝙧𝙖𝙗𝙡𝙚 𝙧𝙚𝙨𝙪𝙡𝙩 𝘁𝗵𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘄𝗮𝗻𝘁𝘀 𝘁𝗼 𝗮𝗰𝗵𝗶𝗲𝘃𝗲?  • “More leads per week”  • “Faster deal close times”  • “Fewer security incidents per month” That’s your destination. But getting there might take weeks (or even months). So here’s the real key: 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 “𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝘀𝘂𝗹𝘁” 𝘁𝗵𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗰𝗼𝘂𝗹𝗱 𝗮𝗰𝗵𝗶𝗲𝘃𝗲.  • First lead from your system  • First deal closed using your platform  • First security incident prevented through your product Because 𝘛𝘪𝘮𝘦 𝘵𝘰 𝘝𝘢𝘭𝘶𝘦 is really just: 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗙𝗶𝗿𝘀𝘁 𝗥𝗲𝘀𝘂𝗹𝘁. And if you get that right — engagement skyrockets. Adoption improves. Churn drops. 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗿𝗲𝗮𝗹 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: I worked with an ecommerce email marketing SaaS company. Their product helped brands drive more sales through email. Sounds clear, right? But many new customers spent their first month building social proof or welcome emails — the ones that don’t drive sales. So their first email sale? Didn’t happen for weeks, if at all. The result? High churn. 𝗦𝗼 𝘄𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝘁𝗵𝗲 𝗴𝗮𝗺𝗲: In the first 7 days, the entire onboarding focused on 3 steps:  1. Create a sales email campaign  2. Send it out  3. Make their first dollar Retention improved. Expansion grew. All because we shifted focus from features… to 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 “𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝘀𝘂𝗹𝘁” 𝘆𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗰𝗵𝗶𝗲𝘃𝗲? #customersuccess

Blending IO psychology with digital innovation flipped the results of our last tech rollout. Most teams never connect these dots—here's why it changes everything ↓ Tech implementations often fail not because of the technology, but due to human factors. The deployment to a large international pharma company was heading for disaster until we brought in IO psychologists. They helped us understand: - How different personality types interact with new systems - The impact of change on team dynamics - Ways to reduce resistance and boost adoption We tailored our approach based on these insights: - Customized training for different learning styles - Change champions selected based on influence networks - Communication strategies aligned with team cultures The results were staggering: - 94% adoption rate within 3 months - 40% increase in user satisfaction scores - 25% boost in productivity post-implementation Key takeaway: Technology and human behavior are deeply intertwined. By considering both, we unlocked synergies we never thought possible. Next time you're planning a tech rollout, remember: The most powerful integration isn't between systems, but between tech and human psychology. Embrace this approach to transform your digital initiatives. PS - and if you know this story, you also know how it set me on the path for my PhD in IO Psychology.

Most companies still don’t know how AI is really being used. So we measured it. We analyzed how AI is adopted inside real teams. Not what vendors say. What people actually do. And we found 6 clear ways to boost adoption from the inside: 1. Share success stories. AI usage climbs faster when peers share wins and tips. Spotlight team leads who are finding real impact. 2. Show the data. Display org-wide metrics to track usage over time. Set clear goals and make progress visible. 3. Focus on key teams. Sales, HR, and Marketing trail in usage. These teams need the most support and see the fastest gains. 4. Start with managers. Manager usage drives team adoption by 75%. Set expectations, track usage, and build usage norms. 5. Build AI skills. Reskill programs help lagging teams catch up. Embed AI familiarity in onboarding and hiring. 6. Lower fear. Raise clarity. Publish approved tools and clear data rules. Emphasize that using AI is innovation, not cheating. The real secret? You don’t need a shiny new tool. You need visibility, consistency, and a plan. Early adopters don’t wait for mandates. They build momentum. And the teams that get it right will win the next era of work. What are you doing to increase AI adoption on your teams?

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