Strategies to Boost Automation Adoption

Everyone’s publishing “10 things your org should do for AI adoption.” Most of it is wrong. Or at least, incomplete. Here’s what I’ve learned working with orgs on the ground - not theoretically, but watching what actually moves the needle vs what sounds good in a strategy deck. AI adoption isn’t a rollout. It’s an energy problem. You need activation energy to get people to try something new. And you need to sustain that energy long enough for it to become habit. Most orgs get the first part. Almost none plan for the second. Here’s what actually works: 1. Hub and spoke, not top-down mandate. One central team setting direction. Multiple spokes embedded in real teams solving real problems. The hub provides frameworks and guardrails. The spokes provide context and use cases. Neither works without the other. 2. Leadership has to go first — visibly. Not “leadership supports AI.” Leadership uses AI. In meetings. In decisions. In front of their teams. If your CXO talks about AI but hasn’t rebuilt a single workflow, your teams will read that signal instantly. 3. Build activation energy deliberately. Most orgs do one big training, declare victory, and wonder why nothing changed three months later. Adoption needs repeated, structured nudges — workshops, office hours, challenges, showcases — spaced over weeks, not crammed into a single afternoon. 4. Celebrate the wins. Especially the small ones. Someone automated a 3-hour weekly report into 20 minutes? That’s not a minor efficiency gain. That’s proof of what’s possible. Make it visible. Make it a story. Let it pull others forward. 5. Encourage failure. Loudly. The biggest blocker to AI adoption isn’t access to tools. It’s fear of looking stupid. When someone tries to build a workflow with AI and it doesn’t work — that’s data. That tells you where the gaps in context, process documentation, or tooling actually are. Punishing that or ignoring it kills adoption faster than any technology gap. The org that gets this right doesn’t have “an AI strategy.” It has people who’ve changed how they work - and can’t imagine going back. —————- I am Priyadeep Sinha and I help AI Adoption Stick - for Leaders and Organizations at Work in Beta Every week, I share one complete AI workflow system for leaders, consultants and knowledge workers in my newsletter Work in Beta: https://lnkd.in/gPqYEzaJ

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 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?

We rolled out AI across our team in 60 days. No chaos. No confusion. Just clear wins and real results. I've seen marketing departments jump into tools like ChatGPT and Claude without a plan, only to end up with inconsistent usage, security risks, and wasted time. So here’s a reality check: Giving your team access to AI tools is not the same as making them AI-ready. What works? A clear, structured rollout that builds confidence, protects your brand, and drives performance. Here’s the 7-step sequence I recommend getting your marketing team fully ready to use AI: 🔹 1. Leadership Alignment Before anyone writes a prompt, you need to answer this: → What are we actually trying to improve with AI? → Clarify your goals: content speed? campaign performance? lead quality? 💡Assign an internal AI Champion to lead adoption and make this someone’s job, not everyone’s maybe. 🔹 2. Create Your AI Usage Policy Yes, before the first prompt. Set ground rules: → No client data or credentials in tools → Human review before anything goes public → Approved tools only → A go-to person for AI questions 💡Keep it simple. A 1-page doc is better than a 20-page one no one reads. 🔹 3. Train the Team Don’t assume “digital native” means “AI fluent.” Run a short onboarding: → Demo real-world prompts for their roles → Share a centralized prompt library → Walk through how to use your company’s Custom GPT (if you have one) 💡Make it practical. Confidence creates momentum. 🔹 4. Start With Small Pilots Want to build trust in AI fast? Deliver small wins early. Assign 1–2 people per function to test real use cases: → AI for email writing → Content repurposing → Campaign briefs 💡Document results. Share what worked and build internal buy-in. 🔹 5. Bake AI Into Daily Workflows AI should enhance what already works. → Add AI to your content creation SOPs → Use it for meeting note summaries → Integrate it into campaign planning templates 💡The more friction you remove, the faster usage scales. 🔹 6. Build a Feedback Loop Set a bi-weekly or monthly check-in: → What’s saving time? → What’s confusing? → What should we expand next? 💡Refine as you go. This isn't a one-and-done rollout. It's a capability you're building. 🔹 7. Enable Long-Term Growth This isn’t just about productivity. It’s about transformation. → Encourage ongoing experimentation → Recognize team AI wins → Offer certifications or incentives to deepen adoption 💡You’re not just introducing a tool. You’re building a smarter, faster, more strategic team. ✅ Final Thought If you're leading a marketing team, you don’t need to rush into every AI trend. But you do need a clear path for AI readiness. Because the biggest risk today isn’t overusing AI. It’s being the last team in your category that doesn’t know how to use it well. ____________ ♻️ Repost if your network needs to see this. DM me if you need help creating an AI rollout plan for your team.

Is your enterprise struggling with AI adoption? Try these ten practices. In a recent HFS Research webinar, industry leaders, Phil Fersht, Malcolm Frank, Steven Hill, Mark Hodges, Cliff Justice, Jesús Mantas (and I) explored bridging the "velocity gap" between rapid individual AI use and slow enterprise execution. Moving from "AI theater" to real value requires addressing deep structural and cultural hurdles. These practices can help: 1. The "Make it Worth it" Framework: To nudge behavior, leaders must make AI adoption clear (define the behavior), easy (make the AI path the path of least resistance), and worth it (align rewards and recognition). 2. Single Accountable Individuals (SAIs): Stop managing by committee. Empower one specific person with the mission and competence to reinvent a process outcome by any means necessary. 3. Outside-In Automation: Build internal confidence by first automating high-spend outside vendor services (like PR, marketing, or IT) where there is no direct threat to internal employees. 4. People-Led, Tech-Powered Culture: Invest in massive-scale training and communicate that AI is "in service to humanity" to transform fear into excitement and action. 5. Acquire to Experiment: Use smaller acquisitions as "guinea pigs," giving them permission to break things and fail in ways the larger parent organization cannot. 6. Build an AI Observability Layer: Implement a system to factually track token consumption and agent use, distinguishing between surface-level tasks (like email) and high-value execution (like coding or decision-making) to motivate impactful adoption. 7. Formalize AI Use for high-value execution through KPIs: Integrate "agentic AI use" into official Key Performance Indicators for high-value execution and annual evaluations to formally reward and prioritize automation over maintaining head-count. 8. Adopt a "Minimal Governance" Framework: Utilize a "Goldilocks" approach to governance that is faster than traditional, slow-moving oversight but less risky than an "all-in" strategy. (See MIT CISR paper: https://lnkd.in/geYmZXP6) 9. Reset "Clock Speed" via Benchmarking: Send teams to witness high-velocity AI execution in other markets (such as China) to reset internal expectations and condense multi-year roadmaps into months. 10. The "Kill Switch" for Agents: Enterprises should govern digital agents like human employees—monitoring for "rogue" behavior and maintaining a "kill switch" to isolate and deny access if needed. Please share your emerging practices on gaining business value from AI. University of Arkansas ­- Sam M. Walton College of Business https://lnkd.in/gBzZrbRu

GenAI adoption is all about people, not about tools. Pharma giant Novo Nordisk offers a great case study of working out what supports useful uptake of AI across a large organization. A case study in MIT Sloan Management Review uncovers a range of useful lessons. Here are some of the most interesting. 🚀 Recognize a mid-cycle drop as normal. Novo Nordisk grew Copilot use from a few hundred to 20,000 users in just over a year, with 23% becoming frequent users within one month. However, by month three or four, 15% of early adopters dropped off and average time saved per week declined. Recognizing this dip as natural helped avoid panic and kept the focus on re-engagement strategies rather than getting staff to try tools for the first time. 🛠 Deliver function-specific training through champion networks. Generic AI onboarding failed to meet the needs of specialized roles. Novo Nordisk succeeded by creating domain-specific training, leveraging internal champions to contextualize AI use, and allowing teams to shape guidance based on their actual work. This addressed “AI shaming” and bridged confidence gaps across functions. 🤝 Use internal champions to overcome cultural resistance. Skepticism wasn’t solved by policy, it was shifted by influence. Novo Nordisk identified trusted, high-status employees to openly adopt and advocate for AI tools. Their visible endorsement encouraged hesitant peers to try AI without fear of judgment or failure. 📈 Treat adoption as a change process, not a tech rollout. Rather than pushing a one-time launch, Novo Nordisk framed GenAI as a long-term transformation. This meant investing in ongoing communication, support structures, and iterative learning. The approach acknowledged that adoption would ebb and flow, and prepared the organization to adapt accordingly. 🎯 Emphasize strategic value over time saved. Though average users saved about 2 hours per week, the most meaningful wins came from higher-quality work—more strategic thinking, clearer writing, and better planning. By highlighting these human-centric gains, Novo Nordisk built a stronger case for AI’s workplace relevance beyond mere productivity. 📊 Use employee data to shape the deployment strategy. Over 3,000 employee surveys and interviews helped Novo Nordisk spot where and why adoption lagged. This feedback guided real-time adjustments—like where to invest in new use cases, where to scale back, and how to tailor messaging. It also surfaced which functions became tool-reliant versus those needing more support.

AI Adoption: Reality Bites After speaking with customers across various industries yesterday, one thing became crystal clear: there's a significant gap between AI hype and implementation reality. While pundits on X buzz about autonomous agents and sweeping automation, business leaders I spoke with are struggling with fundamentals: getting legal approval, navigating procurement processes, and addressing privacy, security, and governance concerns. What's more revealing is the counterintuitive truth emerging: organizations with the most robust digital transformation experience are often facing greater AI adoption friction. Their established governance structures—originally designed to protect—now create labyrinthine approval processes that nimbler competitors can sidestep. For product leaders, the opportunity lies not in selling technical capability, but in designing for organizational adoption pathways. Consider: - Prioritize modular implementations that can pass through governance checkpoints incrementally rather than requiring all-or-nothing approvals - Create "governance-as-code" frameworks that embed compliance requirements directly into product architecture - Develop value metrics that measure time-to-implementation, not just end-state ROI - Lean into understanability and transparency as part of your value prop - Build solutions that address the career risk stakeholders face when championing AI initiatives For business leaders, it's critical to internalize that the most successful AI implementations will come not from the organizations with the most advanced technology, but those who reinvent adoption processes themselves. Those who recognize AI requires governance innovation—not just technical innovation—will unlock sustainable value while others remain trapped in endless proof-of-concept cycles. What unexpected adoption hurdles are you encountering in your organization? I'd love to hear perspectives beyond the usual technical challenges.

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

The psychology of AI adoption: Why your best employees will lead the revolution Here's something that surprised me during our recent home care leadership roundtable: the strongest advocates for AI aren't always the tech-savvy millennials. They are the most experienced, highest-performing clinical staff who were frustrated with administrative overhead eating into patient care time. This insight changes about how we should approach AI implementation. The nurses who provide the best patient care are often the most frustrated with current systems because they can see how much time they're wasting on tasks that don't improve outcomes. These high performers aren't threatened by AI, they're energized by it. Your best clinicians have pattern recognition skills that took years to develop. AI doesn't replace that expertise, it amplifies it. They can see how AI-generated insights could help them make better decisions faster. The most committed healthcare workers didn't enter the field to fill out forms. They came to help people. AI that reduces administrative burden doesn't just improve efficiency, it restores professional purpose. When your most respected clinical staff become AI advocates, adoption accelerates across the entire organization. Their endorsement carries more weight than any executive mandate. The resistance typically comes from middle management who worry that AI will make their coordinating roles obsolete. But smart AI implementation shows how their expertise becomes more valuable when enhanced by intelligent automation. This has huge implications for change management strategy: - Start with your stars: Instead of trying to convince skeptics first, empower your best performers to experiment with AI capabilities. Let them become your internal advocates. - Frame it as expertise enhancement: Don't position AI as replacing human judgment, position it as giving your best people superpowers to do what they do best even better. - Measure what matters to them: Track metrics that resonate with clinical staff — more time with patients, better outcome detection, reduced documentation burden — not just operational efficiency. - The strategic advantage: Organizations that tap into their clinical staff's intrinsic motivation to provide better care will see faster, more sustainable AI adoption than those that focus only on cost reduction. Remember: Your people aren't obstacles to AI implementation — they're the key to making it transformational.

🔎 The latest WEF report on enterprise AI adoption is incredibly detailed and well-researched! It’s one of those reports that feels more like a story than just numbers & numbers. ⛳ Some patterns that stood out to me 👉 GenAI adoption is led by human-centric industries like healthcare, finance, media, and entertainment—not just tech companies. These industries are using AI for automation, personalization, and content creation, shifting the focus from pure tech to human-centered applications. 👉 Scaling AI is *still* a major challenge—74% of enterprises struggle to move beyond PoCs, and only 16% are truly prepared for AI-driven transformation. Many remain stuck in early adoption phases with fragmented experiments and no clear strategy. 👉 The most successful AI adoption relies on "fusion skills"—where AI augments human intelligence, not replaces it. Organizations that combine critical thinking, judgment, and collaboration with AI see far better results than those pushing pure automation. 👉 Workforce concerns are a real barrier. Many employees fear job displacement and burnout, leading to resistance. Companies that focus on reskilling and AI literacy will see smoother adoption and long-term success. 😅 These are unprecedented times, and learning from others’ experiences is invaluable. The key patterns keep seeing in multiple reports: ⛳ Start with the problem first: A solid strategy that prevents AI PoCs from getting stuck. ⛳Augment before automating: Don’t rush to replace humans, make them more powerful. ⛳ Invest in upskilling employees: AI adoption is smoother when people feel equipped, not threatened. ⛳ A good strategy is everything: Without one, AI initiatives fail before they even start. Link: https://lnkd.in/gsRJT2D5