Key Strategies for Successful Digital Adoption

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.

New tech rarely dies in testing. It dies when real people have to use it. The pilot works. The demo lands. The use case makes sense. And still, it never scales. Why? Adoption measures behavior. And behavior is where the brain gets involved. Here’s the neural map to getting past the pilot phase: 👇 1️⃣ Don’t assume a successful pilot means people are ready Do this: ↳ Design for behavior change, not just proof of concept The science: ↳ The brain can like an idea and still resist changing routines ↳ The basal ganglia prefers familiar patterns over new effort 2️⃣ Don’t lead with technical performance Do this: ↳ Lead with what gets easier, safer, or faster for the user The science: ↳ The brain scans for personal relevance first ↳ If value doesn’t feel immediate, attention drops 3️⃣ Don’t ignore the fear underneath adoption Do this: ↳ Surface and reduce the emotional risk of using the tech The science: ↳ New tools can trigger fear of failure, exposure, or replacement ↳ People protect status before they embrace change 4️⃣ Don’t make the new workflow feel too different Do this: ↳ Anchor adoption to behaviors users already know The science: ↳ The brain prefers familiarity and predictability ↳ High perceived effort creates resistance fast 5️⃣ Don’t treat training like a side task Do this: ↳ Make training simple, repeated, and tied to real use moments The science: ↳ The brain learns through repetition and reward ↳ Memory strengthens when learning is applied in context 6️⃣ Don’t overload users with too much information Do this: ↳ Simplify the message and narrow the actions The science: ↳ Working memory is limited ↳ Cognitive overload reduces confidence and follow-through 7️⃣ Don’t assume logic will override politics Do this: ↳ Make adoption feel safe socially and professionally The science: ↳ Social pain lights up many of the same brain regions as physical pain ↳ If adoption feels politically dangerous, scale dies 8️⃣ Don’t make the first experience slow or clunky Do this: ↳ Create a fast first win users can feel The science: ↳ Early wins create dopamine ↳ If the first experience feels frustrating, the brain tags it as costly 9️⃣ Don’t leave the middle managers out Do this: ↳ Equip frontline leaders to reinforce the change daily The science: ↳ The brain looks to authority and peer behavior for safety cues ↳ Local managers shape whether a new behavior feels normal 🔟 Don’t stop at proving the tech works Do this: ↳ Prove people can adopt it consistently under real conditions The science: ↳ The brain trusts repeatability more than novelty ↳ Scale requires lower friction, lower threat, and clearer reward P.S. What's the last pilot you saw fail? ➡️ If your new tech is getting interest but still not making it past pilot, try this --> https://lnkd.in/gvZNBKq9 -------------------------------------------------------------------- ♻️ Share this with a founder building new tech ➕ Follow Shannon for more brain-based GTM tactics

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

🚨 Buying Tech is Easy. Getting Adoption is Hard. I’ve seen it time and time again in property management and beyond: company gets excited about new software, signs the contract, flips the switch…and then nothing changes. Why? Because buying tech is the easy part. Getting people to use it—really use it—is the hard part. Even the smartest, most intuitive platforms aren’t truly “plug-and-play.” People aren’t machines. If your teams don’t understand the why, don’t feel supported in the how, and don’t see results in their day-to-day workflow, adoption fizzles. And when adoption fails, the ROI disappears. Here’s what makes successful rollouts, successful rollouts: 🔑 Build a Rollout Strategy (Not Just an Implementation) Adoption doesn’t happen by accident. It starts with a plan: • Early champions: Identify team members who can test, troubleshoot, and advocate. • Updated SOPs: Bake the tool into your standard processes—if it’s optional, it won’t stick. • Milestone-based training: Ongoing, bite-sized training beats one overwhelming launch day session. • Real-time support: Create a safety net for questions, frustrations, and quick fixes. 🛡️ Create a Safe Space Too often, tech rollouts are met with fear: “Is this replacing me?” or “What if I can’t learn it?” Leaders need to make it clear: adoption isn’t about replacing people—it’s about freeing them up for higher-value work. The best tech amplifies human impact; it doesn’t diminish it. 📊 Track the Right Metrics If you don’t measure adoption, you won’t know if it’s working. A few indicators that matter: • Adoption rates (logins, active use, feature utilization) • Process time saved (turn times, work orders, reporting cycles) • Error reduction and accuracy improvements • Employee satisfaction with the tool (survey it!) These tell you whether the technology is improving the business and the people’s experience. 🔄 Build Feedback Loops End-users are your greatest consultants. Set up feedback channels so they can tell you what works, what doesn’t, and what would make the tool even better. When people feel ownership of the process, adoption skyrockets. 💡 The bottom line: If you want new tech to succeed, don’t just budget for the tool. Budget for change management. Because buying is easy—but adoption is what actually delivers the results.

Buying technology is easy. Getting people to use it? That’s the hard part. Too often, companies invest in new software expecting it to transform operations overnight—only to hit major roadblocks with operational alignment and adoption. The system gets underutilized, workarounds emerge, and the promised efficiencies never materialize. Sound familiar? Here’s why technology adoption stalls: ❌ Poor process alignment – If tech doesn’t fit how people actually work, they won’t use it. ❌ Lack of user buy-in – People resist change when they don’t see the value. ❌ Insufficient training – A one-time demo isn’t enough. Users need hands-on learning and job aids aligned to their day-to-day activities. ❌ No accountability – Without clear expectations and leadership support, adoption suffers. A successful implementation isn’t just about turning the system on—it’s about making sure people actually use it. That’s why a change management strategy is essential to drive adoption and long-term success. When we help clients select and implement new vendor management systems, we focus on more than just system setup—we develop a change strategy to drive adoption. This includes: ✅ Setting clear adoption goals and success metrics to measure impact and progress. ✅ Engaging users early to gather requirements and build buy-in from the start. ✅ Optimizing workflows to ensure processes align with and fully leverage the technology. ✅ Designing tailored training, support, and feedback mechanisms to reinforce adoption. ✅ Ensuring leadership actively supports and champions the change to drive accountability. Technology alone doesn’t drive change—people do. Investing in adoption strategy is just as important as investing in the software itself. What’s been your biggest challenge with technology adoption? Drop a comment below! ⬇️

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

Is your GenAI strategy missing a key ingredient? Successful AI adoption is about change on three fronts: 1) operational development, 2) people, and 3) tech change, not just tech upgrades. Successful AI adoption needs a two-pronged approach LLM + HLM (Large Language Model + Large Human Model): 1. Operational Development Change: Adapt workflows, processes, and IT infrastructure for AI. Think of it as preparing soil for a new plant. Examples: streamline data collection, redesign workflows, train employees on AI tools, and upgrade IT systems. 2. Cultural Change: Shift mindsets to embrace AI. Create an environment where people are comfortable and excited about AI. Examples: address employee concerns, communicate benefits, and foster a culture of experimentation and learning. >> Why Both Matter: Implementing the latest AI tech alone won’t guarantee success. Your operations, including IT infrastructure, must support it. Without employee buy-in, AI investments may go to waste. Think of it as building a house: Operational changes lay the foundation. While cultural changes ensure employees feel comfortable and fully utilize AI. Both are essential for successful AI adoption. Thoughts? ------------------------------- 👋 I'm Mariana Saddakni. I help businesses grow with AI by enhancing business efficiency and keeping teams up-to-date with tech evolution.

In our recent work with organisations, I keep seeing the same patterns emerge when it comes to adopting AI. Yes, there are technical considerations like security and privacy, but at the heart of it these are people issues. Nobody wants to use a technology if they feel it puts them or the business at risk. Trust matters, and without it, adoption stalls. Change management and training are also critical. Helping people develop an AI mindset allows them to use these tools in increasingly creative ways, producing higher-quality outcomes rather than just faster ones. Another big one is executive-level commitment. This cannot sit only with the CIO. Every leader, from the CEO to the CFO and beyond, needs to be able to explain why AI matters for the organisation. When leaders can clearly articulate that story, it signals to the whole business that this is a strategic priority, not just an IT project. Equitable access is just as important. Too often I see organisations give AI tools to a select group to control costs. While that makes sense in the short term, the result can be a cultural divide between the haves and the have-nots. People left out either disengage or start using unapproved tools, both of which create risk. Providing broad access, with the right guardrails and support, helps avoid that divide and encourages responsible experimentation across the organisation. These human, cultural, and leadership factors are what really drive successful AI adoption. The technology is only part of the equation.

💡 The Secret to Successful AI Adoption? It’s NOT Just About the Tech 🤖✨ Everyone’s talking about AI models, tools, and algorithms… but here’s the truth: Technology alone won’t make your AI initiative succeed. The real differentiator? People, leadership, and culture. Here’s how top-performing companies are making AI work for everyone. 👇 1️⃣ Why the Human Side of AI Matters ♠️ AI fails when teams feel left out, blindsided, or unprepared. ♠️ Clear leadership vision + open communication builds trust and engagement. ♠️ AI adoption is a change management journey, not just an IT rollout. 2️⃣ Leadership, Vision & Culture Make or Break AI ♠️ Transparency: Show teams what AI will change and what will stay human-led. ♠️ Ethics & Trust: Encourage open dialogue about bias, fairness, and privacy. ♠️ Reskilling: Equip teams — from front-line staff to executives — to work confidently with AI. ♠️ Culture of Experimentation: Encourage learning, iteration, and collaboration between people and tech. 3️⃣ How to Align People, Processes & Technology ♠️ Establish Leadership & Vision: Set clear, strategic AI objectives tied to business goals. ♠️ Engage Stakeholders Early: Co-create AI use cases with managers and key employees. ♠️ Invest in Training: Deliver hands-on AI training, mentoring, and continuous education. ♠️ Redesign Workflows: Integrate AI into daily processes to remove busywork and enhance impact. ♠️ Embed Governance: Create clear policies on privacy, ethics, and accountability. ♠️ Monitor & Evolve: Track adoption, engagement, and results — then refine your approach. 4️⃣ Real-World AI Adoption Wins ♠️ Enterprises with governance + staff engagement report smoother rollouts and higher trust. ♠️ Financial services & healthcare leaders focusing on reskilling saw faster adoption AND better results. ♠️ SMEs piloting with employee input achieved stronger morale and early ROI. 🌟 Bottom Line: AI success isn’t just measured in teraflops — it’s built on trust, teamwork, and a clear, human-first vision. 💬 Your Turn: Where have YOU seen AI adoption succeed (or fail) because of leadership, culture, or communication — not just tech? Drop your story in the comments and let’s help each other get it right. #AI #DigitalTransformation #Leadership #ChangeManagement #AIAdoption #FutureOfWork #OrganisationalCulture #Innovation #ResponsibleAI #PeopleFirstAI #WorkforceTransformation

Is your digital transformation destined to become another statistic? A staggering 70% of software implementations fail. Think about that. All the budget, the planning, and the effort... wasted. But the reason they fail isn't the technology. It's the rollout strategy. The "big-bang" launch, where you try to convince everyone at once, is doomed. Why? Because it defies a fundamental law of human behavior. You're trying to sell a new vision to a skeptical majority who are wired to resist change and demand proof. This approach erodes trust and leaves you with fuzzy metrics that can't prove a win. So, what's the solution? Stop fighting human nature. Leverage it. The Law of Diffusion of Innovations provides the blueprint. To achieve mass success, you must first win over the hearts and minds of your innovators and early adopters (the first ~16% of your team). These are your champions. They are moved by purpose, not by a long list of features. They jump on board because they believe in the why—the vision of a smarter, better, more empowered way to work. Once you win them over, you create the visible proof and momentum needed for the rest of the organization to follow. This isn't just a theory; it's a field-tested production recipe for cultural change. In our new video, I break down the 90-day blueprint to successfully implement new technology by winning over the right people in the right order. Ready to de-risk your next rollout and drive real adoption? #DigitalTransformation #ChangeManagement #Innovation #Leadership #Industry40 #Manufacturing #PlantManager #ITManager #FutureOfWork