Common mistakes in tech trust evaluation

𝐎𝐧𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐔𝐒 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬: Convenience sounds like a win… But in reality—control builds the trust that scales. 𝐋𝐞𝐭 𝐦𝐞 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 👇 We were working on improving product adoption for a US-based platform. Most founders would instinctively look at cutting down clicks and removing steps in the onboarding journey. Faster = Better, right? That’s what we thought too—until real usage patterns showed us something very different. Instead of shortening the journey, we tried something counterintuitive: -We added more decision points -Let the user customize their flow -Gave options to manually choose settings instead of setting defaults And guess what? Conversion rates went up. Engagement improved. And most importantly—user trust deepened. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐫𝐞𝐚𝐥𝐢𝐬𝐞𝐝: You can design a sleek 2-click journey…  …but if the user doesn’t feel in control, they hesitate. Especially in the US market, where data privacy and digital autonomy are hot-button issues—transparency and control win. 𝐒𝐨𝐦𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐬𝐭𝐨𝐨𝐝 𝐨𝐮𝐭 𝐭𝐨 𝐦𝐞: → People often disable auto-fill just to manually type things in.  → They skip quick recommendations to do their own comparisons.  → Features that auto-execute without explicit confirmation? Often uninstalled. 💡 Why? It’s not inefficiency. It’s digital self-preservation. It’s a mindset of: “Don’t decide for me. Let me drive.” And I’ve seen this mistake firsthand: One client rolled out a smart automation feature that quietly activated behind the scenes. Instead of delighting users, it alienated 15–20% of their base. Because the perception was: "You took control without asking." On the other hand, platforms that use clear confirmation prompts (“Are you sure?”, “Review before submitting”, toggles, etc.)—those build long-term trust. That’s the real game. Here’s what I now recommend to every tech founder building for the US market: -Don’t just optimize for frictionless onboarding. -Optimize for visible control. -Add micro-trust signals like “No hidden fees,” “You can edit this later,” and clear toggles. -Let the user feel in charge at every key point. Because trust isn’t built by speed. It’s built by respecting the user’s right to decide. If you’re a tech founder or product owner: Stop assuming speed is everything. Start building systems that say, “You’re in control.” That’s what creates adoption that sticks. What’s your experience with this? Would love to hear in the comments. 👇 #ProductDesign #UserExperience #TrustByDesign #TechForUSMarket #DigitalAutonomy #businesscoach #coachishleenkaur Linkedin News LinkedIn News India LinkedIN for small businesses

Private equity firms spend months on financial due diligence. They spend days on technology due diligence. Then they're surprised when the tech stack becomes the bottleneck post-acquisition. I've helped PE firms evaluate technology in potential acquisitions. Here's what most miss: 1. Technical debt isn't on the balance sheet That "working" software might need a complete rebuild in 18 months. It's not broken yet, but it's fragile. One key developer leaves, and you're in trouble. 2. The founder IS the technology strategy If the CEO is making every technical decision, you don't have a scalable technology function. You have a dependency. 3. Systems that don't talk cost more than you think When employees manually move data between systems, you're not just paying for their time. You're paying for the errors, the delays, and the opportunities you miss. 4. Security issues don't show up until they do A data breach six months after acquisition doesn't just cost money. It destroys the value you bought. The technology can't support the growth plan You're planning to 3x revenue in three years. Will the current technology scale? Or will you hit a wall at 1.5x? Here's what smart PE firms do: They evaluate technology the same way they evaluate finances. With rigor. With expertise. With an eye toward what needs to change to hit the value creation plan. At Caxy, we've built software for manufacturing, financial services, healthcare, and higher education companies. We know what good technology infrastructure looks like. And what it costs when it's not there. Technology due diligence isn't about finding the perfect tech stack. It's about understanding what you're buying and what you'll need to invest to make it work for your growth plans.

You've spent $10M building the future. And $0 understanding how humans buy it. Your deep tech is brilliant. Your sales are terrible. And it's not because buyers are stupid. Most deep tech companies think their biggest challenge is building the technology. Wrong. The hardest part isn't making it work. It's making humans want it to work. Here's what's really killing your adoption: MISTAKE #1: You explain tech too soon Buyers don't need a science lecture. They need to know why it matters. Why now. Why it's safe to care. Stop leading with quantum algorithms. Start with why their current approach bleeds money. MISTAKE #2: You confuse interest with traction Fascination isn't adoption. "This is amazing!" doesn't mean "I'm buying this." Adoption needs trust, clarity, urgency, and internal belief. Interest is just the starting line. MISTAKE #3: You ignore the brain's alarm system New technology creates uncertainty. Uncertainty triggers threat response. Your prospect's brain screams: "What could go wrong?" "Who loses power?" "Will I look stupid if this fails?" You're fighting biology, not budgets. MISTAKE #4: You pitch features before killing fear Buyers worry about risk first, benefits second. Will this break our operations? Will my team actually use it? Will I get fired when it fails? Kill the fear before you sell the future. MISTAKE #5: You forget the internal sell Your buyer becomes your internal salesperson. To finance. To ops. To legal. To users. If they can't explain it simply, they won't fight for it. Make them the hero of a clear story, not a translator of complex tech. MISTAKE #6: You threaten people's identity New tech attacks expertise, status, control. Especially AI. Robotics. Automation. If people feel replaced, they resist. If they feel amplified, they adopt. Frame your tech as a superpower, not a pink slip. MISTAKE #7: You treat pilots like victories A pilot isn't the finish line. It's where adoption lives or dies. Without clear success metrics, champions, behavior change, and contract pathways, your pilot becomes expensive proof that goes nowhere. MISTAKE #8: You skip the trust-building Trust grows through repetition. Show it. Let them touch it. Make it predictable. Make value obvious. Make risk small. Adoption isn't sold. It's trained. Deep tech doesn't fail because people are stupid. It fails because humans are human. The companies that win build the clearest path for the human brain to say yes. P.S. Deep tech founders: Which of these mistakes has burned you the most? What did you learn when you finally fixed it? Free commercialization audit --> https://lnkd.in/gvZNBKq9 ----------------------------------------------------------- ➕ Follow Shannon for more physical ai adoption tips ♻️ Share with others to help build traction

Tech leaders fail the same way Again and again I’ve seen it 100+ times And it’s always these 7 mistakes: 1. Choosing tools over people ↳ Assuming tech is needed first ↳ The need is different. People ignore the tools → Fix: Ask your team first. Then select tech if needed. 2. Changing things without context ↳ Drop system changes in without warning ↳ Teams push back or check out → Fix: Explain changes in plain words. Create support. 3. Ignoring past problems ↳ Build new features fast ↳ Old bugs keep adding. Never go away → Fix: Address old problems before adding new work 4. Complicating the work ↳ Add too many steps ↳ Planning never ends → Fix: Start small. Deliver fast. Contain rework. 5. Not asking the users ↳ Building based on assumptions and guesses ↳ Missing the real issues and needs → Fix: Talk to users every week. Just listen. 6. Trusting vendors promises ↳ Believe the pitch without diligence ↳ Getting half the value. Or none. → Fix: Always check references, dig in. Ask for proof. 7. Delaying security and risk ↳ Treating it like an extra requirement ↳ Paying for it later → Fix: Address security, risk, compliance throughout. Good tech leadership is simple But simple is not easy. It takes discipline What mistake would you add to this list? Drop it below 👇 ↓ Save this to help your team avoid these traps ♻️ Repost to share and help tech leaders on your team ➕ Adi Agrawal posts on Leadership, Business, Careers

This morning, I was reflecting on the fact that I have participated in 2,500 technology evaluations, and I asked myself, "What is the important thing that I have learned through helping people evaluate technology in order to solve their challenges?" This was the result: The biggest mistake companies make when running a technology evaluation is failing to define the problem well. It doesn't matter whether it is a company looking for a data integration solution, an analytics platform, a CRM, an EMR/EHR, or an RCM (Revenue Cycle Management). They all have a problem-definition problem. I see it all the time. A team starts evaluating vendors and immediately asks: “Do you have this feature?” “Can your system do X?” But those questions reveal something important… They’ve already jumped to a solution without fully understanding the problem. And that’s where things break down. Because the “solution” they’re chasing is based on: - Their limited view of what’s possible - Past experience with other tools - Assumptions about what’s actually broken Here’s the hard truth: If you ask most teams to clearly document the problem they’re trying to solve…they can’t. And when you dig deeper, you uncover even more gaps: - What’s actually causing the issue? - What are we trying to do that we can’t today? - Why does this problem exist in the first place? Without those answers, you don’t choose the right technology. The result of my reflection was a 5-Step Framework for Defining the Problem Before You Buy Technology. Take a peek at the framework. Before you evaluate another vendor, try this: 1️⃣ Define the problem in one sentence → If you can’t explain it simply, you don’t understand it yet 2️⃣ Identify root causes (not symptoms) → What’s really creating the issue behind the scenes? 3️⃣ Map the breakdown → Where in the workflow does this fail—and who does it impact? 4️⃣ Clarify the “why” → Why does solving this matter for patients, staff, and outcomes? 5️⃣ Define success → What does “fixed” actually look like in measurable terms? Technology should solve problems. But only if you’ve taken the time to truly understand them first. How does your team define problems before evaluating solutions?