How to Address Skepticism in Technology

The best thing I ever did for my AI projects? I invited the biggest critics into the room. Sounds counterintuitive, right? Here's what I've learned across 200+ AI deployments: The skeptics, the curmudgeons, the people who question everything — they're not trying to kill your project. They're showing you exactly how to bulletproof it. But here's the catch: timing is everything. Bring them in too early (during brainstorming or exploration), and they'll suffocate momentum before ideas have room to develop. Bring them in too late (after you've committed resources), and their insights can't save you anymore. The sweet spot? Post-design. When you have a concrete solution that needs stress-testing. When the strategy is formed enough to withstand scrutiny but flexible enough to improve. That's when skeptics deliver maximum value. Here's how to make it work: Set expectations upfront. Tell your team you're deliberately bringing in critics to strengthen the project. Frame it as quality assurance, not a threat. Give them a clear job: Find every weakness. Surface every risk. Reveal organizational realities that need addressing. Document everything they say. Every objection becomes a blind spot you can now account for. The result? → Your plan becomes stronger. → Your strategy accounts for real resistance. → Your risk mitigation addresses actual organizational dynamics. Everything is already accounted for before implementation begins. The critics aren't sabotaging your AI initiative. You're sabotaging it by not leveraging them properly. What's been your experience with managing skeptics in transformation projects?

Let skepticism shape your innovation, not stall you. Most rooms I’m in are brimming with Al-assisted development demos and genuine optimism about how quickly software teams can now move. That energy is real and valuable. AI is no longer just helping developers write a few lines of code faster. It increasingly helps teams refactor across files and repos, produce tests, explain unfamiliar code, and advance work through the SDLC workflows. Yet, I sometimes notice the quiet pauses before the tough questions. People worry about sounding negative, or slowing momentum, or being the only one who is uneasy. Those instincts are not only okay, but they are also just as valuable. The skepticism matters more now, not less, because the question is no longer whether AI can generate code. For me, bringing the hard questions supports progress: • What business or engineering outcome is this improving, beyond developer velocity? • Where can this fail: logic, resiliency, security, privacy, or maintainability? • What is the smallest production-relevant test that proves value? • What review, monitoring, and rollback mechanisms need to exist before we scale it? • How do we preserve human judgment where it matters most? I invite challenges to my ideas because that is how we build better ones. A few principles I’ve found useful, especially in the context of mission-critical platforms: • Challenge constructively. Do not just identify the risk and admire the problem, help design the safer path forward. • Trade “no” with “how.” If this approach is not ready, what is the fastest responsible way to learn? • Pair excitement with evidence. Instrument outcomes, test rigorously, and keep a clean rollback path. • Treat trust as a deliverable. In AI-assisted development, control is not friction. It makes speed sustainable. Our best outcomes happen when excitement fuels ambition while skepticism sharpens it. Because in this new environment, skepticism is not the enemy of innovation but is part of the engineering discipline that keeps innovation real and production worthy.

If you’re introducing AI’s involvement with your customer service employees, you’re probably running into some hesitation. And honestly, that hesitation makes sense. They are asking themselves: Is this going to make my job harder? Replace me? Monitor me more closely? Micromanage me? The real challenge isn’t just implementing AI. It’s building trust. And the good news is that there are some very practical ways to do that. Here are five that I’ve seen work consistently. 1.     Show, don’t tell. It’s tempting to roll out AI with big announcements about transformation and efficiency. But what builds trust is experience. Let your employees feel the difference. Because once they experience AI helping them in a real moment, especially under pressure, that’s when skepticism starts to shift. 2.     Position AI as a partner, not a replacement. If agents believe AI is there to replace them, resistance is natural. But when they see AI helping with routine, repetitive tasks, such as summarizing interactions, pulling knowledge, or documenting conversations and commitments, that changes the picture. 3.     Involve your employees early. One of the fastest ways to create resistance is to introduce tools to your employees rather than with them. When your team sees their input shaping the tools, something important happens. They stop feeling like AI is being imposed on them…and start feeling like they’re helping build it. 4.     Build confidence and skills. AI tools can be powerful, but only if people know how to use them. And that doesn’t come from a one-time overview. It comes from hands-on training, real scenarios, and practice using AI during actual interactions. 5.     Measure and share wins. Show improvements in things such as effort reduction, quality, and customer outcomes. And tie those improvements back to how AI is helping. Because when employees see real results, not just promises, it reinforces trust. And that builds momentum. Power Tip: AI empowers. We often focus on showing how AI makes jobs easier. And that’s good. But what really speaks to us is when AI empowers us to make a difference. We all want to know our work matters. One organization was facing real resistance to AI. What changed things was how they used AI-driven interaction analytics. AI helped consolidate what employees were hearing from customers and push that information upstream to other parts of the organization to fix broken processes, improve products, and address recurring issues. Employees could see that what they were dealing with every day wasn’t just activity. It was insight. It was driving real change. They weren’t just handling interactions anymore. They were helping improve the business. So if you’re looking to encourage employees to trust and embrace AI, it’s about being thoughtful in how you introduce it. Show your team how AI helps them make a difference, not just work faster. That’s when adoption really takes off.

Yesterday, I was able to give a talk on data storytelling. What do we do if people don't want to buy into the data or push back? We spend millions on data infrastructure, AI tools, and advanced analytics. But the biggest bottleneck to ROI is rarely the technology—it’s the psychology. The question around overcoming the doubts can come up often. Data teams presents a data story or insight, and the stakeholders don't listen, don't buy in, or listen and go back to the old way of doing things. "That number feels wrong." "My gut says otherwise." "We’ve always done it this way." When people don't "buy in" to the data, they don't may not just ignore it—they may actively push back, causing disruption and stalling transformation. How do we handle the skeptics and the resisters? We stop treating resistance like a math problem and start treating it like a people problem. Here are 4 ways to bridge the gap between data and belief: 1. Invite them into the kitchen. Don't just serve the final meal (the dashboard). Show them the ingredients (the data sources) and the recipe (the logic). When stakeholders are involved in the definition and calculation phase, they feel ownership rather than suspicion. 2. Validate the "Gut Feeling." Never dismiss intuition. Intuition is just internalized experience or maybe call it personal data. Instead of saying "You're wrong," say, "Let's see if the data supports that experience." Make the data a partner to their expertise, not a replacement for it. 3. Master the Narrative (Data Storytelling). A spreadsheet appeals to logic; a story appeals to emotion. If you want buy-in, you have to connect the data point to a business outcome they actually care about. Context creates conversion. 4. Transparency over Complexity. A "black box" AI model is a magnet for distrust. If you can't explain how the model reached the conclusion in plain English, you can't expect a non-technical leader to bet their P&L on it. Data doesn't change organizations. Trust in data and people change organizations. How do you handle it when a stakeholder flat-out rejects the data? Leaders and stakeholders matter, so, get them on board. Stay nerdy, my friends. #DataLiteracy #AI #ChangeManagement #DataStorytelling #Leadership #Culture

Your team is nodding in agreement. They still don't trust it. Every AI deployment triggers two signals: trust or suspicion. Suspicion wins by default. Your team fears AI will replace them, freeze salaries, limit growth. Even those nodding along harbor these suspicions. Most leaders ignore this. They sell AI benefits, talk ROI, and present efficiency decks. Then wonder why adoption fails. At RIGHTSURE, we asked first: "What are you afraid of? Tell me your suspicions." We didn't hide concerns. We unpacked them. People feared job loss. We showed AI compressed tasks, not headcount. People feared salary freezes. We proved commissions went up. People feared losing purpose. We showed AI freed them for higher-value work. Suspicion is the default, not defiance. Treat it as information, not resistance. Address fears first, deploy second. Trust isn't built once. It's maintained through ongoing dialogue. Stop selling AI benefits. Start addressing AI fears. #AIForwardLeadership #ChangeManagement #Leadership #AI

After 10+ years leading change, one thing I’ve learned: Skeptics aren’t obstacles. They’re signals. They show you where trust is weak. Win them over, and they often become your strongest allies. Here’s how to do it in practice: 1/ Start by Listening → Ask: “What worries you most about this shift?” → Write it down. Patterns reveal real resistance points. 2/ Acknowledge the Loss → Say it out loud: “I know this means re-learning old processes, it’s not easy.” → Naming the cost earns respect. 3/ Show Proof, Not Promises → Run a small pilot. Collect results and share them. → Tangible wins quiet doubts faster than speeches. 4/ Involve Skeptics Directly → Invite them into workshops or feedback sessions. → Ownership reduces pushback. 5/ Connect to Shared Purpose → Link the change to something bigger: “This helps clients get answers faster.” → Purpose gives change meaning. 6/ Be Visible and Consistent → Host regular check-ins. Show up every time. → Skeptics notice and trust grows. 7/ Celebrate Small Wins Publicly → Highlight early adopters and results. → Recognition turns them into examples others follow. The truth is: you don’t “convince” skeptics overnight. You build trust one action at a time. And trust is what makes change stick. -- Follow me, Daniel Lock, for practical tips for leading change, consulting & thought leadership

What if some stakeholders are sceptical about your AI integration strategy? How to transform scepticism or fear into success? 👩🏼💻Scepticism about AI integration is inevitable, especially for multi-generational companies, but it is also largely determined by the industry in which the company operates and how technology-driven it is. On one side, there is different adaptability and affinity, and on the other side, the higher risk-taking mindset is typical of younger Millennials and GenZ. ❓So, how leaders and internal AI advocates can work together to address these concerns? 👩🏼💻Here, I’ve put together the most important aspects you need to know. 1️⃣ START WITH AN ASSESSMENT — to identify the reasons and root causes of why different stakeholders are sceptical. ↳ Gather feedback and then group different concerns, and analyse them. ▶︎ THE MOST COMMON REASONS FOR SCEPTICISM based on research data: ▻ Mistrust in The Quality and Reliability of AI ▻ Fear of Job Displacement ▻ Losing The “ Human Touch” ▻ Worries of Data Privacy and Security ▻ Ethical Concerns ▻ Low Confidence Because of Low Level of AI Skills ▻ Rapidly Changing/Unpredictable Market Conditions ▻ High Costs ⮂ Slow ROI 2️⃣ CLEAR COMMUNICATION ▶︎ Communicate regularly and refine it with the different groups of stakeholders. ↳ Outline the goals of the AI Integration Strategy and how it aligns with the overall business objectives. ↳ Communicate how AI supports decision-making, minimises risks and improves project outcomes. 3️⃣ DEMONSTRATE TANGIBLE RESULTS ▶︎ Start with pilot projects to showcase relevant data on improvements and failures. ▶︎ Provide them with KPIs and charts. ▶︎ Quote feedback and concerns from different stakeholders during the pilot project and what actions have been taken to solve them. 4️⃣ INTERNAL KNOWLEDGE SHARING ▶︎ Make internal sources of information, documents and policies accessible to all stakeholders. ▶︎ Organize workshops and training sessions. ▶︎ Ensure continuous learning opportunities and ongoing support. ▶︎ Share success stories and case studies that demonstrate the benefits of AI. 5️⃣ CREATE A SENSE OF PARTNERSHIP ▶︎ Show interest in their input and validate their concerns. ▶︎ Inform them about any potential adverse events or changes that may impact the successful implementation of the strategy in the short or long term. ▶︎ Provide them with regular updates. ▶︎ Involve them in decision-making as much as possible. 6️⃣ TAKE ETHICAL CONCERNS SERIOUSLY ▶︎ Create the “AI Manifesto” of the company including the ethical guidelines of AI use and the “Data Privacy Policy” of your employees and customers. ↳ Ensure that these topics are continually reviewed and adapted to changing market conditions and government regulations. ⚠️Remember, people adapt to change differently and learn at diverse paces, so the primary consideration is for leaders to gain their TRUST rather than force the use of AI on them❗️ 📲 What Would You Add?

Don't silence your naysayers. Give them a seat at the table. Are you spearheading a digital finance transformation and meeting a wall of resistance? It's a familiar story. You paint a compelling picture of a more efficient, insightful finance function, but you're met with hesitant silence and doubtful looks from your team. It’s easy to label anyone who pushes back as a "naysayer." But what if we're using the wrong label? Often, the people we dismiss are actually your most valuable sources of insight in disguise. They fall into two key groups you can’t afford to ignore: The Skeptic: They aren't negative; they're unconvinced. The skeptic thinks, "Show me why this is better. Prove it." Their doubt forces you to build a stronger business case. The Critic: They see the flaws you've missed. The critic thinks, "This part of the plan won't work, and here's why." Their detailed feedback is a roadmap to a more robust solution. Their resistance stems from real, valid concerns. They’ve spent years mastering the current processes and are the keepers of institutional knowledge. So, how do you harness their power? Instead of trying to win them over from a distance, bring them into the inner circle. Use their skepticism and critiques to your advantage with this four-step approach: 1. Listen to Them: Invite their toughest questions and most detailed critiques. A skeptic's doubt will expose weaknesses in your argument before your stakeholders do. 2. Embrace Their Ideas: When a critic points out a flaw, they are giving you a gift. Incorporate their feedback to show you value their expertise and are building a better plan with them, not for them. 3. Show Deep Respect: Acknowledge their role as guardians of process and stability. When people feel their experience is respected, they shift from defending the past to building the future. 4. Ask for Their Help: Directly ask your most insightful critics to help you lead the change. Give them ownership over a part of the process. This turns their critical eye from a source of resistance into a tool for success. When you stop seeing naysayers and start seeing skeptics and critics, everything changes. You don't just gain their support, you gain a co-pilot. You build a more resilient transformation and prove that the goal isn't just to innovate, but to elevate the collective expertise of your team. What are your go-to strategies for turning resistance into a strategic advantage? #digitaltransformation #financetransformation #changemanagement #leadership #CFO #accounting #innovation #BlackLine

One of the #1 questions I get from other founders and leaders is how we get buyers over AI skepticism. Here’s what I learned after running 500+ demos of Hyperbound: Promises of AI automation is not new. Roughly 70,000 AI companies have launched in just the past year. Skepticism is expected. I ran our entire sales motion myself for the first year, and I’ve had to face that doubt head-on. How to turn AI skeptics into believers: 1. Don’t sell the technology. Sell the problem. AI is exciting, but most buyers don’t care about how sophisticated your model is. They care about how you’re helping them hit quota, onboard reps faster, or close deals more efficiently. If you don’t ground your pitch in a problem they already feel, they’ll assume your product is a solution in search of one. 2. Don’t fight the skepticism. Acknowledge it. Most buyers have been burned by flashy demos and underwhelming execution. So when they raise concerns or ask hard questions, don’t get defensive. Agree with them. Show them you’re just as allergic to hype as they are. Suddenly, you’re not selling at them, you’re solving with them. 3. Get them in the product fast. No amount of slides or soundbites will convert a skeptic like a hands-on experience. AI is one of those things that has to be felt. Get them using it quickly—even if it’s a limited workflow—so they can judge for themselves. Live interaction is the fastest path to belief. 4. Don’t show them everything. Show them their thing. Your product might have 50 features, but they only need one to believe in. Instead of overwhelming them with a menu of capabilities, laser in on one pain they care about. Walk through how you solve just that. Focus builds confidence. Breadth creates confusion. 5. Use their language, not yours. Founders and product leaders love talking about what they built. But AI skeptics don’t want to hear about vector embeddings and fine-tuning. They want to know: will this help my team sell better? Speak like an operator, not an engineer. Translate your tech into their business. 6. Let the product speak for itself. Our most effective sales asset? A bot on our homepage. It’s not flashy, but it lets people experience the value of Hyperbound in seconds. When people use what you’ve built instead of just hearing about it, it builds trust in a way no marketing site ever will. 7. Follow up with proof, not pitches. When a call wraps, don’t send them another deck. Send them a case study. A video. A data point. Something that shows the result of your product in action, not just the promise. Skeptics need evidence, not excitement. AI fatigue is real. And when you’re trying to sell in this environment, it’s easy to feel like everyone is just tired of hearing the pitch. But skepticism isn’t the enemy. It’s a signal that they’re still trying to make sense of it all. Meet them there and help them find clarity. That’s when they go from doubters to champions.

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!