Time and Trust in Technology Adoption

At PwC, we've learned that the biggest barrier to scaling enterprise AI isn't model capability: it's trust. Here's how we think about that problem. Every new technology faces the same deadlock: you don't use it because you don't trust it, and you don't trust it because you don't use it. The way out is usually a trust proxy, a visible marker that tells people it's safe to change their behavior. The SSL padlock is the classic example. Ecommerce was technically possible in the 1990s, but adoption stalled because typing a credit card into a browser felt reckless. The padlock didn't create security, the encryption was already there. It made security visible. Enterprise AI faces the same issue. The models work. Real solutions exist. But capability is compounding faster than confidence. You see it in cautious adoption: professionals double-checking outputs the system got right. Not because the models aren't good enough, but because there's no structured way to show they've been rigorously evaluated by people who know what good looks like. These aren't capability problems. They're trust infrastructure problems. That's what we built Evaluation Navigator and the Human Alignment Center to address. 📊 Evaluation Navigator gives AI teams a consistent, repeatable way to evaluate solutions across the development lifecycle, with shared guidance and standardized reporting. By embedding evaluation directly into developer workflows through an SDK, trust markers are built into the solution as it's constructed, not stapled on before deployment. 🧐 The Human Alignment Center adds structured expert review at scale. Automated metrics can assess technical correctness, but in professional services the real question is whether the output reflects experienced professional judgment. The Human Alignment Center translates that judgment into dashboards and audit trails that governance leaders can actually act on. The padlock made invisible security visible. Evaluation infrastructure does the same for AI. Adoption is a trailing indicator of trust, so as evaluation becomes visible and accessible, adoption follows.

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.

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!

AI adoption in enterprises rarely follows a straight line. You can build a capable agent that solves a real problem and still find no one using it. One extra click from the usual process can become an inhibitor. A new window, and your DAU/WAU/MAU can tank. Adoption isn’t just about rolling out a tool; it’s about reshaping ingrained habits. Teams grow so comfortable with existing workflows that AI tools can initially feel like a liability rather than a productivity enhancer. The journey moves through three stages: adoption, adaptation, and transformation. Strategy often starts with the end state (transformation), but execution must begin with the first step: adoption. Each stage requires building trust, lowering friction, and proving value in small, tangible increments. Without that, even the most well-designed AI solutions risk becoming "shelfware". AI isn’t a solo game. It’s a team sport. One weak link, one reluctant user, can cause the whole purpose to fall flat. Success depends not just on technology but on shared conviction. Real transformation happens when every click, every process, and every team member feels like AI isn’t an extra step but the obvious next one. #ExperienceFromTheField #WrittenByHuman

#AI in #healthcare isn’t failing because of technology. It’s failing because of trust. In the ER, I don’t reject AI because it’s powerful. I hesitate when it’s unexplainable. Patients sense that hesitation immediately. So do clinicians. That’s the quiet truth behind most “AI adoption problems.” Not resistance. Not fear. But trust that hasn’t been earned. This image captures the principles I’ve learned the hard way: • Trust before automation If clinicians don’t trust the output, AI becomes background noise not support. • Context over computation Without longitudinal history and human nuance, intelligence turns into guessing. • Human-centered design The real risk isn’t AI replacing doctors. It’s AI built without them. • Collaborative progress Technology moves fast. Medicine moves carefully. Progress happens when both respect the pace of the other. AI should make care more human not less. More listening. More clarity. More time where it matters most. When you think about AI in healthcare, what’s the one thing you believe must not be compromised as we scale it? #HealthcareAI #HumanCenteredCare #PatientTrust #DigitalHealth #FutureOfMedicine #DrGPT

AI doesn’t stumble on technology. It stumbles on trust. Most companies still deploy AI like old IT systems: top-down, pre-baked, “here’s your new workflow.” And then they wonder why adoption stalls. The numbers say it all: Trust in company-provided gen-AI fell 31% in two months. Trust in autonomous tools fell 89%. That’s not resistance — that’s feedback. You can’t mandate trust. You have to earn it — and track it. If you can measure sentiment, friction, and confidence, then Trust Health becomes a KPI. Treat it like latency or uptime: if the trust baseline drops, you stop the rollout. Simple. And once trust is a KPI, the approach shifts: - Co-create workflows with the people who actually do the work. - Ship in small loops to reveal friction early. - Make “No trust → No scale” a rule, not a slogan. The companies winning with AI aren’t the ones with the flashiest models. They’re the ones that understand one thing: Technology is cheap. Trust is the moat. What’s the one trust metric you’d track before scaling any AI tool in your organisation? https://lnkd.in/eRShuVSs #AI #Transformation #Business #Strategy

There is a growing gap I am observing with technology. Tools are advancing. People are hesitating. → Leaders underestimate behavioural resistance. → Teams lack shared literacy. → Governance feels heavy rather than enabling. → Success is measured in pilots, not decision quality. The result? Impressive demos. Limited enterprise impact. A Digital strategy is NOT a technology roadmap. It is an adoption and trust agenda. Boards and executive teams that recognise this early avoid the cycle of excitement followed by disillusionment. Transformation occurs when capability, culture, and accountability evolve in tandem. Anything else remains surface-level. If you are seeing adoption friction despite strong investment, there is usually a deeper structural reason.

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 mortgage industry just learned a $100,000,000 lesson about technology adoption. And it's happening right now with Day One Certainty. Only 35% of lenders actually use this GSE-subsidized verification tool. The largest lender? Just 26% adoption. Think about that: The government has been subsidizing 65% of something most lenders ignore for seven years. Now the bill is coming due. When FHFA Director Calabria signaled changes were coming, the writing was on the wall. When Pulte announced "Fannie Mae and Freddie Mac are too expensive," he wasn't talking about general expenses. He was targeting the tech stack. And Day One Certainty is the biggest target. Here's what kills me about this situation: Lesson 1: Technology without adoption is just expensive decoration. Every mortgage company has this problem. You buy the latest verification system. Leadership loves the demo. IT integrates it perfectly. Then loan officers hit reality: • They need data they don't have yet • The integration works differently than promised • Nobody knows when to use which tool • They create workarounds in Excel So they avoid it. And when borrowers ask about "instant verification," they fumble through excuses. This destroys confidence. This kills momentum. This is exactly what happened with Day One Certainty. Lesson 2: Shadow systems reveal your real operations. Want to know what technology your team actually uses? Don't check your vendor invoices. Check their desktops. I guarantee you'll find: • Processors tracking loans in personal spreadsheets • Underwriters with custom Word checklists • Loan officers running separate CRMs in Google Sheets These aren't signs of rebellion. They're solutions your people created because they needed to get work done. Day One Certainty failed because it never replaced these shadow systems—it just added another layer nobody wanted. Lesson 3: Your loan officers must use every tool you buy—in real life. Not in demos. Not in training. In actual loan files with real borrowers. If you have Truv, Argyle, and Trueworks, your loan officers should know exactly when each works best. They should confidently tell borrowers: "I've tested all three systems. Based on your situation, this one will work best." That's not just good service. That's competitive advantage. But most lenders never get there because they treat technology like a checkbox, not a capability. Here's my challenge to every mortgage executive: Stop buying technology and hoping for adoption. Start with this simple audit: • List every tool you pay for • Have your CEO personally use each one on a real file • Ask every team member what they actually use daily • Kill anything with less than 50% adoption The companies that survive won't have the most technology. They'll have the most used technology. Because as Day One Certainty just proved: Seven years of opportunity means nothing if nobody actually uses it. What's your adoption rate?

Most AI tools for agriculture fail for a simple reason. They are built as if adoption is a technical decision. For farmers, adoption is usually a risk decision. It is shaped by trust, timing, and what other people in the community are saying. Everett Rogers called this the diffusion of innovations. The point is not the theory itself. The point is what it helps us notice when we design and deploy AI in real farming systems. Diffusion depends on four things. The innovation. Communication channels. Time. The social system. In agriculture, that social system is not abstract. It includes neighbors, cooperatives, extension officers, agronomists, input dealers, and sometimes WhatsApp groups. These networks decide what gets tried, what gets dismissed, and what becomes normal. This is why adoption is rarely a straight line. Farmers often move through stages: awareness, interest, evaluation, trial, then continued use. Many projects stop at awareness and call it success. But awareness is not adoption. A demo day does not mean a tool is trusted enough to influence decisions that affect income and food security. If we want AI-enabled advisory, diagnostics, or forecasting to be used, we need to work with the factors that shape adoption speed. Relative advantage: Is the benefit clear, not only in ideal conditions, but under local weather, prices, and labor constraints? Compatibility: Does the tool fit existing practices, languages, and decision rhythms, or does it demand a complete change in how work is done? Complexity: Is it easy to understand and act on, or does it require constant data entry, stable connectivity, and technical support that is not available? Trialability: Can farmers try it safely on a small plot, with low cost and low regret if it fails? Observability: Can others see results on farms like theirs, not just in presentations? Adopter categories matter here too. Early adopters in farming communities are often not “tech enthusiasts.” They are practical experimenters with social credibility. When they test something and talk about it, they reduce uncertainty for the early majority. When they reject it, diffusion stalls. Responsible AI in agriculture means designing for these realities. It means budgeting for training and support, not only model development. It means building feedback loops so errors are corrected quickly. It means communicating clearly about limitations, not overselling accuracy. Technology can be impressive and still be unadoptable. Adoption is a social process first. Where have you seen a strong tool fail because the communication and trust pathway was weak? Who has the most influence on adoption in your context: peers, advisors, cooperatives, or companies?