Building Trust to Drive Revenue in the AI Ecosystem

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

🤝 How Do We Build Trust Between Humans and Agents? Everyone is talking about AI agents. Autonomous systems that can decide, act, and deliver value at scale. Analysts estimate they could unlock $450B in economic impact by 2028. And yet… Most organizations are still struggling to scale them. Why? Because the challenge isn’t technical. It’s trust. 📉 Trust in AI has plummeted from 43% to just 27%. The paradox: AI’s potential is skyrocketing, while our confidence in it is collapsing. 🔑 So how do we fix it? My research and practice point to clear strategies: Transparency → Agents can’t be black boxes. Users must understand why a decision was made. Human Oversight → Think co-pilot, not unsupervised driver. Strategic oversight keeps AI aligned with values and goals. Gradual Adoption → Earn trust step by step: first verify everything, then verify selectively, and only at maturity allow full autonomy—with checkpoints and audits. Control → Configurable guardrails, real-time intervention, and human handoffs ensure accountability. Monitoring → Dashboards, anomaly detection, and continuous audits keep systems predictable. Culture & Skills → Upskilled teams who see agents as partners, not threats, drive adoption. Done right, this creates what I call Human-Agent Chemistry — the engine of innovation and growth. According to research, the results are measurable: 📈 65% more engagement in high-value tasks 🎨 53% increase in creativity 💡 49% boost in employee satisfaction 👉 The future of agents isn’t about full autonomy. It’s about calibrated trust — a new model where humans provide judgment, empathy, and context, and agents bring speed, precision, and scale. The question is: will leaders treat trust as an afterthought, or as the foundation for the next wave of growth? What do you think — are we moving too fast on autonomy, or too slow on trust? #AI #AIagents #HumanAICollaboration #FutureOfWork #AIethics #ResponsibleAI

It took us two years to grow from $10mm to $100mm in ARR and 15 months to reach $200mm. Nine months later, we crossed $300mm. Vanta’s growth rate increased each of the past four quarters – compounding really is the eighth wonder of the world! “But wait,” you might be thinking. “How does a software company founded before 2022 increase its growth rate?” Narrative violation! Trust is the defining problem of the AI era. AI winners build trust with their customers and the market. We founded Vanta to help businesses earn and prove trust. 60% of the companies on the Forbes AI 50 are Vanta customers. While many started small (Cursor at two employees; Decagon three days after company incorporation; the list goes on), they’ve grown large with Vanta: the combined market cap of Vanta’s customers on the Forbes AI 50 is $560bn. AI shifted how companies approach trust: from point-in-time checks to continuous monitoring and verification. For years, teams proved trust during once-a-year audits littered with screenshots and obtuse documentation, and their customers accepted static PDFs. That era is over. Building trust now requires sharing real-time data with customers, partners, and auditors. GitHub Copilot’s launch triggered a wave of security questionnaires that could have stymied the product. Vanta’s questionnaire automation and Trust Center gave GitHub a scalable customer assurance program to support rapid AI adoption. The Github team delivered high-quality, human-in-the-loop responses to 93% of inbound questionnaires in six months – six months ahead of their own plan. AI inside of Vanta’s products saves teams time and reduces risk. Samsara's team was managing 820 controls across ten frameworks as new frameworks and customer requirements added more controls. The Vanta Agent consolidated the sprawl into a common controls framework, which their Security Compliance Manager Elizabeth Walker described as "truly, like having a 24/7 GRC engineer on our team." We’re seeing AI companies use compliance and trust as differentiators, cutting through the noise. Pinpoint Applicant Tracking System sells AI recruiting software into European enterprises, some of the world’s most skeptical buyers. They needed to evolve their compliance baseline (SOC 2, ISO 27001) to build trust with AI skeptics, so they rolled out Vanta, completed ISO 42001 readiness in under three months, and used their framework trifecta to show they’ve invested more than their competitors in managing AI risk. The throughline across 16,000 Vanta customers: AI is raising the stakes for trust, and the companies that build trust grow faster. Finally, anything worth doing takes a village: thank you to our customers, past and present, for being demanding in the best ways. Without your time and feedback, we wouldn't have this opportunity. Thank you to Vanta’ns, past and present, for trusting us with your time and careers. It remains the privilege of my career to hold a megaphone to your work.

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

Financial institutions are discovering that strong governance is becoming a key driver of AI-related revenue growth. For years, many firms viewed AI primarily as a tool for efficiency gains such as identifying ledger discrepancies or optimizing trading performance. Today, the focus is shifting toward compliant and governed AI deployments that support broader business growth and competitive advantage. Secure governance frameworks emphasize explainability, compliance, data lineage, and risk management. These elements help organizations scale AI safely while meeting regulatory expectations. The goal is straightforward: build trust in AI systems so they can move from pilot programs into revenue-generating production environments. When governance is embedded into AI strategies, financial institutions are more confident deploying AI across customer-facing services, credit decisioning, and predictive analytics. This shift allows firms to unlock new revenue opportunities and improve customer lifetime value. Strong governance also reduces operational risk, improves transparency, and supports long-term adoption across the organization. As AI continues to expand across financial services, secure governance is emerging not as a constraint but as an accelerator for growth. It is another sign that responsible AI implementation is becoming directly tied to measurable business outcomes. #ArtificialIntelligence #FinancialServices #AIGovernance

𝗛𝗼𝘄 𝘁𝗼 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗩𝗮𝗹𝘂𝗲 𝗳𝗼𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀  AI adoption in B2B software is moving quickly, but adoption alone doesn’t build customer confidence. What matters is whether AI can reliably improve how customers manage risk, protect data, and operate more efficiently. For many organizations, while enthusiasm around AI is increasing, there is still palpable hesitation about accuracy, governance, and control. That trust gap is where technology providers have an opportunity to lead. AI is most meaningful when it strengthens customer relationships by making outcomes predictable and governance easier to enforce. When customers can clearly see how AI empowers them to do more while also safeguarding critical content, trust follows naturally. Here are five principles that tend to be important when applying AI in ways customers actually value: 1. Use AI to make customer outcomes more predictable AI is most impactful when it reduces uncertainty. Automatically classifying sensitive content, querying complex documents for instant answers, or improving policy enforcement accuracy are just a few areas that could deliver immediate, tangible impact. When customers can depend on consistent outcomes, adoption follows naturally. 2. Apply AI where complexity already exists AI delivers the most value in environments with high content volume, regulatory pressure, or distributed teams. In these scenarios, AI helps customers manage risk and scale operations without increasing overhead. 3. Communicate AI in the language of risk, productivity, and governance Different stakeholders evaluate AI differently. Security leaders care about data exposure risks, whereas IT teams prioritize control and visibility. AI messaging should reflect those realities instead of focusing on obscure problem-solving capabilities. 4. Build AI enablement into the product experience Documentation doesn’t drive adoption, context does. In-product guidance, intelligent recommendations, and workflow-level assistance help customers understand how AI works, and, more importantly, when to trust it. The faster customers see value, the faster confidence grows. 5. Treat customers as participants in AI evolution AI systems improve through real-world usage and feedback. Creating structured ways for customers to validate outputs and influence roadmap decisions strengthens both performance and long-term trust. AI-driven value ultimately comes down to confidence. Before making financial commitments, customers need to trust that AI is improving how their content is used to drive real business outcomes. As software developers continue to integrate more AI into their solutions, customers are entering an unprecedented era. However, to set them up for success, it's important that solution providers deeply understand their workflows and chart a customized path to ensure output optimization.

Organizations are rapidly adopting AI agents, but there's tons of evidence that many are overestimating their readiness for autonomous systems. Most organizations are still in the early to mid-level stages of maturity and that only a small fraction report being in the most advanced stage, the level of maturity that is critical to adopting autonomous AI agents successfully. Building AI That Actually Delivers Business Value means we need to: ❇️ Align AI goals with business strategy AI without strategy is wasted capital. Every agent and automation should tie directly to measurable business outcomes such as revenue, cost efficiency, risk reduction, or experience improvement. Prioritize high impact use cases and pace adoption based on competitive and regulatory realities. ❇️ Invest in scalable infrastructure Strong AI depends on strong foundations. Build on clean data, resilient cloud platforms, secure APIs, and disciplined governance. Without reliable pipelines and security controls, performance and trust erode quickly. ❇️ Upskill and empower talent AI transformation requires workforce transformation. Build cross functional fluency, create hybrid strategy to execution roles, and align KPIs to value creation, adoption, and risk management. Equip people to design, deploy, and oversee AI effectively. ❇️ Evaluate maturity levels Use maturity models to assess readiness across governance, data, infrastructure, and operating model. Identify gaps, sequence investments realistically, and avoid scaling before capability exists. ❇️ Accelerate integration timelines Speed drives advantage. Start with contained, high value use cases, embed AI into existing platforms, prove ROI quickly, then scale. Avoid rebuilding systems from scratch when integration will suffice. ❇️ Develop ethical AI protocols Define transparent standards for accountability, bias mitigation, monitoring, and human oversight. Responsible AI builds trust, reduces regulatory exposure, and protects long term value. ❇️ Focus on resilience over capability Full autonomy is not always optimal. Semi autonomous systems with human oversight often deliver stronger, safer results. Design for adaptability and controlled escalation. ❇️ Strengthen AI agent governance Establish clear ownership, lifecycle management, performance monitoring, and risk controls. Governance converts experimentation into sustainable enterprise capability. AI only creates advantage when it is deliberately aligned, operationally grounded, and responsibly governed. I'm Thomas. I don't design screens. I design businesses. Business is good. #IOPsychology #OrganizationalDesign #BusinessTransformation

Most conversations about AI start with the technology, and what it can automate, predict, or replace. What often gets overlooked is that success with AI has less to do with how advanced the technology is, and more to do with how well organizations are prepared to use it. The findings from Varicent’s latest research with 150+ senior revenue leaders, reinforced what I’ve seen across revenue organizations: the biggest barriers to progress aren’t technical. They’re rooted in how people adopt, trust, and apply AI in their daily work. Even among the most advanced companies, leaders said what they learned from adopting AI had little to do with data or models. The biggest lessons were about people: how trust is built, how processes are designed, and how culture supports change. 44% said human skepticism was a bigger barrier to realizing impact than any technical issue. Nearly 40% said outcomes depended on the processes and training behind the tools. And one-third said adoption improved when AI became less visible, integrated into existing workflows instead of introduced as another system to manage. Those findings show that the real work of AI adoption happens inside the organization, in how we train, communicate, and build trust around new systems. For leaders, that means focusing less on the rollout of technology and more on the readiness of their teams. The organizations getting it right are treating adoption like any other core business process. They plan intentionally, assign ownership, build training into everyday work, and create transparency around outcomes so trust can take root. Get the full findings here: https://hubs.ly/Q03TBNlp0