Data Layer Impact on Customer Trust in Tech Products

Trust is the real bottleneck to AI impact, not GPUs or models. I went through the SAS Data and AI Impact Report. It is one of the clearest looks at what actually drives outcomes in the enterprise. Here is the short version. You can also find the complete report here – https://lnkd.in/d7XfVKNM What the report highlights • Generative AI usage is up, and agentic AI is rising, but traditional ML still underpins real production work. • Most teams say they “trust” AI, yet many lack the governance, explainability, and monitoring needed to prove it. That gap lowers ROI. • ROI improves when goals are value focused. Customer experience, growth, resilience, and time to value outperform pure cost cutting. • The biggest blockers are weak data foundations, inconsistent governance, and skills gaps. • Maturity varies by industry, but leaders share the same pattern. Centralized data, accountable governance, and an end to end AI lifecycle. Why this helps enterprises • It gives a benchmark. Use trust and impact indices to see where you stand and where to invest next. • It links trust to hard results. Governance is not a checkbox. It is how you improve returns and reduce surprises. • It focuses on foundations. Good data, clear policy, and lifecycle oversight beat ad hoc pilots. My take • Move from “save cost” to “create value.” Prioritize customer experience, decision speed, and new revenue paths. • Treat trust like an operating system. Build a reusable layer for governance, explainability, bias testing, evaluation, and monitoring. Use it across all use cases. • Prepare for agentic AI with data work first. Consolidate data, define permissions, and track lineage. Agents will only be as good as the operating environment you give them. • Invest in skills. Teach builders evaluation and safety. Teach business teams how to measure decision quality. • Start small, measure fast, scale what works. Make ROI reviews a habit, not a milestone. Why this matters now AI has moved from pilots to core workflows. If trust lags, risk scales faster than value. If trust leads, value compounds. This report offers a practical map for leaders to shift from enthusiasm to impact. If you lead data or AI in your company, block time with your team this week. Align on foundations, governance, and near term value. Then execute. #data #ai #agenticai #sas #theravitshow

The CIA Triad isn’t just a cybersecurity principle it’s the foundation of trustworthy AI/ML systems. As organizations scale AI, protecting data pipelines is becoming as critical as building the models themselves. 🔐 Confidentiality AI models consume massive volumes of sensitive data customer profiles, logs, internal documents, behavioural patterns. Ensuring confidentiality through encryption, differential privacy, secure APIs, role-based access, and isolated training environments prevents model leakage and unauthorized data exposure. Even a small breach can reveal training data or expose proprietary model weights. 🛡 Integrity ML models are only as reliable as the data feeding them. Hashing, checksums, digital signatures, and version-controlled datasets protect against data poisoning or silent corruption. A single manipulated data point can shift model predictions, distort features, or bias outcomes especially in fraud detection, credit scoring, or recommendation engines. ⚙️ Availability AI systems must run at low latency, high uptime, and continuous throughput. Failover clusters, distributed training, scalable GPU environments, and automated model recovery keep inference services always accessible. If an AI-driven scoring engine or chatbot goes down, the entire business workflow can stall. Why CIA matters even more in AI/ML: 🧠 Breach of confidentiality → leaked datasets or stolen models 🧠 Breach of integrity → poisoned datasets → wrong predictions 🧠 Breach of availability → model downtime → halted decisions In high-impact areas like fraud detection, medical diagnosis, autonomous systems, or financial risk modeling, compromising even one pillar can lead to catastrophic outcomes. CIA isn’t just security it’s the trust layer that makes AI reliable, ethical, and production-ready. Without secure, accurate, and available pipelines, AI simply cannot scale safely. #CIAtriad #SecureAI #ModelSecurity #DataIntegrity #DigitalTransformation

Where's the ROI in Data Governance? Data governance often feels like an abstract concept to many business leaders - another operational layer with unclear returns. But when you dig into it, the value of robust data governance becomes clear in ways that resonate across the entire organization. So, where’s the real return on investment (ROI)? Reduced risks Proper data governance is like a shield for your organization. It minimizes the risk of data breaches, regulatory fines, and costly operational errors. In an age where data privacy laws are tightening, the ability to maintain compliance can save your business from legal headaches and significant financial penalties. Consider the potential cost of a major data breach - data governance could be the safety net that prevents such a disaster. Operational efficiency Cleaner, well-managed data means fewer resources wasted on correcting inaccuracies and inconsistencies. Teams can work more efficiently, with confidence that the data they rely on is accurate. This efficiency allows your employees to focus on delivering value, rather than spending time cleaning up messy data or working around systemic issues. Improved decision-making Data governance ensures that your data is trustworthy, making it a reliable foundation for decision-making. When leaders can trust their data, they make better, more informed decisions that drive business success. Whether it's forecasting future trends or optimizing current operations, good data governance provides the clarity needed to navigate complex business landscapes. Customer trust and compliance Maintaining customer trust is paramount, especially in industries where data privacy is critical. Ensuring data is handled with the highest standards of governance, your organization not only remains compliant with regulations but also reassures customers that their information is safe. Avoiding costly fines for non-compliance is just one part of the equation - customer loyalty and reputation are equally at stake. In short, the ROI in data governance is real, though it may not always be immediately visible. It's about creating a strong foundation that supports sustainable growth, mitigates risk, and enhances decision-making. While the returns may unfold gradually, they are invaluable for the long-term health and success of your organization.

Before AI can transform business, It has to pass a simpler test of credibility. Every organisation is accelerating its AI agenda. Yet progress depends on one invisible factor: trust. Trust is the layer that converts automation into adoption. It ensures every prediction, recommendation, and decision is understood, explainable, and reliable. When teams understand how an AI system thinks, they move from using it occasionally to relying on it consistently. That shift: from curiosity to confidence is what defines sustainable adoption. The trust layer transforms AI from a technical tool into a decision partner that people can depend on. The trust layer is built through: 1/. Transparent logic: Decisions that can be traced back to data and reasoning. 2/. Accountable data pipelines: Every transformation is documented and reviewable. 3/. Explainable outcomes: Users understand why an output was generated. 4/. overnance by design: Compliance, traceability, and oversight built into workflows. Here’s how it plays out across industries: Finance: ☞ Credit models that display input factors behind each approval. ☞ Dashboards that allow auditors to verify model behaviour. ☞ Risk teams using explainability as part of due diligence. Healthcare: ☞ Diagnostic systems built on traceable clinical data. ☞ Validation logic visible to medical experts. ☞ Transparent models that enhance patient confidence. HR: ☞ Algorithms evaluated for bias before deployment. ☞ Scoring frameworks that remain consistent across reviews. ☞ Continuous feedback loops that align fairness with performance. Social proof reinforces credibility. When teams see audited models, validated results, and trusted outcomes in practice, adoption accelerates naturally. The more AI proves itself under real-world pressure, the stronger its cultural acceptance becomes. The trust layer is not a feature, it is a governance mindset. It combines clarity, accountability, and validation to make AI a dependable business partner. Transformation scales when confidence scales. How is your organisation strengthening the trust layer in its AI systems?

Customers want you to know them better, but they also want you to know less about them. As we get started on 2026, those contrary expectations will only get stronger. Now we’ve hit January, I'm thinking about what’s really going to change over the next twelve months. The technology will continue to evolve, obviously, but I think the more interesting change will be in the trust equation itself. Customers have grown to expect Netflix-level personalisation while simultaneously growing more sceptical about what happens to their data. They've been reading the headlines, experiencing the spam, and they're (rightly, in my view) warier than they once were. The firms that succeed in balancing these expectations will be the ones that make customers feel genuinely in control of how their data is used. It’s not going to be enough to just use the data in a clever way (have we seen too many ‘wrapped’ posts now?!) in 2026. In my experience, this means building transparency and customer control into the product itself: don't bury privacy settings in a menu and make opting out easy. Counterintuitively, the easier you make it to leave, the more willing customers are to stay. It also means showing your working. Has the AI recommended something? Explain why. Used customer data to make a decision? Show them what you learned and what value they got in return. Every data use should answer an implicit question: what did the customer gain from this? I've written before about cognitive offloading in AI deployment and the same principle applies here. AI should handle the transactional while humans handle the emotional. But there's a third dimension now: customers need to feel that AI is working in their interest, and isn’t something that is being done to them. The moment that belief changes, their trust is lost. In regulated industries, we're somewhat ahead of the curve; compliance frameworks have forced us to build trust mechanisms that will become standard elsewhere. But meeting requirements and earning trust are different things, and customer expectations are evolving faster than ever before. This is fundamentally a leadership challenge. It requires aligning the CFO, CRO, and the COO around a shared understanding: customer trust hits the bottom line. LTV, churn, ARPU... all of these sit downstream of whether customers believe you're using their data in their interests. Are we ready to re-earn customer trust in 2026?

This morning I was cleaning out my home office and found a gem. A printed deck from an HBS Reunions presentation on branding and trust — June 2, 2000 — by Harvard Business School Professor (now emeritus) Richard Tedlow Twenty-five years old. Still sharper than most strategy decks I see today. People sometimes ask why BrandRank.AI obsesses over trust ("Does that really SELL CASES?")— and why our framework centers on concepts like Vulnerability and Brand Alignment rather than just "AI rankings." Here's the answer, straight from Tedlow: "At the heart of every brand is TRUST. Trust is the capitalized value between a company and its customer. And trust is what de-commodifies anything." That's it. That's the whole game." Every day our team delivers a version of the same uncomfortable memo to clients: AI platforms don't believe you. ChatGPT, Gemini, Claude, Grok — they're not reading your ad copy with fresh eyes. They're cross-referencing it against everything ever written about your brand. Your Amazon listing. Your product page. Your reviews. Your press coverage. Your complaint history. And when your marketing claim doesn't square with the aggregated signal, they quietly discount it. Or worse — contradict it. That's not a ranking problem. That's a trust problem. The AI answer layer is, functionally, the world's largest BS detector. And it runs 24/7. Which is exactly why — in an Answer Economy where AI agents are shaping decisions before a human ever clicks — the trust layer doesn't disappear. It amplifies. When these platforms describe your brand, they're drawing on aggregated signals of credibility, accuracy, consistency, and reputation over time. That's trust, rendered as data. So yes: - Vulnerability matters → trust breaks in public, fast - Accuracy matters → AI amplifies truth signals and penalizes inconsistency - Brand Alignment matters → AI rewards brands that do what they say - Measurement matters → trust behaves like an asset — it appreciates or depreciates A brand isn't your logo or your tagline. It's the evidence of your trustworthiness — now stress-tested by machines at scale. Tedlow saw it in 2000. The Answer Economy just made it undeniable. More musings at TheAnswerEconomy.com. Also book from Wiley by that same title is coming around the corner. Tara Marotti Paul Baier Betsy Cohen Esther Uhalte Cisneros Bob Liodice Michael Donnelly Jeff Minsky David Cohen Jeffrey Bussgang Teymour H. FARMAN-FARMAIAN John Sviokla Kendra Ramirez Helen Todd (Human) Andrew Susman Steven Wolfe Pereira ⚡️Rishad Tobaccowala Drew Ianni Robyn Streisand Thania Farrar Jeff Goldstein Milen Mahadevan Tracey Cooke Stuart Aitken RL D. Greg Stuart Doug Chavez John Carmichael Jeffrey F Rayport Russ Wilcox Max Kalehoff

📖 Let me tell you a story of how I think we can solve the data trust and quality crisis we face today... 📖 Imagine this: Your company has just launched a new data product. Everyone is excited, the KPIs look great, and users are relying on it for key business decisions. But soon, questions start popping up. "Why don’t these numbers match what we saw last quarter?" "Are these KPIs based on solid data?" The data team assures them that the numbers are correct—but they know the reality. Behind the scenes, data quality isn’t always perfect, and sometimes they’re forced to deliver results based on optimistic estimates. The trust gap begins to grow. This is where the Trust-Tiered Interfaces pattern comes into play. 💡 With this approach, instead of delivering one opaque interface, the product offers users three clear choices: - High Confidence Interface 🔒: Where users get only the rock-solid, validated data—perfect for making high-stakes decisions with confidence. - Optimistic Interface 🌟: Optional, but more comprehensive, where corrected data is included. It gives a broader view, while still based on accurate info. - Data Quality Interface 🔍: Here's the game-changer—an interface that shows exactly how reliable the data is. It’s fully transparent about the sources, gaps, and uncertainties, so users know what they’re dealing with. Before this, most teams offered either the high confidence or optimistic view without giving users insight into data quality. But hiding those imperfections was a loophole—one that quietly allowed issues to slip from one data product to another. 🔑 Here’s the truth: Data will never be perfect, and that’s okay! The key is being upfront about it. By offering the Trust-Tiered Interfaces, data teams can empower users to understand the quality of the data they’re working with. This increases trust not only in the data but in the product and the team itself. Imagine a world where every business decision is made on the right data, with full awareness of its limitations. That’s the kind of maturity this pattern can bring. #DataProducts #DataMesh #DataManagement

We cannot focus on product data right now, we need to focus on selling. Often, product data is not the priority. Often, product data is not identified as the culprit. But when we dig deeper, we often find that product data is seriously holding back organisations. Let's take OEM sales as an example. Component suppliers want to be specced in the next model years. OEM sales managers activate their network, highlight the latest technologies, give highly secretive product presentations and then - send out a PDF. Or a Power Point with some pretty pictures. Or a price list with one line descriptions. The product manager has questions - what are the tech specs? Drawings? Tests? Certificates? Compatibility? And now the back and forth starts: 🔎 The OEM sales manager has to check three locations internally: ERP, sharepoint, emails from 8 months ago. But dammit, no access to the the right folder in the sharepoint, so let's email the engineering team. ⚙️ The question arises: which variation out of the 1000s of articles is the right one for the customer. Don't we have the requested measurements already? Let's just start a new project, who cares about engineering resources. 📃 And is the price list up to date? Who cares, let's get it out first and update it later once you made the specs. OEM sales cycles are long and technical. Product data speeds it up. Customers are evaluating dozens of components simultaneously — geometry, weight, compatibility, pricing, certifications. They need accurate, complete data to make decisions. So if you are focusing on sales, why aren't you focusing on supporting your customer's development process instead of slowing it down? Now imagine this process with a single source of truth. When product data is structured, up to date, and accessible, sales stops being a bottleneck and starts being an asset. They can answer technical questions on the spot. They can share specs in the format the brand actually needs. They build trust with the customer before the sale. And when a brand is building next season's lineup under serious time pressure, the supplier who makes their life easier is more likely to get designed in. If you need to focus on selling, get your product data in order.

Security rarely gets the spotlight until something breaks. Most conversations about customer experience focus on speed, personalization, or AI. And those things matter. But there is a quieter layer underneath every digital interaction that matters just as much: trust. When a customer receives a message from a business, they assume it’s legitimate. They assume the system behind it is secure. They assume someone is watching for abuse. The reality is that messaging fraud has become both more automated and more subtle. Artificial traffic inflation. OTP exploitation. SIM swap attacks. These are not edge cases anymore. They are operational realities for any company communicating with customers at scale. What I’ve learned over the years is that security only works when it is built into the system itself. Not bolted on. Not outsourced. Not something operators discover after the damage is done. That's what we believe at 8x8 It has to be embedded directly into the communication layer where signals, traffic patterns, and anomalies are visible in real time. That is why it is encouraging to see work like Omni Shield being recognized at the Asian Telecom Awards this year with Cybersecurity Initiative of the Year in Singapore. Not because of the award itself. But because it reflects a shift in how our industry is thinking about communications infrastructure. The goal is no longer just to connect people. It is to protect those connections as well. In the end, the best customer experience is not just fast or intelligent. It is trustworthy. #Cybersecurity #CustomerTrust

Trust is your most valuable asset. Privacy and data governance failures destroy it. Are you engineering for trust, or just making hopeful promises? Every data usage decision, from customer experience to marketing automation to AI model training, contains a vital promise to users. Modern business requires trust to operate and innovate, especially when you include the AI innovation that will safeguard their future. In this environment, every broken promise erodes the trust, and increases the likelihood of a catastrophic breakdown a little further down the line. Most enterprises treat small governance failures as minor incidents. They're not. They're breaks in the trust contract you've established with users, regulators, and business teams depending on your AI systems to keep creating value. The fundamental disconnect: • Data is increasingly valuable to businesses’ AI capabilities • AI deployment requires stakeholder trust • Trust requires reliable data governance • Data governance requires engineered infrastructure • Most businesses treat privacy and AI governance as an afterthought You can't claim data is your most valuable asset while simultaneously treating privacy and data governance as a checkbox exercise. The very thing that makes data valuable - user trust - is undermined when privacy is seen as a burden rather than a core business function. This is the fundamental contract between businesses and users in the AI age. And it requires a lot more than privacy policies or compliance frameworks. Because once trust is broken, no amount of marketing or governance theater can fully rebuild it. This is why enterprises need a trusted data layer, infrastructure that makes AI innovation reliable by design, not policy by hope. When AI powers competitive advantage, trust isn't optional. It's the foundation that determines whether you can scale AI safely or get stuck in a series of costly bottlenecks. How is your company engineering trust into data-driven AI adoption? I'd love to hear how your org is tackling this challenge. Send me a DM if you’d prefer your experience wasn’t public.