Balancing modernization with customer trust

𝐇𝐨𝐰 𝐭𝐨 𝐑𝐞𝐢𝐧𝐯𝐞𝐧𝐭 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐋𝐨𝐬𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐋𝐨𝐲𝐚𝐥 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 Reinvention isn’t about starting over—it’s about evolving while staying true to your roots. Every business faces the need to pivot. But change can be risky, especially if your loyal customers feel left behind. The good news? You can modernize and still keep their trust. Here’s how: 𝟏. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 𝐄𝐚𝐫𝐥𝐲 𝐚𝐧𝐝 𝐎𝐟𝐭𝐞𝐧 ↳ Keep your customers in the loop about upcoming changes. Transparency builds trust. 𝟐. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞 𝐘𝐨𝐮𝐫 𝐂𝐨𝐫𝐞 𝐕𝐚𝐥𝐮𝐞𝐬 ↳ Show how the changes align with the values your customers already love about your brand. 𝟑. 𝐆𝐚𝐭𝐡𝐞𝐫 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 ↳ Survey your audience to understand what they value most and what they’re excited to see improve. 𝟒. 𝐏𝐢𝐥𝐨𝐭 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 𝐰𝐢𝐭𝐡 𝐒𝐦𝐚𝐥𝐥 𝐆𝐫𝐨𝐮𝐩𝐬 ↳ Roll out new features, services, or branding to a segment of loyal customers first. Use their feedback to refine. 𝟓. 𝐂𝐞𝐥𝐞𝐛𝐫𝐚𝐭𝐞 𝐘𝐨𝐮𝐫 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 ↳ Highlight customer stories that connect to your transformation journey. 𝟔. 𝐎𝐟𝐟𝐞𝐫 𝐅𝐚𝐦𝐢𝐥𝐢𝐚𝐫𝐢𝐭𝐲 𝐀𝐦𝐢𝐝 𝐂𝐡𝐚𝐧𝐠𝐞 ↳ Retain key aspects of your brand experience while integrating new elements. 𝟕. 𝐒𝐡𝐨𝐰 𝐭𝐡𝐞 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐂𝐥𝐞𝐚𝐫𝐥𝐲 ↳ Demonstrate how the reinvention improves the customer experience without compromising what they love. 𝟖. 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐒𝐨𝐜𝐢𝐚𝐥 𝐏𝐫𝐨𝐨𝐟 ↳ Share testimonials from customers who have embraced the changes and benefited from them. 𝟗. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐚 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐋𝐨𝐨𝐩 ↳ Open channels for ongoing feedback during and after the reinvention. Show you’re listening and adapting. 𝟏𝟎. 𝐓𝐡𝐚𝐧𝐤 𝐘𝐨𝐮𝐫 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 ↳ Acknowledge their loyalty through personalized offers, rewards, or public appreciation. --------- Change doesn’t have to cost you loyalty—it can deepen it. When handled strategically, reinvention can re-energize your business and strengthen customer relationships. How will you reinvent while keeping your biggest supporters on board? 🔔 Let’s create a transformation strategy that keeps your audience engaged and excited for what’s next. Ready to chat?

🏦 The Next Technology Evolution for Community Banks: Innovation with Purpose Community banks have long been pillars of trust, personal service, and local connection. Yet the banking landscape is changing faster than ever, driven by digital expectations, cybersecurity demands, and competition from fintechs and large financial institutions. To remain relevant and resilient, community banks must evolve technologically — not by spending more, but by investing smarter. Competing in a Rapidly Digitizing Market Customers now expect seamless digital experiences: mobile onboarding, instant lending, 24/7 access, and proactive fraud alerts. Competing in this new environment doesn’t require the scale of a national bank; it requires clarity of purpose and targeted investment. The goal is not to chase every new technology, but to select those that directly enhance client experience, operational efficiency, and security. Smart Investment: Doing More with Less Digital transformation doesn’t need to be overwhelming. Cloud-based core platforms, API-driven integrations, and AI-enabled analytics now offer modular, scalable paths forward. These solutions allow community banks to modernize incrementally, achieving measurable results while maintaining financial discipline. Strategic modernization can streamline operations, improve risk management, and empower employees to focus on what matters most — building meaningful relationships with customers. The Power of Strategic Partnerships Partnerships are essential to this evolution. Collaborations with fintechs, technology providers, and shared-service networks enable community banks to access innovation without bearing the full cost or complexity of development. Open-banking ecosystems and API connectivity make it possible to deliver competitive digital experiences while preserving each institution’s agility and local insight. Client-Centric Transformation At the heart of every technology decision should be the client. The purpose of innovation is not to replace human interaction, but to enhance it — transforming data into understanding and automation into personalization. Community banks that succeed will be those that integrate technology into their culture of service, ensuring every digital improvement translates into a better client experience. Information Security: A Strategic Imperative As operations expand into the cloud and third-party integrations multiply, protecting customer data becomes non-negotiable. Robust information-security frameworks preserve not only compliance, but the very foundation of client trust. The Path Ahead The future of community banking will belong to institutions that combine innovation, prudence, and partnership. By investing wisely, collaborating strategically, and keeping clients at the center, community banks can remain vital players in a digital world — bridging tradition and technology to deliver the next era of trusted financial relationships.

Modernizing a legacy platform is like rebuilding an airplane mid-flight. You’ve built something better—maybe even a whole new version of your core product. It’s faster, cleaner, more scalable. But there’s a catch: You’re already serving a large customer base on the old platform. And moving them all to the new one at once? Too risky. You’d be inviting breakage, support chaos, and a hit to customer trust. But not launching the new platform? That’s even riskier long-term. Because while you hesitate, your competitors aren’t waiting. So how do you balance progress and stability at scale? Here are six release strategies we’ve seen work—especially in fintech, where trust is everything and legacy systems run deep: 1️⃣ Let users choose when to switch (like Salesforce Lightning Mode) Allow end users to opt into the new experience. This gives them time to adjust—and gives your team space to gather feedback and make refinements before going wide. 2️⃣ Roll out by user role Start with a specific persona. For example, upgrade your loan officers before your servicing team. This narrows the blast radius and helps your team learn fast in smaller, safer increments. 3️⃣ Route a small % of traffic to the new version Think of it like controlled randomness. Divert a small, randomized slice of users to the new experience (a classic A/B approach), monitor the impact, and refine from there. 4️⃣ Launch for new customers only New customers have no prior expectations—no habits to unlearn. Starting here lets you prove the new platform works without disrupting active workflows. 5️⃣ Let existing customers self-select into early adopter or laggard groups Some customers love to be on the bleeding edge. Invite them to opt in as early adopters—offering advanced access to the new platform and, if appropriate, incentives like preferential pricing. Meanwhile, let your more risk-averse customers remain on the legacy platform until the new experience is fully validated. This creates a natural adoption curve without forcing change on anyone before they're ready. 6️⃣ Start with your simplest customers The bigger and more complex the client, the more edge cases. Begin with smaller, simpler accounts to reduce risk and accelerate learning.

It’s rewarding to see a nearly 200-year-old institution reimagine itself for the AI era. During a recent conversation with the team at Citizens Bank, I was impressed by how they're shifting data management from a back-office function into a strategic competitive advantage. This example also reminded me that the most powerful transformations happen when you build on a foundation of trust.     Citizens took a bold approach with their master data management (MDM) modernization. They moved from batch processing that took days to near real-time data synchronization across their 1,000+ branches in 14 states. Using Informatica's Intelligent Data Management Cloud (IDMC) platform on Amazon Web Services (AWS), they've reduced data onboarding time by approximately 85% and transformed MDM into what they call a "Tier 1" operational asset, meaning it’s always available, accurate and ready to power customer interactions.    The results speak volumes. What used to take three days — even something as simple as updating a customer's phone number — now happens instantly. Their contact center call volumes decreased, their mobile experience became seamless and every customer interaction now draws from a single, trusted source of truth.    What I find particularly compelling is how Anand Vijai M R and his team built flexibility into their architecture while maintaining consistency across every customer touchpoint. The cloud-native approach freed their teams from infrastructure complexities so they could focus on what truly matters: ensuring data accuracy and powering AI use cases. With CLAIRE as an AI copilot, they've democratized access to trusted data across the organization.    This is the transformation I'm seeing across industries. Organizations that treat data as a strategic platform are building sustainable competitive advantages in the AI era. For a bank with roots dating back to 1828, Citizens proves that innovation and tradition can coexist harmoniously. https://lnkd.in/gZCua-M9  

Confidence, Trust, & Readiness are the real metrics of modernization. When I took the challenge of CIO at USPTO in 2019, I faced a crisis of confidence: a decades-old infrastructure crashed the summer before halting patent processing for over a week. Inventors lost time. Examiners lost momentum. Trust eroded. That realization crystallized a truth I shared at Gartner’s Enterprise Risk, Audit & Compliance Conference: government can’t settle for risk awareness. The mission demands risk intelligence-systems that anticipate, adapt, & act in real time. This shift isn’t just technical. It’s cultural. Success isn’t measured in terabytes or toolkits, but in three outcomes: confidence, trust, & readiness. How did we tackle decades of technology debt - mission systems on mainframes, over-customized code where patches risked breaking core functions? With discipline:    •   Retired mainframes. Replaced liabilities with scalable, cloud-native services. Replaced single points of failure / slashed operating risks.    •   Embraced cloud-first. Moving from data centers brought resilience. Outages no longer meant days offline; services scaled with demand.    •   Mission focus on patent & trademark systems / USPTO’s crown jewels.    •   Enforced quarterly reviews. Every product team delivered measurable progress in 90 days, embedded transparency & accountability. The result? Users regained confidence, innovators trusted process, & USPTO was ready for future demands. Modernization became risk management. At US AI, the next phase of modernization is not chasing AI tools, but building discipline called Intelligent Computing. Platforms are:    •   Inspection-ready. Decisions must be transparent, explainable, & auditable. Black-box AI has no place in government; systems must withstand scrutiny.    •   Agentic in defense. Our Agentic Cyber Intelligence & Defense (ACID) platform detects anomalies, predicts threats, & responds in real time, turning risk awareness into action.    •   Mission-aligned. Technology should accelerate benefits processing, strengthen fraud prevention, & enhance citizen services. For experts, this translates to governance frameworks, auditable architectures, & operating models that hold up under audit & attack. Confidence, trust, & readiness are not abstract. They are operational realities:    •   A citizen files a claim, trusting it will process without delay.    •   An auditor reviews a system, finding clear documentation & controls.    •   A leader decides with confidence, backed by systems that model risks & flag gaps. That’s what standard Intelligent Computing delivers when executed with rigor. Every outdated system erodes confidence, wastes taxpayer dollars, & undermines readiness. Modernization isn’t about buzzwords—it’s about earning trust at scale. Risk awareness is obsolete. Risk intelligence is the duty. Leaders who deliver it won’t just modernize systems—they’ll modernize trust. In government, trust IS the mission.

I believe the lift industry is at a crossroads. For decades, we’ve built our reputation on safety, engineering excellence, and reliability. But in today’s world — where live data and AI are reshaping industries all around us — relying on reactive maintenance and scheduled visits is no longer enough. If we don’t have a real-time view of the very assets we’re trusted to manage, then we’re not leading — we’re already behind. As a supplier of AI-driven, live monitoring technology, I’ve seen first-hand how this shift transforms businesses: 10–15% improvements in uptime when faults are predicted, not reacted to. 30% fewer wasted callouts, as engineers arrive with the right parts, the right tools, and the right knowledge the first time. Customer trust that grows when performance is transparent and measurable, not hidden in reports after the fact. This is not about replacing engineers, it’s about equipping them with better tools. It’s about turning data into foresight, and foresight into efficiency, safety, and sustainability. The crossroads is clear: One path leads to transformation, AI as the backbone of proactive service, data as the standard, efficiency redefined. The other leads to being left behind, as customers demand more and competitors move faster. My message to the industry is simple: don’t wait until your customers ask why you don’t offer this — by then, it’s too late. The lifts will keep moving. The future is already moving with them. The question is: will you move with it, or be left behind by it?

In #publicsector, a bad AI response isn't just a glitch or a lost customer; it’s a denied benefit or a compromised service that erodes civic trust. That’s why moving toward autonomous digital workers requires a complete shift in how we think about risk mitigation. In a recent diginomica feature, Salesforce's Chief Ethical and Humane Use Officer, Paula Goldman, highlights why #AIethics must move from a passive #compliance checklist to an active operational framework. Three key takeaways from Paula's perspective I felt were highly relevant for public sector modernization leaders: ➖ Active Adversarial Testing: Agencies should actively run adversarial simulations to find vulnerabilities, toxicity, or algorithmic bias before systems interface with the public. ➖ Guardrails Over Models: Scaling AI safely means utilizing secure enterprise platforms with built-in runtime guardrails, data privacy protections, and clear human-in-the-loop oversight out of the box. ➖ Radical #Transparency: As AI agents assist with citizen services, systems must cleanly surface source citations, data origins, and clear indicators that the user is interacting with an AI system. Modernization isn’t just about driving efficiency, it’s about building secure, transparent, and equitable systems for the citizens we serve. https://lnkd.in/e2F-jQx4

From Outdated to Outstanding: Are You Ready to Meet Gen Z's High Expectations? Most contact centers struggle with evolving customer expectations: - They haven’t integrated the latest tech, like Gen AI, for efficient service; - They haven’t adapted to Gen Z’s unique engagement preferences; - They lack a cohesive, omnichannel approach; - Their services remain static and lack personalization. The result? Customer care feels outdated and inefficient. Gen Z wants things fast, personal, and flexible. Brands that don’t keep up risk losing them to competitors who do. Three challenges traditional customer care faces: - Outdated → Fails to utilize tools like Gen AI for efficient problem-solving. - Siloed → Lacks a unified strategy across communication channels. - Reactive → Fails to predict and address recurring customer needs. And you’ve seen it before: - The endlessly looped "please hold" messages. - The frustration of repeating issues across multiple agents. - The lack of proactive solutions or personalized care. For instance, Gen AI could reduce contact center calls by 50% in five years, but its potential might go untapped without a clear strategy. Progressive companies are integrating Gen AI and customer insights to meet Gen Z’s expectations better and boost satisfaction. Here’s a roadmap to evolve customer care into a future-ready function: 1/ Balance AI with Human Interaction Using Gen AI to automate simple tasks lets human agents handle complex issues. This balance is key since 70% of customers, including Gen Z, prefer phone support for complex issues. Automation enhances efficiency, while empathy in high-stakes interactions keeps loyalty strong. Automation → Accuracy → Human Empathy → Loyalty 2/ Build a Seamless, Omnichannel Experience An omnichannel approach offers consistency across all touchpoints, accommodating the 70% of customers who still favor email for issue resolution. Despite management challenges, seamless integration across channels builds trust. Channels → Consistency → Trust → Satisfaction 3/ Develop a Future-Ready Workforce Recruiting agents with customer care and tech skills prepare contact centers for complex queries. With labor shortages in places like Germany and the US, attracting skilled talent ensures effective AI integration and customer care success. Skills → Training → Innovation → Growth What’s Next? Customer care is evolving fast—driven by Gen Z demands and AI advancements. To keep up, brands must be proactive and strategic. Winning brands will: 1️⃣ Blend AI with empathy for real impact 2️⃣ Create seamless, cross-channel support 3️⃣ Cultivate a forward-ready workforce The future of customer experience starts now. Are you prepared to lead? #CustomerCare #AI #GenZ #Innovation #CustomerExperience

I am seeing banking leadership teams being under tremendous pressure and this is true worldwide. They are expected to launch faster, respond quicker and deliver digital-first experiences. However, their internal reality is that most core systems were never built for that pace. Those systems were built for availability, transaction integrity, regulatory compliance, operational resilience and stability. The tension today is real. Businesses want faster product launches, personalisation and rapid experimentation. At the same time, technology teams still have to protect uptime, data integrity and customer trust. Banks are in a tough situation – on one side, they are being asked to move at startup speed on infrastructure built for institutional certainty. That’s hard. So, how to approach modernisation? It doesn't have to scrap everything. The more practical path is incremental: API enablement, decoupling experience layers from core systems, domain-by-domain transformation and controlled coexistence of old and new. Big-bang replacements are risky. Thoughtful, staged modernisation is a better approach. "How do we create agility without introducing fragility?" – Many ask me this question. While there is no single answer. But the strongest programmes I have seen don't treat legacy as the enemy. They respect what it was built to do, while creating room for innovation alongside it. Stability and agility aren't opposing choices. Banking customers expect innovation and they also expect reliability. The winners need to deliver both. #BankingExperience #BankingModernization #TechnologyModernization

The ROI Problem No One Likes to Talk About in Product Management Every product leader eventually runs into the same brick wall. Some work is essential, but it will never produce a clean ROI slide. It will never be monetized. It will never land on a pricing sheet. Yet the product will fall behind without it. -Modernization. -Experience improvements. -Infrastructure that keeps your NPS from sinking. These are the investments that are critical and nearly impossible to quantify. Early in my career, I spent too much time trying to reverse engineer a financial model to defend work that could not be monetized. It never felt right because the math was forced. Many teams still fall into that trap. Here is the reality PMs need to embrace. Not everything worth building shows up in a revenue column. When the value shows up in retention, satisfaction, or risk reduction, the signals take longer and the measurement is softer. Yet the work is no less strategic. So how do you get approval for essential work when the ROI model looks skinny? A few approaches help teams lead the conversation instead of getting stuck defending it. 1. Reframe the decision as a risk choice. You are not calculating upside. You are protecting the integrity and competitiveness of the product. Executives respond when you quantify the downside of inaction. 2. Anchor the discussion in customer trust. You may not be monetizing these capabilities, but you are stabilizing the experience that allows monetizable features to succeed. Higher satisfaction is an enabling investment, not a standalone one. 3. Tie the work to long term viability. A modern product reduces technical drag and gives teams leverage for future innovation. Without that leverage, every future dollar becomes more expensive. 4. Quantify anything you reasonably can. You cannot model exact retention value, but you can model trend risk, competitive gaps, and operational cost reduction. These are directional indicators that still guide decision making. 5. Make the strategic case with confidence. Leaders expect PMs to advocate for the nonmonetized work that keeps the company competitive. When you treat this work as optional, others will too. The strongest product teams know how to blend measurable ROI with strategic necessity. The decision is rarely clean. The conversation always matters. If you are a PM facing this challenge, stay steady. Your instinct to protect the foundation of your product is usually right. And handled well, these conversations build trust in your leadership and in the long term direction of the product.