Evolving Corporate Values

𝟔𝟔% 𝐨𝐟 𝐀𝐈 𝐮𝐬𝐞𝐫𝐬 𝐬𝐚𝐲 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐜𝐨𝐧𝐜𝐞𝐫𝐧. What does that tell us? Trust isn’t just a feature - it’s the foundation of AI’s future. When breaches happen, the cost isn’t measured in fines or headlines alone - it’s measured in lost trust. I recently spoke with a healthcare executive who shared a haunting story: after a data breach, patients stopped using their app - not because they didn’t need the service, but because they no longer felt safe. 𝐓𝐡𝐢𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐥𝐢𝐯𝐞𝐬 - 𝐭𝐫𝐮𝐬𝐭 𝐛𝐫𝐨𝐤𝐞𝐧, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐬𝐡𝐚𝐭𝐭𝐞𝐫𝐞𝐝. Consider the October 2023 incident at 23andMe: unauthorized access exposed the genetic and personal information of 6.9 million users. Imagine seeing your most private data compromised. At Deloitte, we’ve helped organizations turn privacy challenges into opportunities by embedding trust into their AI strategies. For example, we recently partnered with a global financial institution to design a privacy-by-design framework that not only met regulatory requirements but also restored customer confidence. The result? A 15% increase in customer engagement within six months. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐫𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭’𝐬 𝐥𝐨𝐬𝐭? ✔️ 𝐓𝐮𝐫𝐧 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧𝐭𝐨 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭: Privacy isn’t just about compliance. It’s about empowering customers to own their data. When people feel in control, they trust more. ✔️ 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: AI can do more than process data, it can safeguard it. Predictive privacy models can spot risks before they become problems, demonstrating your commitment to trust and innovation. ✔️ 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐄𝐭𝐡𝐢𝐜𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Collaborate with peers, regulators, and even competitors to set new privacy standards. Customers notice when you lead the charge for their protection. ✔️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐭𝐲: Techniques like differential privacy ensure sensitive data remains safe while enabling innovation. Your customers shouldn’t have to trade their privacy for progress. Trust is fragile, but it’s also resilient when leaders take responsibility. AI without trust isn’t just limited - it’s destined to fail. 𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐠𝐚𝐢𝐧 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧? 𝐋𝐞𝐭’𝐬 𝐬𝐡𝐚𝐫𝐞 𝐚𝐧𝐝 𝐢𝐧𝐬𝐩𝐢𝐫𝐞 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 👇 #AI #DataPrivacy #Leadership #CustomerTrust #Ethics

Can law help build ethical AI systems by design, or does ethics resist formalization? In earlier posts, I argued that ethics is about reasoned judgement under uncertainty, and that regulation can create clarity where organizations otherwise struggle. With today’s post I want to connect law and ethics to technical implementation; specifically, the role that law can play in facilitating ethical data practices by design. Privacy professionals are well familiar with this concept, as epitomized by Art. 25 GDPR which requires organizations to implement data protection by design and default. But as Prof. Christian Djeffal outlines in a recent article, law by design has since become a fixture of EU law: Law by design translates legal and ethical goals into technical and organizational obligations. At the same time, it deliberately leaves discretion as to implementation. ➡️ What law can do well Frameworks like the GDPR and the AI Act show how law can meaningfully support ethical data practices by design: ✅ They shape how organizations structure the lifecycle of data processing, starting with an initial assessment of the necessity and proportionality of processing. ✅ They require organizations to clearly define roles and responsibilities from the beginning, and document any relevant risks. ✅ They encourage organizations to seek diverse perspectives when developing and deploying new technologies, thus reflecting the inherently interdisciplinary nature of sociotechnical design. ➡️ What this means for ethical AI Ethics is no longer a nice-to-have when it is hardcoded into legal requirements. As I argued in my master's thesis, the AI Act, for instance, translates ethical obligations into technical requirements, specifically mandating: ✅ Respect for human autonomy by requiring human oversight of the development and deployment of AI systems. ✅ The prevention of harm through accuracy, robustness, and security. ✅ Fairness and explainability through robust data governance and record-keeping. ➡️ Where law reaches its limits At the same time, law by design does not resolve any dilemmas or trade-offs. Ethical behavior is not a technological fact, but the result of human deliberation. Procedure matters just as much as outcome, and legal requirements alone do not tell organizations how to weigh competing priorities in practice. ➡️ What this means for leaders on ethical AI Law by design is not a shortcut to ethical AI. But it can create the right incentives. Leaders should: ✅ Leverage law by design requirements as a foundation for responsible data processing. ✅ Facilitate ethical deliberation to translate law by design requirements into concrete deliverables. ✅ Open up the room for innovation by, in Djeffal's words, "prompting the development of solutions where none yet exist." Link to Djeffal's article: https://bit.ly/45Sj76P. #ResponsibleAI #AIGovernance #DataEthics #Leadership

24 years ago on 9/11), Kenneth Chenault—just months into his role as CEO of American Express and only the third African American to ever lead a Fortune 500 company—found himself leading in the midst of one of America’s darkest and most trying moments. American Express’s headquarters sat in Three World Financial Center, directly across from the Twin Towers. That morning, Chenault who was away on a business trip, was on a conference call with colleagues who were overlooking the World Trade Center when the first plane struck. Amidst the horror that followed, as the towers collapsed, Chenault realized he was facing the greatest test of his leadership. Stranded away from New York on a routine business trip, he set up a command center from his hotel room. He ordered the evacuation of AmEx offices, ensured employees and their families were accounted for, and arranged new workspace. He instructed operators to reach every worker, while also serving the public: waiving late fees, raising credit limits, assisting distressed customers, and even chartering buses for stranded travelers. Chenault later said, “In a crisis, you can’t manage by manual; you must manage by values and beliefs.” On 9/11, American Express employed more than 3,500 people in Three World Financial Center and hundreds more across lower Manhattan. Eleven Amex employees lost their lives. Chenault’s calm and compassionate leadership helped steady thousands of lives while honoring those who were lost. #leadership #valuesbasedleadership

“Another Boeing plane has crashed…” That headline didn’t just inform the world. It shook it. Airlines grounded fleets. Passengers canceled bookings. Families waited in grief. And in those painful moments, everyone turned to Boeing — waiting for reassurance, compassion, and clarity. But what they received instead was silence, technical statements, and corporate coldness. ⸻ 💬 The Dialogue That Never Happened Imagine if Boeing’s CEO had stood before the world and said: 👉 “We are devastated by this tragedy. Our deepest condolences go to the families who lost their loved ones. We take full responsibility to uncover the truth, fix it, and make sure this never happens again. Every passenger’s life matters. We will not rest until trust is restored.” Instead, the company issued vague technical explanations about “software updates” and “pilot procedures.” The difference? One statement speaks to the heart. The other hides behind jargon. 📉 The Fallout of Silence Boeing didn’t just lose billions in market value. They lost something far more precious: trust. • Passengers felt unsafe. • Governments demanded groundings. • Airlines questioned contracts. • Employees lost pride. A global brand that once symbolized safety became a symbol of fear. And the leadership lesson? 👉 In crisis, your communication is your reputation. ⸻ When tragedy strikes, the human brain looks for three things immediately: 1. Reassurance (Pathos): “Do you see my pain? Do you care?” 2. Clarity (Logos): “What exactly happened? Am I safe?” 3. Responsibility (Ethos): “Can I trust you to fix this?” ⸻ Here’s a 3-step Crisis Communication Framework every CEO must remember: 1. Acknowledge Emotion (Pathos): • Show empathy immediately. • Example: “We are heartbroken by this tragedy. Lives were lost. Families are grieving.” 2. Share Facts Clearly (Logos): • State what you know, what you don’t know, and what you’re investigating. • Example: “The incident involves [details]. Investigations are ongoing. Safety checks are underway globally.” 3. Commit to Responsibility (Ethos): • Show accountability and promise change. • Example: “We take full responsibility. Here’s how we are fixing it: [specific steps].” ⸻ ✅ Do’s & ❌ Don’ts of Crisis Communication ✅ Do’s • Respond quickly. Speed signals responsibility. • Lead with humanity. Speak to emotions first, facts second. • Be transparent. Say what you know and admit what you don’t. • Take responsibility. Even partial acknowledgment builds trust. • Be consistent. Updates must be regular, not one-time. ❌ Don’ts • Stay silent. Silence is filled with rumors. • Use jargon. “Software anomaly” means nothing to grieving families. • Deflect blame. Saying “pilot error” erodes credibility. • Downplay loss. Even one life lost must be honored. • Overpromise. “It will never happen again” sounds hollow if unproven. ⸻ 💡 The Bigger Leadership Lesson Crisis doesn’t just test your company. It tests your character.

Before diving headfirst into AI, companies need to define what data privacy means to them in order to use GenAI safely. After decades of harvesting and storing data, many tech companies have created vast troves of the stuff - and not all of it is safe to use when training new GenAI models. Most companies can easily recognize obvious examples of Personally Identifying Information (PII) like Social Security numbers (SSNs) - but what about home addresses, phone numbers, or even information like how many kids a customer has? These details can be just as critical to ensure newly built GenAI products don’t compromise their users' privacy - or safety - but once this information has entered an LLM, it can be really difficult to excise it. To safely build the next generation of AI, companies need to consider some key issues: ⚠️Defining Sensitive Data: Companies need to decide what they consider sensitive beyond the obvious. Personally identifiable information (PII) covers more than just SSNs and contact information - it can include any data that paints a detailed picture of an individual and needs to be redacted to protect customers. 🔒Using Tools to Ensure Privacy: Ensuring privacy in AI requires a range of tools that can help tech companies process, redact, and safeguard sensitive information. Without these tools in place, they risk exposing critical data in their AI models. 🏗️ Building a Framework for Privacy: Redacting sensitive data isn’t just a one-time process; it needs to be a cornerstone of any company’s data management strategy as they continue to scale AI efforts. Since PII is so difficult to remove from an LLM once added, GenAI companies need to devote resources to making sure it doesn’t enter their databases in the first place. Ultimately, AI is only as safe as the data you feed into it. Companies need a clear, actionable plan to protect their customers - and the time to implement it is now.

As businesses integrate AI into their operations, the landscape of data governance and privacy laws is evolving rapidly. Governments worldwide are strengthening regulations, with frameworks like GDPR, CCPA, and India’s DPDP Act setting higher compliance standards. But as AI becomes more embedded in decision-making, new challenges arise: 🔍 Key Trends in Data Governance & Privacy Compliance ✔ Stricter AI Regulations: The EU AI Act mandates greater transparency, accountability, and ethical AI deployment. Businesses must document AI decision-making processes to ensure fairness. ✔ Beyond GDPR: Laws like China’s PIPL and Brazil’s LGPD signal a global shift toward tougher data protection measures. ✔ AI and Automated Decisions Scrutiny: Regulations are focusing on AI-driven decisions in areas like hiring, finance, and healthcare, demanding explainability and fairness. ✔ Consumer Control Over Data: The push for data sovereignty and stricter consent mechanisms means businesses must rethink their data collection strategies. 💡 How Businesses Must Adapt To remain compliant and build trust, companies must: 🔹 Implement Ethical AI Practices: Use privacy-enhancing techniques like differential privacy and federated learning to minimize risks. 🔹 Strengthen Data Governance: Establish clear data access controls, retention policies, and audit mechanisms to meet compliance standards. 🔹 Adopt Proactive Compliance Measures: Rather than reacting to regulations, businesses should embed privacy-by-design principles into their AI and data strategies. In this new era of ethical AI and data accountability, businesses that prioritize compliance, transparency, and responsible AI deployment will gain a competitive advantage. 𝑰𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒓𝒆𝒂𝒅𝒚 𝒇𝒐𝒓 𝒕𝒉𝒆 𝒏𝒆𝒙𝒕 𝒘𝒂𝒗𝒆 𝒐𝒇 𝑨𝑰 𝒂𝒏𝒅 𝒑𝒓𝒊𝒗𝒂𝒄𝒚 𝒓𝒆𝒈𝒖𝒍𝒂𝒕𝒊𝒐𝒏𝒔? 𝑾𝒉𝒂𝒕 𝒔𝒕𝒆𝒑𝒔 𝒂𝒓𝒆 𝒚𝒐𝒖 𝒕𝒂𝒌𝒊𝒏𝒈 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒂𝒉𝒆𝒂𝒅? #DataPrivacy #EthicalAI #datadrivendecisionmaking #dataanalytics

Data privacy and ethics must be a part of data strategies to set up for AI. Alignment and transparency are the most effective solutions. Both must be part of product design from day 1. Myths: Customers won’t share data if we’re transparent about how we gather it, and aligning with customer intent means less revenue. Instacart customers search for milk and see an ad for milk. Ads are more effective when they are closer to a customer’s intent to buy. Instacart charges more, so the app isn’t flooded with ads. SAP added a data gathering opt-in clause to its contracts. Over 25,000 customers opted in. The anonymized data trained models that improved the platform’s features. Customers benefit, and SAP attracts new customers with AI-supported features. I’ve seen the benefits first-hand working on data and AI products. I use a recruiting app project as an example in my courses. We gathered data about the resumes recruiters selected for phone interviews and those they rejected. Rerunning the matching after 5 select/reject examples made immediate improvements to the candidate ranking results. They asked for more transparency into the terms used for matching, and we showed them everything. We introduced the ability to reject terms or add their own. The 2nd pass matches improved dramatically. We got training data to make the models better out of the box, and they were able to find high-quality candidates faster. Alignment and transparency are core tenets of data strategy and are the foundations of an ethical AI strategy. #DataStrategy #AIStrategy #DataScience #Ethics #DataEngineering

SOME leaders got it ALL WRONG 🔥 Perks like pizza and bean bags? Cool, but they’re not what keeps people invested. The real glue is respect, fairness, and opportunity - the kind of fundamentals that build culture, not just vibes. 1. Respect and Fairness • Let them be heard: Make space for voices. When people feel seen, trust grows. • Keep it real: Recognition should be earned, not handed out like party favours. Reward merit - it’s what keeps the culture honest. 2. Opportunities That Matter • Growth isn’t optional: People need to see a way forward. Create space for them to level up in skills and responsibility. • Access for all: Don’t gatekeep. Give everyone the same shot to thrive. 3. Pay What They’re Worth • Respect their value: Competitive pay isn’t a bonus - it’s the baseline. Undervalue people, and you lose them. 4. Balance is Power • Flexibility is the future: Time is currency. Respect their personal lives as much as their output. • Support > Pressure: Build a culture that lets people take care of themselves without guilt. 5. Well-being is Non-Negotiable • Safety is everything: From mental health to physical spaces, make sure they know they’re protected. 6. Feedback That Hits • Guide, don’t micromanage: Feedback should empower growth, not tick a box. • Open up the floor: Honest conversations build stronger teams. 7. Empowerment Through Trust • Let them own it: Autonomy isn’t just freedom - it’s a vote of confidence in their skills. • Push for bold ideas: Back their risks with resources and belief. 8. Recognition With Depth • Make it personal: A thank-you isn’t enough. Show them you see the real work behind the scenes. • Celebrate like it matters: Forget cookie-cutter celebrations. Honour wins in ways that reflect your team’s energy. The extras are surface-level. The essence is what sticks. When you nail the fundamentals - respect, fairness, and opportunity - you’re not just building a team. You’re building culture. Something real, something lasting. 💡Reno Perry

Beware of empowerment being abdication in disguise. Some leaders pride themselves on being hands-off. They say it’s trust. They say it’s empowerment. There are times when I’ve been guilty of this myself, as an overcorrection for micromanagement. But I’ve realised, far from being empowering, it leaves the teams feeling abandoned. Because there’s a difference between delegating and disappearing. A HUGE one. I’ve seen leaders boast that their teams function without them and that they now have time for “higher-order” work. And while well-intentioned, this version of empowerment often leaves teams feeling unclear and unsupported. Delegation doesn’t mean detachment. Empowerment doesn’t mean stepping so far back that teams lose the sense that someone is paying attention. So what’s the middle ground? It means creating a system where people feel both trusted and supported, even when you're not intervening. True empowerment comes with 3 things: - Regular communication. - Clarity of ownership. - Accountability. Teams don’t just need freedom. They need to know someone cares, without hovering. It’s a fine balance to strike and leaders struggle with it. When leaders delegate well, they create space. When they abdicate, they leave confusion in their wake. Empowerment isn’t about walking away. It’s about staying close enough to matter. #leadership #accountability #beyondtheplaybook

What actually breaks transformation programmes, technology or fragmentation? Three legacy systems. Zero single source of truth. One transformation programme to fix it. We led the mobilisation phase for a major public sector transformation replacing three legacy systems that had operated independently for years. The challenge was not technical complexity. It was operational fragmentation. Data existed in multiple places with no master version. Teams worked in silos using different methodologies. Deployment frequency was constrained by lack of data led insights. Enterprise data architecture suffered because nobody owned the complete picture. Here's what we actually did, 1. Established integrated project teams pairing our experts with client resources. 2. Teams worked together to build capability that stays after we finished. 3. Conducted workshops and hands on sessions on Agile data management. 4. Implemented master data management processes and data governance tools. 5. Created insights dashboards that gave visibility into what was actually happening. 6. Introduced KPI monitoring and feedback mechanisms so teams could see impact. 7. Delivered comprehensive training through train the trainer programmes. As a result, → 60 percent improvement in data team Agile development competency. → 40 percent increase in deployment frequency. → Pool of master trainers created who can upskill new joiners. → Single source of truth for data established across previously siloed systems. → Culture of continuous learning fostered instead of reliance on external expertise. The insight most organisations miss. Transformation fails when it treats capability building as separate from delivery. The best programmes are the ones where external specialists work alongside internal teams, not instead of them. Where knowledge transfer is designed in from day one, not added as an afterthought when contracts end. The work is not finished when systems go live. It is finished when the organisation can run, improve, and evolve those systems without external dependency. How much of your transformation budget goes to building internal capability versus buying external delivery?