Digital Transformation Steps

In a recent discussion with Priscilla Ng, Prudential plc’s Group Chief Customer and Marketing Officer, we delved into Prudential’s shift towards customer-centricity. This conversation underscored the seamless integration of digital innovation and the essential human touch in the insurance sector.   Here are five key insights from our discussion applicable across industries:   🔹Strategic Integration of AI and Human Insight: Prudential is not just using AI to streamline processes; they are using it to significantly enhance personalization and customer service. From simplifying underwriting to transforming service at customer touchpoints like call centers, AI is proving to be transformative. How can other industries use AI not merely for efficiency but as a catalyst for customer connection?   🔹Empowering Employees: In the journey of digital transformation, the role of technology is as crucial as the people behind it. Priscilla emphasized the importance of equipping over 15,000 employees with the necessary mindset, skills, and tools to excel in a digitally evolving landscape. What strategies can companies implement to ensure their teams thrive amidst technological change?   🔹Balanced Approach to Digital and Human Interaction: Despite extensive technological integration, the human element remains critical at Prudential. Their approach ensures that digital enhancements support rather than replace human interactions, thereby strengthening customer relationships. How can businesses maintain this balance to enhance, not undermine, human connections?   🔹Navigating Challenges in Transformation: Adapting to digital transformation comes with challenges, from aligning large teams with new strategies to continuously adapting to emerging technologies. Priscilla shared that a steadfast focus on customer-centricity is essential for navigating these challenges. How can other organizations keep their focus on customer needs while managing transformation complexities?   🔹Continuous Learning and Adaptation: A crucial aspect of Prudential’s transformation is fostering an environment of continuous learning and adaptation. This involves training in new technologies and developing a deeper understanding of customer needs and behaviors. How can continuous learning be structured to keep pace with rapid technological advancements and evolving customer expectations?   This dialogue is part of McKinsey’s ongoing series exploring how leaders steer their companies through transformations. Stay tuned for more insights shaping today’s business landscape. Full interview: https://lnkd.in/gtjphW2s   #Leadership #DigitalTransformation #CustomerCentricity #InsuranceIndustry #AI

In an unprecedented and concerted effort to shape the legal and ethical landscape of AI, a tsunami of AI standards are currently in various stages of development. These standards, spearheaded by different ISO/IEC Joint Technical Committee working groups, are set to clarify key terminologies, shape system requirements, and guide users in implementing AI technologies effectively and responsibly. Some notable standards include ISO/IEC CD 5339 and ISO/IEC 25059:2023, which respectively offer guidelines for AI applications and a quality model for AI systems. ISO/IEC DTS 25058 and ISO/IEC CD TR 24030 provide crucial guidance for evaluating the quality of AI systems and present a diverse range of AI use cases. Data quality is a major focus, with a family of ISO/IEC DIS 5259 standards dealing with addressing data quality for analytics and machine learning, including data quality management requirements, the data quality process framework, and data quality governance. AI transparency is tackled by ISO/IEC AWI 12792, while issues of unwanted bias in machine learning tasks are addressed by ISO/IEC CD TS 12791. ISO/IEC CD TS 8200 further ensures the controllability of automated AI systems. In the domain of ethical and societal concerns, ISO/IEC TR 24368:2022 and ISO/IEC TR 24030:2021 provide overviews of ethical and societal considerations and use cases for AI respectively. Meanwhile, standards like ISO/IEC 23894:2023 offer guidelines for risk management of AI applications. Compliance with these ISO/IEC standards could play a role in assisting companies to align with the new AI regulations like the forthcoming EU AI Act. The AI Act will regulate high-risk AI systems, mandate transparency, fairness, robustness, and human oversight, among other requirements. Standards such as ISO/IEC AWI 12792 and ISO/IEC CD TS 12791, which cover AI transparency and unwanted bias, could provide companies with guidelines on meeting the EU's requirement for transparency and non-discrimination. Demonstrable compliance could then serve as evidence in the case of any legal disputes relating to these aspects. Likewise, the ISO/IEC 23894:2023 standard, which offers guidelines for risk management of AI applications, aligns with the AI Act's emphasis on safety and risk management. Adherence to this standard could potentially provide a framework for demonstrating compliance of these regulatory obligations. Data quality management is another area where the ISO/IEC DIS 5259 standards could assist in ensuring adherence to the AI Act. The Act requires that high-risk AI systems are trained, validated, and tested with good quality datasets, and compliance with these ISO/IEC standards could help companies fulfill this requirement. However, it's important to stress that while these standards can guide and support compliance, they are not a replacement for comprehensive legal advice tailored to the specifics of a company's situation and jurisdiction.

Let me explain... ▶️ Attackers Are Weaponizing Trust Itself Cyber criminals are increasingly focusing on getting better at hijacking trust signals that fool users into taking harmful actions, developers into downloading harmful packages, etc. Worse off, we've spent years, training users to rely on and look out for the very trust signals that attackers are getting better at convincingly mimicking. Consequently, traditional security tools are being bypassed ever more often. Trust is broken! ▶️ Trust Transcends Perimeters In modern architectures, trust lives in identities, tokens, APIs, supply chains, and even human relationships. When we grant an application, partner, or employee a high level of trust, we're effectively enlarging our “attack surface” to WHEREVER that trust extends. A compromised cloud credential or an abused API token can bypass traditional defenses undetected, because the system assumes “trusted” traffic is not harmful. ▶️Supply-Chain Dependencies Each third-party library, managed service, or vendor relationship is a trust link; a vulnerability or breach in any link immediately widens the attacker’s reach into your environment. ▶️The Zero Trust Paradox The rise of “zero trust” architectures means every request must be authenticated, every session evaluated, every transaction authorized. Ironically, the constant negotiation of trust doubles as an attack surface. Here's why; if your policy engine or identity provider is misconfigured, overloaded, or compromised, attackers can gain unfettered access. So here's my prognosis: - Expect adversaries to increasingly target IAM systems, API gateways, and CI/CD pipelines, exploiting the very mechanisms organizations rely on to grant access and permissions. - Personalized deep fake attacks will surpass mass phishing by 2027. - Discerning leaders will deploy tools that operationalize context at scale. CONTEXT IS NOW KING!!! Organizations will shift to context-aware trust assessments; monitoring behavioral anomalies, device posture, and risk signals at every transaction to detect misuse of “trusted” assets. - As orchestration tools become universal, attackers will shift to poisoning CI/CD pipelines. A malicious change to a shared workflow or action could inject backdoors into every deployment, turning your “automation trust” into a systemic vulnerability. In fact, Gartner predicts a 50% rise in breaches traceable to vendor software flaws or misconfigurations. - By 2026, both defenders and attackers will leverage AI for behavior modeling. Attackers will focus on “data poisoning”, through faux-legitimate actions making anomaly detection. Building Trust Is The Only Future That Matters!

Your AI can be 100% compliant and still be unsafe. This has happened more than a few times in recent months, and it’s worth surfacing: AI launch meetings treating compliance as the finish line… when it should be the starting point. On paper, the project looked perfect. 🔸 Documentation? Complete. 🔸 Legal sign-offs? Secured. 🔸 Regulatory boxes? All ticked! But here’s the problem, the compliance review never asked: 🔸 How were training datasets sourced and validated? 🔸 Could patients understand how the AI reached its conclusions? 🔸 Who’s accountable when the AI gets it wrong? Here's the thing, Compliance checks boxes, Responsible AI earns trust. 🔹 Compliance is like passing a driving test 🔹 Responsibility is how you drive when no one’s watching 🔹 Compliance protects you from penalties 🔹 Responsibility protects people. With AI tools moving from pilot to frontline faster than policies can catch up, the gap between compliant and responsible is where harm happens. A compliant AI might flag a patient as low-risk, but without transparency, the clinician can’t see it missed a crucial symptom. One missed symptom → delayed care → worse outcomes → mistrust that can last years. Responsible AI starts with three pillars: 🔹 Ethical frameworks: Ground decisions in fairness, accountability, and beneficence, not just legal allowances. 🔹 Transparency: Let clinicians, patients, and regulators see how the AI works, its limits, and its data sources. 🔹 Oversight: Ensure a human is always answerable for AI actions, with mechanisms to detect and correct harm quickly. The real test of AI in healthcare isn’t whether it passes an audit, it’s whether it can earn and sustain trust. If you’re leading AI in healthcare today, this is the question your patients would want you to answer - which are you building? 💡This post is part of 'Rethinking Digital Health Innovation' (RDHI), empowering professionals to transform digital health beyond IT and AI myths. 💡The ongoing series and additional resources are available at www•enabler•xyz 💡Repost if this message resonates with you!

Enterprise leaders must update their 2026-2027 AI strategies. This year brings major changes: AI agents and automation are outpacing governance, sharply increasing risk. "Sticking an AI on it" is insufficient; leaders must redesign how we augment human decision making (humans-in-the-loop) and automate at scale (human-on-the-loop). Governance practices and platforms are essential to avoid costly mistakes. Gartner predicts the by 2027, 25% of ungoverned decisions using large language models (LLMs) will cause financial or reputational loss due to human biases, insufficient critical thinking, and AI sycophancy. This stems from users' over-trusting confident-sounding LLM outputs. Leaders must govern decisions more carefully, as automation often scales the risks just as fast as it scales the gains! Most clients I speak with still focus on human decision makers being “data‑driven” by dashboards, analytics, and data, etc. However, this fails to overcome human biases, does not prevent "AI sycophancy," nor does it make major decisions transparent and accountable (the black box problem). As #AIAgents increasingly automate part of our businesses, the data-driven dogma (dashboard watching humans) really breaks down. Gartner research shows clients evolving from “data‑driven” to “decision‑centric,” where the business decision is modeled, monitored, and governed - that is why we are hearing much more about decision intelligence in 2026. The Magic Quadrant for Decision Intelligence Platforms offers leaders three key benefits: 1️⃣ Clarity on essential technical capabilities like decision modeling, monitoring, and governance. 2️⃣ A framework for vendor evaluation based on combining AI agents, data, analytics, ML, knowledge graphs, and context for strategic and operational decisions. 3️⃣ Evidence that a decision-centric approaches deliver results; explicitly modeled decisions will be five times more trusted and 80% faster than ungoverned ones. For instance, a client (major bank) leveraged this research to secure their budget, adopt a decision-centric vision, transform a large team into a DI division, and select a platform for governing regulated decisions - boosting their influence and providing a safer path to scale AI. Using LLMs for decision making without governance is an enterprise risk. Becoming decision-centric is the safest way to connect AI to enterprise data. Q. Are you still data-driven, or adopting a #DecisionCentric vision to govern AI-enabled decisions? If "data-driven" is where you are at, this Magic Quadrant shows how connecting data-to-decisions explains the deeper value of data. If you're already exploring #DecisionIntelligence, then let's explore it together. Which capabilities and platforms are on your 2026 roadmap. Now you know why I say that in 2026, “D is for Decisions”. Clients are reading Gartner's new Magic Quadrant for Decision Intelligence Platforms 🔗 https://lnkd.in/eMq4gynh (requires log in)

Most people think #VR in enterprise is all about training. At MARIN (Maritime Research Institute Netherlands), it goes much further than that. Giorgio Ballestin, PhD walked me through how his team answered a very difficult question: What if a ship has not been built yet, and you need to know whether a deck operator can safely perform their job in 5-meter waves? They put an operator in a VR headset, on a motion platform, inside a CAVE system that replicates the bridge of a ship that exists only on paper. The deck operator walks the length of the virtual vessel, grabs a messenger line, and judges the threshold where the operation becomes unsafe. This advanced simulation was built out of several parts: - Multiple people in the same scenario, each in a different physical location, experiencing different perspectives of the same vessel - An expert seafarer acting as instructor, controlling traffic behavior in real time - Researchers collecting data on where people look, how they respond, what breaks down under pressure - Physical objects tracked and overlaid in VR to preserve haptic feedback without losing visual coherence This was such an interesting angle that people don't talk about enough. The goal is never training. It is answering questions that would otherwise require building the real thing first. Make sure to check out the full interview on the XR AI Spotlight Newsletter (link on the contact info on my profile)

This week's defining shift for me is that XR is a practical tool for reducing real-world risk. It helps people see what they are dealing with before they commit to a choice or an action. Teams can spot problems before they happen, drivers can get comfortable with harder scenarios before hitting the road, and shoppers can get a better feel for fit and style before purchase. Better awareness at the start tends to pay off later. This week’s news surfaced signals like these: 🏎️ Mercedes-AMG PETRONAS F1 is using TeamViewer’s AR tools to speed up how its test rigs are put together. Engineers can point a tablet at the setup and see step-by-step guidance placed directly on the hardware. The overlays come from the team’s CAD files and help staff check part placement and confirm that everything is ready before testing starts. 😎 Tom Ford Fashion has added an AR try-on feature for its eyewear on its online stores. The experience, powered by Perfect Corp., uses a person’s pupillary distance to show frames at the right size on their face. This gives shoppers a more accurate sense of how different styles will look and can help cut down on returns. 🚘 South Carolina State University opened a VR training lab for commercial drivers, using full-size simulators to prepare people for roadway hazards such as fatigue, congestion, and aggressive driving. The system also captures physiological data to support safety research and improve training design. Why this matters: Tools that help people understand things earlier can lead to better outcomes. XR does this by making moments that used to feel uncertain easier to anticipate. As more organizations adopt it, the technology becomes a powerful way to bring more confidence into everyday decisions. #spatialcomputing #XR #virtualreality #VR #augmentedreality #AR

AI is already making decisions about people. Ethics decides whether those decisions help or harm. If you’re building, deploying, or teaching AI, these 10 principles are non-negotiable. 1. Fairness and bias   AI must not amplify inequality. Decisions should not disadvantage people based on race, gender, or income. 2. Transparency   People deserve to know how their data is collected, used, and protected. 3. Privacy   User data is not a resource to exploit. It’s a responsibility to safeguard. 4. Safety   AI systems must be designed to prevent harm, errors, and unintended consequences. 5. Explainability   If users cannot understand how decisions are made, trust collapses. 6. Human oversight   Humans must remain accountable, especially in high-impact decisions. 7. Trustworthiness   Clear processes and governance build confidence over time. 8. Human-centered design   AI should solve real human problems, not showcase technical capability. 9. Responsibility   Developers and organizations must own the outcomes, including failures. 10. Long-term impact   AI decisions today shape society, work, and the planet tomorrow. Ethical AI is not a compliance checkbox. It’s a design mindset. If you work with AI, which of these do you find hardest to implement in practice? Follow Amit Kumar Soni for more curated and Simple AI content to stay ahead.

Healthcare’s next interoperability challenge is not simply moving more data. It is establishing confidence in who is requesting the data, which organization they represent, and what authority they have to act. Scott Stuewe FACHDM, President and CEO of DirectTrust, highlights an increasingly important reality: identity alone is not enough. As healthcare moves toward API-based exchange across TEFCA, payer networks, providers, applications, and consumer-directed services, we also need trusted organizational identity and verifiable delegated authority. This is where healthcare’s digital trust infrastructure must evolve. We need scalable approaches that can: 🔹 Reliably identify the legal entity behind a transaction 🔹 Connect individuals, applications, and endpoints to the organizations they represent 🔹 Communicate delegated authority and the context of an exchange 🔹 Reduce duplicative, manual onboarding across networks 🔹 Support trust decisions across organizational and jurisdictional boundaries This challenge closely aligns with the work underway through HL7 FAST Identity and our collaboration with DirectTrust, the CARIN Alliance, GLEIF, credential service providers, and other industry partners. Our collective goal is to help establish standards-based identity and trust frameworks that can support secure FHIR exchange at national—and ultimately global—scale. Interoperability cannot scale through connectivity alone. It requires an infrastructure through which identity, authority, purpose, consent, and accountability can be consistently understood and verified. Excellent perspective from Scott and an important foundation for the next phase of trusted healthcare data exchange. https://lnkd.in/g8a_228r #HealthcareInteroperability #DigitalIdentity #FHIR #HL7FAST #DirectTrust #TEFCA #HealthIT #DigitalTrust #DataExchange #PatientAccess

As a Senior Expert in Digital Transformation, I am always on the lookout for groundbreaking innovations that shape the future of industries. Recently, I came across a fascinating use case presented by Siemens at Realize LIVE 2023, focusing on battery pack assembly powered by the Industrial Metaverse and Process Simulate software. 🌐 Understanding the Industrial Metaverse The Industrial Metaverse is a fusion of physical and digital realms, bridging the gap between real and virtual worlds. It leverages technologies like IoT, AI, AR, and VR to create seamless interactions between physical assets and their virtual counterparts. 👥 The Role of Real-Time Digital Twin At the heart of this innovation lies the real-time digital twin, enabled by Tecnomatix Process Simulate software. It allows companies to plan, simulate, and validate manufacturing processes, including robotics, automation, and human tasks, throughout the entire product development lifecycle. 🔧 Enabling the Battery Industry Siemens demonstrated how the battery industry can adopt the Industrial Metaverse using Process Simulate software. This example showcases how companies can gain valuable insights and optimize production processes using digital twin technology. 🛠️ Seamless Integration with NVIDIA Omniverse The newly released Tecnomatix connector to Omniverse enables realistic and high-fidelity visualization simulations. It paves the way for a seamless update of digital twins in Process Simulate, reflecting immediate changes on the shop floor. 🏭 The End Result: Realistic Visualization & Closed-Loop Asset Management The ability to visualize the digital twin in its real-world context provides a realistic environment for decision-making. One compelling feature is the integration with real assets in a closed loop, ensuring seamless and efficient operations. 🚀 A Game-Changer for Forward-Thinking Organizations Realize LIVE 2023 unveiled a future that promises to revolutionize industries through the Industrial Metaverse and real-time digital twin technology. Embracing this innovation will undoubtedly be a game-changer for any forward-thinking organization. As we move forward into this exciting era, it's essential for leaders to recognize the potential of these technologies in optimizing production processes, improving collaboration, and gaining valuable insights. Feel free to reach out if you'd like to discuss these and other innovations. Let's shape the future together! More at: https://lnkd.in/g2s2U6du #IndustrialMetaverse #DigitalTwin #Innovation #BatteryIndustry #Manufacturing #FutureTech