India's digital governance model sets global benchmark

🔎 How ALS Global Built AI Maturity Long Before ChatGPT From testing soil and water to verifying gold purity, ALS Global has earned global trust over 150 years — and now it’s redefining what data maturity means in the age of AI. In the final installment of this three-part series, Thibault Bonneton, Chief Digital and Information Officer at ALS, speaks with Dr. Julian Schirmer, Co-Founder of OAO, about the company’s journey from early cloud adoption to tackling GenAI hallucinations head-on. Key insights: 🔹Early cloud migration built a decade-long data advantage 🔹Strategic acquisitions infused new AI expertise and culture 🔹In-house software ensures data ownership and trust 🔹Balancing innovation and accuracy remains ALS’s top AI priority 🔹“Even a 1% hallucination rate is too high” for a trust-driven enterprise ➡️ Read the full article: https://hubs.ly/Q03NrdFj0 #DataLeadership #AITransformation #GenAI #SmartLabs #DigitalTrust #CDOMagazine

Many enterprises in the Asia-Pacific region are showing strong interest in AI and believe in its transformational potential. However, recent findings point out that strategy, spending alignment, and governance frameworks are lagging. In several markets, AI’s share of overall tech budgets has actually declined despite rising enthusiasm. Organisations that clearly define a shared AI vision and strengthen oversight across all functions are better positioned to translate ambition into results. https://lnkd.in/gvc7Ride #AIAdoption #DigitalTransformation #APACBusiness #EnterpriseStrategy #GovernanceMatters #TechInvestment #BusinessLeadership #AIReadiness #Innovation #OperationalExcellence #UnderstandingEnterpriseTech #EnterpriseTechnologyNow #EnterpriseTechnologyToday

🔍 The $4.8T trust gap in AI — and the fix no one can do alone A new World Economic Forum article highlights a stark reality: while AI adoption jumped dramatically in recent years, only 62% of business leaders believe it’s deployed responsibly. What’s holding us back isn’t just technology—it’s trust. The article argues convincingly that public-private partnerships (PPPs) are the only way to knit together legitimacy, technical capability, oversight, and accountability at scale. Three levers for change that stood out: 👮 Governance: embedding risk tiers, registries, sandboxes 🛟 Assurance: audits, reporting, third-party validation 🫂 Inclusion & data: enabling fair data access and shared infrastructure across regions If you’re a marketing leader, executive, or decision-maker steering AI in your org, this piece is a must-read. Because growth without trust is fragile — but when we get trust right, the upside is massive. https://lnkd.in/dCACXXGb Let me know: what’s one “trust control” you wish your AI systems had built in? #AI #Governance #PublicPrivatePartnership #Trust #DigitalTransformation

A recent survey by Red Hat found that AI is a top priority for UK IT strategies, yet only 11% of organizations are able to translate this technology into real value for customers.

Too often, organizations focus on the technical “build” without giving equal weight to how people, processes, and culture must shift in tandem. This article highlights real risks—from underestimating change resistance to failing to embed continuous learning—and offers practical guardrails. As change leaders, our role is to ensure AI doesn’t just get deployed, but gets adopted — so that value is realized, not just promised. #ChangeManagement #AI #DigitalTransformation https://lnkd.in/gBa-_GNQ

In our Digital Transformation and reform work, many countries consider how to assure Cloud Sovereignty. The EU AI Continent Action Plan could establish criteria to measure sovereignty across various dimensions, including strategic, legal, operational, and environmental considerations. Its recommendations could be translated into practical actions: 1. Rapidly scale sovereign computing: Invest in multi‑tier sovereign data centre computing with capacity tripling within ~5–7 years. If affordable, “AI Factories” could train and finetune models. Tier facilities with long‑term power contracts and accelerate siting/permitting for low‑carbon centres. Tie government R&D grants to domestic capacity builds. 2. Make data usable at scale (without losing control). Pass interoperability and portability rules, standardise consent/contracting templates, and fund neutral labs that certify and steward national datasets (health, mobility, industry) for AI use under your laws. 3. Close the talent gap: Stand up a national AI skills academy, maybe via a scholarship/fellowship scheme bonded to local work; embed AI course frameworks across universities and technical institutes; use regional digital‑innovation hubs to retrain public servants and SMEs. 4. Pull demand through in strategic sectors: Drive adoption in priority sectors with public–private pilots. Run mission‑driven challenge funds (e.g., “AI for health,” “AI for local manufacturing”), with shared tools hosted in sovereign cloud regions to avoid data export and lock‑in. 5. Simplify rules to build trust and speed, pairing innovation with governance: implement an AI Act to give legal certainty and boost citizen trust. Create a “one‑stop” AI compliance desk for SMEs, publish model risk‑management templates, and provide open tools so businesses can meet requirements without heavy overhead. #CloudSovereignty #AIInfrastructure #DataStrategy #DigitalPolicy #SovereignCloud #AIGovernance #Skills #PublicSectorInnovation #digitaltransformation https://lnkd.in/efvsdpmN

Last week, we were proud to sponsor the Responsible AI Summit in London. It was energizing to be in the room with global leaders shaping how AI can be built responsibly, at scale. Here are the key themes we took away, and what they mean in practice: ✅ 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗿𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿 𝘁𝗿𝘂𝘀𝘁 Transparency is about more than “explainability,” it’s about meeting global standards, including frameworks like 𝗜𝗦𝗢 𝟰𝟮𝟬𝟬𝟭 (𝗔𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀), 𝗜𝗦𝗢 𝟮𝟳𝟬𝟬𝟭 (𝗜𝗻𝗳𝗼𝗦𝗲𝗰), 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗘𝗨 𝗔𝗜 𝗔𝗰𝘁, which require clear documentation and measurable KPIs. Accountability now means building systems that can be audited, benchmarked, and externally reviewed. ✅ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 Responsible AI is moving from whitepapers to operational frameworks. We saw examples like Intesa Sanpaolo’s 𝗖𝗲𝗻𝘁𝗲𝗿 𝗼𝗳 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗔𝗜, which develops rules, tools, and training to govern AI across the enterprise. Mature governance also includes policy-based guardrails, periodic red-teaming, and external compliance reviews that embed responsibility directly into product lifecycles. ✅ 𝗕𝗶𝗮𝘀, 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗮𝗿𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 It’s no longer enough to check for bias once. Risks like hallucinations, prompt injection, and sensitive data leakage need continuous monitoring and dedicated tools. Companies showcased solutions such as Microsoft's Presidio, Hugging Face Detoxify, and Microsoft Azure's Content Safety, all working to make AI outputs safer and more trustworthy. ✅ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝘁𝗼𝗼𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 Cross-sector collaboration is critical, and it’s materializing in shared tooling. Organizations are benchmarking guardrail solutions (e.g., LlamaGuard, Google DeepMind's #Gemini Flash) against pillars like accuracy, latency, robustness, and cost. The collective push is toward modular, redundant guardrails that can scale across industries. ✅ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗶𝘀 𝗮 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 We left convinced: responsible AI is more than compliance. In regulated sectors like #banking and #finance, companies that can demonstrate AI security (threat modeling, lifecycle protections) and strong governance are already gaining market trust. Responsible AI is becoming a differentiator, a way to win customers and build resilience in a shifting regulatory landscape. At ActiveFence, we believe that safe, secure, and responsible AI are the base for growth. Building safe online ecosystems requires not only innovation, but also guardrails, governance, and continuous risk management. We’re excited to keep contributing to this conversation and helping organizations adopt AI responsibly, at scale. #ResponsibleAI #TrustAndSafety #AISummit #ActiveFence

What if the next competitive moat isn't AI capability—but AI governance? By 2026, organizations without operationalized Responsible AI will face a strategic paradox: the faster they deploy AI, the faster they erode trust, compliance, and market position. Here's the future taking shape right now: What's really happening: 1️⃣ Agentic AI systems demand governance infrastructure most companies haven't built. Autonomous agents making decisions across supply chains, customer interactions, and operations require real-time monitoring, control planes, and systematic risk management. Without it? Unmanaged liability at scale. 2️⃣ Regulatory fragmentation will force a "comply up" strategy. Organizations like Infosys already apply the highest global standards (like the EU AI Act) across all markets. This eliminates complexity and future-proofs against jurisdictional shifts. 3️⃣ Responsible AI becomes a market signal—not an internal practice. Customers, investors, and partners increasingly demand transparency. The Dubai AI Seal, voluntary reporting frameworks, and third-party certifications indicate trust as currency. 4️⃣ AI literacy gaps create systemic vulnerabilities. 53% of the US population uses generative AI tools. Only 1% have basic AI literacy. This gap doesn't just risk individual misuse—it undermines organizational governance when employees can't identify risks, limitations, or escalation needs. The World Economic Forum and Accenture's new playbook outlines how organizations and governments must act in parallel: clear jurisdictional goals, context-specific frameworks, cross-border interoperability, workforce transition support. Pattern recognition: This mirrors the digital transformation cycle—where laggards paid acquisition premiums, lost talent, and faced existential disruption. The implication? Responsible AI isn't a compliance checkbox. It's the foundation for unlocking agentic AI, maintaining public trust, and scaling innovation without regulatory or reputational blowback. Organizations that invest now in AI governance leaders, systematic risk management, and technology enablement will dominate the next phase. Those that don't will face the consequences when trust erodes faster than they can rebuild it. 👉 Tools to act now: ⚡ Manus - It doesn't assist. It executes. You delegate. It delivers. https://lnkd.in/d3Ami8eK 💬 Comment ♻️ Repost ➕ Follow me Christian Schmidt for AI & business strategy insights. #AIStrategy #ResponsibleAI #AgenticAI #OrganizationalChange #LeadershipAccountability https://lnkd.in/d6tkH6Nw

Advancing Responsible AI Innovation: A Playbook | prepared by World Economic Forum in collaboration with Accenture The 2025 report, Advancing Responsible AI Innovation: A Playbook, prepared by the World Economic Forum in collaboration with Accenture, provides a comprehensive framework for embedding ethical, transparent, and resilient practices into AI-driven innovation across industries. The report’s mission is to guide organizations, policymakers, and innovators in aligning AI deployment with societal values, regulatory compliance, and long-term strategic growth. It positions responsible AI not merely as a compliance obligation but as a core driver of sustainable competitive advantage and trust in digital ecosystems. Drawing on cross-industry case studies and survey data from over 120 global enterprises, the report identifies that organizations implementing responsible AI frameworks experience up to 20% higher adoption rates, improved stakeholder trust, and measurable reductions in operational and reputational risk. Investment in AI governance, explainability, and ethical auditing has increased by 15% annually, while enterprises integrating socio-technical risk assessments report 12–18% gains in decision accuracy and predictive reliability across AI applications, including finance, healthcare, and supply chain management. Analytically, the playbook emphasizes three pillars for responsible AI innovation: (1) governance and accountability, embedding clear roles, audit mechanisms, and risk controls; (2) human-centric design, ensuring AI systems augment human judgment, mitigate bias, and respect ethical norms; and (3) ecosystem collaboration, fostering shared standards, interoperability, and multi-stakeholder engagement. The report further underscores the role of continuous learning, model validation, and transparent reporting as critical for maximizing ROI while maintaining societal trust. In conclusion, the playbook positions responsible AI innovation as both a strategic imperative and a societal safeguard. Organizations capable of integrating governance, ethics, and collaborative design into AI systems are better poised to achieve sustainable growth, resilience, and legitimacy in the rapidly evolving digital economy. Responsible AI emerges not as a constraint but as a catalyst for innovation, trust, and long-term value creation. #ResponsibleAI #EthicalAI #AIInnovation #DigitalTrust #Governance #HumanCentricAI #SustainableTech