Digital Transformation And Culture

The $3 million AI system sat unused for eight months. Perfect accuracy. Impressive speed. Zero adoption. I walked into that company to figure out what went wrong. Took me about an hour to see it: → Nobody told the operations team why they should trust a machine over their 15 years of experience → Nobody redesigned workflows around the new system → Nobody gave them a reason to change what was already working The executives thought they bought an AI solution. What they actually bought was expensive software that required massive organizational change and they skipped that part… This happens everywhere. I've seen it at Fortune 100 companies with unlimited budgets and at startups burning through their Series A. Same pattern. It's completely preventable. Before you spend a dollar on AI technology, answer three questions: → Can your team explain why this change matters to them personally? → Do your workflows support how the AI actually works? → Does your culture reward trying new approaches or punish deviating from the norm? If you can't answer those clearly, organizational readiness is your problem. Not the technology. The companies winning with AI figured this out early: → They spent months on change management before the first line of code → They redesigned workflows with input from people who'd actually use the system → They built trust before they built features Technology teams hate hearing this because it's not sexy. But organizational readiness determines whether your AI initiative transforms operations or becomes another failed pilot. Follow for the reality of AI implementation that nobody else talks about. Find out more at https://vist.ly/4gvpt #ai #aistrategy #leadership #aiimplementation #changemanagement #digitaltransformation #enterpriseai

I've done 127 AI readiness assessments in the past two years. Only three actually measured what matters. The others focused on beautiful dashboards. Impressive tech scores. Data cleanliness metrics. Automation percentages. All the wrong things. They miss the critical factor. Whether your team trusts this is happening for them, not to them. A healthcare company with ninety million in revenue had a perfect readiness score on paper last quarter. Clean data. Solid infrastructure. Two successful pilots. Six months after rollout, adoption sat at nine percent. I asked the operations manager what happened. She said nobody explained why they were doing this. Just that they had to. A manufacturing client I'm working with now has messy data. Their systems aren't integrated. But their teams know exactly what problems the AI is solving for them. Ninety days in, sixty-eight percent usage rate. The difference isn't the technology. It's whether you asked your people what they actually need before you started building. Most companies treat AI readiness like a technical assessment. Infrastructure check. Data quality check. Security protocols check. They're auditing the wrong thing. AI readiness isn't a tech audit. It's a trust audit. #AIReadiness #AIAdoption #DigitalTransformation #FutureOfWork #HumanCenteredAI #ChangeManagement #AIBusiness #TrustInTech #AICulture #LeadershipInAI

Leading change isn't just about having a compelling vision or a well-crafted strategy. Through my years as a transformation leader, I've discovered that the most challenging aspect lies in understanding and addressing the human elements that often go unnoticed. The fundamental mistake many leaders make is assuming people resist change itself. People don't resist change - they resist loss. Research shows that the pain of losing something is twice as powerful as the pleasure of gaining something new. This insight completely transforms how we should approach change management. When implementing change, we must recognize five core types of loss that drive resistance. * First, there's the loss of safety and security - our basic need for predictability and stability. * Second, we face the potential loss of freedom and autonomy - our ability to control our circumstances.  * Third, there's the fear of losing status and recognition - particularly relevant in organizational hierarchies.  * Fourth, we confront the possible loss of belonging and connection - our vital social bonds. * Finally, there's the concern about fairness and justice - our fundamental need for equitable treatment. What makes these losses particularly challenging is their connection to identity.  When change threatens these aspects of our work life, it doesn't just challenge our routines and who we think we are. This is why seemingly simple changes can trigger such profound resistance. As leaders, our role must evolve. We need to be both champions of change and anchors of stability.  Research shows that people are four times more likely to accept change when they clearly understand what will remain constant. This insight should fundamentally shift our approach to change communication. The path forward requires a more nuanced approach. We must acknowledge losses openly, create space for processing transition and highlight what remains stable. Most importantly, we need to help our teams maintain their sense of identity while embracing new possibilities. In my experience, the most successful transformations occur when leaders understand these hidden dynamics. We must also honour the present and past. This means creating an environment where both loss and possibility can coexist. The key is to approach resistance with curiosity rather than frustration. When we encounter pushback, it's often signaling important concerns that need addressing. By listening to this wisdom and addressing the underlying losses, we can build stronger foundations for change. These insights become even more crucial as we navigate an increasingly dynamic business environment. The future belongs to leaders who can balance the drive for transformation with the human need for stability and meaning. True transformation isn't just about changing what we do - it's about evolving who we are while honouring who we've been. #leadership #leadwithrajeev

Digital Circular Economy 🌎 In the shift towards sustainable business practices, digital technologies offer transformative potentials for the circular economy. These technologies facilitate significant improvements across various circular business models, from design and manufacturing to life extension and resource recovery. As depicted in the recent visual framework, each stage of the circular process can be optimized through the strategic deployment of technologies such as the Internet of Things (IoT), blockchain, artificial intelligence (AI), and big data analytics. For instance, IoT can enhance product lifecycle tracking, enabling more efficient reverse logistics and better product lifecycle management. Blockchain technology introduces unparalleled transparency and security in supply chains, making it easier to track the origin and handling of materials, which is crucial for recycling and remanufacturing processes. Meanwhile, AI and big data analytics can predict maintenance needs and optimize resource use, significantly extending the life of products and components. However, while technology provides opportunities for advancing circular business models, it's crucial to recognize and address potential adverse effects. The increased use of digital tools can lead to higher energy demands and contribute to electronic waste. These negative impacts necessitate a balanced approach where the benefits of digital applications are leveraged to enhance sustainability while mitigating undesirable outcomes. This balance is achieved by designing systems and frameworks that not only incorporate digital tools into circular business practices but also ensure that these tools are used in ways that prioritize environmental integrity and resource efficiency. For example, employing cloud computing solutions can decrease the need for physical infrastructure, reducing material use and energy consumption. As industries continue to integrate these technologies, it is imperative to continually assess their impacts, both positive and negative. By understanding and addressing these dynamics, businesses can more effectively harness the potential of digital technologies to drive the development of a more sustainable and economically viable circular economy. This approach ensures that technological advancements contribute effectively to environmental goals and the resilience of business operations. Source: OECD #circulareconomy #sustainability #climateaction #esg #circular #circularity

Sustainability isn’t a coat of paint. It’s part of the blueprint. In digital health transformation, “green” has moved from a nice-to-have to a core part of responsible change. And lately, it’s a recurring topic in many meeting rooms. Ignoring sustainability in transformation isn’t just bad for the planet, it exposes organizations to rising energy costs, regulatory penalties, and reputational risk. Every transformation decision, from strategy to procurement, deployment to retirement, carries an environmental footprint. Treating sustainability as an afterthought leads to waste: 🔸 Systems overbuilt for prestige rather than need 🔸 Infrastructure running far below capacity 🔸 Devices replaced on schedule, not condition I’ve seen entire racks of perfectly good hardware decommissioned, not because they failed, but because refresh cycles didn’t account for reuse or repurposing. It’s a reminder that sustainability isn’t always obvious at first glance. In one study comparing two T-shirts: 🔹 The one labelled as “sustainably produced” wore out quickly, requiring multiple replacements. 🔹 The other, not marketed as green, lasted far longer, and over its full lifecycle, had a smaller environmental footprint. Digital transformation works the same way. True sustainability comes from durability, efficiency, and total lifecycle impact, not just how “green” it looks at launch. Embedding sustainability means building it into every phase of transformation: 1️⃣ Strategy & design Set sustainability goals alongside clinical and operational goals.  Select cloud providers with renewable energy commitments. 2️⃣ Build & deploy Use modular architectures to extend system life.  Prioritize energy-efficient code, devices, and configurations. 3️⃣ Operate & maintain Monitor resource usage, consolidate storage, and optimize workloads for off-peak energy demand. 4️⃣ Retire & replace Plan for secure decommissioning, refurbishment, and recycling from the outset. Before approving your next transformation initiative, run it through the "Green Lens": ✅ Can we meet the need with fewer resources? ✅ Can this run on renewable-powered infrastructure? ✅ Can we extend the life of what we already have? If the answer is “no” across the board, you don’t have a sustainable transformation plan. If you’re leading digital transformation today, are you building it for the next launch… or the next generation? 💡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!

As AI reshapes the global economy, its environmental implications remain significant. Data centre workloads are set to more than triple by 2027, driven by surging compute demand. Without intervention, this trajectory risks trading digital progress for ecological strain. Sustainable AI embeds efficiency across the tech lifecycle—anchoring Green IT through optimised software, cleaner infrastructure, smart hardware management, and circular design. There are strategic signals: 1. Adoption Metrics: KPMG reports that 68% of organisations have Green IT goals, but formal strategies nearly double as AI scales—47% among early adopters compared to 93% among more advanced users. However, only 4% have fully optimised their AI impact with specific targets, strong partner networks, sustainability-focused SLAs, and Scope 3 reporting. 2. Cost Efficiency: Efficient AI models reduce cloud and compute expenses. When scaled properly, AI helps identify operational waste, streamline logistics, and enhance asset use. 3. ROI Impact: Global confidence in AI returns has increased, with 86% expecting ROI within three years, up from 21% last year. Sustainable AI ensures each dollar spent delivers more value through resource efficiency, reduced operating costs, and improved performance. Continuous feedback loops powered by AI measure results and track ROI over time. At ReNew, we witness this shift firsthand. As India’s leading clean energy company, we see AI as essential. Embedding efficiency into AI operations goes beyond lowering emissions—it aligns digital growth with climate ambition, ensures resilience, and strengthens stakeholder trust. An impact in action: a 26% reduction in our asset downtime, a 150% surge in predictive analytics adoption over the past two years, and AI-powered market price forecasting driving a 66% increase in trading team productivity. AI can improve operations and accelerate corporate decarbonisation, but if it relies on fossil fuel infrastructure, it undermines the sustainability goals it is meant to support. #SustainableAI #AIForGood #ReNewTheFuture

🌍 New Article: Data Stewardship as Environmental Stewardship 🌱 ✍️Co-authored with Sara Marcucci ➡️ As the world becomes increasingly reliant on data and artificial intelligence (AI), the environmental impact of data-related activities is growing—raising urgent questions about sustainability in the digital age. The rise of generative AI, fueled by massive datasets and computational power, risks exacerbating these challenges. 🤔 In our latest article, we propose that responsible data stewardship is the most common-sense pathway to mitigate the environmental footprint of data-related activities. By promoting practices such as: 🌐 Data minimization, reuse and circular economies: maximizing value while minimizing environmental costs. ♻️ Reducing digital waste and energy consumption: streamlining storage and minimizing resource use. 🔍 Transparent and shared data: enabling better decision-making for sustainability. ➡️ We argue that positioning data stewardship as environmental stewardship offers a dual benefit—advancing technological innovation while safeguarding our planet. 📊 The stakes are high: ✅Data centers alone consumed 460 TWh of electricity in 2022 (2% of global usage) and are projected to double by 2026 due to the rise of AI. And water resources are getting depleted as a result... ✅Rare earth mining for data-related infrastructure leads to biodiversity loss, habitat destruction, and water scarcity. ✅ Increased space activities, satellites, and poorly managed data processes add to the growing environmental strain. 💡 What’s the way forward? We call for: 1️⃣ Practical guidelines for sustainable data stewardship. 2️⃣ Recognizing data stewards as strategic sustainability leaders. 3️⃣ Adoption of circular data economies. 4️⃣ Integration of environmental metrics into data governance. 5️⃣ Cross-sector collaboration to align sustainability goals. 👉 Read the full article:https://lnkd.in/g2zbF_c5 #Sustainability #DataStewardship #EnvironmentalResponsibility #AI #CircularEconomy #DataGovernance

𝘛𝘩𝘦 𝘴𝘬𝘪𝘭𝘭𝘴 𝘨𝘢𝘱 𝘪𝘴 𝘢 𝘱𝘦𝘳𝘴𝘪𝘴𝘵𝘦𝘯𝘵 𝘢𝘯𝘥 𝘨𝘳𝘰𝘸𝘪𝘯𝘨 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦. While demand for talent remains high, the hiring rate has fallen sharply, and a shocking number of job openings remain unfilled even as the number of unemployed people is steadily rising. One major reason for these developments is a growing disconnect between the skills employers need and those job seekers have. To meet this challenge, we need to rethink how we approach talent development. Reskilling and upskilling are a necessity. Businesses can’t wait for the perfect candidate with the perfect skill set to show up—they need to invest in developing the skills of the workers they already have. How? There’s mentorship, training programs, and leveraging technology like AI. All these can ensure workers are equipped with the skills we need today and in the future. But it’s not just about developing one’s in-house talent. Employers must also be open to diversifying their approach to attracting external talent, including identifying and engaging with untapped talent pools—people who might not have followed traditional career paths but have the skills to thrive in the right environment. In an era characterized by rapid technological change, employers must take a proactive, forward-looking approach to investing in talent, offering the right opportunities for growth, and developing skills that align with tomorrow’s needs. Only through these efforts can we close the skills gap and build a future-ready workforce.

Digital empowerment goes beyond just access to technology. It’s about creating equity in critical sectors like education, healthcare, finance, and employment, especially for marginalized communities, rural populations, and persons with disabilities (PwDs). To address these needs, tailored digital solutions are crucial, and public-private partnerships (PPP) will play a key role in shaping this transformation. 1️⃣ 𝐓𝐚𝐢𝐥𝐨𝐫𝐞𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐋𝐢𝐭𝐞𝐫𝐚𝐜𝐲 & 𝐒𝐤𝐢𝐥𝐥𝐢𝐧𝐠 Programs focused on digital literacy are the bedrock of empowering rural populations. The PMGDISHA (Pradhan Mantri Gramin Digital Saksharta Abhiyan), initiated by the Indian government, aims to enhance digital literacy in rural areas. As of 2024, over 5 crore people have been trained, highlighting the immense demand for digital literacy and skilling. Similiary, the Digital Empowerment Foundation (DEF) has been at the forefront, with initiatives like eMitra centers in Rajasthan, where digitizing government services has not only simplified access but also saved community members time and travel costs. In Ziro Valley, Arunachal Pradesh, DEF’s program to train rural women in e-commerce skills has increased financial independence and profitability, enabling them to access broader markets. 2️⃣ 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: 𝐒𝐞𝐫𝐯𝐢𝐧𝐠 𝐋𝐨𝐜𝐚𝐥 𝐍𝐞𝐞𝐝𝐬 A critical component of digital empowerment is ensuring that technology serves local needs and is not merely consumer-driven. India’s vast rural terrain demands tailored solutions. Low-bandwidth apps and voice-enabled technology are making access to services more inclusive, especially in remote areas. Private companies like Jio have partnered with the government to extend internet connectivity, but more work is needed to bridge this connectivity gap. 3️⃣ 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 𝐑𝐢𝐠𝐡𝐭𝐬 & 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭 Digital empowerment is also about giving marginalized communities the autonomy to make informed decisions. Persons with disabilities (PwDs) in cities like Jaipur are using voice-enabled apps to access telehealth services and government welfare schemes, thereby gaining greater control over their healthcare and livelihood. Microsoft’s AI for Accessibility initiative has also been instrumental in creating digital tools that cater to PwDs, enabling them to interact more seamlessly with technology. Empowering these communities to have decision-making rights and consent in digital platforms ensures that technology is not just an instrument of access but also an enabler of autonomy and inclusion. 4️⃣ 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 & 𝐆𝐚𝐩𝐬 Despite the progress, there are significant challenges such as lack of Modern infrastructure, affordable devices, and quality internet access in rural areas persist. The Indian government’s Digital India initiative aims to address these gaps by establishing more research labs and digital villages.

𝗪𝗵𝗮𝘁'𝘀 𝗵𝗶𝗱𝗱𝗲𝗻 𝗯𝗲𝗹𝗼𝘄 𝘁𝗵𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗜𝗰𝗲𝗯𝗲𝗿𝗴? 𝟳𝟬% of all the knowledge which you accumulate in any organisation. The implicit and tacit knowledge, the why and how which drives actions and decisions and is lost when employees leave. Only 𝟯𝟬% of what we truly know is captured in documents, data, facts and figures. I recently wrote about the challenge of retaining knowledge in Procurement teams, particularly in times of high fluctuation. Left unaddressed, it's causing productivity leakage and lowering employee morale. But not all knowledge is of the same kind. And not all can be harvested the same way. 𝗙𝗶𝗻𝗱 𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝟯 𝗺𝗮𝗶𝗻 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀: 𝗘𝘅𝗽𝗹𝗶𝗰𝗶𝘁: The "What" is known and documented (e.g., SOPs, processes, spend reports). 𝗜𝗺𝗽𝗹𝗶𝗰𝗶𝘁: The "How" of actionable insights, often unspoken but transferable (e.g., negotiation tactics, best practices). 𝗧𝗮𝗰𝗶𝘁: The "Why," deeply embedded in experience and values, hard to express but crucial (e.g., personal insights about market trends or suppliers). 𝗦𝘂𝗿𝗳𝗮𝗰𝗶𝗻𝗴 𝘁𝗮𝗰𝗶𝘁 𝗮𝗻𝗱 𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗶𝘀 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗶𝗻𝗴. Turning experience into tangible, documented information and data points is like reverse engineering and is often resisted by knowledge owners when it comes to sharing. Technologies, such as Knowledge Graphs, Ontologies, and AI assistants, can collaborate with employees to harvest knowledge at the source, whether from structured data (files, tables, logs) or unstructured data (voice, audio, video, documents). This can help to reduce the burden of knowledge capture, centralise its management and make it accessible for everyone. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, 𝗹𝗶𝗸𝗲 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁, 𝗶𝘀 𝗮 𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗵𝗮𝗯𝗶𝘁 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝘄𝗶𝘁𝗵 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗼𝗻𝗹𝘆. 𝗜𝘁 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝘀𝘁𝗲𝗽𝘀. Here are some practical tips to kickstart knowledge sharing to surface tacit and implicit knowledge: ▪️𝗕𝗿𝗼𝘄𝗻 𝗯𝗮𝗴 𝗹𝘂𝗻𝗰𝗵𝗲𝘀 where category teams share use cases and insights ▪️𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗻𝘂𝗴𝗴𝗲𝘁𝘀 captured in documents and support tickets ▪️𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗟𝗲𝗮𝗿𝗻𝘁 sessions to review project outcomes post-mortem ▪️𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 & 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀 used for knowledge dissemination ▪️𝗪𝗼𝗿𝗸 𝘀𝗵𝗮𝗱𝗼𝘄𝗶𝗻𝗴 & 𝗿𝗼𝘁𝗮𝘁𝗶𝗼𝗻𝘀 to skill-up new people in a role ❓What kind of knowledge assets are most valuable in Procurement? ❓How is your company tapping into your submerged knowledge #knowledgemanagement #procurement #lessonslearnt #artificialintelligence