Digital Transformation Initiatives

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Scaling from 50 to 100 employees almost killed our company. Until we discovered a simple org structure that unlocked $100M+ in annual revenue. In my 10+ years of experience as a founder, one of the biggest challenges I faced in scaling was bridging the organizational gap between startup and enterprise. We hit that wall at around 100~ employees. What worked beautifully with a small team suddenly became our biggest obstacle to growth. The problem was our functional org structure: Engineers reporting to engineering, product to product, business to business. This created a complex dependency web: • Planning took weeks • No clear ownership  • Business threw Jira tickets over the fence and prayed for them to get completed • Engineers didn’t understand priorities and worked on problems that didn’t align with customer needs That was when I studied Amazon's Single-Threaded Owner (STO) model, in which dedicated GMs run independent business units with their own cross-functional teams and manage P&L It looked great for Amazon's scale but felt impossible for growing companies like ours. These 2 critical barriers made it impractical for our scale: 1. Engineering Squad Requirements: True STO demands complete engineering teams (including managers) reporting to a single owner. At our size, we couldn't justify full engineering squads for each business unit. To make it work, we would have to quadruple our engineering headcount. 2. P&L Owner Complexity: STO leaders need unicorn-level skills: deep business acumen and P&L management experience. Not only are these leaders rare and expensive, but requiring all these skills in one person would have limited our talent pool and slowed our ability to launch new initiatives. What we needed was a model that captured STO's focus and accountability but worked for our size and growth needs. That's when we created Mission-Aligned Teams (MATs), a hybrid model that changed our execution (for good) Key principles: • Each team owns a specific mission (e.g., improving customer service, optimizing payment flow) • Teams are cross-functional and self-sufficient,  • Leaders can be anyone (engineer, PM, marketer) who's good at execution • People still report functionally for career development • Leaders focus on execution, not people management The results exceeded our highest expectations: New MAT leads launched new products, each generating $5-10M in revenue within a year with under 10 person teams. Planning became streamlined. Ownership became clear. But it's NOT for everyone (like STO wasn’t for us) If you're under 50 people, the overhead probably isn't worth it. If you're Amazon-scale, pure STO might be better. MAT works best in the messy middle: when you're too big for everyone to be in one room but too small for a full enterprise structure. image courtesy of Manu Cornet ------ If you liked this, follow me Henry Shi as I share insights from my journey of building and scaling a  $1B/year business.

𝗥𝗶𝗴𝗵𝘁 𝗻𝗼𝘄, 𝟳𝟰% 𝗼𝗳 𝘁𝗵𝗲 𝗙𝗼𝗿𝘁𝘂𝗻𝗲 𝟱𝟬𝟬 𝗮𝗿𝗲 𝘂𝗻𝗱𝗲𝗿𝗴𝗼𝗶𝗻𝗴 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀. 𝗨𝗽 𝘁𝗼 𝟵𝟱% 𝗼𝗳 𝘁𝗵𝗲𝗺 𝘄𝗶𝗹𝗹 𝗳𝗮𝗶𝗹. 𝗪𝗵𝘆? 𝗣𝗼𝗼𝗿 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. When I stepped in as CTO, it was clear that if our transformation was going to succeed, we had to improve execution. So, instead of chasing shiny tools or trendy models, we relentlessly focused on the basics. 🧱 Here’s my advice for anyone on this journey: 1️⃣ 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗳𝗼𝗿 𝗦𝗽𝗲𝗲𝗱 Standardization doesn’t limit creativity — it removes roadblocks. Certified pipelines, test plans, and frameworks eliminate chaos, helping teams deliver faster. 2️⃣ 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 You need rules, but only enforce the “no-regret” ones. This gives teams the flexibility to innovate solutions for different regions or customers. 3️⃣ 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Take it step by step and front-load complexity. Doing everything in parallel or saving the hardest for last will result in gridlock and deflating surprises. 4️⃣ 𝗧𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 Tech teams know a lot, but the business knows best. Demand clear requirements so you can build what's needed... and not bridges to nowhere. 5️⃣ 𝗜𝘁'𝘀 𝗮 𝗧𝗲𝗮𝗺 𝗦𝗽𝗼𝗿𝘁 They’re called ‘digital transformations,’ but they’re really business transformations. Everyone — not just tech — must own it. There's always more to do, but we’ve made huge strides this year:   ✅ Cut over four 40+ year-old mainframes to the cloud ✅ Migrated all North American mainframe pipelines to data fabric ✅ Closed data centers from Alpharetta to Australia ✅ Beat our all-time stability records ✅ Achieved our best-ever tech hygiene stats 𝗧𝗵𝗲 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀? We won’t be in the 95%. 𝗧𝗵𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗻𝗲𝘄𝘀? We’re now seeing the transformation benefits we envisioned at the start: AI innovation, model precision, next-gen services, enhanced resilience, and more. 🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘁𝗿𝗲𝗻𝗱𝘀—𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. What are digital transformation lessons you've learned? I’d love to know! 👇

Ever heard of the Lippitt-Knoster Model for Managing Complex Change? It's a classic in the change management world, laying out the essential pieces needed to navigate big transformations. Taking a cue from that, I've adapted it to fit the world of digital transformation. There are seven key elements you can't afford to miss: Vision, Strategy, Objectives, Capabilities, Architecture, Roadmap, and Projects & Programs. Skip any one of these, and you're asking for trouble. Here’s why each one matters: • 𝐕𝐢𝐬𝐢𝐨𝐧: This is the 'what' of your transformation. A clear vision gives everyone a target to aim for, aligning all efforts and keeping the team focused. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Think of this as the 'why' and 'how.' A solid strategy explains the logic behind your vision, showing how you plan to get there and why it's the best route. It’s designed to guide everyone in the company on how to make decisions that support the vision, aligning all efforts and keeping the team focused. • 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: These are your milestones. Clear, specific objectives make it easy to measure success and ensure everyone knows what's important. Without them, you can easily veer off course and waste resources. • 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: These are what your company will now be able to do that it wasn't able to before in order to achieve the objectives. These can be organizational capabilities (like improved decision-making), technical capabilities (such as real-time operational visibility), or other types like enhanced customer engagement or streamlined processes. • 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: A robust architecture ensures all your tech works together smoothly, preventing inefficiencies and costly headaches. This includes various types of architecture such as data architecture, IT infrastructure architecture, enterprise architecture, and functional architecture. Effective architecture is central to reducing technical debt and aligning software with broader business transformation goals. • 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: Your roadmap is the game plan. It lays out the sequence of actions, helping you avoid uncertainty and missteps. It's your guide to getting things done right. • 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬: These are where the rubber meets the road. Actionable projects and programs turn your strategy into reality, making sure your plans lead to real, tangible outcomes. From my experience, I think '𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬' and '𝐑𝐨𝐚𝐝𝐦𝐚𝐩' are the two most overlooked. What do you think? ******************************************* • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

This is a 𝗺𝘂𝘀𝘁 𝗿𝗲𝗮𝗱 for anyone interested in the #FutureOfWork and the role that generative #AI #agents will play. Microsoft analyzed survey data from 31,000 people across 31 countries and combined insights with LinkedIn labor market trends, trillions of Microsoft 365 productivity signals, interviews with AI startups, academics, economists, scientists, and thought leaders to develop a picture of what the future of work may look like. There's so much to unpack - some quick highlights: - they envision "frontier firms" with hybrid human-agent teams - a new role for everyone: "agent boss" - ideal human-agent team ratios - "intelligence resources" emerges to manage digital labor at scale - "work chart" replaces "org chart" I recently posted about the need to rethink "human resources" when we now have non-human resources able to perform knowledge work. Microsoft believes some companies will blend HR and IT or create new leadership roles like "Chief Resources Officer" responsible for managing the optimal balance of human and digital/AI labor. Regarding the "work chart" concept, Microsoft's report shares that "Until now, companies have been built around domain expertise siloed in functions like finance, marketing, and engineering. But with expertise on demand, the traditional org chart may be replaced by a Work Chart - a dynamic, outcome-driven model where teams form around goals, not functions, powered by agents that expand employee scope and enable faster, more impactful ways of working." So much food for thought in this excellent report. Have a read, share with others, and please let me know your thoughts! This is without a doubt the most exciting time to be anywhere within HR. At the same time it is also deeply concerning, given that 33% of companies are planning on using AI to reduce headcount, and I think that this % will only increase. https://lnkd.in/eUBCPhw4

Most change initiatives don't fail because of the change that's happening, they fail because of how the change is communicated. I've watched brilliant restructurings collapse and transformative acquisitions unravel… Not because the plan was flawed, but because leaders were more focused on explaining the "what" and "why" than on how they were addressing the fears and concerns of the people on their team. People don't resist change because they don't understand it. They resist because they haven't been given a compelling story about their role in it. This is where the Venture Scape framework becomes invaluable. The framework maps your team's journey through five distinct stages of change: The Dream - When you envision something better and need to spark belief The Leap - When you commit to action and need to build confidence The Fight - When you face resistance and need to inspire bravery The Climb - When progress feels slow and you need to fuel endurance The Arrival - When you achieve success and need to honor the journey The key is knowing exactly where your team is in this journey and tailoring your communication accordingly. If you're announcing a merger during the Leap stage, don't deliver a message about endurance. Your team needs a moment of commitment–stories and symbols that anchor them in the decision and clarify the values that remain unchanged. You can’t know where your team is on this spectrum without talking to them. Don’t just guess. Have real conversations. Listen to their specific concerns. Then craft messages that speak directly to those fears while calling on their courage. Your job isn't just to announce change, but to walk beside your team and help your team understand what role they play in the story at each stage. #LeadershipCommunication #Illuminate

Some thoughts on the Future of Work. 1. Most companies will have significantly fewer full time employees than they have now because of in addition to AI, companies will leverage marketplaces and the growth of fractional employment. 2. There will be far more companies in the future as a combination of distributed work, new technology, market places and side hustles, low code and no code solutions make it easier than ever to create and scale new firms. 3. The least important challenge in the future of work is where one works. Companies who are focussed on getting everybody back to the office are asking the wrong question. The question should be how to maximize the impact of in person interaction in ways that are personalized and customized to Client needs, type of job, seniority, personal situation and market place dynamics for any particular expertise. How can a company talk about personalization, agility, flexibility, cost competitiveness and being future forward and then enforce a one size for all model that insists everybody return to a container of the past and be expected to be taken seriously as a company of tomorrow ? 4. We have entered an age of de-bossification as people are rejecting “boss- like” behavior and are looking for leaders. Bosses spend almost all their time measuring, monitoring, overseeing, allocating, nitpicking and “checking-in” while Leaders spend most of their time creating, selling, guiding, building, mentoring and growing. The modern leader will not be just full stack but wide spectrum. They will focus not on zone of control but zone of influence. They will combine a growth mindset, an ability to connect dots in creative ways, and communicate and inspire with data driven story telling. 5. Three criteria will be key to the the future of work both for the individual and company. a) Investments in learning and training across all levels. b) The ability to connect people, data, interfaces and opportunities inside and outside the firm in flexible and cost effective ways. We are living in a connected age and connection is the key. c) Trust and distinctiveness. Trust will be critical in a world of algorithms, agents, and AI for companies, for brands and individuals. Distinctiveness which can be defined as differentiation through excellence in key criteria that matter will will be a key to compete. 6. The individuals and companies that will win in the future will rethink the strategy of their firm for a world of a) declining and aging populations, b) shifts of power from scale of size, resources and spending to that of data, networks and talent, c) for a world where knowledge will be free ( but not wisdom, insights and ideas) , and d) where the ability to charge for hours of input and FTE’s (Full Time Equivalents) will be destroyed by Generative, Agentic and Physical AI. Everything and more about the book at https://lnkd.in/gw6iEQvD Much more here: https://lnkd.in/g2ESaxBV

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

When most people hear automation, they think of expensive robots, complex integrations, and big enterprise budgets. It feels out of reach for smaller businesses. But it doesn’t have to be. Research from MIT Sloan School of Management shows that SMEs can approach automation differently, and affordably, without losing sight of competitiveness. One way is to start with peripheral processes. These aren’t the core operations that need high reliability, but the supporting activities that often drain time and attention. Think QR codes to monitor container unloading, sensors to track equipment health, or simple smartphone apps to give real-time visibility. Small steps like these can improve efficiency by 10–15% with minimal cost. Another way is to use stand-alone solutions. These don’t require deep IT integration, which makes them easier to deploy and scale. AI chatbots, IoT sensors, or plug-and-play analytics tools can be rolled out gradually, growing with the business rather than demanding heavy upfront investment. The lesson is simple: automation costs spiral when you chase customisation, tight integration, or unnecessary reliability. Costs come down when you design for compatibility, modularity, and just-enough functionality. For SMEs, the challenge isn’t whether automation is possible, but it’s learning where to start, and being smart about how far to go. Start small, scale at your own pace, and you’ll find automation doesn’t have to break the bank. #Automation #DigitalTransformation #SMEs #Innovation

I just finished reading Flywheel's "The Big Shift" report on redefining ROI with Return on Consumer, and it crystallized something I've been thinking about for months. Here are my key takeaways and what they mean for Amazon advertisers. While ROAS has served us well for immediate conversion optimization, it falls short in identifying and nurturing long-term customer relationships. What's exciting about Amazon's canvas is the quality of identity resolution we can achieve. When customers interact with ads and make purchases, we can connect those touchpoints with much higher confidence than other platforms. This isn't just about tracking sales – it's about understanding the complete customer journey. Amazon Marketing Cloud: A Bridge to the Future The recent expansion of AMC's lookback window to five years is more than just a feature update. It represents a fundamental shift in how brands can understand and activate their customer data. This unprecedented access to purchase history, combined with privacy-safe behavioral insights, allows brands to: • Measure true customer lifetime value • Identify high-potential audience segments • Optimize point of market entry (POME) • Drive sustainable growth through data-driven decisions Beyond Last-Touch Attribution One of the most common conversations I have with advertisers centers around breaking free from last-touch attribution. The reality is that customer journeys are complex and non-linear. With AMC, brands can now see how different touchpoints – from Sponsored Products to Streaming TV – work together to drive both immediate sales and long-term customer value. Real-World Impact The report illustrates this perfectly: a consumer might enter a brand's portfolio with hand soap one year, then purchase detergent and dryer sheets the next year, followed by air fresheners and storage products in the third year. This insight, only possible through long-term customer journey analysis, completely transforms how we should think about acquisition strategy and budget allocation. Looking Ahead • The future belongs to brands that can effectively: • Verticalize their ROI approach within Amazon's canvas • Focus on customer lifetime value rather than individual transactions • Use behavioral signals to fuel sustainable growth • Balance immediate performance with long-term customer value The Question for Advertisers The shift to ROC isn't just about new metrics – it's about fundamentally rethinking how we measure success. Are you still optimizing for short-term ROAS, or are you building for sustainable customer lifetime value? Want to learn more? Read the report: https://lnkd.in/gd2DNBfT Like to listen? Check out the podcast: https://lnkd.in/gPzvS7ci How is your organization adapting to this evolution in measurement and optimization?