Technology Integration in Strategy

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 👇

Innovation is only as valuable as the problem it solves. We live in an age where technological advancements move faster than our ability to strategically adopt them. It’s no longer a question of can we implement this? but rather, should we? The real challenge isn’t access to innovation. 𝐈𝐭’𝐬 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞. Discipline to pause before we purchase. Discipline to align tools with outcomes. Discipline to measure impact before we declare success. 𝐓𝐡𝐞 𝐃𝐫𝐢𝐯𝐞𝐫𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡 𝐏𝐚𝐫𝐚𝐝𝐨𝐱: • 𝐒𝐡𝐢𝐧𝐲 𝐍𝐞𝐰 𝐎𝐛𝐣𝐞𝐜𝐭 𝐒𝐲𝐧𝐝𝐫𝐨𝐦𝐞: The irresistible pull towards the ‘new’ and ‘novel’, often at the expense of sustained objectives and an overarching strategic vision. • 𝐅𝐞𝐚𝐫 𝐨𝐟 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐎𝐮𝐭 (𝐅𝐎𝐌𝐎): The anxiety that failing to adopt new technologies or trends could result in missed opportunities for growth or competitive advantage. 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤: • 𝟑𝟎% of App deployments fail • 𝟕𝟎% of Digital Transformation initiatives don’t meet goals • 𝟕𝟎%+ of manufacturers worldwide are stuck in pilot purgatory • 𝟓𝟖% of IoT projects are considered not to be successful • 𝟔𝟏% of manufacturers don’t have specific metrics to measure the effectiveness or impact of AI deployments 𝐀𝐝𝐯𝐢𝐜𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐓𝐞𝐜𝐡-𝐂𝐮𝐫𝐢𝐨𝐮𝐬 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: 1. 𝐀𝐬𝐬𝐞𝐬𝐬, 𝐃𝐨𝐧'𝐭 𝐀𝐬𝐬𝐮𝐦𝐞: Evaluate whether the technology fills a need or optimizes current operations before investing. 2. 𝐀𝐥𝐢𝐠𝐧, 𝐓𝐡𝐞𝐧 𝐀𝐜𝐭: Ensure that any new tech acquisition is in alignment with your strategic business goals. 3. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐨 𝐌𝐚𝐧𝐚𝐠𝐞: Develop clear metrics or KPIs to track the success and relevance of your technology investments. 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞 𝐨𝐧 𝐭𝐡𝐢𝐬 𝐭𝐨𝐩𝐢𝐜, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐬𝐨𝐮𝐫𝐜𝐞𝐬:  https://lnkd.in/eX89kQ6n ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

I found this meme funny… but also strikingly accurate. Many CEOs are rushing into AI with huge enthusiasm, but often without clarity on what specific problem they’re solving. The result? Exactly what you see here. After 3+ years partnering with companies on conversational AI solutions, I’ve seen this pattern repeat countless times. Organizations invest in AI, then wonder why they’re not seeing ROI. The real challenge isn’t “Do we need AI?” (we do). It’s “How do we implement it to create measurable, sustainable value?” Here’s what I’ve learned separates successful AI implementations from expensive experiments: Start with the problem, not the technology – Define outcomes before choosing tools. Establish clear success metrics – If you can’t measure it, you can’t improve it Align strategy across stakeholders – Technical teams and business leaders must speak the same language. Focus on value, not features – Shiny doesn’t always mean useful The technology is ready. What’s often missing is the strategic bridge between business objectives and technical execution. I’ve worked with CTOs who knew exactly what they wanted to build but couldn’t quantify business impact. I’ve advised executives who had clear ROI targets but no technical roadmap. The magic happens when strategy and execution align. What’s been your experience with AI implementation? Are you seeing real value — or just expensive experiments? #AI #ConversationalAI #DigitalTransformation #BusinessStrategy #TechLeadership

Just when you think you’ve got all your #sustainability  focus areas humming along nicely, you read a report by The Carbon Bankroll. That was our team last year when we learned how many tech companies' liquid assets are held in emissions-heavy investment vehicles and in some cases, the investments are contributing to emissions that exceed the company’s scope 1-3 emissions combined. Talk about a wake-up call. “Financial supply chain” emissions have historically been considered immaterial for the tech sector, but we didn't want to ignore them. This realization led Atlassian to examine our own financial supply chain, and we found that some of our investments didn't line up with what we were learning about the Net Zero transition and climate related financial risk. So we collaborated with the finance team to ensure we were taking a long term view. For example, we no longer use investment vehicles involving companies that get more than 10% of revenue from fossil fuel extraction or development. We're aiming for better ROI for the company *and* the climate. This is just a start and there’s more we can do. Here’s the best part, though: what began as curiosity has turned into another avenue for building a more sustainable business. Sometimes subtle really can be thrilling. More details in our “Don’t #@!% the Planet” guide if you want to go deeper: https://lnkd.in/gqEppj6H

In today's rapidly evolving business landscape, leveraging AI is no longer a luxury but a strategic imperative. Let's explore the critical components that can empower organizations to thrive in the AI-driven era: ✔️ Identifying AI-Ready Opportunities: Embracing AI begins with identifying areas within your business that can benefit from its transformative potential. By analyzing processes, data, and customer touchpoints, you can pinpoint opportunities where AI can enhance efficiency, accuracy, and customer experience. ✔️ Data-Driven Decision Making: Data is the fuel that powers AI success. The article underscores the significance of cultivating a data-driven culture and investing in robust data infrastructure. A well-curated data repository allows AI algorithms to uncover valuable insights, make informed predictions, and support proactive decision-making. ✔️ AI Talent Acquisition and Development: Attracting and nurturing AI talent is crucial for achieving a competitive edge. Developing a workforce well-versed in AI technologies and methodologies ensures the successful implementation and ongoing optimization of AI initiatives. ✔️ Collaboration between Humans and AI: The article emphasizes that AI isn't about replacing human intelligence but augmenting it. Establishing effective collaboration between AI systems and human teams unlocks new possibilities, enabling organizations to deliver more innovative and personalized solutions. ✔️ Ethics and Responsible AI: As AI adoption grows, so does the importance of ethical considerations. Ensuring that AI applications are designed and used responsibly fosters trust among customers, employees, and stakeholders alike. ✔️ Continuous Learning and Adaptation: The AI landscape is dynamic, and so must be your strategy. Regularly reassessing your AI roadmap, staying abreast of industry trends, and embracing a culture of continuous learning are vital to stay ahead in the AI race. Building a winning AI strategy demands a holistic approach that integrates data, talent, ethics, and adaptability. By embracing AI as a strategic imperative, organizations can revolutionize their operations, deliver unparalleled customer experiences, and secure a sustainable competitive advantage. #AIstrategy #BusinessTransformation #Innovation #DataDrivenDecisionMaking

When will business leaders learn that you can’t go from Excel to AI? Trying to kludge legacy tools into modern infrastructure stacks doesn’t work. Businesses must let go of tools that are older than some of their employees. I got pushback for that take in 2019, but today, my clients don’t have the technical debt that’s preventing their competitors from implementing agents. A core tenet of technical strategy is that decisions made today must amplify the value of future technology waves. Looking at BI tools strategically makes it obvious that they are AI disruptors, not amplifiers. Transitioning away from low maturity BI tools to self-service analytics platforms early set businesses up for AI success today. It freed technical resources to work on contextual data gathering and information architecture rather than spending 80% of their time on reporting and data cleaning. Data literacy and tool maturity have had years to take hold, so the business is filled with semi-technical teams. They’re early adopters of generative AI self-service tools and agent builders. They’re getting more value from AI and avoiding the hype. Products and capabilities have matured iteratively with a cohesive, holistic vision. Transformation is continuous, but a big picture view makes it consistent rather than a series of disconnected pivots and knee-jerk reactions. CIOs must position technology as a pillar of business strategy, so technology decisions must be forward-looking and prescriptive. Technical strategy must be holistic and enterprise-wide. #AI #DataEngineering #AIStrategy #Data

Driving Your Company’s AI Strategy and Execution from the GCC: OKRs for Success AI is not about technology—it’s about driving your company’s strategic goals and delivering measurable business outcomes. A well-designed AI Center of Excellence (CoE) at your GCC can become the cornerstone of your AI strategy and execution, provided it is guided by clear objectives and key results (OKRs). Here’s a framework to align your GCC’s AI CoE with your company’s vision: 1. Objective: Build a Strategic AI Team • Key Result: Hire and onboard N experts by Q2, blending technical and business expertise to align with company priorities. 2. Objective: Ensure Business Integration • Key Result: Conduct workshops with stakeholders to uncover customer pain points and key company processes, completing 5+ sessions by Q3. 3. Objective: Deliver Business Value Through AI • Key Result: Execute 3 pilot projects that drive measurable impact, such as reducing costs by 10% or improving efficiency by 15%, within the first 12 months. 4. Objective: Build Scalable Expertise • Key Result: Launch an upskilling program to ensure 80% of the team is certified in business-critical AI applications by Q4. 5. Objective: Align AI with Corporate Strategy • Key Result: Establish a governance model to ensure all AI initiatives tie back to broader company goals by Q2. An AI CoE designed with these OKRs in mind ensures that your GCC doesn’t just execute AI initiatives—it drives your company’s strategic transformation. Zinnov Rohit Nair Dipanwita Ghosh Mohammed Faraz Khan Amita Goyal Karthik Padmanabhan Hani Mukhey Sagar Kulkarni Saurabh Mehta Komal Shah ieswariya k Namita Adavi

They were hemorrhaging money on digital tools their managers refused to use. The situation: A retail giant in the diamond industry with post-COVID digital sales tools sitting unused. Store managers resisting change. Market volatility crushing performance. Here's what every other company does: More training on features. Explaining benefits harder. Pushing adoption metrics. Here's what my client did instead: They ignored the technology completely. Instead, they trained 200+ managers on something nobody else was teaching; how to fall in love with change itself. For 8 months, we didn't focus on the digital tools once. We taught them Change Enthusiasm®, how to see disruption as opportunity, resistance as data, and overwhelm as information. We certified managers in emotional processing, not technical skills. The results were staggering: → 30% increase in digital adoption (without a single tech training session) →  2X ROI boost for those who embraced the mindset →  25% sales uplift in stores with certified managers →  96% of participants improved business outcomes Here's the breakthrough insight: People don't resist technology. They resist change. Fix the relationship with change, and adoption becomes automatic. While competitors were fighting symptoms, this company cured the disease. The secret wasn't better technology training, it was better humans. When managers learned to thrive through change, they stopped seeing digital tools as threats and started seeing them as allies. Most companies are solving the wrong problem. They're trying to make people adopt technology. We help people embrace transformation. The results speak for themselves. What would happen if you stopped training on tools and started training on change? ♻️ Share if you believe the future belongs to change-ready organizations 🔔 Follow for insights on making transformation inevitable, not optional

𝗪𝗵𝘆 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 <> 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗶𝘀 𝗕𝗿𝗼𝗸𝗲𝗻 Most pilots, PoCs, and “design partner” deals never turn into real revenue. Not because startups don’t have great products. Not because enterprises don’t want innovation. It’s because both sides struggle to align expectations. 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗗𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝗛𝗮𝗽𝗽𝗲𝗻𝘀 🔹 Startups: • Expect enterprises to guide them through procurement instead of being plug-and-play. • Build powerful tech but don’t always tie it to business impact. • Struggle to 𝗽𝗿𝗼𝘃𝗲 𝗥𝗢𝗜 𝗲𝗮𝗿𝗹𝘆, 𝗺𝗮𝗸𝗶𝗻𝗴 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗱𝗲𝗮𝗹𝘀 𝗵𝗮𝗿𝗱 𝘁𝗼 𝗷𝘂𝘀𝘁𝗶𝗳𝘆. 🔹 Enterprises: • Want innovation but evaluate startups like traditional vendors. • Have rigid procurement processes that slow down promising ideas. • Need turnkey solutions but often require customization to fit existing systems. 𝗛𝗼𝘄 𝘁𝗼 𝗕𝗿𝗶𝗱𝗴𝗲 𝘁𝗵𝗲 𝗚𝗮𝗽 → 𝗠𝗮𝗸𝗲 𝗜𝘁 𝗘𝗮𝘀𝘆 𝘁𝗼 𝗧𝗿𝘆 – If enterprises can’t self-test your product, they won’t buy. → 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗲 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻 – If your solution needs custom work just to show value, you’ve lost 90% of buyers. → 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗙𝗿𝗼𝗺 𝗗𝗮𝘆 1 – Security, compliance, and governance aren’t optional—they are table stakes. → 𝗦𝗵𝗼𝘄 𝗥𝗢𝗜 𝗘𝗮𝗿𝗹𝘆 – Pilots should demonstrate clear business value, not just feature testing. → 𝗠𝗲𝗲𝘁 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗪𝗵𝗲𝗿𝗲 𝗧𝗵𝗲𝘆 𝗔𝗿𝗲 – Cloud-native, flexible integration, and clear pricing reduce friction. The startups that get this right? They don’t just get design partners; they get real contracts. 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗳𝗼𝘂𝗻𝗱𝗲𝗿𝘀 & 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀—𝘄𝗵𝗮𝘁’𝘀 𝗯𝗲𝗲𝗻 𝘆𝗼𝘂𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗼𝘁𝗵𝗲𝗿 𝘀𝗶𝗱𝗲? 𝗗𝗿𝗼𝗽 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲. 👇

We hear all about the amazing progress of AI BUT, enterprises are still struggling with AI deployments - latest stats say 78% of AI deployments get stall or canceled - sounds like we’re still buying tools and expect transformation. But those that have succeeded? They don’t just license AI, they redesign work around them. Because adoption isn’t about the tool. It’s about the people who use it. Let’s break this down: 😖 Buying AI tools just adds to your tech stack. Nothing more, nothing less! Stat you can’t ignore: 81% of enterprise AI tools go unused after purchase. (Source: IBM, 2024) 🙌🏼 But adoption, adoption requires new workflows, new roles, and new routines - this means redesigning org charts, updating SOPs, and rethinking “a day in the life.” Why? Because AI should empower decisions—not just automate tasks. It should amplify human strengths—not quietly sideline them. That’s where the 65/35 Rule comes in! 65% of a successful AI deployment is redesigning business processes and preparing the workforce. Only 35% is tools and infrastructure. But most companies still do the reverse. They invest 90% in tech and 10% in training… and wonder why they’re stuck in “perpetual POC purgatory” (my term for things that never make production. It’s like buying a Formula 1 car and expecting your team to win races—without ever learning to drive. Here’s the better way: Step 1: Start with the “day in the life” Map how work actually gets done today. Not hypothetically. Not aspirationally. Just reality. Step 2: Identify friction points Where do delays, errors, or bad decisions happen? Step 3: Redesign with intent Now—and only now—do you introduce AI. Not to replace the human. But to support and strengthen them. Recommendation #1: Design AI solutions with your workforce, not just for them. Co-create roles, rituals, and reviews. Recommendation #2: Adopt the 65/35 Rule as your north star. If your AI strategy doesn’t spend more time on people and process than tools and tech… it’s not ready. ⸻ AI doesn’t fail because it’s flawed. It fails because the org using it is unprepared. #AI #FutureOfWork #DigitalTransformation #Leadership #OrgDesign #HumanInTheLoop #AIAdoption #DataDrivenDecisions #Innovation >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol Rashidi was the 1st “Chief AI Officer” for Enterprise (appointed back in 2016). 10 patents. Best-Selling Author of “Your AI Survival Guide”. FORBES “AI Maverick & Visionary of the 21st Century”. 3x TEDx Speaker