Over the last year, Iâve 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 â like response time or number of users. Thatâs not enough. To create AI systems that actually deliver value, we need ðµð¼ð¹ð¶ððð¶ð°, ðµððºð®ð»-ð°ð²ð»ðð¿ð¶ð° ðºð²ðð¿ð¶ð°ð that reflect: ⢠User trust ⢠Task success ⢠Business impact ⢠Experience quality   This infographic highlights 15 ð¦ð´ð´ð¦ð¯ðµðªð¢ð dimensions to consider: â³ ð¥ð²ðð½ð¼ð»ðð² ðð°ð°ðð¿ð®ð°ð â Are your AI answers actually useful and correct? â³ ð§ð®ðð¸ ðð¼ðºð½ð¹ð²ðð¶ð¼ð» ð¥ð®ðð² â Can the agent complete full workflows, not just answer trivia? â³ ðð®ðð²ð»ð°ð â Response speed still matters, especially in production. â³ ð¨ðð²ð¿ ðð»ð´ð®ð´ð²ðºð²ð»ð â How often are users returning or interacting meaningfully? â³ ð¦ðð°ð°ð²ðð ð¥ð®ðð² â Did the user achieve their goal? This is your north star. â³ ðð¿ð¿ð¼ð¿ ð¥ð®ðð² â Irrelevant or wrong responses? Thatâs friction. â³ ð¦ð²ððð¶ð¼ð» ððð¿ð®ðð¶ð¼ð» â Longer isnât always better â it depends on the goal. â³ ð¨ðð²ð¿ ð¥ð²ðð²ð»ðð¶ð¼ð» â Are users coming back ð¢ð§ðµð¦ð³ the first experience? â³ ðð¼ðð ð½ð²ð¿ ðð»ðð²ð¿ð®ð°ðð¶ð¼ð» â Especially critical at scale. Budget-wise agents win. â³ ðð¼ð»ðð²ð¿ðð®ðð¶ð¼ð» ðð²ð½ððµ â Can the agent handle follow-ups and multi-turn dialogue? â³ ð¨ðð²ð¿ ð¦ð®ðð¶ðð³ð®ð°ðð¶ð¼ð» ð¦ð°ð¼ð¿ð² â Feedback from actual users is gold. â³ ðð¼ð»ðð²ð ððð®ð¹ ð¨ð»ð±ð²ð¿ððð®ð»ð±ð¶ð»ð´ â Can your AI ð³ð¦ð®ð¦ð®ð£ð¦ð³ ð¢ð¯ð¥ ð³ð¦ð§ð¦ð³ to earlier inputs? â³ ð¦ð°ð®ð¹ð®ð¯ð¶ð¹ð¶ðð â Can it handle volume ð¸ðªðµð©ð°ð¶ðµ degrading performance? â³ ðð»ð¼ðð¹ð²ð±ð´ð² ð¥ð²ðð¿ð¶ð²ðð®ð¹ ðð³ð³ð¶ð°ð¶ð²ð»ð°ð â This is key for RAG-based agents. â³ ðð±ð®ð½ðð®ð¯ð¶ð¹ð¶ðð ð¦ð°ð¼ð¿ð² â Is your AI learning and improving over time? If you're building or managing AI agents â bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system â these are the metrics that will shape real-world success. ðð¶ð± ð ðºð¶ðð ð®ð»ð ð°ð¿ð¶ðð¶ð°ð®ð¹ ð¼ð»ð²ð ðð¼ð ððð² ð¶ð» ðð¼ðð¿ ð½ð¿ð¼ð·ð²ð°ðð? Letâs make this list even stronger â drop your thoughts ð
Technology Integration in Strategy
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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. Itâs no longer a question of can we implement this? but rather, should we? The real challenge isnât access to innovation. ððâð¬ ðð¢ð¬ðð¢ð©ð¥ð¢ð§ð. Discipline to pause before we purchase. Discipline to align tools with outcomes. Discipline to measure impact before we declare success. ðð¡ð ðð«ð¢ð¯ðð«ð¬ ð¨ð ðð¡ð ðððð¡ ððð«ððð¨ð±: ⢠ðð¡ð¢ð§ð² ððð° ððð£ððð ðð²ð§ðð«ð¨ð¦ð: The irresistible pull towards the ânewâ and ânovelâ, often at the expense of sustained objectives and an overarching strategic vision. ⢠ð ððð« ð¨ð ðð¢ð¬ð¬ð¢ð§ð ðð®ð (ð ððð): The anxiety that failing to adopt new technologies or trends could result in missed opportunities for growth or competitive advantage. ðð¡ð ðððð¥ð¢ðð² ðð¡ððð¤: ⢠ðð% of App deployments fail ⢠ðð% of Digital Transformation initiatives donât meet goals ⢠ðð%+ of manufacturers worldwide are stuck in pilot purgatory ⢠ðð% of IoT projects are considered not to be successful ⢠ðð% of manufacturers donât have specific metrics to measure the effectiveness or impact of AI deployments ððð¯ð¢ðð ðð¨ð« ðð¡ð ðððð¡-ðð®ð«ð¢ð¨ð®ð¬ ðð¨ð¦ð©ðð§ð¢ðð¬: 1. ðð¬ð¬ðð¬ð¬, ðð¨ð§'ð ðð¬ð¬ð®ð¦ð: Evaluate whether the technology fills a need or optimizes current operations before investing. 2. ðð¥ð¢ð ð§, ðð¡ðð§ ððð: Ensure that any new tech acquisition is in alignment with your strategic business goals. 3. ðððð¬ð®ð«ð ðð¨ ððð§ðð ð: Develop clear metrics or KPIs to track the success and relevance of your technology investments. ð ð¨ð« ð ðððð©ðð« ðð¢ð¯ð ð¨ð§ ðð¡ð¢ð¬ ðð¨ð©ð¢ð, ð¢ð§ðð¥ð®ðð¢ð§ð ð¬ð¨ð®ð«ððð¬: 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 ð for notifications!
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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 theyâre solving. The result? Exactly what you see here. After 3+ years partnering with companies on conversational AI solutions, Iâve seen this pattern repeat countless times. Organizations invest in AI, then wonder why theyâre not seeing ROI. The real challenge isnât âDo we need AI?â (we do). Itâs âHow do we implement it to create measurable, sustainable value?â Hereâs what Iâve learned separates successful AI implementations from expensive experiments: Start with the problem, not the technology â Define outcomes before choosing tools. Establish clear success metrics â If you canât measure it, you canât improve it Align strategy across stakeholders â Technical teams and business leaders must speak the same language. Focus on value, not features â Shiny doesnât always mean useful The technology is ready. Whatâs often missing is the strategic bridge between business objectives and technical execution. Iâve worked with CTOs who knew exactly what they wanted to build but couldnât quantify business impact. Iâve advised executives who had clear ROI targets but no technical roadmap. The magic happens when strategy and execution align. Whatâs been your experience with AI implementation? Are you seeing real value â or just expensive experiments? #AI #ConversationalAI #DigitalTransformation #BusinessStrategy #TechLeadership
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Just when you think youâve 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 companyâs scope 1-3 emissions combined. Talk about a wake-up call. âFinancial supply chainâ 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 thereâs more we can do. Hereâs 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 âDonât #@!% the Planetâ guide if you want to go deeper: https://lnkd.in/gqEppj6H
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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: âï¸ 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. âï¸ 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. âï¸ 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. âï¸ 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. âï¸ 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. âï¸ 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
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When will business leaders learn that you canât go from Excel to AI? Trying to kludge legacy tools into modern infrastructure stacks doesnât 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 donât have the technical debt thatâs 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. Theyâre early adopters of generative AI self-service tools and agent builders. Theyâre 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
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Driving Your Companyâs AI Strategy and Execution from the GCC: OKRs for Success AI is not about technologyâitâs about driving your companyâs 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). Hereâs a framework to align your GCCâs AI CoE with your companyâs 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 doesnât just execute AI initiativesâit drives your companyâs 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
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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: â 30% increase in digital adoption (without a single tech training session) â 2X ROI boost for those who embraced the mindset â 25% sales uplift in stores with certified managers â 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? â»ï¸ Share if you believe the future belongs to change-ready organizations ð Follow for insights on making transformation inevitable, not optional
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ðªðµð ð¦ðð®ð¿ððð½ <> ðð»ðð²ð¿ð½ð¿ð¶ðð² ð£ð¹ð®ðð¯ð¼ð¼ð¸ ð¶ð ðð¿ð¼ð¸ð²ð» Most pilots, PoCs, and âdesign partnerâ deals never turn into real revenue. Not because startups donât have great products. Not because enterprises donât want innovation. Itâs because both sides struggle to align expectations. ðªðµð²ð¿ð² ððµð² ðð¶ðð°ð¼ð»ð»ð²ð°ð ðð®ð½ð½ð²ð»ð ð¹ Startups: ⢠Expect enterprises to guide them through procurement instead of being plug-and-play. ⢠Build powerful tech but donât always tie it to business impact. ⢠Struggle to ð½ð¿ð¼ðð² ð¥ð¢ð ð²ð®ð¿ð¹ð, ðºð®ð¸ð¶ð»ð´ ð¹ð¼ð»ð´-ðð²ð¿ðº ð±ð²ð®ð¹ð ðµð®ð¿ð± ðð¼ ð·ðððð¶ð³ð. ð¹ 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. ðð¼ð ðð¼ ðð¿ð¶ð±ð´ð² ððµð² ðð®ð½ â ð ð®ð¸ð² ðð ðð®ðð ðð¼ ð§ð¿ð â If enterprises canât self-test your product, they wonât buy. â ð ð¶ð»ð¶ðºð¶ðð² ðð¿ð¶ð°ðð¶ð¼ð» â If your solution needs custom work just to show value, youâve lost 90% of buyers. â ðð»ðð²ð¿ð½ð¿ð¶ðð² ð¥ð²ð®ð±ð¶ð»ð²ðð ðð¿ð¼ðº ðð®ð 1 â Security, compliance, and governance arenât optionalâthey are table stakes. â ð¦ðµð¼ð ð¥ð¢ð ðð®ð¿ð¹ð â Pilots should demonstrate clear business value, not just feature testing. â ð ð²ð²ð ðð»ðð²ð¿ð½ð¿ð¶ðð²ð ðªðµð²ð¿ð² ð§ðµð²ð ðð¿ð² â Cloud-native, flexible integration, and clear pricing reduce friction. The startups that get this right? They donât just get design partners; they get real contracts. ð¦ðð®ð¿ððð½ ð³ð¼ðð»ð±ð²ð¿ð & ð²ð»ðð²ð¿ð½ð¿ð¶ðð² ð¹ð²ð®ð±ð²ð¿ðâððµð®ðâð ð¯ð²ð²ð» ðð¼ðð¿ ð¯ð¶ð´ð´ð²ðð ð°ðµð®ð¹ð¹ð²ð»ð´ð² ðð¼ð¿ð¸ð¶ð»ð´ ðð¶ððµ ððµð² ð¼ððµð²ð¿ ðð¶ð±ð²? ðð¿ð¼ð½ ðð¼ðð¿ ðð®ð¸ð². ð
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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 weâre still buying tools and expect transformation. But those that have succeeded? They donât just license AI, they redesign work around them. Because adoption isnât about the tool. Itâs about the people who use it. Letâs break this down: ð Buying AI tools just adds to your tech stack. Nothing more, nothing less! Stat you canât ignore: 81% of enterprise AI tools go unused after purchase. (Source: IBM, 2024) ðð¼ But adoption, adoption requires new workflows, new roles, and new routines - this means redesigning org charts, updating SOPs, and rethinking âa day in the life.â Why? Because AI should empower decisionsânot just automate tasks. It should amplify human strengthsânot quietly sideline them. Thatâs 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 theyâre stuck in âperpetual POC purgatoryâ (my term for things that never make production. Itâs like buying a Formula 1 car and expecting your team to win racesâwithout ever learning to drive. Hereâs the better way: Step 1: Start with the âday in the lifeâ 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 Nowâand only nowâdo 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 doesnât spend more time on people and process than tools and tech⦠itâs not ready. ⸻ AI doesnât fail because itâs flawed. It fails because the org using it is unprepared. #AI #FutureOfWork #DigitalTransformation #Leadership #OrgDesign #HumanInTheLoop #AIAdoption #DataDrivenDecisions #Innovation >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol Rashidi was the 1st âChief AI Officerâ for Enterprise (appointed back in 2016). 10 patents. Best-Selling Author of âYour AI Survival Guideâ. FORBES âAI Maverick & Visionary of the 21st Centuryâ. 3x TEDx Speaker