Effects of Enterprise AI on Business Operations

One of the biggest implications of an AI-first enterprise will be that the AI stack an enterprise chooses will be a critical driver of any individual’s productivity in a company, which eventually compounds to determine the firm level productivity and thus competitiveness. This means that specific technology decisions that a company makes will determine the rate of software they can code, contracts they can review, leads that can be generated, campaigns that can be launched, breakthrough innovations that are discovered, customers that can be supported, and so on. The AI-first enterprise -and one that makes the right technology decisions- will see compounding returns and acceleration in all of these areas; conversely, the slower moving companies will tend to fall behind over time. As a result, AI will likely accelerate differences between firms based on their tech decisions, and even impact the kind of talent they can attract and retain, further hastening these competitive differences. We’ve actually seen this with software in the past, albeit at a smaller scale. For instance, for years employees at Facebook and Google benefited from a unique developer stack and tooling that’s long created efficiencies for shipping or scaling software that the rest of the world didn’t have. Fast growing startups notoriously leverage their lack of tech debt, and ability to implement a modern tech stack, as a means of moving faster than incumbents. Companies like Netflix leverage data to make better acquisition decisions. It all comes down to the tech stack. AI just takes this far further, because it will be a meaningful force multiplier on the execution of any employee. And with AI Agents, it’s almost akin to the quality of colleagues that you’re working with. Even subtle differences in the AI Agents you use for customer support, code writing, question answering, or workflow automation will lead to very different business results over time. And the need will only accelerate in the future as a new demographic enters the workforce. A younger workforce coming into the enterprise will have completely different technology habits than generations before, ultimately requiring an AI-first stack to be productive. We’re only in the earliest innings of fully understanding what an AI-first enterprise will look like, but it’s clear that work will likely look very different in a decade from now. And there’s a huge opportunity for those that are adapting early.

I've often emphasized that making AI work in the enterprise isn’t just about technology—it’s about delivering real business outcomes. Here’s what we’ve heard from our customers: ✅ A leading real estate firm reduced time spent searching for information and is on track to save $5.5 million this year. ✅ A home improvement retailer cut engineering debugging time, leading to $2.4 million in annual savings. ✅ A telecommunications company slashed customer support resolution time from 2 minutes and 21 seconds to just 18 seconds. ✅ One company re-deployed 12 engineers from an internal support project, saving 24,000 hours annually for higher-impact work. ✅ An online home retailer automated responses in high-volume Slack channels, enabling the redeployment of 1–3 full-time employees. ✅ A collaboration platform accelerated account research, cutting annual report analysis time from 2 hours to 10 minutes. This is what real AI-driven impact looks like. What’s the most impactful way AI has changed the way your team works?

𝗔𝗜 𝗛𝘆𝗽𝗲 𝘃𝘀. 𝗔𝗜 𝗩𝗮𝗹𝘂𝗲: 𝗛𝗼𝘄 𝘁𝗼 𝗰𝘂𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗻𝗼𝗶𝘀𝗲 𝗮𝗻𝗱 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝘄𝗵𝗮𝘁 𝗱𝗿𝗶𝘃𝗲𝘀 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 We’re living in a time of AI overload.Every day, there’s a new tool, a viral demo, or a promise that AI will transform everything. But for CXOs, the essential question remains: 𝗪𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲? In my work with Fortune 500 clients leading cloud and AI transformations, one thing is clear: Success with AI doesn’t come from chasing trends. It comes from identifying the 𝑟𝑖𝑔ℎ𝑡 𝑝𝑟𝑜𝑏𝑙𝑒𝑚, having 𝑟𝑒𝑙𝑖𝑎𝑏𝑙𝑒 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔 𝑑𝑎𝑡𝑎 𝑠𝑒𝑡𝑠, and 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑛𝑔 𝑖𝑛 𝑡ℎ𝑒 𝑟𝑖𝑔ℎ𝑡 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑐𝑜𝑛𝑡𝑒𝑥𝑡. Here’s a practical lens I use with executive teams to prioritize AI investments: 𝗧𝗵𝗲 𝟯𝗣 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 – 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 – 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹𝗶𝘁𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: What specific business issue are we solving? Is it a speed, experience, or insight challenge? 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹: What’s the tangible upside of solving it with AI? Are we talking about revenue growth, New revenue streams, operational efficiency, or improved accuracy? 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹𝗶𝘁𝘆: Do we have the data, skills, and platform to deploy it at scale with the right market timing? Proofs of concept are easy; scalable success is not. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗥𝗲𝗮𝗹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 – 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝗻 𝗟𝗮𝗿𝗴𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 A Fortune 100 healthcare organization deployed generative AI to surface internal documentation and expert insights. Employees now retrieve critical answers in seconds, not hours—accelerating onboarding and reducing duplication of effort. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗥𝗲𝘁𝗮𝗶𝗹 – 𝗟𝗶𝗳𝘁𝗶𝗻𝗴 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗥𝗮𝘁𝗲𝘀 𝗯𝘆 𝟭𝟴% A global retailer applied machine learning to personalize product recommendations based on browsing behavior and inventory trends. Customers received more relevant suggestions, and e-commerce conversions jumped by nearly 20%. 𝗔𝗜 𝗶𝗻 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 – 𝗖𝘂𝘁𝘁𝗶𝗻𝗴 𝗘𝘅𝗰𝗲𝘀𝘀 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗯𝘆 𝟮𝟱% A manufacturing firm integrated AI-based demand forecasting into its planning cycle. With more accurate predictions, they reduced excess inventory and saved millions in carrying costs. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: If your AI project doesn’t move the needle—on revenue, speed, or experience—it’s probably tech theater. AI is here to stay, but 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 𝗡𝗼𝗿𝘁𝗵 𝗦𝘁𝗮𝗿. 𝗪𝗼𝘂𝗹𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝗼𝘁𝗵𝗲𝗿𝘀: 𝑊ℎ𝑎𝑡’𝑠 𝑎 𝑟𝑒𝑎𝑙-𝑤𝑜𝑟𝑙𝑑 𝐴𝐼 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑡ℎ𝑎𝑡’𝑠 𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑚𝑒𝑎𝑠𝑢𝑟𝑎𝑏𝑙𝑒 𝑖𝑚𝑝𝑎𝑐𝑡 𝑓𝑜𝑟 𝑦𝑜𝑢𝑟 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠? #AI #Cloud #DigitalTransformation #TheHeartOfProgress

A year ago, AI was considered a side project. Now it is a core strategy. Forward-looking businesses are moving from hype to implementation, using AI to solve targeted pain points with measurable outcomes. According to McKinsey's latest State of AI report, organizations are rewiring their entire operations around AI to capture measurable value. Here's 11 ways companies are seeing AI-driven ROI: 1/ Customer Service Automation Companies are moving beyond basic chatbots to full-service AI agents. ↳ 45% reduction in response time ↳ 30% cost savings in support operations 2/ Predictive Maintenance AI analyzes equipment data to prevent costly downtime. ↳ 20% decrease in equipment downtime ↳ $2M average annual savings for manufacturing 3/ Personalized Marketing Deep learning models predict customer behavior and optimize campaigns. ↳ 3x increase in conversion rates ↳ 40% reduction in customer acquisition costs 4/ Supply Chain Optimization AI-driven forecasting revolutionizes inventory management. ↳ 15% inventory reduction ↳ 25% improvement in forecast accuracy 5/ Sales Intelligence Advanced analytics turn data into actionable sales insights. ↳ 35% increase in qualified leads ↳ 28% shorter sales cycles 6/ Document Processing NLP transforms unstructured data into business intelligence. ↳ 80% reduction in manual processing time ↳ 60% decrease in errors 7/ Product Development AI accelerates innovation and reduces time-to-market. ↳ 40% faster time-to-market ↳ 25% reduction in development costs 8/ Risk Management Machine learning spots patterns humans miss. ↳ 50% better fraud detection ↳ 30% reduction in false positives 9/ Employee Productivity AI assistants augment human capabilities. ↳ 4 hours saved per employee weekly ↳ 20% increase in output quality 10/ Process Mining AI identifies inefficiencies and optimization opportunities. ↳ 35% efficiency improvement ↳ $3M average operational savings 11/ Knowledge Management AI transforms company data into accessible insights. ↳ 60% faster information retrieval ↳ 40% reduction in training time The key difference in 2025? Custom-built solutions tailoring models to your unique workflows, data sets, and industry context. As AI matures, the gap will widen between companies that customize and those that generalize. What AI initiatives are delivering the best ROI in your organization? Share below 👇 Sign up for my newsletter: https://lnkd.in/gyJ3FqiT ♻️ Repost to your network if they are looking for AI-related content.

The hype (AI replaces people) and doom (AI doesn’t work) are both wrong. AI works, but few businesses know how to get it working. AI improves productivity by over 30% in some areas, but doesn’t replace people. If your business isn’t growing, AI won’t do much for your business. AI can also be the engine that restarts growth, but few businesses see the need for both sides (AI as growth and productivity engines) to be successful. If your business isn’t turning data into information, AI won’t do much either. AI is only reliable enough to power growth and productivity with the support of information architecture and knowledge management systems. AI requires customer/product process reengineering for growth and internal process reengineering for productivity gains. Higher productivity allows the business to scale and support new growth without hiring. AI productivity initiatives must align with AI growth initiatives. Think of AI products and the business processes that support them as living on two sides of the business’s technology model. Use AI to reduce the cost of scaling and the time it takes to do it. For example, AI helps businesses spend more on ads by spending less on ad agencies. However, without new products and features to advertise, that doesn’t lead to significant cost savings or revenue growth. AI helps software and AI engineers be more productive, but if there aren’t more customer-facing features and products in the backlog, higher productivity won’t cause significant cost savings or revenue growth. Businesses that try to take productivity gains by laying people off quickly realize that without people, AI-driven operations collapse. Businesses that try to scale AI products quickly realize that it’s impossible to keep up with their growth rate without AI-driven operations.

SAP’s key message at Sapphire 2025 was clear ERP transformation doesn’t need to be disruptive, nor is it one-size-fits-all SAP is reshaping ERP with AI, making it more accessible and impactful for businesses of all sizes. SAP is integrating AI to meet ERP customers where they are, enhancing flexibility and productivity. 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 🔹 AI Integration: SAP's Joule AI co-pilot is embedded across its software suites, including ERP, finance, and supply chain management. 🔹 Cloud Compatibility: Joule operates seamlessly across various cloud environments, including SAP's own cloud, Microsoft Azure, AWS, and Google Cloud. 🔹 Cross-Platform Collaboration: Joule can interact with other AI tools like Microsoft Copilot and Google Gemini, enhancing flexibility for businesses using multiple software sources. 🔹 Data Utilization: SAP leverages anonymized data from over 30,000 customers to fine-tune AI models, improving accuracy and relevance. 🔹 Productivity Gains: SAP engineers have experienced a 30% to 40% increase in productivity through AI tools. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗠𝗼𝘃𝗲𝘀: 🔹 Acquisitions: SAP acquired WalkMe for $1.5 billion to enhance user guidance and adoption. 🔹 Partnerships: Collaborations with tech giants like Apple, Amazon, Nvidia, IBM, and Accenture to advance generative AI capabilities. 🔹 Ethical Commitment: SAP has committed to adopting UNESCO's 10 guiding principles in artificial intelligence ethics. 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀: 🔹 Small to Medium Enterprises (SMEs): SAP's initiatives focus on AI-driven enhancements across finance, supply chain management, and other enterprise functions, promising significant productivity gains. 🔹 Consulting Ecosystem: SAP's developer tools are being tested by firms like Accenture, Deloitte, and PwC, aiming to save 600 million working hours per year in productivity gains. SAP's approach to integrating AI into ERP systems is setting a new standard for enterprise software, making advanced capabilities more accessible and impactful for businesses worldwide. 𝗣.𝗦. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗦𝗔𝗣'𝘀 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀, 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗰𝗹𝗼𝘂𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝘁𝗼 𝗲𝗻𝗵𝗮𝗻𝗰𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. Save 💾 ➞ React 👍 ➞ Share ♻️ Follow Alok Kumar for insights on SAP and AI in enterprise solutions.

🚀 Harnessing AI for Modern Business Risk Intelligence 🌐 Today, businesses encounter risks that can quickly disrupt operations, such as supply chain breakdowns, data breaches, or reputational challenges. Traditional risk management strategies often struggle to keep pace with these threats. This is where AI-powered risk intelligence comes in, offering a transformative solution for modern businesses. A recent blog post highlights how AI transforms risk management by analyzing complex data to provide predictive insights, enabling organizations to make informed decisions about operational and reputational risks. Here are some key takeaways: Operational Risk Intelligence: How AI Helps - Real-time Monitoring & Alerts: AI dashboards track patterns and alert decision-makers before crises occur. - Predictive Supply Chain Analysis: AI forecasts potential disruptions, allowing proactive adjustments. - Workforce & Compliance Risk Management: AI identifies training gaps and behavior trends to reduce errors and breaches. Reputation Management with AI AI doesn’t stop at operations—it also monitors sentiment across news, social media, and blogs to detect negative chatter early. Additionally, it flags ESG (Environmental, Social, and Governance) non-compliance, helping businesses avoid reputational pitfalls tied to unethical practices. Balancing Benefits with Challenges While AI enhances transparency and decision-making, human judgment remains critical. Algorithms alone can’t navigate the ethical complexities of every situation. Combining human expertise with AI precision is the key to success. One example of this hybrid approach is Datasurfr by MitKat, which integrates AI-driven insights with human analysis to deliver critical event monitoring and operational risk reports tailored to business needs. 💡How is your organization leveraging AI to stay ahead of risks? Let’s discuss how this technology can empower businesses to thrive in an unpredictable world! #ArtificialIntelligence #RiskManagement #BusinessResilience #Innovation Sources [1] the-role-of-ai-in-modern-business-risk-intelligence-9929050c1f05 https://lnkd.in/ew_2dNTD

Navigating the AI Landscape: Insights from the Business of Tech From this episode of the Business of Tech, we delve into the evolving dynamics of AI in the workplace and its implications for businesses: AI Adoption and Burnout: A recent report reveals a staggering 233% increase in daily AI usage among desk workers, leading to significant productivity gains. However, this surge comes with a downside—workers heavily engaged with AI tools are 88% more likely to experience burnout. As organizations embrace AI, understanding its impact on employee well-being is crucial. Shadow AI Risks: The rise of "shadow AI" is a growing concern, with over 80% of technology leaders acknowledging that unauthorized AI tool adoption is outpacing their ability to manage risks. This trend raises significant cybersecurity issues, particularly as employees inadvertently input sensitive information into unapproved platforms. C-Suite Confidence Decline: Despite increasing investments in AI, confidence among C-suite executives is waning. Trust in AI strategies has dropped from 69% to 58% in just a year, highlighting a critical need for organizations to bolster their governance and training around AI initiatives. For a deeper dive into these topics and more, tune in to our latest episode. 🔊 Listen on Business of Tech: https://lnkd.in/eHX8hJP9 📺 Watch on YouTube: https://lnkd.in/egUgPtJa 🇪🇸 ¿Habla español?: https://lnkd.in/e5ur76Wp #AI #BusinessStrategy #EmployeeWellbeing #Cybersecurity #Leadership #TechTrends

In preparing for a upcoming keynote speech on #genai and the impact on #work; I found these Insights global study by Google #Cloud and National Research Group some of the best I have seen. As a management consulting leader, I'm struck by the clear imperative for organizations to educate themselves on gen AI today. Here are some key takeaways: 1) 74% of enterprises using gen AI report ROI within the first year - faster than most #software deployments 2) 86% of organizations seeing revenue growth estimate a 6%+ increase in annual revenue (real revenue growth!) 3) 84% can move a gen AI use case from idea to production in under 6 months (once again, speed WINS) 4) 45% of organizations report employee productivity has doubled or more due to gen AI (maybe some technology to make our lives easier!) The message is clear: gen AI is not just another tech trend, but a key driver of business transformation and competitive advantage. The study also reveals a "gen AI #leadership gap" - only 16% of organizations are truly leading in this space. These leaders are seeing outsized gains in revenue, productivity, and innovation. To close this gap, organizations must prioritize gen AI education at all levels. This means: 1) Building unified C-suite support and vision for gen AI initiatives 2) Focusing gen AI efforts on core business functions 3) Investing in AI talent development across the organization 4) Prioritizing data quality and infrastructure to support gen AI It is more clear to me than ever that the time to act is now. Those who invest in understanding and strategically implementing gen #AI today will be best positioned to thrive in the AI-driven future of business. Link to the complete study if interested - https://lnkd.in/gmn-yAwE #GenerativeAI #BusinessStrategy #Innovation #Leadership Mercer Ravin Jesuthasan, CFA, FRSA JESS VON BANK #google Adriana O'Kain Ryan Malkes

𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈: 𝐅𝐨𝐮𝐫 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐨𝐫 𝐂-𝐒𝐮𝐢𝐭𝐞 𝐋𝐞𝐚𝐝𝐞𝐫𝐬 In my recent interview with CTO Magazine, I highlighted lessons from enterprise AI projects that moved beyond promises to measurable results. For executives looking to translate AI investment into real-world impact, here’s what we’ve learned: 1. 𝐓𝐡𝐢𝐧𝐤 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 𝐅𝐢𝐫𝐬𝐭, 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐒𝐞𝐜𝐨𝐧𝐝 Set a clear 90-day milestone: if an AI initiative can't deliver measurable time, cost, or risk improvements quickly, reconsider funding it. 2. 𝐖𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐘𝐨𝐮𝐫 𝐄𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 Avoid lengthy data migrations. Real-world AI connects directly to your existing systems - whether SAP, mainframes, or legacy databases - to generate immediate insights. 3. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐇𝐢𝐠𝐡-𝐕𝐨𝐥𝐮𝐦𝐞 𝐓𝐚𝐬𝐤𝐬 Instead of offshoring repetitive workflows, automate them in-house. Companies we’ve partnered with have reduced processes like invoice reconciliation from 2 days down to minutes. 4. 𝐏𝐚𝐢𝐫 𝐇𝐮𝐦𝐚𝐧 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐋𝐢𝐭𝐞𝐫𝐚𝐜𝐲 AI-driven decisions should always have a clearly defined human owner. Support this with internal training to ensure your teams are confident and accountable when deploying AI solutions. The bottom line: Successful enterprise AI is about measurable, achievable steps that produce real outcomes - safely and quickly. (𝘍𝘶𝘭𝘭 𝘢𝘳𝘵𝘪𝘤𝘭𝘦 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘴𝘦𝘤𝘵𝘪𝘰𝘯 𝘣𝘦𝘭𝘰𝘸) And if this is the kind of thinking you're tracking: 👉 Follow AI One for more insights on where Enterprise AI is actually delivering value 𝐑𝐮𝐧 𝐋𝐢𝐤𝐞 𝐚 𝐎𝐧𝐞-𝐏𝐞𝐫𝐬𝐨𝐧 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 Start now. Scale to autonomy. #EnterpriseAI #AIShoring #AIExecution #CTOMagazine #RunAsOne #AIOne